{"index":{"version":"0.5.12","fields":[{"name":"title","boost":10},{"name":"keywords","boost":15},{"name":"body","boost":1}],"ref":"url","documentStore":{"store":{"./":["core","hadoop","hadoop概念扩展","hadoop概述","hbase","hdf","hive","mllib的区别","shell的优缺点","shell的基本使用","spark","sql","sql对json数据的处理","sql的相关概念","sql进行数据清洗","standalone模式的启动","stream","streaming实现实时数据处理","streaming的特点","streaming的状态操作解决实际问题","yarn&mapreduc","了解hbase的基本架构","了解hdfs读写流程","了解hive原理和架构","了解mapreduce概念","了解spark概念","了解spark的安装部署","了解yarn概念和产生背景","了解基于内容推荐算法概念","了解推荐相关常用概念","了解推荐系统的冷启动问题","了解推荐系统的评估","了解物品冷启动的推荐方法","了解物品画像，用户画像概念","分布式文件系统","和","完成提交作业到yarn上执行","掌握happybase的常用api","掌握hbase的shell操作","掌握transformation和action算子的基本使用","推荐系统基础","推荐系统案例","推荐系统简介","推荐系统算法","独立完成mrjob实现wordcount","独立实现spark","知道ctr预估概念","知道hadoop生态组成","知道hadoop的优势","知道hadoop的概念及发展历史","知道hbase和关系型数据库的区别","知道hdf","知道hive的udf（自定义函数）","知道hive的内部表、外部表、分区表","知道hql和sql的区别","知道rdd的概念","知道spark作业提交集群的过程","知道spark的特点（与hadoop对比）","知道什么是hdf","知道列式数据库与行数据库的区别","知道协同过滤推荐的相关原理","知道基于回归模型的协同过滤推荐原理","知道基于矩阵分解的协同过滤推荐原理","知道常用的基于模型的推荐算法","知道推荐系统的工程架构和算法架构","知道推荐系统的常用算法","简介","能够应用spark","能够应用sparkmllib训练lr模型","能够应用sparkml训练als模型","能够掌握hdf","能够掌握hdfs的环境搭建","说出dataframe与rdd的联系","说出dstreaming的常见操作api","说出hadoop发行版本的选择","说出hadoop的核心组件","说出hdfs的架构","说出mapreduce原理","说出spark","说出sparkml","说出yarn执行流程","说出处理缺失值的常件办法","说出广播变量的概念"],"day01_推荐系统介绍/01_推荐系统简介.html":["&","(gross","(video","1","1.1","1.1_推荐系统简介","2","3","baidu","merchandis","v.s.","view)","volume电商网站成交金额)/视频网站vv","web项目:","yahoo","不确定思维","且服务的物品对用户构成了信息过载,","个性化","个性化推荐(推荐系统)经历了多年的发展，已经成为互联网产品的标配，也是ai成功落地的分支之一，在电商(淘宝/京东)、资讯(今日头条/微博)、音乐(网易云音乐/qq音乐)、短视频(抖音/快手)等热门应用中,推荐系统都是核心组件之一。","主动","了解推荐系统与web项目区别","了解推荐系统概念及产生背景","什么是推荐系统","信息过载","分类⽬录（1990s）：覆盖少量热门⽹站。典型应用：hao123","向朋友咨询,","基于内容的推荐","基于协同过滤的推荐","基于流行度的推荐","处理复杂业务逻辑，处理高并发，为用户构建一个稳定的信息流通服务","复杂","够满⾜他们兴趣和需求的信息。","学习目标","对结果有确定预期","弱","强","快速满足","意图","户的历史⾏为给⽤户的兴趣进⾏建模，从⽽主动给⽤户推荐能","打开搜索引擎,","找到和自己历史兴趣相似的用户,","持续服务","推荐","推荐系统","推荐系统:","推荐系统和web项目的区别","推荐系统概念及产生背景","推荐系统的作用","推荐系统的工作原理","推荐系统的工作原理及作用","推荐系统的应用场景","推荐系统简介","推荐系统（2010s）：不需要⽤户提供明确的需求，通过分析⽤","提高用户停留时间和用户活跃程度","搜索","搜索引擎","搜索引擎（2000s）：通过搜索词明确需求。典型应用：googl","明确","有效的帮助产品实现其商业价值","查看票房排行榜,","模糊","没有明确需求的用户访问了我们的服务,","流量分布","然后看看返回结果中还有什么电影是自己没看过的","用户需求不明确","留存率/阅读时间/gmv","目标","看看他们最近在看什么电影","确定","社会化推荐","社会化推荐,","稳定的信息流通系统","简明","系统通过一定的规则对物品进行排序,并将排在前面的物品展示给用户,这样的系统就是推荐系统","结果是概率问题","行为方式","被动","让好友给自己推荐物品","记忆推荐系统工作原理及作用","评估指标","输入自己喜欢的演员的名字,","追求指标增长,","通过信息过滤实现目标提升","长尾效应","马太效应","高效连接用户和物品"],"day01_推荐系统介绍/02_推荐系统架构设计.html":["&","(lambda架构)","(海选)","(点击率预估","1","1.2","1.2_推荐系统架构设计","2","ctr预估","flume","hadoop","hadoop、spark","kafka","lambda架构图","lambda架构是由实时大数据处理框架storm的作者nathan","lambda架构的将离线计算和实时计算整合，设计出一个能满足实时大数据系统关键特性的架构，包括有：高容错、低延时和可扩展等。","marz提出的一个实时大数据处理框架。","mysql","nosql(hbase/cassandra)","redis/memcach","spark","streaming/storm/flink","ue(前端界面)","ui","业务知识","了解推荐系统要素","估计用户是否会点击某个商品","使用lr算法)","决定了最终的推荐效果","几分钟~几小时(计算量和数据量不同)","分层架构","分布式存储：","分布式计算：","协同过滤","召回决定了最终推荐结果的天花板","召回决定了最终推荐结果的天花板,","召回阶段","可水平扩展","可进行任何计算,","和","基于内容","大数据lambda架构","存储和分析某个窗口期内的数据（一段时间的热销排行，实时热搜等）","学习目标","实时处理层","实时数据分析","实时数据收集","常用算法:","批处理层","持续计算","排序逼近这个极限,","排序阶段","推荐算法架构","推荐系统整体架构","推荐系统架构","推荐系统的整体架构","推荐系统要素","推荐系统设计","支持随机读","数据","数据不可变,","日志收集：","服务层","流式处理,","策略调整","算法","视图存储数据库","记忆推荐系统架构","读取批处理层和实时处理层结果并对其归并","需要在非常短的时间内返回结果","需要用户的点击数据","高延迟","（精选）"],"day01_推荐系统介绍/03_推荐算法.html":["\"item","\"user2\",","\"user3\",","\"user4\",","\"user5\"]","#","#sort_valu","#遍历所有的最相似用户","*","0.0000","0.1231","0.1612","0.3101","0.33","0.4276","0.4666","0.4677","0.4767","0.4781","0.4900","0.5322","0.5817","0.6415","0.6455","0.7071","0.7206","0.7921","0.8528","0.9001","0.9695","1","1,1]或[0,1]之间,一般可以使用","1,1]来计算，","1,1之间","1.0000","1.3","1.3_推荐算法","1/3","1表示强负相关，+1表示强正相关","1表示正相关","1表示负相关,","2","2/4","3","3.91","4","4.63","=","=\\cfrac{0.85*3+0.71*5}{0.85+0.71}",">features(特征)",">ml",">predict","[","[\"buy\",\"buy\",\"buy\",none,\"buy\"],","[\"buy\",none,\"buy\",\"buy\",none],","[\"buy\",none,\"buy\",none,none],","[\"buy\",none,none,\"buy\",\"buy\"],","[\"item","[\"user1\",","[0,1,0,1,1],","[1,0,0,1,1],","[1,0,1,0,0],","[1,0,1,1,0],","[1,1,1,0,1],","[1,5,5,2,1],","[3,1,2,3,3],","[3,3,1,5,4],","[4,3,4,3,5],","[5,3,4,4,none],","[none,\"buy\",none,\"buy\",\"buy\"],","\\cfrac","]","_df","_df.sort_values(ascending=false)","_df_sort","a\",","a\"],","algorithm(选择算法训练模型)","b","b\",","b\"]))","base","b各自减去向量的均值后,","b的相似度","c","c\",","calculation)","cart","cf","cf的评分结果也是存在差异的，因为严格意义上他们其实应当属于两种不同的推荐算法，各自在不同的领域不同场景下，都会比另一种的效果更佳，但具体哪一种更佳，必须经过合理的效果评估，因此在实现推荐系统时这两种算法往往都是需要去实现的，然后对产生的推荐效果进行评估分析选出更优方案。","cf预测评分和item","cf）","click","columns=items,","columns=users,","comment","d","d\",","data(数据)","dataset","df","df.corr()","df.index:","df.ix[user].replace(0,np.nan).dropna().index:","df.t.corr()","df[\"item","e","e\"]","event","filtering）","histori","i_5)","i_{rated}}sim(i,j)*r_{uj}}{\\sum_{j\\in","i_{rated}}sim(i,j)}","import","index=items)","index=users)","item","item_similar","item_similar.index:","item_similar.loc[i].drop([i])","jaccard","jaccard_similarity_scor","list(_df_sorted.index[:2])","log","metric=\"jaccard\")","np","numpi","n物品，构建初始推荐结果","n相似的人或物品","n相似结果，并进行推荐了","n结果生成初始推荐结果，然后过滤掉用户已经有过记录的物品或明确表示不感兴趣的物品","order","output(预测输出)","page","pairwise_dist","pairwise_distances(df,","pairwise_distances(df.t,","panda","pandas中corr方法可直接用于计算皮尔逊相关系数","pd","pd.dataframe(datasets,","pd.dataframe(item_similar,","pd.dataframe(user_similar,","pprint","pprint(rs_results)","pprint(topn_items)","pprint(topn_users)","pred(u,i)=\\hat{r}_{ui}=\\cfrac{\\sum_{j\\in","pred(u,i)=\\hat{r}_{ui}=\\cfrac{\\sum_{v\\in","pred(u_1,","print(\"top2相似物品：\")","print(\"top2相似用户：\")","print(\"最终推荐结果：\")","print(\"物品之间的两两相似度：\")","print(\"用户之间的两两相似度：\")","print(df)","print(item_similar)","print(item_similar.round(4))","print(jaccard_similarity_score(df[\"item","print(user_similar)","print(user_similar.round(4))","rate","rs_result","rs_result.union(set(df.ix[sim_user].replace(0,np.nan).dropna().index))","rs_result.union(topn_items[item])","rs_results[user]","search","set()","set(df.ix[user].replace(0,np.nan).dropna().index)","sim_us","sim_users:","sklearn.metr","sklearn.metrics.pairwis","thru","top2","topn_item","topn_items[i]","topn_us","topn_users.items():","topn_users[i]","user","user,","user1","user2","user3","user4","user5","user_similar","user_similar.index:","user_similar.loc[i].drop([i])","u}sim(u,v)*r_{vi}}{\\sum_{v\\in","u}|sim(u,v)|}","view","{0.97*5+0.58*4}{0.97+0.58}","{}","​","“跟你喜好相似的人喜欢的东西你也很有可能喜欢”","“跟你喜欢的东西相似的东西你也很有可能喜欢","”：基于物品的协同过滤推荐（item","≈","一个给定的商品，可能被拥有类似品味或需求的用户购买","不过先对向量做了中心化,","不适合计算布尔值向量之间的相关度","与向量长度无关,余弦相似度计算要对向量长度归一化,","两个向量只要方向一致,无论程度强弱,","两个向量的夹角为0是,余弦值为1,","两个物体,","两个集合的交集元素个数在并集中所占的比例,","了解推荐模型构建流程","产生推荐结果","从排序之后的结果中切片","从数据中筛选特征","以下是一个简单的示例，数据集相当于一个用户对物品的购买记录表：打勾表示用户对物品的有购买记录","余弦相似度","余弦相似度/皮尔逊相关系数适合用户评分数据(实数值),","余弦相似度在度量文本相似度,","余弦相似度的特点,","使用协同过滤推荐算法对用户进行评分预测","使用用户行为数据描述商品","假如叫做p,q,","关于协同过滤推荐算法使用的数据集","关于用户","关于相似度计算这里先用一个简单的思想：如有两个同学x和y，x同学爱好[足球、篮球、乒乓球]，y同学爱好[网球、足球、篮球、羽毛球]，可见他们的共同爱好有2个，那么他们的相似度可以用：2/3","关于评分预测的方法也有比较多的方案，下面介绍一种效果比较好的方案，该方案考虑了用户本身的评分评分以及近邻用户的加权平均相似度打分来进行预测：","再求元素和","再计算余弦相似度","准备空白dict用来保存推荐结果","分别都是n个坐标,","分子是两个布尔向量做点积计算,","分母是两个布尔向量做或运算,","利用top","加购物车","协同过滤","协同过滤推荐算法代码实现：","历史订单","取出前两条（相似度最高的两个）","取出每一列数据，并删除自身，然后排序数据","取出每个用户当前已购物品列表","可以看到与用户1最相似的是用户2和用户3；与物品a最相似的物品分别是物品e和物品d。","向量a","和item","因此在协同过滤推荐算法中其实会更多的利用用户对物品的“评分”数据来进行预测，通过评分数据集，我们可以预测用户对于他没有评分过的物品的评分。其实现原理和思想和都是一样的，只是使用的数据集是用户","在前面的demo中，我们只是使用用户对物品的一个购买记录，类似也可以是比如浏览点击记录、收听记录等等。这样数据我们预测的结果其实相当于是在预测用户是否对某物品感兴趣，对于喜好程度不能很好的预测。","基于内容","基本的协同过滤推荐算法基于以下假设：","如下转化公式:","如何选择余弦相似度","存储推荐结果","学习目标","实现协同过滤推荐有以下几个步骤：","实际上也是余弦相似度,","对推荐结果进行评估（评估方法后面章节介绍），评估通过后上线","对比可见，user","将所有用户行为合并在一起","应用杰卡德相似度实现简单协同过滤推荐案例","度量两个变量是不是同增同减","度量的是两个向量之间的夹角,","当夹角为90度是余弦值为0,为180度是余弦值为","得到的就是交集元素的个数","我们要预测用户1对物品e的评分，那么可以根据与物品e最近邻的物品a和物品d进行预测，计算如下：","我们要预测用户1对物品e的评分，那么可以根据与用户1最近邻的用户2和用户3进行预测，计算如下：","打分","找出最相似的人或物品：top","按照相似度降序排列","排序","推荐模型构建流程","推荐算法","搜索记录","数据来源","数据清洗/数据处理","数据量/数据能否满足要求","数据集：","是一个欧式空间下度量距离的方法.","是否收藏,是否点击,是否加购物车)","显性数据","最大值正无穷,","最终预测出用户1对物品5的评分为3.91","最经典的推荐算法：协同过滤推荐算法（collabor","有了两两的相似度，接下来就可以筛选top","有了数据集，接下来我们就可以进行相似度的计算，不过对于相似度的计算其实是有很多专门的相似度计算方法的，比如余弦相似度、皮尔逊相关系数、杰卡德相似度等等。这里我们选择使用杰卡德相似系数[0,1]","来表示。","杰卡德相似度","杰卡德相似度适用于隐式反馈数据(0,1布尔值","杰卡德距离=杰卡德相似度","构建初始的推荐结果","构建推荐结果","构建数据集","构建数据集：","构建数据集：注意这里构建评分数据时，对于缺失的部分我们需要保留为none，如果设置为0那么会被当作评分值为0去对待","根据每个物品找出最相似的top","根据相似的人或物品产生推荐结果","欧氏距离,","欧氏距离不适用于布尔向量之间","欧氏距离的值是一个非负数,","注意：我们在预测评分时，往往是通过与其有正相关的用户或物品进行预测，如果不存在正相关的情况，那么将无法做出预测。这一点尤其是在稀疏评分矩阵中尤为常见，因为稀疏评分矩阵中很难得出正相关系数。","添加到结果中","点击","物品之间的两两相似度：","物品的评分数据。","物品的评分矩阵，根据评分矩阵的稀疏程度会有不同的解决方案","物品相似度的时候较为常用","物品评分矩阵","特征工程","理解协同过滤原理","用夹角的余弦值来度量相似的情况","用户","用户之间的两两相似度：","用户相似度","用户购买记录数据集","用数据表示特征","皮尔逊相似度计算结果在","皮尔逊相关系数pearson","皮尔逊相关系数度量的是两个变量的变化趋势是否一致,","目的：预测用户1对物品e的评分","直接计算某两项的杰卡德相似系数","直接计算皮尔逊相关系数","相似度的计算方法","相似度计算(similar","矩阵","稀疏评分矩阵","稠密评分矩阵","算法思想：物以类聚，人以群分","计算item","计算所有的数据两两的杰卡德相似系数","计算时我们数据通常都需要对数据进行处理，或者编码，目的是为了便于我们对数据进行运算处理，比如这里是比较简单的情形，我们用1、0分别来表示用户的是否购买过该物品，则我们的数据集其实应该是这样的：","计算物品间相似度","计算用户间相似度","计算相似度：对于评分数据这里我们采用皮尔逊相关系数[","记忆相似度计算方法","评分预测：","评分预测：使用物品间的相似度进行预测","评分预测：使用用户间的相似度进行预测","评论/评价","过滤掉已经购买过的物品","过滤掉用户已购的物品","运行结果：","这里先介绍稠密评分矩阵的处理，稀疏矩阵的处理相对会复杂一些，我们到后面再来介绍。","这里利用物品相似度预测的计算同上，同样考虑了用户自身的平均打分因素，结合预测物品与相似物品的加权平均相似度打分进行来进行预测","选择合适的算法","通常计算相似度的结果希望是[","通过前面两个demo，相信大家应该已经对协同过滤推荐算法的设计与实现有了比较清晰的认识。","通过计算两两的相似度来进行排序，即可找出top","遍历所有用户","遍历每一行数据","那么欧式距离就是衡量这两个点之间的距离.","都可以视为'相似'","都在同一个空间下表示为两个点,","隐形数据","非常适用于布尔向量表示","页面浏览","默认是按列进行计算，因此如果计算用户间的相似度，当前需要进行转置","，形成一个user","：基于用户的协同过滤推荐（user"],"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":["\"./datasets/ml","\"movieid\":","\"rating\":","#","#计算用户之间相似度","%","'''","'__main__':","(1,","(uid,","*","+=","0","1","1,","1.","1.4","1.4_案例","1用户对相似物品物品的评分","2","2)","2.","3","3.","4",":param",":return:","=","==","__name__","base","cf","columns=[\"movieid\"],values=\"rating\")","data_path","dataset","def","denomin","dtype","dtype=dtype,","e:","else:","except","exception(\"用户没有相似的用户\"","finally_similar_item","finally_similar_items.iteritems():","finally_similar_us","finally_similar_users.iteritems():","id","iid","iid))","iid,","iid:","import","item","item_id","item_ids:","item_similar","item_similar)","item_similar):","item_similar:","item_similar[1].drop([1]).dropna()","key=lambda","latest","movie的评分矩阵","np","np.float32}","np.int32,","numer","numerator/denomin","numpi","os","panda","pass","pd","pd.read_csv(data_path,","pprint","pprint(result)","pred(u,i)=\\hat{r}_{ui}=\\cfrac{\\sum_{v\\in","predict(uid,","predict_all(1,","predict_all(uid,","predict_r","predict_rating))","print(\"开始预测用户对电影的评分...\"%(uid,","print(\"预测出用户对电影的评分：%0.2f\"","print(e)","rais","rate","ratings.pivot_table(index=[\"userid\"],","ratings_matrix","ratings_matrix,","ratings_matrix.column","ratings_matrix.corr()","ratings_matrix.ix[sim_uid].dropna()","ratings_matrix.t.corr()","ratings_matrix:","ratings_matrix[sim_iid].dropna()","result","return","reverse=true)[:k]","round(predict_rating,","set(ratings_matrix.ix[1].dropna().index)&set(similar_items.index)","set(ratings_matrix[1].dropna().index)&set(similar_users.index)","set(ratings_matrix[iid].dropna().index)&set(similar_users.index)","sim_iid,","sim_item_rated_movi","sim_item_rated_movies[1]","sim_item_rating_from_us","sim_uid,","sim_user_rated_movi","sim_user_rated_movies[1]","sim_user_rated_movies[iid]","sim_user_rating_for_item","similar","similar_item","similar_items.ix[list(ids)]","similar_items.where(similar_items>0).dropna()","similar_us","similar_users.empti","similar_users.ix[list(1)]","similar_users.ix[list(ids)]","similar_users.where(similar_users>0).dropna()","small","small.zip，数据量小，便于我们单机使用和运行","small/ratings.csv\"","sorted(results,","sum_up/sum_down","top_k_rs_result(20)","top_k_rs_result(k):","true:","try:","uid)","uid,","uid:","usecols=range(3))","user","user_similar","user_similar)","user_similar):","user_similar:","user_similar[1].drop([1]).dropna()","user_similar[uid].drop([uid]).dropna()","u}sim(u,v)*r_{vi}}{\\sum_{v\\in","u}|sim(u,v)|}","x:","x[2],","yield","{\"userid\":","下载地址：movielen","为某一用户预测所有电影评分","从iid物品的近邻相似物品中筛选出uid用户评分过的物品","从uid用户的近邻相似用户中筛选出对iid物品有评分记录的近邻用户","从用户1的近邻相似用户中筛选出对物品1有评分记录的近邻用户","准备要预测的物品的id列表","加载ratings.csv，转换为用户","加载数据，我们只用前三列数据，分别是用户id，电影id，已经用户对电影的对应评分","基于协同过滤的电影推荐","学习目标","封装成方法","应用基于物品的协同过滤实现电影评分预测","应用基于用户的协同过滤实现电影评分预测","建议下载ml","找出iid物品的相似物品","找出uid用户的相似用户","数据集下载","根据评分为指定用户推荐topn个电影","案例","物品id","物品两两间的相似度","物品打分矩阵","物品评分矩阵","生成器，逐个返回预测评分","用户","用户id","用户两两相似度矩阵","用户两两间的相似度","电影评分矩阵并计算用户之间相似度","相似物品筛选规则：正相关的物品","相似用户筛选规则：正相关的用户","结合iid物品与其相似物品的相似度和uid用户对其相似物品的评分，预测uid对iid的评分","结合uid用户与其近邻用户的相似度预测uid用户对iid物品的评分","计算分子的值","计算分母的值","计算预测的评分值","计算预测的评分值并返回","评分公式","评分预测公式的分子部分的值","评分预测公式的分母部分的值","近邻物品的评分数据","近邻用户对iid物品的评分","近邻用户的评分数据","透视表，将电影id转换为列名称，转换成为一个us","逐个预测","预测任意用户对任意电影的评分","预测全部评分","预测用户对物品的评分","预测电影评分","预测的评分值","预测给定用户对给定物品的评分值","（以用户1对电影1评分为例）"],"day01_推荐系统介绍/07_ 推荐系统评估.html":["&","(业务角度)","(理论角度)","1","1.5","1.5_推荐系统评估","2","50%","a/b测试","ee可能带来的问题","ee问题实践","exploit","exploitation(开发","explor","exploration(探测","kpi压力大","mae","netflix","rmse","topn推荐","usercf","•","⽤户留存","下载","不损害用户体验","且跟线上真实效果存在偏差","了解推荐系统的常用评估指标","了解推荐系统的评估方法","人群算法:","低","使⽤历史⾏为预测⽤户对某个物品的喜爱程度","例子","信任度","信息熵","全量上线","兴趣扩展:","内容提供方的共赢","准确性","利用)：选择现在可能最佳的⽅案","只能在用户看到过的候选集上做评估,","只能评估少数指标","召回率","可扩展性","可能导致用户流失","商业⽬标","在做两类决策的过程中，不断更新对所有决策的不确定性的认知，优化","在线评估:","多","多样性","多样性&新颖性&惊喜性","多样性：推荐列表中两两物品的不相似性。（相似性如何度量？","好的推荐系统可以实现用户,","如何平衡大众口味和小众需求","如何平衡实时兴趣和长期兴趣","如何平衡短期产品体验和长期系统生态","学习目标","定期做问卷调查","实时性","实践:","对于推荐越大越好","少","常用评估指标","平衡个性化推荐和热门推荐比例","往往需要牺牲准确性","惊喜度","惊喜性：历史不相似（惊）但很满意（喜）","成本高","探索与利用问题","探索伤害用户体验,","探索带来的长期收益(留存率)评估周期长,","推荐系统的评估指标","推荐系统评估","推荐系统评估方法","搜索)：选择现在不确定的⼀些⽅案，但未来可能会有⾼收益的⽅案","搭配推荐","播放/点击","数量","新颖性","新颖性：未曾关注的类别、作者；推荐结果的平均流⾏度","是否喜欢这个推荐","显式反馈","服务提供方,","满意度","灰度发布","用户聚类","电影/书籍评分","相似话题,","离线评估:","离线评估和在线评估结合,","精准率","系统过度强调实时性","美国录像带租赁","获取成本","覆盖度","覆盖率","评估数据来源显示反馈和隐式反馈","评估方法","评分预测","评论","购买","速度快,","长期的⽬标","问卷调查:","随机丢弃用户行为历史","随机扰动模型参数","隐式反馈","高","鲁棒性"],"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html":["1","1.6","1.6_推荐系统的冷启动问题","1.收集⽤户特征","2","3","4","exploit⼒度","explor","⽤户冷启动：如何为新⽤户做个性化推荐","⽤户注册信息：性别、年龄、地域","举例","了解处理推荐系统冷启动的常用方法","今日头条&抖音","使⽤单独的特征和模型预估","使用其它站点的行为数据,","例如腾讯视频&qq音乐","利用物品的内容信息，将新物品先投放给曾经喜欢过和它内容相似的其他物品的用户。","加权求和得到最终推荐结果","基于内容的推荐","基于内容的推荐和协同过滤的推荐结果都计算出来","基于内容的推荐逐渐过渡到协同过滤","处理推荐系统冷启动问题的常用方法","学习目标","引导用户填写兴趣","性别与电视剧的关系","推荐系统冷启动概念","推荐系统的冷启动问题","新⽤户在冷启动阶段更倾向于热门排⾏榜，⽼⽤户会更加需要长尾推荐","新老用户推荐策略的差异","本质是推荐系统依赖历史数据，没有历史数据⽆法预测⽤户偏好","物品冷启动","物品冷启动：如何将新物品推荐给⽤户（协同过滤）","用户冷启动","社交信息、推⼴素材、安装来源","系统冷启动","系统冷启动：⽤户冷启动+物品冷启动","系统早期","给物品打标签","记忆推荐系统冷启动概念","设备信息：定位、⼿机型号、app列表"],"day02_推荐算法/01_基于模型的协同过滤推荐.html":["2.1_基于模型的协同过滤推荐","base","cf算法做一个大致的分类：","model","协同过滤算法","基于分类算法、回归算法、聚类算法","基于回归模型的协同过滤推荐","基于图模型算法","基于矩阵分解的协同过滤推荐","基于矩阵分解的推荐","基于神经网络算法","接下来我们重点学习以下几种应用较多的方案：","随着机器学习技术的逐渐发展与完善，推荐系统也逐渐运用机器学习的思想来进行推荐。将机器学习应用到推荐系统中的方案真是不胜枚举。以下对model"],"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":["\"))","\",","\"iid\",","\"mae\":","\"mae:","\"movieid\",","\"movieid\":","\"rating\":","\"rating\"])","\"rating\"]):","\"rmse\":","#","#number_epoch","%","&:=b_i","&:=b_u","&=","&=r_{ui}","&j(\\theta)=\\sum_{u,i\\in","&j(\\theta)=cost=f(b_u,","'''","'''评分预测'''","'''预测测试集数据'''","'__main__':","(\"movieid\",","(\"rating\",","(2,","(3,","(\\mu+b_u+b_i)","(error","(global_mean","(pred_rat","(reg_bi","(reg_bu","(self.global_mean","(self.reg_bi","(self.reg_bu","*","**","+","+=","...])","/","0","0.1,","0.24.2","0.5)+1.2​，也就是4.2分。","0.5；","1","1)","15,","1:","1：梯度下降法推导","2","2,","2.2_基于回归模型的协同过滤推荐","25,","2:","2\\lambda*b_u","2\\lambda{b_u}","2\\sum_{u,i\\in","2：随机梯度下降","3:","3]","3：算法实现","4)","4),","4:","5),","5,","6)]","6]",":=",":param",":return:","=","==",">>>","[\"userid\",","[(\"userid\",","[(1,","[1,","[1,2,3]","[4,","[4,5,6,7,8]","[4,5,6]","[]","\\\\","\\\\&=","\\\\&=2\\sum_{u,i\\in","\\\\&=\\sum_{u,i\\in","\\alpha*(","\\alpha*((r_{ui}","\\alpha*(\\sum_{u,i\\in","\\alpha*(error","\\alpha\\cfrac{\\parti","\\begin{split}","\\cfrac","\\cfrac{\\partial}{\\parti","\\end{split}","\\hat{r}_{ui}","\\hat{r}_{ui})^2","\\lambda","\\lambda*","\\lambda*(\\sum_u","\\lambda*b_i)","\\lambda*b_i)\\\\","\\lambda*b_u)","\\mu","\\sum_i","\\sum_{u,i\\in","\\theta_j:=\\theta_j","\\theta_j}j(\\theta)","__init__(self,","__name__","_index","_mae_sum","_rmse_sum","_sum","a1,","a2","abs(pred_r","accuray(pred_results)","accuray(predict_results,","agg（aggregation聚合）","alpha","alpha,","alpha学习率","als(self):","api","b","b_i","b_i&:=b_i","b_i)","b_i)(","b_i)\\\\","b_i)^2","b_i)^2是用来寻找与已知评分数据拟合最好的b_u和b_i​","b_i)}{\\lambda_1","b_i:=b_i","b_u","b_u&:=b_u","b_u)","b_u)\\\\","b_u)}{\\lambda_2","b_u}","b_u​更新(因为alpha可以人为控制，所以2可以省略掉)：","b_u和b_i​分别属于用户和物品的偏置，因此他们的正则参数可以分别设置两个独立的参数","b_{ui}","baselinecfbyals(20,","baselinecfbyals(object):","baselinecfbysgd(20,","baselinecfbysgd(object):","baseline目标：","baseline设计思想基于以下的假设：","baseline：基准预测","bcf","bcf.fit(dataset)","bcf.fit(trainset)","bcf.test(testset)","bi","bi[iid]","bi[iid])","bi的正则参数","bu","bu,","bu[uid]","bu[uid])","bu的正则参数","c","class","column","columns=[\"uid\",","cost","cost=\\sum_{u,i\\in","data_path:","data_split(\"datasets/ml","data_split(data_path,","dataset","dataset):","dataset.groupby('movieid').agg([list])","dataset.groupby('userid').agg([list])","dataset.groupby(self.columns[0]).agg([list])[[self.columns[1],","dataset.groupby(self.columns[1]).agg([list])[[self.columns[0],","dataset.itertuples(index=false):","dataset:","dataset['rating'].mean()","def","dict(zip(items_ratings.index,","dict(zip(self.items_ratings.index,","dict(zip(self.users_ratings.index,","dict(zip(users_ratings.index,","docs/stable/reference/groupby.html","dtype","dtype=dict(dtype))","dtype=dtype,","e:","elif","else:","error","except","exception(\"无法预测用户对电影的评分，因为训练集中缺失的数据\".format(uid=uid,","f(b_u,","fit(self,","global_mean","groupbi","groupby('userid')","http://pandas.pydata.org/panda","i)","iid","iid)","iid))","iid):","iid,","iid=iid))","iids,","import","index","int(input(\"iid:","int(input(\"uid:","item","items_r","items_ratings.itertuples(index=true):","j(\\theta)&=\\cfrac{\\partial}{\\parti","j(\\theta)=\\sum_{u,i\\in","latest","len(iids))","len(uids))","length","length),","length,","list()","list(a1)","list(a2)","list(index[_index:])","list(user_rating_data.index)","list(user_rating_data.index.values[index:])","list(zip(a,c))","list(zipped)","mae","mae(predict_results)","mae(predict_results):","mae)","mae评估指标","method.lower()","method:","method=\"all\"):","np","np.float32)]","np.float32}","np.int32),","np.int32,","np.random.shuffle(index)","np.zeros(len(items_ratings))))","np.zeros(len(self.items_ratings))))","np.zeros(len(self.users_ratings))))","np.zeros(len(users_ratings))))","number_epoch","number_epochs,","numpi","panda","pd","pd.read_csv(\"datasets/ml","pd.read_csv(\"ml","pd.read_csv(data_path,","pred_rat","pred_result","predict(self,","predict(uid,","predict_r","predict_results:","print(\"iter%d\"","print(\"rmse:","print(\"完成数据集切分...\")","print(\"开始切分数据集...\")","print(bcf.predict(uid,","print(e)","r_{ui}","rais","random:","random=false):","random=true)","range(number_epochs):","range(self.number_epochs):","rate","ratings):","ratings.drop(testset_index)","ratings.groupby(\"userid\").any().index:","ratings.loc[testset_index]","ratings.where(ratings[\"userid\"]==uid).dropna()","rating字段的名称","real_rat","real_rating)","real_rating,","reg","reg,","reg_bi","reg_bi,","reg_bu","reg_bu,","return","rmse","rmse(predict_results)","rmse(predict_results):","rmse,","rmse_mae(predict_results)","rmse_mae(predict_results):","rmse和mae评估指标","rmse评估指标","round(_mae_sum","round(len(user_rating_data)","round(np.sqrt(_rmse_sum","r}(r_{ui}","self.alpha","self.als()","self.bi","self.bi[iid]","self.bu,","self.bu[uid]","self.column","self.columns[2]]]","self.dataset","self.dataset.itertuples(index=false):","self.dataset[self.columns[2]].mean()","self.global_mean","self.items_r","self.items_ratings.index:","self.items_ratings.itertuples(index=true):","self.number_epoch","self.predict(uid,","self.reg","self.reg_bi","self.reg_bu","self.sgd()","self.users_r","self.users_ratings.itertuples(index=true):","sgd(self):","shuffle方法作用，所以需要强行转换为列表","small/ratings.csv\",","step","test(self,testset):","testset","testset.itertuples(index=false):","testset_index","tip","trainset","trainset,","true:","try:","uid","uid,","uids,","usecols=range(3))","usecols=range(3),","user_rating_data","users_r","users_ratings.itertuples(index=true):","x)","x:","x=0.8,","yield","zip","zip()","zip(*zip(a,b))","zip([iterable,","zip(a,b)","zip(iids,","zip(uids,","{\"userid\":","{\\sum_{u,i\\in","{b_i}^2)","{b_i}^2)​是正则化项，用于避免过拟合现象","{b_u}^2","|r(i)|}","|r(u)|}","}{\\partial","λ","​","“阿甘正传”比较热门且备受好评，评分普遍比平均评分要高1.2分，“阿甘正传”的偏置是+1.2","一些物品的评分普遍高于其他物品，一些物品的评分普遍低于其他物品。比如一些物品一被生产便决定了它的地位，有的比较受人们欢迎，有的则被人嫌弃。","与","为了保证每个用户在测试集和训练集都有数据，因此按userid聚合","为正则化系数）","举例：通过baseline来预测用户a对电影“阿甘正传”的评分","交替最小二乘法应用","交替最小二乘法推导","使用baseline的算法思想预测评分的步骤如下：","使用交替最小二乘法优化算法预测baseline偏置值","使用随机梯度下降优化算法预测baseline偏置值","元素个数与最短的列表一致","公式第一部分","公式第二部分\\lambda*(\\sum_u","公式解析：","关于zip","其中|r(i)|表示物品i​收到的评分数量","其中|r(u)|表示用户u的有过评分数量","准确性指标计算方法","准确性指标评估","函数求导：","函数用于将可迭代的对象作为参数，将对象中对应的元素打包成一个个元组，然后返回由这些元组组成的对象，这样做的好处是节约了不少的内存。","分组","切分数据集，","初始化bu","初始化bu、bi的值，全部设为0","利用随机梯度下降，优化bu，bi的值","加入l2正则化：","加载数据，我们只用前三列数据，分别是用户id，电影id，已经用户对电影的对应评分","单样本损失值：","参数更新：","可理解为解压，返回二维矩阵式","号操作符，可以将元组解压为列表。","同样，损失函数：","同理可得，梯度下降更新b_i​:","同理可得：","因为不可变类型不能被","因此就可以预测出用户a对电影“阿甘正传”的评分为：3.5+(","基于回归模型的协同过滤推荐","如果各个迭代器的元素个数不一致，则返回列表长度与最短的对象相同，利用","如果我们将评分看作是一个连续的值而不是离散的值，那么就可以借助线性回归思想来预测目标用户对某物品的评分。其中一种实现策略被称为baseline（基准预测）。","如求b_u时，将b_i看作是已知；求b_i时，将b_u​看作是已知；如此反复交替，不断更新二者的值，求得最终的结果。这就是交替最小二乘法（als）","学习率","对于所有电影的平均评分是直接能计算出的，因此问题在于要测出每个用户的评分偏置和每部电影的得分偏置。对于线性回归问题，我们可以利用平方差构建损失函数如下：","对于最小过程的求解，我们一般采用随机梯度下降法或者交替最小二乘法来优化实现。","将每个用户的x比例的数据作为训练集，剩余的作为测试集","我们可以使用","我们的目标也就转化为寻找最优的b_u和","打乱列表","找出每个用户普遍高于或低于他人的偏置值b_u","找出每件物品普遍高于或低于其他物品的偏置值b_i​","指标方法，类型为字符串，rmse或mae，否则返回两者rmse和ma","损失函数偏导推导：","损失函数：","数据初始化","数据加载","数据加载初始化与之前完全相同","数据集中user","数据集路径","整体封装","方法一：随机梯度下降法优化","方法二：交替最小二乘法优化","是否随机切分，默认fals","更多关于groupby的","更新bu","最小二乘法和梯度下降法一样，可以用于求极值。","最小二乘法思想：对损失函数求偏导，然后再使偏导为0","有些用户的评分普遍高于其他用户，有些用户的评分普遍低于其他用户。比如有些用户天生愿意给别人好评，心慈手软，比较好说话，而有的人就比较苛刻，总是评分不超过3分（5分满分）","根据用户id分组","梯度下降参数更新原始公式：（公式中α为学习率）","梯度下降更新b_u:","梯度下降最高迭代次数","正则化系数","正则参数","添加test方法，然后使用之前实现accuary方法计算准确性指标","版本不要过低","物品评分数据","物品评分矩阵","用户","用户a比较苛刻，普遍比平均评分低0.5分，即用户a的偏置值b_i​是","用户评分数据","由于随机梯度下降法本质上利用每个样本的损失来更新参数，而不用每次求出全部的损失和，因此使用sgd时：","相反，zip(*)","示例：","算法实现","经过交替最小二乘","计算全局平均分","计算其中一项，先固定其他未知参数，即看作其他未知参数为已知","计算所有电影的平均评分\\mu​（即全局平均评分）","计算每个用户评分与平均评分\\mu的偏置值b_u​","计算每部电影所接受的评分与平均评分\\mu的偏置值b_i","训练集的比例，如x=0.8，则0.2是测试集","设置要加载的数据字段的类型","详见","语法","调用sgd方法训练模型参数","转换为列表","转换来输出列表。","返回一个对象","这个用户或物品普遍高于或低于平均值的差值，我们称为偏置(bias)","这里为了保证用户数量保持不变，将每个用户的评分数据按比例进行拆分","迭代更新bu","迭代次数","通过最小二乘推导，我们最终分别得到了b_u和b_i​的表达式，但他们的表达式中却又各自包含对方，因此这里我们将利用一种叫交替最小二乘的方法来计算他们的值：","预测用户对电影的评分：","预测结果，类型为容器，每个元素是一个包含uid,iid,real_rating,pred_rating的序列","预测评分","首先计算出整个评分数据集的平均评分\\mu​是3.5分","（"],"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html":["2.3_基于矩阵分解的协同过滤推荐","biassvd:","funksvd（lfm）","svd++:","svd++是基于这样的假设：在biassvd基础上，认为用户对于项目的历史浏览记录、购买记录、收听记录等可以从侧面反映用户的偏好。","svd:","svd也被称为最原始的lfm模型","svd分解的形式为3个矩阵相乘，中间矩阵为奇异值矩阵。如果想运用svd分解的话，有一个前提是要求矩阵是稠密的，即矩阵里的元素要非空，否则就不能运用svd分解。","svd分解降维，但这样做明显对数据的原始性造成一定影响。","svd的方法，它不在将矩阵分解为3个矩阵，而是分解为2个用户","svd首先需要填充矩阵，然后再进行分解降维，同时存在计算复杂度高的问题，因为要分解成3个矩阵，所以后来提出了funk","svd（传统并经典着）其公式如下：","svd，一般的做法是先用均值或者其他统计学方法来填充矩阵，然后再运用tradit","tradit","人们后来又提出了改进的biassvd，被称为svd++，该算法是在biassvd的基础上添加了用户的隐式反馈信息：","以上两种最优化函数都可以通过梯度下降或者随机梯度下降法来寻求最优解。","借鉴线性回归的思想，通过最小化观察数据的平方来寻求最优的用户和项目的隐含向量表示。同时为了避免过度拟合（overfitting）观测数据，又提出了带有l2正则项的funksvd，上公式：","刚才提到的tradit","在funksvd提出来之后，出现了很多变形版本，其中一个相对成功的方法是biassvd，顾名思义，即带有偏置项的svd分解：","基于矩阵分解的cf算法","它基于的假设和baseline基准预测是一样的，但这里将baseline的偏置引入到了矩阵分解中","很显然我们的数据其实绝大多数情况下都是稀疏的，因此如果要使用tradit","显示反馈指的用户的评分这样的行为，隐式反馈指用户的浏览记录、购买记录、收听记录等。","矩阵分解发展史","通常svd矩阵分解指的是svd（奇异值）分解技术，在这我们姑且将其命名为tradit","隐含特征的矩阵，funk","隐含特征，项目"],"day02_推荐算法/05_LFM算法实现.html":["\")","\"iid\",","\"movieid\",","\"rating\"])","\"rating\"]):","#","##","#梯度下降优化损失函数","#物品向量","#用户向量","#计算损失","#遍历","&=","&=\\vec","&p_{uk}:=p_{uk}+\\alpha","'''","'''预测测试集数据'''","'__main__':","(\"movieid\",","(\"rating\",","(err","(r_{ui}","))","*","**","+","+=","0.01*v_puk)","0.01*v_qik)","0.01,","0.01正则化系数","0.02*(error*v_puk","0.02*(error*v_qik","0.02学习率","1","10","10,","100,","2)","2.4_lfm算法实现","5","610","610*k","9700","9700*k",":param",":return:","=","==","[\"userid\",","[(\"userid\",","[(r_{ui}","[\\sum_{u,i\\in","[]","\\\\&=\\sum_{u,i\\in","\\\\&={\\sum_{k=1}}^k","\\\\&q_{ik}:=q_{ik}","\\alpha[(r_{ui}","\\alpha[\\sum_{u,i\\in","\\begin{split}","\\end{split}","\\hat","\\hat{r}_{ui})^2","\\lambda","\\lambda(\\sum_u{p_{uk}}^2+\\sum_i{q_{ik}}^2)","\\lambda_1","\\lambda_2","\\sum_{u,i\\in","\\vec","\\vec{q_{k,1}}","__init__(self,","__name__","_init_matrix(self):","agg（aggregation聚合）","alpha","alpha,","api","class","column","columns=[\"uid\",","cost","dataset","dataset):","dataset.groupby('movieid').agg([list])","dataset.groupby('userid').agg([list])","dataset.groupby(self.columns[0]).agg([list])[[self.columns[1],","dataset.groupby(self.columns[1]).agg([list])[[self.columns[0],","dataset.itertuples(index","dataset:","dataset['rating'].mean()","def","dict(zip(","dict(zip(items_ratings.index,np.random.rand(len(items_ratings),10).astype(np.float32)","dict(zip(users_ratings.index,np.random.rand(len(users_ratings),10).astype(np.float32)","docs/stable/reference/groupby.html","dtype","dtype=dict(dtype))","e:","else:","err","error","error_list","error_list.append(err","except","factor","false):","fit","fit(self,","global_mean","groupbi","groupby('userid')","http://pandas.pydata.org/panda","iid","iid)","iid):","iid,","import","input(\"iid:","input(\"uid:","int(iid)))","item","items_r","item的评分矩阵（稠密/稀疏）分解为p和q矩阵，然后利用p*q​还原出us","item矩阵，即隐含特征和物品的矩阵","item矩阵，有p*q得来","item评分矩阵r​。整个过程相当于降维处理，其中：","k","k值","latest","lf","lfm","lfm(0.02,","lfm(latent","lfm(object):","lfm.fit(dataset)","lfm也就是前面提到的funk","lfm原理解析","lf矩阵，即用户和隐含特征矩阵。lf有三个，表示共总有三个隐含特征。","model","model)隐语义模型核心思想是通过隐含特征联系用户和物品，如下图：","np","np.dot(p_u,","np.dot(v_pu,","np.dot(v_puk,v_qik)","np.float32(r_ui","np.float32)]","np.int32),","np.random.rand(len(self.items_ratings),","np.random.rand(len(self.users_ratings),","number_epoch","number_epochs=10,","number_latentfactor","number_latentfactors=10,","numpi","p","p,","p[uid]","p_u","p_{uk}&:=p_{uk}+\\alpha","p_{uk}]","p_{uk}q_{ik}","p_{uk}q_{ik})^2","p_{uk}q_{ik})p_{uk}","p_{uk}q_{ik})q_{ik}","panda","pd","pd.dataframe(dataset)","pd.read_csv(\"datasets/ml","pd.read_csv(\"ml","pred_rat","predict(self,","print(\"iter%d\"%i)","print('*'*10,i)","print(e)","print(lfm.predict(int(uid),","print(np.sqrt(np.mean(error_list)))","p矩阵是user","p矩阵正则","q","q[iid]","q_i","q_i)","q_{ik}&:=q_{ik}","q_{ik}]","q矩阵是lf","q矩阵正则","r_ui","range(15):","range(self.number_epochs):","range(self.number_of_latentfactors):","rate","real_rat","real_rating,","reg_p","reg_p,","reg_q","reg_q,","return","r}","r矩阵是user","self._init_matrix()","self.alpha","self.alpha*(err*v_pu[k]","self.alpha*(err*v_qi[k]","self.column","self.columns[2]]]","self.dataset","self.dataset.itertuples(index=false):","self.dataset[self.columns[2]].mean()","self.globalmean","self.items_r","self.items_ratings.index,","self.items_ratings.index:","self.number_epoch","self.number_latentfactor","self.number_latentfactors).astype(np.float32)","self.p,","self.p[uid]","self.predict(uid,","self.q","self.q[iid]","self.reg_p","self.reg_p*v_pu[k])","self.reg_q","self.reg_q*v_qi[k])","self.sgd()","self.users_r","self.users_ratings.index","self.users_ratings.index,","sgd(self):","small/ratings.csv\",","svd矩阵分解","test(self,testset):","testset.itertuples(index=false):","tip","true:","try:","uid","uid,","uid,iid,real_r","usecols=range(3),","user","users_r","v_pu","v_pu)","v_pu[k]","v_puk","v_qi","v_qi)","v_qi))","v_qi[k]","v_qik","yield","{\\sum_{k=1}}^k","{p_{uk}}\\cdot","{q_{ik}}","{r}_{ui}","λ正则化系数）","代表","使用随机梯度下降，优化结果","分组","初始化p","初始化p和q矩阵，同时为设置0，1之间的随机值作为初始值","利用lfm预测用户对物品的评分，$k​$表示隐含特征数量：","利用矩阵分解技术，将原始user","到物品矩阵里获取物品向量","到用户矩阵中获取用户向量","加入l2正则化：","同样对于评分预测我们利用平方差来构建损失函数：","向量乘法","因此最终，我们的目标也就是要求出p矩阵和q矩阵及其当中的每一个值，然后再对用户","基于矩阵分解的cf算法实现（一）：lfm","如果uid或iid不在，我们使用全剧平均分作为预测结果返回","学习率","损失函数","数据初始化","数据加载","更多关于groupby的","最大迭代次数","根据用户id分组","梯度下降优化损失函数","梯度下降更新参数p_{uk}和q_{ik}​：（α学习率","每一个分量相乘","求和","物品的评分数据","物品的评分进行预测。","物品评分数据","用户","用户评分数据","由于p矩阵和q矩阵是两个不同的矩阵，通常分别采取不同的正则参数，如\\lambda_1和\\lambda_2","矩阵值p_{11}​表示用户1对隐含特征1的权重值","矩阵值q_{11}​表示隐含特征1在物品1上的权重值","矩阵值r_{11}就表示预测的用户1对物品1的评分，且r_{11}=\\vec{p_{1,k}}\\cdot","算法实现","能处理稀疏评分矩阵","计算全局平均分","评分预测","详见","通过物品的uid","通过用户的id","随机梯度下降法优化","随机梯度下降：","隐含因子个数是10个","隐式类别数量"],"day02_推荐算法/06_BiasSVD算法实现.html":["\")","\"iid\",","\"rating\"]):","#","&=","&=\\mu","&p_{uk}:=p_{uk}+\\alpha","'''","'__main__':","(\"movieid\",","(\"rating\",","(err","(r_{ui}","))","*","**","+","+=","0.01,","10,","2)","2.5_biassvd算法实现","20)",":param",":return:","=","==","[(\"userid\",","[(r_{ui}","[\\sum_{u,i\\in","[]","\\\\&=\\mu","\\\\&=\\sum_{u,i\\in","\\\\&q_{ik}:=q_{ik}","\\\\+","\\alpha[(r_{ui}","\\alpha[\\sum_{u,i\\in","\\begin{split}","\\end{split}","\\hat","\\hat{r}_{ui})^2","\\lambda","\\lambda(\\sum_u{b_u}^2+\\sum_i{b_i}^2+\\sum_u{p_{uk}}^2+\\sum_i{q_{ik}}^2)","\\lambda_1","\\lambda_2","\\lambda_3","\\lambda_4","\\mu","\\sum_{u,i\\in","\\vec","__init__(self,","__name__","_init_matrix(self):","alpha","alpha,","b_i","b_i:=b_i","b_i]","b_u","b_u:=b_u","b_u]","bi","bi[iid]","bi[iid])","biassvd","biassvd(0.02,","biassvd(object):","biassvd其实就是前面提到的funk","bsvd","bsvd.fit(dataset)","bu","bu,","bu[uid]","bu[uid])","class","column","columns=[\"uid\",","cost","dataset","dataset):","dataset.groupby(self.columns[0]).agg([list])[[self.columns[1],","dataset.groupby(self.columns[1]).agg([list])[[self.columns[0],","dataset:","def","dict(zip(","dict(zip(self.items_ratings.index,","dict(zip(self.users_ratings.index,","dtype","dtype=dict(dtype))","err","error_list","error_list.append(err","fit","fit(self,","iid","iid):","iid,","import","input(\"iid:","input(\"uid:","int(iid)))","item","latest","lf","math","model","np","np.dot(p_u,","np.dot(v_pu,","np.float32(r_ui","np.float32)]","np.int32),","np.random.rand(len(self.items_ratings),","np.random.rand(len(self.users_ratings),","np.zeros(len(self.items_ratings))))","np.zeros(len(self.users_ratings))))","number_epoch","number_epochs=10,","number_latentfactor","number_latentfactors=10,","numpi","p","p,","p[uid]","p_u","p_{uk}&:=p_{uk}+\\alpha","p_{uk}]","p_{uk}q_{ik}","p_{uk}q_{ik})","p_{uk}q_{ik})^2","p_{uk}q_{ik})p_{uk}","p_{uk}q_{ik})q_{ik}","panda","pd","pd.dataframe(dataset)","pd.read_csv(\"datasets/ml","predict(self,","print(\"iter%d\"%i)","print(bsvd.predict(int(uid),","print(np.sqrt(np.mean(error_list)))","q","q,","q[iid]","q_i","q_i)","q_{ik}&:=q_{ik}","q_{ik}]","r_ui","random","range(self.number_epochs):","rate","reg_bi","reg_bi,","reg_bu","reg_bu,","reg_p","reg_p,","reg_q","reg_q,","return","r}","self._init_matrix()","self.alpha","self.bi","self.bi[iid]","self.bu,","self.bu[uid]","self.column","self.columns[2]]]","self.dataset","self.dataset.itertuples(index=false):","self.dataset[self.columns[2]].mean()","self.globalmean","self.items_r","self.items_ratings.index,","self.items_ratings.index:","self.number_epoch","self.number_latentfactor","self.number_latentfactors).astype(np.float32)","self.p,","self.p[uid]","self.q,","self.q[iid]","self.reg_bi","self.reg_bu","self.reg_p","self.reg_q","self.sgd()","self.users_r","self.users_ratings.index","self.users_ratings.index,","sgd(self):","small/ratings.csv\",","svd矩阵分解基础上加上了偏置项。","true:","uid","uid,","usecols=range(3),","user","v_pu","v_pu)","v_qi","v_qi)","v_qi))","{\\sum_{k=1}}^k","{p_{uk}}\\cdot","{q_{ki}}","{r}_{ui}","使用随机梯度下降，优化结果","初始化bu、bi的值，全部设为0","初始化p和q矩阵，同时为设置0，1之间的随机值作为初始值","利用biassvd预测用户对物品的评分，k表示隐含特征数量：","加入l2正则化：","同样对于评分预测我们利用平方差来构建损失函数：","同理：","基于矩阵分解的cf算法实现（二）：biassvd","学习率","损失函数","梯度下降更新参数p_{uk}：","由于p矩阵和q矩阵是两个不同的矩阵，通常分别采取不同的正则参数，如\\lambda_1和\\lambda_2","算法实现","随机梯度下降法优化","随机梯度下降：","隐式类别数量"],"day02_推荐算法/07_基于内容的推荐算法.html":["\"动作\"、\"吴京\"、\"吴刚\"、\"张翰\"、\"大陆电影\"、\"国产\"、\"爱国\"、\"军事\"等等一系列标签是不是都可以贴上","2.6_基于内容的推荐算法","8》\"等，我们是不是就可以分析出该用户的一些兴趣特征如：\"爱国\"、\"战争\"、\"赛车\"、\"动作\"、\"军事\"、\"吴京\"、\"韩三平\"等标签。","based）","n最相似的物品进行相关推荐：如与该商品相似的商品有哪些？与该文章相似文章有哪些？","n物品进行推荐","pgc","ugc","为每个物品产生top","例如，假设已知电影a是一部喜剧，而恰巧我们得知某个用户喜欢看喜剧电影，那么我们基于这样的已知信息，就可以将电影a推荐给该用户。","其他渠道：如爬虫","冷启动","冷启动问题","利用物品画像计算物品间两两相似情况","基于内容推荐的算法流程：","基于内容的推荐实现步骤","基于内容的推荐方法是非常直接的，它以物品的内容描述信息为依据来做出的推荐，本质上是基于对物品和用户自身的特征或属性的直接分析和计算。","基于内容的推荐算法（content","服务提供方设定的属性（服务提供方为物品附加的属性）：如短视频话题、微博话题（平台拟定）","根据pgc/ugc内容构建物品画像","根据pgc内容构建物品画像","根据pgc内容构建的物品画像的可以解决物品的冷启动问题","根据用户画像从物品中寻找最匹配的top","根据用户行为记录生成用户画像","物品冷启动处理：","物品画像","物品画像：例如给电影《战狼2》贴标签，可以有哪些？","物品自带的属性（物品一产生就具备的）：如电影的标题、导演、演员、类型等等","用户在享受服务过程中提供的物品的属性：如用户评论内容，微博话题（用户拟定）","用户画像：例如已知用户的观影历史是：\"《战狼1》\"、\"《战狼2》\"、\"《建党伟业》\"、\"《建军大业》\"、\"《建国大业》\"、\"《红海行动》\"、\"《速度与激情1","画像构建。顾名思义，画像就是刻画物品或用户的特征。本质上就是给用户或物品贴标签。","简介","问题：物品的标签来自哪儿？"],"day02_推荐算法/08_物品画像.html":["\"genres\",\"tags\"]","\"profile\",","\"title\",","\"weights\"])","#","#到inverted_t","#将修改后的值设置回去","#将电影的id","&","'''","(dct[x[0]],","(x[0],",")","**","+",",","...","...)","......","1.0","10])","11,","15,","16,","19]","2","2,","2.7_电影推荐(contentbased)物品画像","2.x","25]","3)).dropna()","3,","3.x","4,","5,","5])","6,","7,","8,","9,","9],",":",":param",":return:","=",">>>",">>>def","[1,","[1,2,3,4,5])","[2,","[3,","[]","[])","[]),","[dct.doc2bow(line)","[i[0]","_","_)","_.append((mid,","_movie_profil","_movie_profile.append((mid,","_tag","_tags.groupby(\"movieid\").agg(list)","bag","columns=[\"movieid\",","corpu","create_inverted_table(movie_profile)","create_inverted_table(movie_profile):","create_movie_profile(movie_dataset):","data","data[0]","data[1]","data[2]","dataset","dataset]","dct","def","dict","dict(map(lambda","dictionari","dictionary(dataset)","document","enumerate(movie_dataset.index):","enumerate(movie_dataset.itertuples()):","frequency，idf）两部分，由tf和idf的乘积来设置文档词语的权重。","frequency，idf）的乘积。tf指的是某一个给定的词语在该文件中出现的次数。这个数字通常会被正规化，以防止它偏向长的文件（同一个词语在长文件里可能会比短文件有更高的词频，而不管该词语重要与否）。idf是一个词语普遍重要性的度量，某一特定词语的idf，可以由总文件数目除以包含该词语之文件的数目，再将得到的商取对数得到。","frequency，tf）和逆转文档频率（invers","function","g","genr","genres:","gensim","gensim.corpora","gensim.model","gensim介绍","gensim基本概念","get_movie_dataset()","get_movie_dataset():","i,","idf与词语在文档中的出现次数成正比，与该词在整个文档集中的出现次数成反比。","idf值","idf值。","idf值作为它们的权重按照对应的顺序依次排列，就得到这篇影评的特征向量，我们就用这个向量来代表这篇影评，向量中每一个维度的分量大小对应这个属性的重要性。","idf值得到top","idf值最大的k个数组成目标文档的特征向量用以表示文档。","idf是一个词语普遍重要性的度量。表示某一词语在整个文档集中出现的频率，由它计算的结果取对数得到关键词k_i的逆文档频率idf_i：idf_i=log\\frac","idf模型，即计算tf","idf的特征提取技术","idf算法便是其中一种在自然语言处理领域中应用比较广泛的一种算法。可用来提取目标文档中，并得到关键词用于计算对于目标文档的权重，并将这些权重组合到一起得到特征向量。","idf算法基于一个这样的假设：若一个词语在目标文档中出现的频率高而在其他文档中出现的频率低，那么这个词语就可以用来区分出目标文档。这个假设需要掌握的有两点：","idf算法的计算可以分为词频（term","idf结果，“海盗”为0，“船长”为0.0225，“自由”为0.05。","idf自然语言处理领域中计算文档中词或短语的权值的方法，是词频（term","idf计算出来并进行对比，取其中tf","idf，word2vec在内的多种主题模型算法","idf，以电影“加勒比海盗：黑珍珠号的诅咒”为例，假设它总共有1000篇影评，其中一篇影评的总词语数为200，其中出现最频繁的词语为“海盗”、“船长”、“自由”，分别是20、15、10次，并且这3个词在所有影评中被提及的次数分别为1000、500、100，就这3个词语作为关键词的顺序计算如下。","import","index_col=\"movieid\")","inplace=true)","instal","inverted_t","inverted_table.get(tag,","inverted_table.setdefault(tag,","iter","iterable,","key=lambda","lambda","latest","latest数据集中","line","map(","map()","map(fun,可迭代对象)","map(function,","map(lambda","map(square,","map函数","mid","mid,","model","model[corpus[i]]","movi","movie_dataset","movie_dataset.set_index(\"movieid\",","movie_dataset:","movie_dataset[\"tags\"].valu","movie_profil","movie_profile.set_index(\"movieid\",","movie_profile[\"weights\"].iteritems():","movie_profile[mid]","movie_tag","movie_tags))","movies.join(new_tags)","movies[\"genres\"]","movies[\"genres\"].apply(lambda","movies_index","new_tag","np","np.nan","numpi","n个关键词作为电影画像标签","n关键词，构建电影画像","n的关键词","panda","pd","pd.dataframe(","pd.dataframe(_movie_profile,","pd.read_csv(\"datasets/ml","pip","pprint","pprint(create_movie_profile(movie_dataset))","pprint(inverted_table)","print(movie_dataset)","python","ret","ret.itertuples())","return","reverse=true)[:30]","set(movies.index)","set(tags.index)","small/al","small/movies.csv\",","small中标签数据太多，因此借助其来扩充","sorted(vector,","square(x)","tag","tag,","tags.csv\",","tags.csv来自ml","tags.loc[list(movies_index)]","tf","tfidfmodel","tfidfmodel(corpus)","tf指的是一个词语在文档中的出现频率。假设文档集包含的文档数为n，文档集中包含关键词k_i的文档数为n_i，f_{ij}表示关键词k_i在文档d_j中出现的次数，f_{dj}表示文档d_j中出现的词语总数，k_i在文档dj中的词频tf_{ij}定义为：tf_{ij}=\\frac","titl","title,","topn_tag","topn_tags,","topn_tags_weight","topn_tags_weights))","topn_tags_weights.items()]","topn_tags_weights[g]","usecols=range(1,","vector","weight","weight))","weights.items():","words)","words），如“是”、“的”之类的，对于文档的中心思想表达没有意义的词，在分词时需要先过滤掉再计算其他词语的tf","x","x,","x.split(\"|\"))","x:","x:(dct[x[0]],","x[1]),","x[1],","x[2]+x[3])","x[2],","x[3]","y,","y:","{f_{ij}}{f_{dj}}·log\\frac","{f_{ij}}{f_{dj}}。并且注意，这个数字通常会被正规化，以防止它偏向长的文件（指同一个词语在长文件里可能会比短文件有更高的词频，而不管该词语重要与否）。","{n}{n_i}","{}","“海盗”出现的词频为20/200＝0.1","“海盗”的idf为：log(1000/1000)=0","“自由”出现的词频为10/200=0.05；","“自由”的idf为：log(1000/100)=1","“船长”出现的词频为15/200=0.075","“船长”的idf为：log(1000/500)=0.3","一个或多个序列","三方库","两者本质上的区别，词袋是在词集的基础上增加了频率的维度，词集只关注有和没有，词袋还要关注有几个。","为了根据指定关键词迅速匹配到对应的电影，因此需要对物品画像的标签词，建立倒排索引","为每部电影匹配对应的标签数据，如果没有将会是nan","以参数序列中的每一个元素调用","会根据提供的函数对指定序列做映射。","但另外一些特征，比如电影的内容简介、电影的影评、图书的摘要等文本数据，这些被称为非结构化数据，首先他们本应该也属于物品的一个特征标签，但是这样的特征标签进行量化时，也就是计算它的特征向量时是很难去定义的。","使用","使用tfidf，分析提取topn关键词","倒排索引介绍","函数","函数语法：","函数返回值的新列表。","函数，返回包含每次","利用tags.csv中每部电影的标签作为电影的候选关键词","利用tf·idf计算每部电影的标签的tfidf值，选取top","前面提到，物品画像的特征标签主要都是指的如电影的导演、演员、图书的作者、出版社等结构话的数据，也就是他们的特征提取，尤其是体征向量的计算是比较简单的，如直接给作品的分类定义0或者1的状态。","加载基于所有电影的标签","加载数据集","加载电影列表数据集","匿名函数","参数","向量（vector）：由一组文本特征构成的列表。是一段文本在gensim中的内部表达。","和","因此这时就需要借助一些自然语言处理、信息检索等技术，将如用户的文本评论或其他文本内容信息的非结构化数据进行量化处理，从而实现更加完善的物品画像/用户画像。","因此，tf","在其他文档出现的频率低。","在本文档出现的频率高；","基于tf","基于tf·idf提取top","基于内容的电影推荐：物品画像","如果取不到就返回[]","如果电影没有标签数据，那么就替换为空列表","安装","完善画像关键词","对于计算影评的tf","将影评中出现的停用词过滤掉，计算其他词语的词频。以出现最多的三个词为例进行计算如下：","将总的影评集中所有的影评向量与特定的系数相乘求和，得到这部电影的综合影评向量，与电影的基本属性结合构建视频的物品画像，同理构建用户画像，可采用多种方法计算物品画像和用户画像之间的相似度，为用户做出推荐。","将电影的分类词直接作为每部电影的画像标签","将类别词分开","将类别词的添加进去，并设置权重值为1.0","并将电影的分类词直接作为每部电影的画像标签","建立tag","按照tf","描述","提供了两个列表，对相同位置的列表数据进行相加","支持包括tf","放到一个tuple中","文本特征提取有两个非常重要的模型：","权重","构建电影数据集，包含电影id、电影名称、类别、标签四个字段","根据关键词提取对应的名称","根据将每条数据，返回对应的词索引和词频","根据数据集建立词袋，并统计词频，将所有词放入一个词典，使用索引进行获取","根据每条数据返回，向量","模型（model）","注意：文档中存在的停用词（stop","添加到list中","物品画像构建步骤：","物品的倒排索引","用tag作为key去取值","用途：在目标文档中，提取关键词(特征标签)的方法就是将该文档所有词语的tf","由1和2计算的结果求出词语的tf","由tf和idf计算词语的权重为：w_{ij}=tf_{ij}·idf_{i}=\\frac","由于ml","示例","第一个参数","算法举例","算法原理","结论：tf","而倒排索引就是用物品的其他数据作为索引，去提取它们对应的物品的id列表","自然语言处理利器","计算列表各个元素的平方","计算平方数","计算词语的逆文档频率如下：","训练tf","词袋模型（bow","词袋模型：在词集的基础上如果一个单词在文档中出现不止一次，统计其出现的次数（频数）。","词集模型：单词构成的集合，集合自然每个元素都只有一个，也即词集中的每个单词都只有一个。","语料（corpus）：一组原始文本的集合，在gensim中，corpus通常是一个可迭代的对象（比如列表）。每一次迭代返回一个可用于表达文本对象的（稀疏）向量。","语法","返回值","返回列表。","返回迭代器。","通常数据存储数据，都是以物品的id作为索引，去提取物品的其他信息数据","通过对比可得，该篇影评的关键词排序应为：“自由”、“船长”、“海盗”。把这些词语的tf"],"day02_推荐算法/09_用户画像.html":["\"bob\",","\"bob\",\"stanley\",","\"lily\",","\"movieid\":","\"peter\",","\"well\",","\"well\"]","#","'''","+","...","......","1.","15","1、2","2.","2.8_电影推荐(contentbased)用户画像","3.","4))",":","=",">>>",">>>def","[\"stanley\",","[(w,round(c/maxcount,","[1,2,3,4,5])","add(x,","collect","collections.counter(reduce(lambda","counter","counter(names)","counter.most_common(50)","create_movie_profile(movie_dataset)","create_user_profile()","create_user_profile():","def","dtype={\"userid\":np.int32,","function","function（有两个参数）先对集合中的第","functool","gensim.model","get_movie_dataset()","import","initi","initializer])","interest_word","interest_words[0][1]","interest_words]","iter","iterable[,","k个词，这里k设为100，作为用户的标签","lambda","latest","list(x)+list(y),","maxcount","mid","movie_dataset","movie_profil","movie_profile.loc[list(mids)]","name","names_count","np","np.int32})","numpi","n作为用户最终的画像标签","panda","pd","pd.read_csv(\"datasets/ml","pprint","pprint(user_profile)","print(watch_record)","profile画像建立：","record_movie_prifol","record_movie_prifole[\"profile\"].values))","reduc","reduce()","reduce(add,","reduce(function,","reduce(lambda","reduce函数","return","small/ratings.csv\",","tfidfmodel","uid,","usecols=range(2),","user","user_profil","user_profile[uid]","w,c","watch_record","watch_record.groupby(\"userid\").agg(list)","watch_record.itertuples():","x","x+y,","x,","y","y)","y:","{}","两数相加","个元素进行操作，得到的结果再与第三个数据用","中的函数","使用","使用collections.counter类统计列表元素出现次数","出现次数最多的词权重为1","出现的次数","函数会对参数序列中元素进行累积。","函数将一个数据集合（链表，元组等）中的所有数据进行下列操作：用传给","函数语法：","函数运算，最后得到一个结果。","函数，有两个参数","利用次数计算权重","匿名函数","参数","取出出现次数最多的前50个词","取出出现次数最多的词","可迭代对象","可选，初始参数","基于内容的电影推荐：用户画像","描述","提取用户观看列表","根据用户的评分历史，结合物品画像，将有观影记录的电影的画像标签作为初始标签反打到用户身上","根据观看列表和物品画像为用户匹配关键词，并统计词频","根据词频排序，最多保留top","用户画像建立","用户画像构建步骤：","示例","计算列表和：1+2+3+4+5","语法","返回值","返回函数计算结果。","通过对用户观影标签的次数进行统计，计算用户的每个初始标签的权重值，排序后选取top"],"day02_推荐算法/10_TOPN用户推荐.html":["\"movieid\":","#","(x[0],","......","100%","2.9_电影推荐(contentbased)top","=","===>","==>","[])","_","_)","_.append(interest_weight)","_.append(interest_weight*related_weight)","_.append(related_weight)","break","create_user_profile()","interest_weight","interest_word","interest_word,","interest_words:","inverted_table[interest_word]","key=lambda","latest","map(lambda","mid,","np.int32,","np.int32})","n推荐结果","n用户推荐","pd.read_csv(\"datasets/ml","pprint(rs_result)","print(uid)","related_movi","related_movies:","related_weight","result_t","result_table.get(mid,","result_table.items())","result_table.setdefault(mid,","reverse=true)[:100]","rs_result","small/ratings.csv\",","sorted(rs_result,","sum(x[1])),","uid,","usecols=range(2),dtype={\"userid\":","user_profil","user_profile.items():","watch_record","watch_record.groupby(\"userid\").agg(list)","x:","x:x[1],","{}","二者都考虑","历史兴趣程度","历史推荐结果","历史数据","只考虑兴趣词与电影的关联程度","只考虑用户的兴趣程度","在线推荐","基于内容的电影推荐：为用户产生top","娱乐(王思聪)","实时计算","我","王思聪","电影id:[0.2,0.5,0.7]","离线推荐","离线计算","近线：最近1天、3天、7天"],"day03_Hadoop/ha1.1.html":["(hdfs)成为hadoop项目的独立子项目。","01_什么是hadoop","1.0","1.1","2003","2004年","2006年2月hadoop成为apache的独立开源项目(","2006年4月—","2008年4月—","2008年—","2009年3月—","2009年5月—","2009年7月—","2012年11月—","2018年4月—","3.1","alpha","apach","apache™","avail","availability)，而是在应用层检测和处理故障，从而在计算机集群之上提供高可用服务","bigtable：一个大型的分布式数据库","cloudera推出cdh（cloudera’","cluster","common;","core项目更名为hadoop","count","cut","cutting等人实现了dfs和mapreduce机制)。","data","distribut","doug","dsitribut","facebook推出h","file","gb每个节点)在188个节点上运行47.9个小时。","gfs：google的分布式文件系统googl","go","google发表了三篇论文","hadoop","hadoop®","hadoop发展史","hadoop名字的由来","hadoop的概念:","hadoop能做什么?","hadoop项目作者的孩子给一个棕黄色的大象样子的填充玩具的命名","hadoop）","hive可以在hadoop上运行sql操作,","includ","intelligence，简称：bi)","larg","mapreduce:","mapreduce和hadoop","pagerank","pb级数据的存储","process","simplifi","siri","system","tb的数据进行排序只花了62秒时间。","word","yahoo的团队使用hadoop对1","不依靠硬件来提供高可用性(high","为机器学习提供燃料","什么是hadoop","仅限于数据库,受数据量和计算能力的限制,","从单个服务器扩展到数千台计算机，每台计算机都提供本地计算和存储","作者：doug","允许使用简单的编程模型跨计算机集群分布式处理大型数据集","关联分析","分析","可以把运行日志,","可扩展:","可扩展的(scalable)分布式计算框架","可靠的(reliable),","可靠的:","商业智能(busi","商业智能通常被理解为将企业中现有的数据(订单、库存、交易账目、客户和供应商等数据)转化为知识，帮助企业做出明智的业务经营决策的工具。从技术层面上讲，是数据仓库、数据挖掘等技术的综合运用。","啤酒尿不湿","处理","大数据提高数据存储能力,","天猫精灵","小爱","广义大数据","应用采集数据,数据库数据放到一起分析","我们只能对最重要的数据进行统计和分析(决策数据,财务相关)","搜索引擎","搜索引擎时代","搭建大型数据仓库","数据仓库时代","数据挖掘","数据挖掘时代","日志分析","是一个开源的,","曾经进行数分析与统计时,","有保存大量网页的需求(单机","机器学习时代","标准排序(10","淘宝开始投入研究基于hadoop的系统–云梯。云梯总容量约9.3pb，共有1100台机器，每天处理18000道作业，扫描500tb数据。","用户画像/物品画像","统计等业务","词频统计","赢得世界最快1tb数据排序在900个节点上用时209秒。","集群)"],"day03_Hadoop/ha1.2.html":["(hdfs™):","/users/sameerp/data/part","02_hadoop核心组件","0，其复制备份数设置为2,","1.2","access","anoth","applic","base","block1的两个备份存储在datanode0和datanode2两个服务器上","block3的两个备份存储在datanode4和datanode6两个服务器上","cluster","common","common:","data","data.(分布式文件系统)","distribut","file","framework","hadoop","hadoop是所有搜索引擎的共性问题的廉价解决方案","hadoop核心组件","hdf","hdfs是gfs的开源实现","hdfs的特点:扩展性&容错性&海量数量存储","high","job","larg","management.(资源调度系统)","mapreduc","mapreduce:","mapreduce是googlemapreduce的开源实现","mapreduce特点:扩展性&容错性&海量数据离线处理","modules.(hadoop的核心组件)","negoti","parallel","process","provid","resourc","schedul","sets.","support","system","throughput","util","v.s.","yarn","yarn:","yarn特点:扩展性&容错性&多框架资源统一调度","下面这张图是数据块多份复制存储的示意","分布式存储","分布式计算","分布式计算框架","单节点","图中对于文件","如何存储持续增长的海量网页:","如何对持续增长的海量网页进行排序:","存储的blockid分别为1、3。","将文件切分成指定大小的数据块,","并在多台机器上保存多个副本","数据切分、多副本、容错等操作对用户是透明的","源于google的mapreduce论文，论文发表于2004年12月","源自于google的gfs论文,","解决分布式存储问题","解决分布式计算问题","论文发表于2003年10月","负责整个集群资源的管理和调度","超算"],"day03_Hadoop/ha1.3.html":["03_hadoop优势","1.3","hadoop优势","hadoop生态系统成熟","一个集群中可以包含数以千计的节点","会自动重新调度作业计算","存储/计算资源不够时，可以横向的线性扩展机器","数据块多副本","数据存储:","数据计算:","某个节点崩溃,","集群可以使用廉价机器，成本低","高可靠","高扩展性"],"day03_Hadoop/ha2.1.html":["./start","/home/hadoop/app/hadoop","0.0.0.0:","01_hdfs的使用","192.168.19.137:50070","2.1","2.6.0","4251","4416","4631","4770","[0.0.0.0]","[hadoop00]","[hadoop@hadoop00","cdh5.7.0/logs/hadoop","datanod","datanode,","datanodes界面查看datanode的情况","datanode启动的日志信息","dfs.sh","hadoop","hadoop00.out","hadoop00:","hdfs的使用","jp","localhost:","log","namenod","namenode,","namenode和","overview界面查看整体情况","sbin]$","secondari","secondarynamenod","secondarynamenode,","start","以及","可以看到","启动hdf","执行start","来到$hadoop_home/sbin目录下","说明启动成功","通过jps命令查看当前运行的进程","通过可视化界面查看hdfs的运行情况","通过浏览器查看"],"day03_Hadoop/ha2.2.html":["...","/hadoop001","/hadoop001/test","/hadoop001/test/","/hadoop001/test/test.txt","/nonexistentfil","/user/hadoop/emptydir","/user/hadoop/file1","/user/hadoop/file2","/user/hadoop/hadoopdir","/user/hadoop/hadoopfil","02_hdf","1。","2.2","2.4.1","[uri","bin/hadoop","cat","fs","hadoop","hadoop001/test","hadoop001/test/","hadoop001/test/test.txt","hadoop001/test/test.txt文件下载到cento","hdf","hdfs://host:port/dir1","hdfs://host:port/fil","hdfs://host:port/file1","hdfs://host:port/file2","hdfs://host:port/file3","hdfs://host:port/hadoop/hadoopfil","hdfs://host:port/user/hadoop/dir1","http://hadoop.apache.org/docs/r1.0.4/cn/hdfs_shell.html","localfil","localfile1","localfile2","ls","mkdir","mv","p","put","r","rm","shell操作","shell操作练习","test.txt","text","touch","uri","vi","…]","中创建","从本地文件系统中复制单个或多个源路径到目标文件系统。也支持从标准输入中读取输入写入目标文件系统。","从标准输入中读取输入。","使用方法：hadoop","修改日期","修改时间","删除hdfs中","删除指定的文件。只删除非空目录和文件。请参考rmr命令了解递归删除。","在cento","在centos中为test.txt","在hdfs中创建","如果是文件，则按照如下格式返回文件信息：","如果是目录，则返回它直接子文件的一个列表，就像在unix中一样。目录返回列表的信息如下：","将hdfs中","将文件从源路径移动到目标路径。这个命令允许有多个源路径，此时目标路径必须是一个目录。不允许在不同的文件系统间移动文件。","将源文件输出为文本格式。允许的格式是zip和textrecordinputstream。","成功返回0，失败返回","把text.txt文件上传到hdfs中","文件内容","文件名","文件大小","文件夹","权限","查看hdfs中","添加文本内容","用户id","的形式","目录名","示例：","组id","调用文件系统(fs)shell命令应使用","返回值："],"day03_Hadoop/ha2.3.html":["03_hdfs设计思路","2.3","hardware)上的分布式文件系统","hdfs的设计目标","hdfs能提供高吞吐量的数据访问，非常适合大规模数据集上的应用","hdfs设计思路","分布式文件系统的设计思路：","容易扩展，为用户提供性能不错的文件存储服务","适合运行在通用硬件(commod","高度容错性的系统，适合部署在廉价的机器上"],"day03_Hadoop/ha2.4.html":["(master","04_hdfs架构","10分钟没有收到datanode报告认为datanode死掉了","1个namenode/nn(master)","1个文件会被拆分成多个block","2.4","datanode(dn)","datanode/dn(slaves)","hdfs优缺点","hdfs架构","metadata","namenode(nn)","slave结构)","优点","低延迟的数据访问","元数据","分布式集群namenode和datanode部署在不同机器上","可构建在廉价机器上","处理流式数据","存储用户的文件对应的数据块(block)","小文件存储","带","描述数据的数据","数据冗余","监控datanode健康状况","硬件容错","缺点","要定期向nn发送心跳信息，汇报本身及其所有的block信息，健康状况","负责元数据（文件的名称、副本系数、block存放的dn）的管理","负责客户端请求的响应","适合存储大文件"],"day03_Hadoop/ha2.5.html":["#保存退出后","#找到下面内容添加java","(这个命令只运行一次)","./hadoop","./start","/root/bigdata/hadoop/hdfs/data","/root/bigdata/hadoop/hdfs/nam","05_hdfs环境搭建","1","2.5","c","cd","configur","configuration节点中添加","core","cp","datanod","dfs.datanode.data.dir","dfs.namenode.name.dir","dfs.replic","dfs.sh","env.sh","etc/hadoop","export","export_java_home=/root/bigdata/jdk","file:/root/bigdata/hadoop/tmp","format","fs.defaultf","hadoop","hadoop.tmp.dir","hadoop_home=/root/bigdata/hadoop","hdf","hdfs://hadoop","hdfs环境搭建","home","java_home=/root/bigdata/jdk","mapr","mapreduce.framework.nam","mapreduce_shuffl","master:9000","namenod","path=$hadoop_home/bin:$path","path=$java_home/bin:$path","sbin","servic","site.xml","site.xml.templ","sourc","tar","vi","yarn","yarn.nodemanager.aux","zxvf","~/.bash_profil","~/app/","~/app目录下","~/software目录下","​","下载jdk","从模板文件复制","修改","修改hadoop","修改hdf","修改yarn","修改配置文件","压缩包名字","启动hdf","启动启动yarn","和","在","在mapr","在sbin中","指定hdfs的访问方式","指定namenod","放到","来到hadoop的bin目录","然后解压到","的configur","的数据存储位置","节点中添加","进入到","进入到解压后的hadoop目录","配置mapreduc","配置yarn","配置文件作用","配置环境变量","默认没有这个"],"day03_Hadoop/ha3.1.html":["(yet","...","01_资源调度框架yarn","1）mapr","1，client提交作业请求","2）yarn","2，resourcemanag","3)","3.1.1","3.1.2","3.1.3","3.1.5","3，在启动的container中创建applicationmast","4）验证","4，applicationmaster启动后向resourcemanager注册进程,申请资源","5）停止yarn相关的进程","5，applicationmaster申请到资源后，向对应的nodemanager申请启动container,将要执行的程序分发到nodemanager上","6，container启动后，执行对应的任务","7，tast执行完毕之后，向applicationmaster返回结果","8，applicationmaster向resourcemanag","anoth","applicationmast","applicationmaster:","client:","contain","hadoop1.x时并没有yarn，mapreduc","hadoop数据分布式存储（数据分块，冗余存储）","hadoop早期,","http://192,168.19.137:8088","jp","kill","mapreduce.framework.nam","mapreduce_shuffl","meso","negotiator)","negotiator,","nm","nodemanag","nodemanager:","resourc","resourcemanag","resourcemanager:","rm","sbin/start","sbin/stop","servic","site.xml","storm","submit,","yarn","yarn.nodemanager.aux","yarn.sh","yarn产生背景","yarn环境搭建","yarn的架构和执行流程","​","不同计算框架可以共享同一个hdfs集群上的数据，享受整体的资源调度","为上层应用提供统一的资源管理和调度，为集群在利用率、资源统一管理和数据共享等方面带来了巨大好处","为应用程序向rm申请资源（core、memory），分配给内部task","什么是yarn","分发到这个容器上面","另一种资源协调者","另一种资源调度器","启动yarn相关的进程","处理客户端的请求：","处理来自am的命令","大数据资源管理产品","如果没有通用资源管理系统，只能为多个集群分别提供数据","定时向rm汇报本节点的资源使用情况","容器:","封装了cpu、memory等资源的一个容器,是一个任务运行环境的抽象","当多个mapreduce任务要用到相同的hdfs数据，","技术只有hadoop,","接收并处理来自rm的各种命令：启动contain","提交作业","整个集群中有多个，负责自己本身节点资源管理和使用","整个集群同一时间提供服务的rm只有一个，负责集群资源的统一管理和调度","既负责进行计算作业又处理服务器集群资源调度管理","服务器集群资源调度管理和mapreduce执行过程耦合在一起带来的问题","查询作业的运行进度,杀死作业","每个应用程序对应一个：mr、spark，负责应用程序的管理","监控我们的nm，一旦某个nm挂了，那么该nm上运行的任务需要告诉我们的am来如何进行处理","节点管理器","计算框架都要用到服务器集群资源","请求kill","资源利用率低","资源管理器","资源调度框架","运维成本高","这个问题不明显","进程和","进程通信，根据集群资源，为用户程序分配第一个container(容器)，并将","通用资源管理系统","随着大数据技术的发展，spark","需要与nm通信：启动/停止task，task是运行在container里面，am也是运行在container里面","需要进行资源调度管理"],"day03_Hadoop/ha3.2.html":["02_分布式计算框架mapreduc","3.2.1","3.2.2",">(out_key,intermediate_value)",">out_valu","count","hadoop的mapreduce是google论文的开源实现","list","map","map(in_key,in_value)","mapreduc","mapreduce优点:","mapreduce分而治之的思想","mapreduce编程分map和reduce阶段","mapreduce编程执行步骤","mapreduce编程模型","mapreduce缺点:","map阶段","reduc","reduce(out_key,intermediate_value)","reduce处理","reduce阶段:","shuffl","task","word","一个人数所有的钞票，数出各种面值有多少张","什么是mapreduc","借鉴函数式编程方式","准备mapper数据","准备mapreduce的输入数据","分布式处理框架","分治策略","分：把复杂的问题分解为若干\"简单的任务\"","单点策略","合：reduc","实时流式计算","将作业拆分成map阶段和reduce阶段","数钱实例：一堆钞票，各种面值分别是多少","每个人分得一堆钞票，数出各种面值有多少张","汇总，每个人负责统计一种面值","海量数据离线处理&易开发","源于google的mapreduce论文(2004年12月)","用户只需要实现两个函数接口：","结果输出","编程模型","解决数据可以切割进行计算的应用","词频统计案例"],"day03_Hadoop/ha3.3.html":["!=","\"\":","#","#介于mapper和reducer之间，用于临时的将mapper输出的数据进行统计","#传入两个step","#利用heapq将数据进行排序，将最大的2个取出","#实现steps方法用于指定自定义的mapper，comnbiner和reducer方法","#每一行从line中输入","'__main__':","03_mapreduce实战","1)指定hadoop任务调度优先级(very_high|high),如：","1、内嵌(","2)map及reduce任务个数限制，如：","2、本地(","3.3.1","3.3.2","3.3.3","3、hadoop(","==",">","[","]","_,","__name__","__name__=='__main__':","class","cnt,word","combiner(self,","combiner=self.combiner,","counts):","def","file","file，比如下面两种运行方式是等价的","hadoop","hadoop)方式","hdfs:///output","hdfs:///test.txt","heapq","heapq.nlargest(2,word_cnts):","import","inlin","inline)可以省略，输出文件使用","inline)方式","input.txt","instal","jobconf","line):","line.split():","line.strip()","line.strip().split():","local","local)方式","main()","main():","mapper(self,","mapreduce.job.priority=very_high。","mapreduce.map.tasks=2","mapreduce.reduce.tasks=5","mapreduce实战","mr_word_count.pi","mrjob","mrjob,mrstep","mrjob.job","mrjob实现wordcount","mrjob是最简单的方式","mrjob程序可以在本地测试运行也可以部署到hadoop集群上运行","mrstep(mapper=self.mapper,","mrstep(reducer=self.top_n_reducer)","mrwordcount(mrjob):","mrwordcount.run()","my_file.txt","none,(sum(counts),word)","o","output","output.txt","output1.txt","pip","python","r","reducer(self,","reducer=self.reducer_sum),","reducer_sum(self,","return","steps(self):","sum(counts)","sy","top_n_reducer(self,_,word_cnts):","topnwords(mrjob):","topnwords.run()","topn统计（实验）","word","word,","word,1","word,cnt","word,sum(counts)","word_count.pi","word相同的","yield","会走到同一个reduc","但是需要利用hadoop写mapreduce代码,mrjob是很好的选择","使用pip安装","使用python开发在hadoop上运行的程序,","利用mrjob编写和运行mapreduce代码","如果不想成为hadoop专家,","安装","定义了执行的顺序","实现","或","打开命令行,","找到一篇文本文档,","敲如下命令:","特点是调试方便，启动单一进程模拟任务执行状态和结果，默认(","用于hadoop环境，支持hadoop运行调度控制参数，如：","用于本地模拟hadoop调试，与内嵌(inline)方式的区别是启动了多进程执行每一个任务。如：","简介","统计数据中出现次数最多的前n个数据","运行mrjob的不同方式","运行wordcount代码"],"day03_Hadoop/ha3.5.html":["04_mapreduce原理","1.x","3.4",">写入数据",">处理数据",">读取数据",">输出数据","buffer","data：输入数据","disk：将所有的\"小的数据\"进行合并。","hadoop计算流程","input","inputformat：对数据进行切分，格式化处理","jobtracker:负责接收客户作业提交，负责任务到作业节点上运行，检查作业的状态","mapreduce2.x架构","mapreduce原理详解","mapreduce架构","map：将前面切分的数据做map处理(将数据进行分类，输出(k,v)键值对数据)","map：将数据进行处理","memory：达到80%数据时，将数据锁在内存上，将这部分输出到磁盘上","merg","nodemanager：由resourcemanager指派任务，定期向resourcemanager汇报状态","outputformat：格式化输出数据","partitions：在磁盘上有很多\"小的数据\"，将这些数据进行归并排序。","reduce：不同的reduce任务，会从map中对应的任务中copy数据","reduce：将map输出的数据进行hash计算，对每个map数据进行统计计算","resourcemanager：负责资源的管理，负责提交任务到nodemanager所在的节点运行，检查节点的状态","shuffle&sort:将相同的数据放在一起，并对数据进行排序处理","tasktracker：由jobtracker指派任务，定期向jobtracker汇报状态，在每一个工作节点上永远只会有一个tasktrack","​","单机程序计算流程","在reduce中同样要进行merge操作","输入数据"],"day03_Hadoop/ha4.1.html":["01_hadoop生态系统","4.1",":","core","flink:","flume:日志收集框架","hadoop生态系统","hadoop生态系统的特点","hbase","hive:数据仓库","kafka:","mahout:机器学习库","ml","mllib","oozie:工作流引擎，管理作业执行顺序","pig：脚本语言，跟hive类似","python操作storm","r:数据分析","spark","spark:","sql","sqoop:数据交换框架，例如：关系型数据库与hdfs之间的数据交换","storm:","stream","vs","zookeeper:用户无感知，主节点挂掉选择从节点作为主的","不算是一个标准的流式计算","准实时","分布式的流式计算框架","分布式的计算框架基于内存","囊括了大数据处理的方方面面","广义的hadoop","广义的hadoop：指的是hadoop生态系统，hadoop生态系统是一个很庞大的概念，hadoop是其中最重要最基础的一个部分，生态系统中每一子系统只解决某一个特定的问题域（甚至可能更窄），不搞统一型的全能系统，而是小而精的多个小系统；","开源、社区活跃","成熟的生态圈","海量数据中的查询，相当于分布式文件系统中的数据库","消息队列","狭义的hadoop"],"day03_Hadoop/ha4.2.html":["02_hdfs读写流程&高可用","4.2hdf","ahead","blockid","client将namenode返回的分配的可写的datanode列表和data数据一同发送给最近的第一个datanode节点，此后client端和namenode分配的多个datanode构成pipeline管道，client端向输出流对象中写数据。client每向第一个datanode写入一个packet，这个packet便会直接在pipeline里传给第二个、第三个…datanode。","client端按128mb的块切分文件。","datanod","datanode故障容错","hdfs如何实现高可用(ha)","hdfs读写流程","log，先写log，再写内存，因为editlog记录的是最新的hdfs客户端执行所有的写操作。如果后续真实写操作失败了，由于在真实写操作之前，操作就被写入editlog中了，故editlog中仍会有记录，我们不用担心后续client读不到相应的数据块，因为在第5步中datanode收到块后会有一返回确认信息，若没写成功，发送端没收到确认信息，会一直重试，直到成功）","master节点选举","namenod","namenode会认为这个datanode已经宕机","namenode故障容错","namenode查找这个datanode上有哪些数据块,","secondari","zookeeper配合","上的数据块，计算并存储校验和（checksum)","主从热备","从其它datanode上读取备份数据","从其它datanode服务器上复制数据","以及这些数据在其它datanode服务器上的存储情况","写完数据，关闭输输出流。","发送完成信号给namenode。","客户端向namenode发出写文件请求。","对于存储在","将数据复制到其他服务器上","将该块磁盘上存储的所有","报告给","数据存储故障容错","校验不正确抛出异常,","检查是否已存在文件、检查权限。若通过检查，直接先将操作写入editlog，并返回输出流对象。","检查这些数据块在哪些datanode上有备份,","每个datanode写完一个块后，会返回确认信息。","监测到本机的某块磁盘损坏","磁盘介质在存储过程中受环境或者老化影响,数据可能错乱","磁盘故障容错","读写流程&","读取数据的时候,","超时未发送心跳,","通知相应datanode,","通过心跳和namenode保持通讯","重新计算读取出来的数据校验和,","高可用","（注：wal，writ","（注：发送完成信号的时机取决于集群是强一致性还是最终一致性，强一致性则需要所有datanode写完后才向namenode汇报。最终一致性则其中任意一个datanode写完后就能单独向namenode汇报，hdfs一般情况下都是强调强一致性）","（注：并不是写好一个块或一整个文件后才向后分发）","（注：并不是每写完一个packet后就返回确认信息，个人觉得因为packet中的每个chunk都携带校验信息，没必要每写一个就汇报一下，这样效率太慢。正确的做法是写完一个block块后，对校验信息进行汇总分析，就能得出是否有块写错的情况发生）"],"day03_Hadoop/ha4.3.html":["/","0","03_hadoop发行版选择","15","17","18","2.6.0","3日留存","4.3","4.4","4.5","4.6","5.7.0","5日留存","7日留存","8","=",">.class",">.jar",">jvm","apach","app/web","cdh","cdh5.7.0","cdh:","cdh版本一致","cdh版本的这些组件","cloudera","data","distribut","echart","flume","flume*","gmv","gmv相关的指标:","growth","hadoop","hadoop企业应用案例之消费大数据","hadoop企业案例之商业零售大数据","hadoop发布的","hadoop发行版的选择","hdf","hdfs需要把数据导出交给应用程序,","hdp:","hive","hortonwork","http://archive.cloudera.com/cdh5/cdh/5/","jar包","java","kafka","mapreduc","merchandis","page","platform","pv","scala","spark","sparkstreaming(秒)","spark几秒钟","storm(毫秒)","ug","user","view","volume)","三方采集数据","上班后就会登陆后台数据系统","不同数据源产生的数据质量可能差别很大","也许可以直接用","了解公司目前发展的状况","于是将问题提交给技术部门调查，工程师查看","互联网产品要求","互联网大数据平台架构:","亚马逊提前发货系统","产品增长性的关键指标","产生的数据&日志同步到大数据系统","价格异常","任务调度系统","关联分析,","关联推荐","几分钟","分布式系统执行任务瓶颈:","分析","利用sql进行数据统计","历史上所有订单","及时调整运营和产品策略,是大数据技术的关键价值之一","反应网站应收能力的重要指标","发现日活没有明显下降","号早晨发现","号的订单量没有恢复正常，运营人员开始尝试寻找原因","各项指标相对稳定","和","在社区版的基础上做了一些修改","垂直领域领头羊,","埋点采集数据","基本判断,","大数据产品与互联网产品结合","大数据存储与计算的核心","大数据平台","大数据平台(互联网企业)运行的绝大多数大数据计算都是关于数据分析的","大数据应用","大量的清洗,转化处理","存储","客单价","对咨询信息分类统计后发现，新用户的咨询量几乎为","对数据清洗","将上面三个部分整合起来","将应用产生的数据导入到大数据系统,","将问题交给数据分析团队","延迟高","开源社区版","当期新增用户数","很多运营管理人员,","得到需要的运营数据报告","总访问用户数","成交总金额(gross","打开app","打开产品就算活跃","打开以后是否频繁操作就用pv衡量,","打开使用产品的用户","打开用户数","打点:","提升活跃是网站运营的重要目标","搜索关键词","搜索打开转化率","搜索用户数","支付","放入购物车","数据分析","数据分析师分析可能性","数据分析案例","数据同步后导入hdf","数据处理","数据库","数据库,日志","数据库同步:sqoop","数据挖掘","数据规模大,","数据输出与展示","数据采集","数据需要写入数据库","数据驱动运营:","整合网站应用和大数据系统之间的差异,","新增用户出现问题","新增用户数","新增访问网站(新下载app)的用户数","日开始，网站的订单量连续四天明显下跌","日当天发布记录,发现有消息队列sdk更新","日志","日志同步:flume","日活","日订单量","是否有负面报道被扩散","是否某类商品缺货","是否竞争对手在做活动","智能推荐","最新的hadoop版本都是从apach","月","月活","有明显降幅的是咨询详情转化率","有购买意向开始咨询","有购买行为的用户数","某电商网站,","查看日活数据,","每次点击,","每秒产生订单数","比如","毫秒级响应(1秒以内完成)","汇总","汇总报告,","没有全部开源","没有找到原因,","活跃用户数","流式计算","浏览搜索结果列表","淘宝卖家量子魔方","淘宝双11","点击商品访问详情","爬虫","版本不兼容的问题","用户在访问网站的过程中,转化出了问题","用户增长","用户画像","用户留存率","电商网站统计营业额,","留存用户数","的各个组件配合是有不会有兼容性问题","监控宣传","离线存储","离线计算,","离线计算特点:","离线计算通常针对(某一类别)全体数据,","经过处理计算后再导出给应用程序使用","结果再保存到hdf","给运营和决策层提供各种统计报告,","统计分析","统计指标","背景:","订单活跃转化率","订单量","让用户实时展示","读取数据进行计算","调节指标对公司进行管理","转化率","转化过程:","转换","运营人员发现从","运营常用数据指标","运营数据是公司管理的基础","运营数据的获取需要大数据平台的支持","运行时间长","通过数据分析指标监控企业运营状态,","需要通过大数据实现","页面跳转都记一次pv"],"Hive&HBase/01_hive介绍.html":["(manag","(内部表)","0.8版本后加入位图索引","01_hive基本概念","1","1.1.0","2","3","4","4.1","5","5.*.jar","ansi","array","bigint","bin/hiv","binari","boolean","bucket：在","c","cdh5.7.0.tar.gz","char","cli(command","cli、jdbc/odbc、webgui。","com.mysql.jdbc.driv","conf/hiv","conf目录,","connector","cp","date","db","db：在","decim","derbi","devic","docker","doubl","env.sh","env.sh.templ","env.sh中指定hadoop的路径","executor","export","extern","facebook","float","fs","hadoop","hadoop_home=/root/bigdata/hadoop","hadoop。配置好环境变量。","hash","hdf","hive","hive.exec.script.wrapp","hive.metastore.warehouse.dir","hive_home=/root/bigdata/h","hiveserver2基于thrift,","hive。","hive中表的类型","hive启动","hive基本概念","hive安装目录的lib目录下","hive支持的数据类型","hive是数据仓库工具，没有集群的概念，如果想提交hive作业只需要在hadoop集群","hive的metastore元数据服务","hive简介","hive，每一个","hql","hql(hive","http://archive.cloudera.com/cdh5/cdh/5/","insert","int","interface)为","java","javax.jdo.option.connectiondrivernam","javax.jdo.option.connectionpassword","javax.jdo.option.connectionurlmysql","javax.jdo.option.connectionusernam","jdbc/odbc","jdbc:mysql://127.0.0.1:3306/hiv","jdk","line","local","map","mapreduc","mapreduce，减少开发人员的学习成本","master节点上装hive就可以了","metastor","mysql","mysql/derbi","mysql驱动到","overwrite\\into","partition：在","password","path=$hive_home/bin:$path","raw","root","servic","shell","site.xml","smallint","sourc","sql","sql)查询功能，底层数据是存储在","start","string","struct","tabl","table(默认)","table)","table：在","table：数据存放位置可以在","tar","timestamp","tinyint","update\\insert\\delet","varchar","vi","webgui","zxvf","~/.bash_profil","~/app/","上。","上传","上的结构化的数据,是一款基于","下载hive的安装包","不完全支持","不支持(默认)","与","与传统数据库对比","中。","中包含以下数据模型：","中所有的数据都存储在","中的元数据包括","中表现为","中表现为同一个表目录下根据","中表现所属","中，并在随后由","中，没有专门的数据存储格式","为什么使用","主要用途：用来做离线数据分析，比直接用","了解为什么使用hive","了解什么是hive","事务","人员学习成本太高","什么是","任务运行，使不熟悉","任意指定路径","低","使用","修改配置文件","允许远程客户端使用多种编程语言如java、python向hive提交请求","元数据存储：通常是存储在关系数据库如","元数据库信息(mysql安装见文档)","关系型数据库","内置","写模式","分区及其属性","利用","功能扩展很方便","原子数据类型","只能用在from子句中","可扩展性","启动","启动docker","启动hive","启动mysql","启动即可使用","命令行","和","在hive","在创建表时指定数据中的分隔符，hive","处理和计算","处理数据所面临的问题：","处理数据规模","复杂数据类型","外部表","大","子查询","存储数据，利用","学习目标","安装前需要安装好","安装部署","完全支持","实现复杂查询逻辑开发难度太大","实现并开源，是基于","实现，与传统数据库jdbc","密码：password","将","将元数据存储在数据库中。","小","就可以映射成功，解析数据。","属于内嵌模式。实际生产环境中则使用","并解压","开发效率更高。","托管表","执行","执行延迟","找到","拥有一套自己的元数据，无法共享","操作接口采用类","支持","散列之后的多个文件","数据存储","数据模型","是","是通过浏览器访问","更新","有复杂的索引","本质:","来进行元数据的存储。","架构","架构图","查询分析数据。","查询语句从词法分析、语法分析、编译、优化以及查询计划的生成。生成的查询计划存储在","查询语言","根据元数据存储的介质不同，分为下面两个版本，其中","模式","版：","用于海量数据的离线数据分析。","用户名：root","用户接口：包括","由","的","的一个数据仓库工具，可以将结构化的数据映射为一张数据库表，并提供","的关系","的用户很方便地利用","目录下一个文件夹","目录下的子目录","直接使用","类似","索引","组件","缺点：不同路径启动","表的列","表的名字","表的属性（是否为外部表等）","表的数据所在目录等。","解压后的hive目录","解释器、编译器、优化器、执行器:完成","计算框架","语句转换为","语法，提供快速开发的能力","读模式","调用执行","进入到","通过docker","避免了去写","配置","配置环境变量","高"],"Hive&HBase/02_hive的shell操作.html":["','","',';","'/home/hadoop/tmp/student.txt'overwrit","'/root/tmp/employee.txt'","'/root/tmp/student.txt'","'/tmp/student';","'\\n'","(classno","(date1","(name","*","/tmp/employee.txt","/user/hive/warehouse/employee/date1=2018","/user/hive/warehouse/test.db/employee/date1=2018","01');","02_hive的shell操作","04","04');","04/employee.txt","1","12","2","3","4","4.2","add","alter","bigint)","by和统计","c01,n0101,82","c01,n0102,59","c01,n0103,65","c02,n0201,81","c02,n0202,82","c02,n0203,79","c03,n0301,56","c03,n0302,92","c03,n0306,72","classno,count(score)","classno;","copyfromloc","count","creat","data","databas","databases;","delimit","desc","drop","employe","employee2","employee;","exist","extern","field","format","fs","group","hadoop","hdfs中任意位置","hive","hive.exec.dynamic.partition.mode=nonstrict;","hive.metastore.warehouse.dir","hive>select","hive不会对student.txt做任何格式处理，因为hive本身并不强调数据的存储格式。","hive中分区表实际就是对应hdfs文件系统上独立的文件夹，该文件夹内的文件是该分区所有数据文件。","hive会自动添加分区列","hive把查询的结果变成了mapreduce作业通过hadoop执行","hive的内部表和外部表","hql操作初体验","inpath","insert","int)","jake,11000","jerry,12000","line","load","local","locat","mike,13000","mkdir","name,salary,date1","overwrit","partit","partition(date1)","partition(date1='2018","repair","rob,10000","row","score","score>=60","select","set","show","store","string)","string,","string,salari","student","student(classno","student2","student2;","student;","stuno","tabl","table)","table_name;","table_name;）","termin","test;","textfile;","tom,4300","不插入数据直接查询查看结果","也必须要通过hql添加分区,","什么是分区表","仅会删除元数据，hdfs上的文件并不会被删除","从执行结果可以看出","使用动态分区需要设置参数","修改会将修改直接同步给元数据","内部表(manag","再次创建外部表","分区仅仅是一个目录名","分区可以理解为分类，通过分类把不同类型的数据放到不同的目录下。","分区字段不是表中的列,","分区表","分区表的意义在于优化查询。查询时尽量利用分区字段。如果不使用分区字段，就会全部扫描。","分类的标准就是分区字段，可以一个，也可以多个。","分组查询group","创建一个外部表student2","创建分区表","创建数据库","创建表","创建表时无external修饰","创建表时被external修饰","删除时影响","删除表查看结果","利用分区表方式减少查询时需要扫描的数据量","加载数据到分区","动态分区","在写入数据时自动创建分区(包括目录结构)","在本地文件系统创建一个如下的文本文件：/home/hadoop/tmp/student.txt","基本操作","外部分区表即使有分区的目录结构,","外部表(extern","多级子目录","如果重复加载同名文件，不会报错，会自动创建一个*_copy_1.txt","导入数据","将数据load到表中","并将文件内容映射到表中。","总结","才能看到相应的数据","指定了字段的分隔符为逗号，所以load数据的时候，load的文本也要为逗号，否则加载后为null。hive只支持单个字符的分隔符，hive默认的分隔符是\\001","支持多级分区,","数据保存位置","数据文件中没有对应的列","数据管理","显示所有数据库","显示表信息","查看数据时,","查看表的分区","查询表中的数据","案例","概念","此时再次查看才能看到新加入的数据","此时查看表中数据发现数据并没有变化,","添加分区","由hdfs管理","由hive自身管理","直接删除元数据（metadata）及存储数据","表结构修改时影响","表结构和分区进行修改，则需要修复（msck","装载数据","跟sql类似","这个命令将student.txt文件复制到hive的warehouse目录中，这个目录由hive.metastore.warehouse.dir配置项设置，默认值为/user/hive/warehouse。overwrite选项将导致hive事先删除student目录下所有的文件,","随着表的不断增大，对于新纪录的增加，查找，删除等(dml)的维护也更加困难。对于数据库中的超大型表，可以通过把它的数据分成若干个小表，从而简化数据库的管理活动，对于每一个简化后的小表，我们称为一个单个的分区。","需要通过hql添加分区","（默认：/user/hive/warehouse）"],"Hive&HBase/03_Hive的函数和自定义函数.html":["'\\t'.join([fname,","'hdfs:///user/hive/lib/h","'org.apache.hadoop.hive.contrib.udf.udfrowsequence'","'python","(fname,","(udafs)","(udfs)","(内容较多，见《hive",",","/root/tmp/udf1.py;","/user/hive/lib","/user/hive/lib/","/user/hive/lib目录","03_hive的函数和自定义函数","1","1.1.0","2","3","4.3","=","actual","add","aggreg","aggregation.","allow","avg","basic","by中的就是reducer。","by和clust","call","cdh5.7.0.jar","cdh5.7.0.jar';","cdh5.7.0.jar;","cluster","contrib","control","creat","custom","defin","desc","distinct","distribut","done","each","employee;","even","exactli","extend","file","fname","formul","fs","function","functions;","function）。","group","hadoop","hand","hdfs:///user/hive/lib/h","hdfs:///user/hive/lib/udf.py;","hive","hive>","hiveql","hive从hdfs中加载python脚本","hive目录下","https://cwiki.apache.org/confluence/display/hive/languagemanual+udf","import","indic","input","insert","jar","l_name","l_name)","lib/hiv","line","line.split('\\t')","line.strip()","lname","lname)","lname.upper()","map","mapper","mean","mkdir","more","number","on","output","perform","plug","possibl","print","put","python","query.","reduc","row","row.","row_sequ","row_sequence(),*","rows,","run","script","select","show","singl","statement","step","str(l_name)])","string);","string,lnam","sum","sy","sys.stdin:","tabl","temporari","therefor","transform","transform(fname,","transform,and","u(fnam","u2","u;","udaf","udaf:就是一个reducer，把一组输入数据映射为一条(或多条)输出数据。","udf","udf.pi","udf1.py'","udf示例(运行java已经编写好的udf)","udf：就是做一个mapper，对每一条输入数据，映射为一条输出数据。","us","user","values('bill','clinton');","values('bill','gates');","values('george','bush');","values('george','washington');","want","way","一个脚本至于是做mapper还是做reducer，又或者是做udf还是做udaf，取决于我们把它放在什么样的hive操作符中。放在select中的基本就是udf，放在distribut","关系运算符","内置函数","内置运算符","准备案例环境","函数","函数名;","分析函数","创建表","创建非临时自定义函数","加载文件到hdf","向表中插入数据","在","在hdfs中创建","在hive中创建临时udf","在之前的案例中使用临时自定义函数(函数功能:","复杂运算","字符串函数","官方文档》》)","当","把","把集群中jar包的位置添加到hive中","提供的内置函数无法满足你的业务处理需要时，此时就可以考虑使用用户自定义函数（udf：us","放到hdfs中","日期函数","显示所有函数","有四种类型的运算符：","添加自增长的行号)","简单函数:","算术运算符","类型转换","统计函数:","编写map风格脚本","自定义函数和","通过hdfs向hive中add","逻辑运算符","集合函数"],"Hive&HBase/04_hive综合案例.html":["#含有outer","','","'/tmp/demo/article_keywords';","'/tmp/demo/user_action';","'|'","(a.article_id","(count(1)","(select",")","*","*/","/*","/tmp/demo","/tmp/demo/user_act","00:04:12","01","02","03","04","04_hive综合案例","05","06:01:10","07:09:12","07:28:12","07:50:14","09:07:12","09:08:12","09:21:12","1","1,kw3:1,kw6:1","101","101,http://www.itcast.cn/1.html,kw8|kw1","102","102,http://www.itcast.cn/2.html,kw6|kw3","103","103,http://www.itcast.cn/3.html,kw7","104","104,http://www.itcast.cn/4.html,kw5|kw1|kw4|kw9","105","105,http://www.itcast.cn/5.html,","11","11,101,2018","11,104,2018","11:00:12","12","12:11:12","13:37:12","16:42:12","18:02:02","18:31:12","18:42:12","19:10:12","2","20:09:11","22","22,102,2018","22,103,2018","22,104,2018","3","33","33,101,2018","33,102,2018","33,103,2018","35","35,102,2018","35,105,2018","4","4.4","77","77,103,2018","77,104,2018","99","99,102,2018","99,105,2018","=","[\"1\",\"1\",\"1\",\"1\",\"1\",\"2\",\"1\"]","[\"1\",\"1\",\"1\",\"1\",\"1\"]","[\"1\",\"kw3\",\"kw6\"]","[\"101\",\"101\",\"101\",\"104\"]","[\"101\",\"102\",\"103\"]","[\"101\",\"104\",\"101\",\"101\"]","[\"101\",\"104\"]","[\"102\",\"103\",\"103\",\"104\"]","[\"102\",\"103\",\"104\",\"103\"]","[\"102\",\"103\",\"104\"]","[\"102\",\"105\"]","[\"103\",\"102\",\"101\"]","[\"103\",\"104\"]","[\"105\",\"102\"]","[\"4\",\"1\",\"1\",\"3\",\"1\"]","[\"kw1\",\"kw3\",\"kw4\",\"kw5\",\"kw6\",\"kw7\",\"kw9\"]","[\"kw1\",\"kw3\",\"kw6\",\"kw7\",\"kw8\"]","[\"kw1\",\"kw4\",\"kw5\",\"kw7\",\"kw9\"]","[\"kw1\",\"kw4\",\"kw5\",\"kw8\",\"kw9\"]","[\"kw5\",\"kw1\",\"kw4\",\"kw9\"]","[\"kw6\",\"kw3\"]","[\"kw7\"]","[\"kw8\",\"kw1\"]","[]","[null,\"1\",\"1\"]","a.user_id,","a.user_id,b.kw","a.user_id,b.kw;","a.user_id,weight","a.user_id;","array","artical_id,artical_url,artical_keyword","articl","article_id","article_id,kw","articles(","articles;","b","b.article_id)","b.kw","b.kw,count(1)","by中的某列转为一个数组返回","cc","cc.user_id,concat_ws(',',collect_set(cc.kw_w))","cc.user_id,str_to_map(concat_ws(',',collect_set(cc.kw_w)))","cc.user_id;","collect","collect_list","collect_list不去重而collect_set去重","collect_set","collect_set/collect_list作用:","concat","concat(str1,str2,…)","concat(user_id,article_id)","concat_ws(':',b.kw,cast","concat_ws(':',user_id,article_id)","concat_ws()","concat_ws:","concat：","content","creat","delimit","desc;","drop","event_tim","exist","explod","explode(key_words)","explode(wm)","explode函数","explode把map中的数据转换成多列","extern","field","format","from(","fs","group","hadoop","hive综合案例","http://www.itcast.cn/1.html","http://www.itcast.cn/2.html","http://www.itcast.cn/3.html","http://www.itcast.cn/4.html","http://www.itcast.cn/5.html","item","join","key:value形式","key:value形式并按用户聚合","key_word","keyword,weight;","kw)","kw1","kw1:1","kw1:1,kw3:1,kw4:1,kw5:1,kw6:1,kw7:2,kw9:1","kw1:1,kw3:1,kw6:1,kw7:1,kw8:1","kw1:1,kw4:1,kw5:1,kw7:1,kw9:1","kw1:4","kw1:4,kw4:1,kw5:1,kw8:3,kw9:1","kw3","kw3:1","kw4","kw4:1","kw5","kw5:1","kw6","kw6:1","kw7","kw7:1","kw7:2","kw8","kw8:1","kw8:3","kw9","kw9:1","kw;","kw_w","later","left","locat","mkdir","null","null。","null，则结果为","order","outer","row","select","separ","sort_array:","string","string))","string,","t","tabl","termin","time_stamp","url","user_act","user_actions(","user_actions;","user_id","user_id,","user_id,collect_list(article_id)","user_id,collect_set(article_id)","user_id,keyword,weight","user_id,map_keys(wm),map_values(wm)","user_id,sort_array(collect_list(article_id))","user_id;","user_kw","user_kws;","view","weight","wm","wm['kw1']","{\"1\":null,\"kw3\":\"1\",\"kw6\":\"1\"}","{\"kw1\":\"1\",\"kw3\":\"1\",\"kw4\":\"1\",\"kw5\":\"1\",\"kw6\":\"1\",\"kw7\":\"2\",\"kw9\":\"1\"}","{\"kw1\":\"1\",\"kw3\":\"1\",\"kw6\":\"1\",\"kw7\":\"1\",\"kw8\":\"1\"}","{\"kw1\":\"1\",\"kw4\":\"1\",\"kw5\":\"1\",\"kw7\":\"1\",\"kw9\":\"1\"}","{\"kw1\":\"4\",\"kw4\":\"1\",\"kw5\":\"1\",\"kw8\":\"3\",\"kw9\":\"1\"}","​","从表中获取map中所有的key","从表中通过key查询map中的值","代表","使用语法为：concat_ws(separator,str1,str2,…)","内容推荐数据处理","分组查询每个用户的浏览记录","创建外部表","原始数据","和","对数组排序","将array","将group","将上面聚合结果转换成map","将用户查看的关键字和频率合并成","将用户的阅读偏好结果保存到表中","所有的valu","拆分","数据上传hdf","数组的元素之间用'|'分割","数组的数据类型","文章数据","文章表","查看数据","查看每一篇文章的关键字","根据文章id找到用户查看文章的关键字","根据文章id找到用户查看文章的关键字并统计频率","根据用户行为以及文章标签筛选出用户最感兴趣(阅读最多)的标签","用later","用户行为表","相关数据","返回结果为连接参数产生的字符串。如有任何一个参数为null","配合使用,将一行数据拆分成多行数据，在此基础上可以对拆分的数据进行聚合","需求","，则返回值为","，是concat()的特殊形式。第一个参数是其它参数的分隔符。分隔符的位置放在要连接的两个字符串之间。分隔符可以是一个字符串，也可以是其它参数。如果分隔符为"],"Hive&HBase/05_hBase简介与环境部署.html":["/home","/home/p","01_hbase简介与环境部署","1","1024","2","3","4","5","5.1","5032","6","7","acid只支持单个row级别","bigtabl","bigtable是google设计的分布式数据存储系统，用来处理海量的数据的一种非关系型的数据库。","bigtable的开源实现","byte","creator","creator:jerri","creator:tom","file","file1.txt","file2.txt","gb","hbase","hbase不同于一般的关系数据库,","hbase不适合有join,","hbase与hdf","hbase使用场景","hbase内部使用哈希表,","hbase可以存储超大数据并适合用来进行大数据的实时查询","hbase在hadoop生态中的地位","hbase基于hdfs进行数据存储","hbase建立在hadoop文件系统上,","hbase提供对数据的随机实时读/写访问功能","hbase是apache基金会顶级项目","hbase是googl","hbase是一个分布式的、面向列的开源数据库","hbase简介","html、各类报表、图像和音频/视频信息等","id","info","jerri","jpg","key","name","name:file1.txt","name:file2.jpg","path","path:/hom","path:/home/p","pb级别","rowkey","save","size","size:1024","size:5032","tb","tom","txt","type","type:jpg","type:txt","不方便用数据库二维逻辑表来表现","不适用于传统关系型数据的存储；","与","且数量会持续增长","丰富的数据类型","事务支持","什么是hbase","什么是非结构化数据存储","传统关系数据库的区别","全面的acid支持,","关系型数据库","关系型数据库中数据示例","分布式、并发数据处理，效率极高；","利用了hdfs的容错能力","办公文档、文本、图片、xml,","只支持row","可以快速查找hdfs中数据","同样数据保存到列式数据库中","吞吐量","多级索引,","大量数据需要长期保存,","对row和表","并存储索引,","支持","数千写入/秒","数据库大小","数据类型","易于扩展，支持动态伸缩","没有预定义的数据模型","百万写入/秒","瞬间写入量很大","索引","结构化数据","行数据库&列数据库存储方式比较","表关系复杂的数据模型","适合大规模海量数据，pb级数据；","适合用二维表来展示的数据","适合非结构化数据存储","适用于廉价设备；","非结构化数据","非结构化数据是数据结构不规则或不完整","面向列的数据库"],"Hive&HBase/06_hbase数据模型.html":["02_hbase数据模型","5.2",":","acid","acid，即对行级别的","cap定理","column","famili","family:","hbase","hbase是cap中的cp系统,即hbase是强一致性的","hbase的列由","hbase的数据模型","key)来进行唯一标识的,","namespace:","qualifi","qualifier)","value),","一致性(所有节点在同一时间具有相同的数据)","以二进制的字节来存储","关系型数据库的\"数据库\"(database)","分区容错性(系统中任意信息的丢失或失败不会影响系统的运行,系统如果不能在某个时限内达成数据一致性,就必须在上面两个操作之间做出选择)","分布式系统的最大难点，就是各个节点的状态如何同步。cap","列(column):","列修饰符(column","列族(columnfamily)：是列的集合。列族在表定义时需要指定，而列在插入数据时动态指定。列中的数据都是以二进制形式存在，没有数据类型。在物理存储结构上，每个表中的每个列族单独以一个文件存储。一个表可以有多个列簇。","列族中的数据通过列标识来进行映射,","区域(region)：hbase自动把表水平划分成的多个区域，划分的区域随着数据的增大而增多。","可以理解为一个键值对(key","可用性(保证每个请求不管成功或失败都有响应,但不保证获取的数据的正确性)","和","如","定理是这方面的基本定理，也是理解分布式系统的起点。","对应关系型数据库的列","就是key","操作保证完全的","支持特定场景下的","时间戳(timestamp)：是列的一个属性，是一个64位整数。由行键和列确定的单元格，可以存储多个数据，每个数据含有时间戳属性，数据具有版本特性。可根据版本(versions)或时间戳来指定查询历史版本数据，如果都不指定，则默认返回最新版本的数据。","由冒号:","组成,","行(row)：在表里面,每一行代表着一个数据对象,每一行都是以一个行键(row","行键(rowkey)：类似于mysql中的主键，hbase根据行键来快速检索数据，一个行键对应一条记录。与mysql主键不同的是，hbase的行键是天然固有的，每一行数据都存在行键。","行键并没有什么特定的数据类型,","表(table)：用于存储管理数据，具有稀疏的、面向列的特点。hbase中的每一张表，就是所谓的大表(bigtable)，可以有上亿行，上百万列。对于为值为空的列，并不占用存储空间，因此表可以设计的非常稀疏。","进行行间隔,"],"Hive&HBase/07_hbase的安装与shell操作.html":["\"表名称\"","#","##","#hbase中一般存储数据量都很大","#limit=>2","#startrow","#显示结果","#返回结果如下","#返回结果：","'1',","'65536'}","'base_info',","'base_info:username'","'delete'","'f2'","'false',","'forever',","'none',","'rowkey_16',","'test'","'test:user','base_info'","'timestampsfilt","'user'","'user',","'user','base_info'","'user','rowkey_10','base_info:address','tokyo'","'user','rowkey_10','base_info:birthday','2014","'user','rowkey_10','base_info:sex','1'","'user','rowkey_10','base_info:username','tom'","'user','rowkey_10',{column=>'base_info:username',timestamp=>1558323904133}","'user','rowkey_10',{column=>'base_info:username',timestamp=>1558323918953}","'user','rowkey_10',{column=>'base_info:username',versions=>10}","'user','rowkey_10',{column=>'base_info:username',versions=>2,filt","'user','rowkey_10',{column=>'base_info:username',versions=>2,timerang","'user','rowkey_10',{column=>'base_info:username',versions=>2}","'user','rowkey_16'","'user','rowkey_16','base_info'","'user','rowkey_16','base_info:address','beijing'","'user','rowkey_16','base_info:birthday','2014","'user','rowkey_16','base_info:sex','1'","'user','rowkey_16','base_info:username'","'user','rowkey_16','base_info:username','mike'","'user','rowkey_22','base_info:address','newyork'","'user','rowkey_22','base_info:birthday','2014","'user','rowkey_22','base_info:sex','1'","'user','rowkey_22','base_info:username','jerry'","'user','rowkey_24','base_info:address','shanghai'","'user','rowkey_24','base_info:birthday','2014","'user','rowkey_24','base_info:sex','1'","'user','rowkey_24','base_info:username','nico'","'user','rowkey_25','base_info:address','soul'","'user','rowkey_25','base_info:birthday','2014","'user','rowkey_25','base_info:sex','1'","'user','rowkey_25','base_info:username','rose'","'user',name=>'base_info',versions=>10","'user',{column","'user',{filt","'列族名1','列族名2','列族名n'","'列族名2'],","'名称空间:表名',","'行名','列名'","'表名'","'表名',","'表名','行名'","'表名','行名','列名:','值","'起始的rowkey'}","(1558323139732,","(1558323904130,",",{column",",{columns=>'列族名:列名'}","/hbase/bin/start","/root/bigdata/zookeep","0.0120","03_hbase的安装与shell操作","07","1","1.2.0","10","10'","10,","1558323139866)'}","1558323139866]}","1558323918954)'}","1558323918954]}","2","2181","3.4.14/datadir","5.3","=>","=>['base_info'],limit=>2,startrow=>'rowkey_16'}","=>['base_info'],limit=>2}","['base_info:username','base_info:sex']}","['列族名1',","[1558323139732,","[1558323904130,","alter","base_info:usernam","blockcach","blocksiz","cache_bloom","cache_index_on_writ","cdh5.7.0.tar.gz","cell","column","column+cel","column=base_info:address,","column=base_info:birthday,","column=base_info:sex,","column=base_info:username,","compress","count","creat","create_namespac","data_block_encod","ddl","delet","desc","descript","disabl","dml","drop","enabl","env.sh","er","evict_blocks_on_clos","export","fals","famili","hadoop","hbase","hbase.cluster.distribut","hbase.mast","hbase.rootdir","hbase.sh","hbase.unsafe.stream.capability.enforc","hbase.zookeeper.property.clientport","hbase.zookeeper.property.datadir","hbase.zookeeper.quorum","hbase_home=/root/bigdata/hbas","hbase_manages_zk=fals","hbase的安装","hdfs://hadoop","he_data_on_writ","http://archive.cloudera.com/cdh5/cdh/5/hbas","in_memori","java_home=/root/bigdata/jdk","limit","list_namespac","list_namespace_t","master:2181","master:60000","master:9000/hbas","mi","name","new_version_b","path=$hbase_home/bin:$path","put","row","row(s)","rowkey","rowkey_10","rowkey_16","rowkey_22","rowprefixfilt","scan","scan会加上一些条件限制","scan查询中添加限制条件","scan查询添加过滤器","se',","second","shell","shell（进入shell命令行）","site.xml","startrow","tabl","timerang","timestamp=1558323139575,","timestamp=1558323139636,","timestamp=1558323139678,","timestamp=1558323139732,","timestamp=1558323139866,","timestamp=1558323139907,","timestamp=1558323139963,","timestamp=1558323140036,","timestamp=1558323140107,","timestamp=1558323140143,","timestamp=1558323140188,","timestamp=1558323758696,","timestamp=1558323904133,","timestamp=1558323918953,","took","true","truncat","ttl","user","value=1","value=2014","value=beij","value=jerri","value=newyork","value=tokyo","value=tom","value=tom2","value=tom3","value=tom4","version","versions=>'1'说明最多可以显示一个版本","{column","{name","{rowprefixfilter=>'rowkey_22'}","‘表名’，‘rowkey的值’，’列族：列标识符‘，’值‘","下载安装包","会保留多个版本数据","修改可以显示的版本数量","修改数据","创建名称空间","创建表","创建表的时候添加namespac","删除一张表","删除表","删除表中的数据","删除记录","前缀过滤器","名称","启动hbase（启动的hbase的时候要保证hadoop集群已经启动）","命令","命令表","命令表达式","和","如果你是使用hbase自带的zk就是true，如果使用自己的zk就是fals","展示现有名称空间","很少使用全表查询","指定具体时间戳的值","指定时间范围","指定显示多个版本","插入数据","操作列簇","显示某个名称空间下有哪些表","更新记录","查看所有记录","查看指定表指定列所有数据","查看表中的记录总数","查看记录","查询某个rowkey的数据","查询某个列簇的数据","查询表中的所有数据","添加记录","清空数据","环境变量配置","的安装与shell操作","第一步","第二步","等条件缩小查询范围","获取最近多个版本的数据","输入hbase","返回结果","连接集群","追加型数据库","通过column","通过timerang","通过指定时间戳获取不同版本的数据","通过时间戳查询","通过时间戳过滤器","配置hbase","配置伪分布式环境","重写覆盖","限制起始的rowkey","限制输出两行"],"Hive&HBase/08_HappyBase操作HBase.html":["\"__main__\":","\"columnprefixfilter('username')\"","#","#api","#connecthbase()","#createtable()","#deletedata()","#deletetable()","#filter","#getquery()","#insertdata()","#result","#row_start","#全表查询","#关闭连接","#函数封装","#创建和hbase的连接","#封装函数","#获取hbase中的所有表","#通过connection找到user表","%","%s","0.9x","04_happybase操作hbas","5.4",":","=","==","__name__","apach","api","applic","autoconnect=false)","base","befor","below","cf_list)","conn.delete_table(table_name,","connect","connecthbase():","connection.close()","connection.create_table('users',{'cf1':","connection.delete_table('users',disable=true)","connection.open()","connection.open():","connection.table('mytable')","connection.table('user')","connection.table('users')","connection.tables():","createtable():","daemon.sh","def","delete_table(table_name):","deletedata():","deletetable():","design","develop","dict()})","filter","first","friendli","gateway,","getquery():","happybas","happybase.connection('192.168.19.137')","happybase.connection('somehost')","happybase.connection('somehost',","happybase操作hbas","hbase","hbase.","import","includ","insertdata():","instal","interact","key,valu","librari","main()","main():","now.'","offer","pip","pretty_print('delet","print(connection.tables())","print(key,value)","print(result)","python","releases.","result","scanquery()","scanquery():","server","server的socket链接.","setups,","standard","start","surface,","tabl","table.delete('rk_01',['cf1:username'])","table.delete(row_key,","table.put('rk_01',{'cf1:address':'beijing'})","table.put(row_key,","table.row('row_key')","table.row('rowkey_22',columns=['base_info:username'])","table.rows(['rowkey_22','rowkey_16'],columns=['base_info:username'])","table.rows([row_keys])","table.scan()","table.scan():","table.scan(row_start='rowkey_10',filter=filter):","table_name)","table类提供了大量api,","thrift","thrift,","true)","us","use:","{'cf:cq':'value'})","什么是happybas","会自动创建一个与","但使用方式比thrift简单,","关闭连接","其基于python","创建和hbase的连接","创建表之后可以传入表名获取到table类的实例:","删除数据","删除表","可以通过参数禁止自动链接,","启动hbase","在上面的示例中，我们已经使用connection.tables（）方法查询hbase中的表。","如何使用happybas","如果还没有任何表，可使用connection.create_table（）创建一个新表：","安装happi","完整代码","已被广泛应用","建立连接","当连接建立时,","指定起始rowkey","插入数据","操作表","是facebook员工开发的操作hbase的python库,","查询一行","查询多行","查询操作","比如获取hbase中所有的表:","添加过滤器","然后再需要连接是调用","缩小查询范围","获得table对象","这个类提供了一个与hbase交互的入口,","这些api用于检索和操作hbase中的数据。","通过connection找到user表"],"Hive&HBase/10_HBase组件.html":["05_hbase组件","1","2","5.6",">","client","client访问regionserver写入数据","election机制保证总有一个master运行。","family。","family会更高效。","family就是一个集中的存储单元，故将具有相同io特性的column放在一个column","family的存储，column","family，就只有一个store。","flush成storefil","hbase","hbase中最核心的模块，主要负责响应用户i/o请求，向hdfs文件系统中读写数据。","hbase启动","hbase存储的核心，由memstore和storefile组成。","hbase模块协作","hbase组件","hlog","hmaster","hmaster启动,","hmaster失效","hmaster将失效regionserver上的region分配到其他节点","hmaster更新hbase:","hregion","hregionserv","hstore","master，避免单点问题；","meta","regionserver失效","regionserver注册到zookeeper,","region会随着插入的数据越来越多，会进行拆分。默认大小是10g一个。","root","rpc机制与hmaster和hregionserver进行通信；","server分配region；","server的上线和下线信息，实时通知给master；","server的状态，将region","server的负载均衡；","serve并重新分配其上的region；","zk返回regionserver地址","zookeep","①与zookeeper通信,","①为region","①保证任何时候，集群中只有一个run","①维护master分配给它的region，处理对这些region的io请求；","②使用hbase","②存贮所有region的寻址入口，包括","②负责region","②负责切分在运行过程中变得过大的region。","③client与hmaster进行通信进行管理类操作；","③发现失效的region","③实时监控region","④client与hregionserver进行数据读写类操作。","④hdfs上的垃圾文件回收；","④存储hbase的schema，包括有哪些table，每个table有哪些column","⑤处理用户对表的增删改查操作。","一个region中会有个多个store，每个store用来存储一个列簇。如果只有一个column","一个表最开始存储的时候，是一个region。","也不能更改表结构","但是不能创建删除表,","作用：","分配region和meta信息","可以启动多个hmaster，通过zookeeper的mast","在分布式系统环境中，无法避免系统出错或者宕机，一旦hregionserver意外退出，memstore中的内存数据就会丢失，引入hlog就是防止这种情况。","基础架构","处于backup状态的其他hmaster节点推选出一个转为active状态","对各个regionserver(包括失效的)的数据进行整理,","并向hmaster汇报","找到数据入口地址","数据存入memstore，一直到memstore满","数据能正常读写,","此外，hregionserver管理一系列hregion对象，每个hregion对应table中一个region，hregion由多个hstore组成，每个hstore对应table中一个column","注册到zookeeper,","用户写入数据的流程为：client访问zk,","等待regionserver汇报","表以保证数据正常访问","表地址、hmaster地址；","角色功能："],"day05_Spark_core/spark_core_1.html":["\"))","('ab',","('abc',","('ac',","('bar',","('bc',","('bec',","('by',","('foo',","('hadoop',","('labs',","('me',","('quux',","('see',","('test',","('welcome',","('you',","(x,","+","./pyspark",".flatmap(lambda",".map(lambda",".reducebykey(lambda","01_spark入门","1))","1),","1)]","1.1","1.2","1、什么是spark","1、速度快（比mapreduce在内存中快100倍，在磁盘中快10倍）","1，map结果写磁盘，reduce写hdfs，多个mr之间通过hdfs交换数据","2),","2、为什么要学习spark","2、易用性（可以通过java/scala/python/r开发spark应用程序）","2，任务调度和启动开销大","3、spark特点","3、通用性（可以使用spark","3，无法充分利用内存","4),","4、兼容性（spark程序可以运行在standalone/yarn/mesos）","4，不适合迭代计算（如机器学习、图计算等等），交互式处理（数据挖掘）","5，不适合流式处理（点击日志分析）","6，mapreduce编程不够灵活，仅支持map和reduce两种操作","=","[('python',","\\","a,","b).collect()","b:","dag引擎，较少多次计算之间中间结果写到hdfs的开销","hadoop生态圈","line.split(\"","line:","local模式的启动","mapreduce中map和reduce任务都是以进程的方式运行着，而spark中的job是以线程方式运行在进程中。","mapreduce框架局限性","sc","sc.textfile('file:///home/hadoop/tmp/word.txt')","spark","spark.sparkcontext","spark中的job中间结果可以不落地，可以存放在内存中。","spark启动（local模式）和wordcount(演示)","spark概述","spark的缺点是：吃内存，不太稳定","sql/spark","streaming/mlib/graphx）","word","x:","了解spark概念","交互式计算：impala、presto","使用多线程模型来减少task启动开销，shuffle过程中避免不必要的sort操作以及减少磁盘io","入门","内存计算引擎，提供cache机制来支持需要反复迭代计算或者多次数据共享，减少数据读取的io开销","启动pyspark","在$spark_home/sbin目录下执行","基于内存的计算引擎，它的计算速度非常快。但是仅仅只涉及到数据的计算，并没有涉及到数据的存储。","批处理：mapreduce、hive、pig","流式计算：storm","独立实现spark","知道spark的特点（与hadoop对比）","课程目标：","输出结果：","需要一种灵活的框架可同时进行批处理、流式计算、交互式计算"],"day05_Spark_core/spark_core_2.html":["\"copyright\",","\"credits\"","\"help\",","\"license\"","\"warn\".","'_/","'abc","'spark'.","(default,","(red","+",".__/\\_,_/_/","/","/_/","/_/\\_\\","/__","02_rdd概念介绍","12:19:55","13","15","15:43:53)","19/03/08","192.168.19.137:4040","2,","2.1","2.2","2.3.0","20150623","2018","2018,","28)]","3,","3.5.0","4,","4.8.5","4个分区）如未指定分区数量，spark会自动设置分区数。","5]","=",">>>",">rdd2","['foo","[1,","[gcc","[hadoop@hadoop000","\\/","_","_\\","__","__/","__/__","___","____","_____/","`/","a,","ab","abc","ac","adjust","applic","avail","b)","b:","bar","bc","bec","builtin","class","collections方式创建rdd","conf","data","dataset:一个数据集，简单的理解为集合，用于存放数据的","dataset）叫做弹性分布式数据集，是spark中最基本的数据抽象，它代表一个不可变、可分区、里面的元素可并行计算的集合.","default","distdata","distdata.reduce(lambda","distribut","distributed：它的数据是分布式存储，并且可以做分布式的计算","foo","hadoop","hat","information.","java","lab","level","librari","linux","load","log","more","nativ","nov","parallel","parallelcollectionrdd[0]","partit","platform...","pyspark","pyspark可以从hadoop支持的任何存储源创建rdd，包括本地文件系统，hdfs，cassandra，hbase，amazon","python","python']","pythonrdd.scala:175","quux","rdd1","rdd1.collect()","rdd会在多个节点上存储，就和hdfs的分布式道理是一样的。hdfs文件被切分为多个block存储在各个节点上，而rdd是被切分为多个partition。不同的partition可能在不同的节点上","rdd概述","rdd的创建","rdd（resili","resilient：弹性的","s3等","sc","sc.parallelize(data)","sc.parallelize(data,5)","sc.setloglevel(newlevel).","sc.textfile('file:///root/tmp/word.txt')","see","set","setloglevel(newlevel).","shell中","sparkconf","sparkconf().setappname(appname).setmaster(master)","sparkcontext","sparkcontext(conf=conf)","sparkcontext,","sparkcontext代表了和spark集群的链接,","sparkr,","sparksess","spark将为群集的每个分区（partition）运行一个任务（task）。","spark程序的入口.","spark计算结束，一般会把数据做持久化到hive，hbase，hdfs等等。我们就拿hdfs举例，将rdd持久化到hdfs上，rdd的每个partition就会存成一个文件，如果文件小于128m，就可以理解为一个partition对应hdfs的一个block。反之，如果大于128m，就会被且分为多个block，这样，一个partition就会对应多个block。","spark读取hdfs的场景下，spark把hdfs的block读到内存就会抽象为spark的partition。","test","test',","type","ui中看到当前的spark作业","ui中观察执行情况","unabl","us","util.nativecodeloader:","version","warn","welcom","~]$","不可变","什么是rdd","创建rdd","创建sparkcontext","创建sparkcontext的时候需要一个sparkconf，","可以在spark","可分区","在spark","在spark集群中通过sparkcontext来创建rdd","在浏览器访问当前centos的4040端口","在通过parallelize方法创建rdd","它表示的是数据可以保存在磁盘，也可以保存在内存中","已经为我们创建好了","并行计算","弹性:并不是指他可以动态扩展，而是容错机制。","支持压缩文件","支持整个目录、多文件、通配符","数据分布式也是弹性的","方法并且传入已有的可迭代对象或者集合","独立实现rdd的创建","用来传递spark应用的基本信息","的时候可以指定分区数量","知道rdd的概念","第一步","课程目标：","调用sparkcontext的","进入pyspark环境","通常，可以根据cpu核心数量指定分区数量（每个cpu有2","通过sc直接使用","通过外部数据创建rdd"],"day05_Spark_core/spark_core_3.html":["\"))","'b',","'c',","'c'],","'d',","'e',","'f',","'f'],","'h',","'i',","'j']","'j']]","(\"a\",","(\"b\",","('1',","('2',","('a',","('b',","('c',","('d',","('fleece',","('had',","('lamb',","('little',","('was',","('white',","('whose',","(key,",")","),",")]","...","03_rdd常用算子练习","1),","1).collect()","1)])","10,","100).filter(lambda","10]","12,","14,","15","16,","18]","2","2),","2).collect()","2)]","3","3)","3),","3)]","3,","3.1","3.2","3.3","3.4","3]","4),","4)]","4,","4]).count()","4]).first()","5),","5),...('white',","5)]","5,","6),","6)]","6,","6]","6]).take(10)","6]).take(2)","7),","7,","8),","8,","9),","9)])","9,","90).take(3)","92,","93]",":","=",">",">>>",">transform","['a',","['d',","['h',","[('1',","[('a',","[('b',","[('c',","[('mary',","[2,","[6,","[91,","[['a',","action","action算子","add(x):","assum","b","c\",\"d","collect","consist","core","count","def","e","f\",\"h","filter","filter(func)","first","flatmap","flatmap会先执行map的操作，再将所有对象合并为一个对象","flatmap和map的区别：flatmap在map的基础上将结果合并到一个list中","groupbykey","groupbykey之后的结果中","intersect","j\"])","k.lower()).collect()","k:","keyfunc=>)","keyfunc=lambda","list(result[2][1])","map(func)","map:","map就是一个transform","numpartitions=none,","pairs.","persist","persist操作用于将数据缓存","rdd","rdd,","rdd.reducebykey(lambda","rdd1","rdd1.flatmap(lambda","rdd1.map(add)","rdd1.map(lambda","rdd1.reduce(lambda","rdd1.union(rdd2)","rdd2","rdd2.collect()","rdd2.filter(lambda","rdd3","rdd3.collect()","rdd3.groupbykey()","rdd3.intersection(rdd2)","rdd4","rdd4.collect()","rdd两类算子执行示意","rdd中的所有元素","rdd常用算子练习","reduc","reducebykey","reduce将rdd中元素两两传递给输入函数，同时产生一个新的值，新产生的值与rdd中下一个元素再被传递给输入函数直到最后只有一个值为止。","result[2]","result[2][1]","return","sc.parallelize([\"a","sc.parallelize([(\"a\",","sc.parallelize([(\"a\",1),(\"b\",2)])","sc.parallelize([(\"c\",1),(\"b\",3)])","sc.parallelize([1,2,3,4,5,6,7,8,9],3)","sc.parallelize([1,2,3,4,5])","sc.parallelize([2,","sc.parallelize(range(100),","sc.parallelize(tmp).sortbykey().first()","sc.parallelize(tmp).sortbykey(true,","sc.parallelize(tmp2).sortbykey(true,","sort","sortbykey","sortbykey(ascending=true,","spark","take","take(num)","tmp","tmp2","tmp2.extend([('whose',","transform","transformation算子","union","value)","value是一个iter","x","x+1","x+1)","x+y)","x,i","x,y:x+y).collect()","x:","x:x*2)","x:x.split(\"","x:x>4)","不会立即计算结果","也可以复制到磁盘的其它节点上","也可以缓存到磁盘上，","从一个已经存在的数据集创建一个新的数据集","以元组中的第0个元素作为key，进行分组，返回一个新的rdd","例如map","只有当数据量较小的时候使用collect","只有调用action一类的操作之后才会计算所有transform","只记下应用于数据集的transformation操作","可以缓存在内存中","因为所有的结果都会加载到内存中","对两个rdd求交集","对两个rdd求并集","将func函数作用到数据集的每一个元素上，生成一个新的rdd返回","将key相同的键值对，按照function进行计算","就是一个action操作，使用某个函数聚合rdd所有元素的操作，并返回最终计算结果","常用操作","所有的transformation操作都是惰性的（lazy）","掌握transformation和action算子的基本使用","操作","操作，map创建的数据集将用于reduce，map阶段的结果不会返回，仅会返回reduce结果。","操作，把数据集中的每一个元素传给一个函数并返回一个新的rdd，代表transformation操作的结果","支持两种类型的操作：","比如，","获取对数据进行运算操作之后的结果","说出rdd的三类算子","课程目标","返回rdd中元素的个数","返回rdd的前n个元素","返回rdd的第一个元素","返回一个list，list中包含","这种设计使spark运行效率更高","选出所有func返回值为true的元素，生成一个新的rdd返回"],"day05_Spark_core/spark_core_4.html":["!=","\"","\"))","\")).filter(lambda","\")).map(lambda","\",","\"dnspod","\"get","\"head","\"http://cos.name/category/software/packages/\"","\"http://www.angularjs.cn/a00n\"","\"mozilla/4.0","\"mozilla/5.0","#取出序列数据中的前n个","#对数据进行累加，按照url出现次数的降序排列","#对每一行按照空格拆分，将ip地址取出","#对每一行按照空格拆分，将url数据取出，把每个url记为1","#把每一行数据记为(\"pv\",1)","#把每个ur记为1","#每条数据代表一次访问记录","%","%i\"","'__main__':","(compatible;)\"","(khtml,","(window","(word,","(x,","+","+0000]",".flatmap(lambda",".map(lambda",".reducebykey(lambda","/","/images/my.jpg","/wp","0","04_spark","1)","1))","101.226.68.137","163.177.71.12","183.49.46.228","185524","194.237.142.21","19939","20","200","222.68.172.190","2:","304","4.1利用pycharm编写spark","4.2","400","6.1)","60.208.6.156","=","==","[18/sep/2013:06:49:18","[18/sep/2013:06:49:23","[18/sep/2013:06:49:33","[18/sep/2013:06:49:36","[18/sep/2013:06:49:42","[18/sep/2013:06:49:45","[18/sep/2013:06:49:48","[18/sep/2013:06:49:57","[18/sep/2013:06:50:08","\\","__name__","a,","a,b:a+b)","a,b:a+b).sortby(lambda","applewebkit/537.36","avg","b)","b:","chrome/29.0.1547.66","content/uploads/2013/07/rcassandra.png","content/uploads/2013/07/rstudio","core","core实战案例_pv&uv统计","count","count)","count))","counts.collect()","count案例","file:///root/bigdata/data/spark_test.log","file=sys.stderr)","gecko)","git3.png","http/1.0\"","http/1.1\"","import","len(sys.argv)","line.split(\"","line:","local","master","monitor/1.0\"","nt","os系统上","output","output:","packages目录下","print(\"%s:","print(\"usage:","pv：网站的总访问量","pyspark.sql","rdd1","rdd1.map(lambda","rdd2","rdd2.collect()","rdd2.distinct().map(lambda","rdd2.reducebykey(lambda","rdd3","rdd3.reducebykey(lambda","rdd3.take(5)","rdd4","rdd4.collect()","rdd4.saveastextfile(\"hdfs:///uv/result\")","rdd的pv","rdd的word","safari/537.36\"","sc","sc.stop()","sc.textfile(\"file:///root/bigdata/data/access.log\")","sc.textfile(sys.argv[1])","spark","spark.sparkcontext","sparksess","sparksession.builder.appname(\"pv\").getorcreate()","sparksession.builder.appname(\"test\").getorcreate()","sparksession.builder.appname(\"topn\").getorcreate()","submit","sy","sys.exit(","uv统计案例","uv：网站的独立用户访问量","wc.pi","wordcount程序","x:","x:(\"pv\",1)).reducebykey(lambda","x:(\"uv\",1))","x:(x[10],1))","x:len(x)>10).map(lambda","x:x.split(\"","x:x[0])","x:x[1],ascending=false)","代码","包含了ip","在新闻类网站中，经常要衡量一条网络新闻的页面访问量，最常见的就是uv和pv，如果在所有新闻中找到访问最多的前几条新闻，topn是最常见的指标。","在系统上执行指令","实战案例","将spark目录下的python目录下的pyspark整体拷贝到pycharm使用的python环境下","将下图中的pyspark","将代码上传到远程cent","拷贝到pycharm使用的：xxx\\python\\python36\\lib\\sit","数据示例","独立实现spark","环境配置","访问时间","访问的pv","访问的topn","访问的uv","访问的地址...信息","访问的请求方式","课程目标：","通过spark实现点击流日志分析"],"day05_Spark_core/spark_core_5.html":["#","#((纬度,精度),1)","#00000000","#1101111100000000","#11011111111100110000000000000000","#‭","#‭11011111‬","#创建一个广播变量","#将ip转换为特殊的数字形式","#根据单个ip获取对应经纬度信息","#根据取出对应的位置信息","'__main__':","((city_broadcast_value[index][2],","(x[2],","+","0","05_spark","0~255","1)","11110011‬","1、","223.243.0.0|223.243.191.255|","255","255.255.255.255","256","2^8","2、","32位2进制数","32位二进制数","3、","4，对相同的经度和纬度做累计求和","5.1通过spark实现ip地址查询","8位2进制数","=","==","__name__","a,","b)","b:","binary_search(ip_num,","city_broadcast","city_broadcast.valu","city_broadcast_valu","city_broadcast_value)","city_broadcast_value[index][3]),","city_id_rdd","core实战","core实战_ip统计","def","dest_data","dest_data.mappartitions(lambda","dest_rdd","dest_rdd.reducebykey(lambda","get_pos(x))","get_pos(x):","get_result(ip):","import","index","int(broadcast_value[mid][0])","int(broadcast_value[mid][1]):","int(i)","ip","ip.split(\".\")#[223,243,0,0]","ip_num","ip_transform(ip)","ip_transform(ip):","ips:","ip日志信息","lambda","main()","main():","map(tuple,[get_result(ip)","mid","print(result_rdd.collect())","pyspark.sql","result_rdd","return","sc","sc.broadcast(city_id_rdd.collect())","sc.stop()","sc.textfile(\"file:///root/tmp/20090121000132.394251.http.format\").map(","sc.textfile(\"file:///root/tmp/ip.txt\").map(lambda","spark","spark.sparkcontext","sparkcontext.broadcast(要共享的数据)","sparksess","sparksession.builder.appname(\"test\").getorcreate()","start","x","x.split(\"|\")[1])","x:","x:x.split(\"|\")).map(lambda","x[13],","x[14]))","x[3],","x])","|","代码","加载城市ip段信息，获取ip起始数字和结束数字，经度，纬度","加载日志数据，获取ip信息，然后转换为数字，和ip段比较","可以通过广播变量,","因此，我们需要通过日志信息（运行商或者网站自己生成）和城市ip段信息来判断用户的ip段，统计热点经纬度。","在ip日志信息中，我们只需要关心ip这一个维度就可以了，其他的不做介绍","在互联网中，我们经常会见到城市热点图这样的报表数据，例如在百度统计中，会统计今年的热门旅游城市、热门报考学校等，会将这样的信息显示在热点图中。","实际上","广播变量的使用","思路","所以这份数据可以共享,没必要每个task复制一份","所有task都会去复制ip表","数据也是只需要进行查询操作的,","来共享这个数据,避免数据的多次复制,可以大大降低内存的开销","每一个task","每一个worker上会有多个task,","每一条数据都会去查询ip表","比较的时候采用二分法查找，找到对应的经度和纬度","独立实现ip地址查询","要统计ip所对应的经纬度,","说出广播变量的概念","课程目标","通知当前worker上所有的task,","都需要这一个ip表,","需求","默认情况下,"],"day05_Spark_core/spark_core_6.html":["./start","06_spark安装部署&standalone模式介绍","1","192.168.19.137","2","27073","27151","3","applic","client：客户端进程，负责提交作业到master。","dagscheduler：","driver：","env.sh(需要将spark","env.sh.template重命名)","executor：即真正执行作业的地方，一个集群一般包含多个executor，每个executor接收driver的命令launch","export","firewalld","h","hadoop","http://192.168.19.137:4040/","http://192.168.19.137:8080/","java_home=java_home_path","job","jps查看进程","master","master.sh","master和work","master：standalone模式中主控节点，负责接收client提交的作业，管理worker，并命令worker启动driver和executor。","path=$path:$spark_home/bin","pyspark_python=/xx/pythonx_home/bin/pythonx","slave","slave.sh","spark","spark://192.168.19.137:7077","spark_home=/xxx/spark2.x","spark_master_host=nod","spark_master_port=7077","spark作业相关概念","spark集群架构(standalone模式)","stage：一个spark作业一般包含一到多个stage。","standalone模式启动","stop","systemctl","taskscheduler：实现task分配到executor上执行。","task，一个executor可以执行一到多个task。","task：一个stage包含一到多个task，通过多个task实现并行运行的功能。","teach","ui查看spark集群及spark","web","worker","worker：standalone模式中slave节点上的守护进程，负责管理本节点的资源，定期向master汇报心跳，接收master的命令，启动driver和executor。","一个spark作业运行时包括一个driver进程，也是作业的主进程，负责作业的解析、生成stage并调度task到executor上。包括dagscheduler，taskscheduler。","修改配置文件","关闭防火墙","启动master","启动slave","启动spark集群","和","学习目标","安装部署","安装部署及standalone模式介绍","实现将spark作业分解成一到多个stage，每个stage根据rdd的partition个数决定task的个数，然后生成相应的task","放到taskscheduler中。","整个集群分为","用户自己写的spark应用程序，批处理作业的集合。application的main方法为应用程序的入口，用户通过spark的api，定义了rdd和对rdd的操作。","的","监控spark","监控spark集群","知道spark作业提交集群的过程","知道spark的安装过程，知道standalone启动模式","节点。","节点和","节点，相当于","进入到$spark_home/sbin目录","通过spark","配置java环境变量","配置master的地址","配置master的端口","配置python环境","配置spark环境变量","集群相关概念"],"day06_Spark_sql&Spark_streaming/s1.1.html":["01_spark","1、spark","1、易整合","1、解决了rdd的缺点","2014年6月1日的时候，spark宣布了不再开发shark，全面转向spark","2、丢失了rdd的优点","2、统一的数据源访问","3、兼容hive","4、提供了标准的数据库连接（jdbc/odbc）","apach","api","catalystoptimizer：catalyst优化器","code","core编写的rdd，不同的语言生成不同的rdd","data.","datafram","dataframe引入schema和off","generation最终生成为rdd","gen：代码生成器","heap(使用操作系统层面上的内存)","hive是目前大数据领域，事实上的数据仓库标准。","independ","languag","less","modul","perform","projecttungsten：钨丝计划，为了提高rdd的效率而制定的计划","python操作rdd，转换为可执行代码，运行在java虚拟机，涉及两个不同语言引擎之间的切换，进行进程间","rdd具有面向对象编程的特性","rdd编译时进行类型检查","schema","shark：shark底层使用spark的基于内存的计算模型，从而让性能比hive提升了数倍到上百倍。","spark","spark'","sparksql特性","sql","sql优势","sql历史","sql概念","sql的开发","sql简介","sql编写转换的rdd慢，涉及到执行计划","structur","work","write","为什么要学习sparksql","优化引擎：类似mysql等关系型数据库基于成本的优化器","在rdd中无法看出，解释性不强，无法告诉引擎信息，没法详细优化。","它是spark中用于处理结构化数据的一个模块","序列化和反序列化开销大","底层很多东西还是依赖于hive，修改了内存管理、物理计划、执行三个模块","是rdd为基础的分布式数据集，类似于传统关系型数据库的二维表，dataframe记录了对应列的名称和类型","概述","用scala/python编写的rdd比spark","用任何语言编写生成的rdd都一样，而使用spark","直接编写rdd也可以自实现优化代码，但是远不及sparksql前面的优化操作后转换的rdd效率高，快1倍左右","结构化数据，可以直接看出数据的详情","通信很耗费性能。","频繁的创建和销毁对象造成大量的gc","首先执行逻辑执行计划，然后转换为物理执行计划(选择成本最小的)，通过code"],"day06_Spark_sql&Spark_streaming/s1.2.html":["\"true\")","#","#================","#==================","#====================数据集拆成两部分","#fraction：采样比例","#seed：随机种子","#withreplacement：是否有放回的采样","#为数据添加列名","#列名","#创建datafram","#创建udf，udf函数需要两个参数：","#删除一列","#加载csv类型的数据并转换为datafram","#在dataframe中需要通过udf将自定义函数封装成udf函数再交给dataframe进行调用执行","#在rdd中可以直接定义函数，交给rdd的transformatioins方法进行执行","#如果操作的是原有列，可以替换原有列的数据","#定义一个新的列，数据为其他某列数据的两倍","#定义一个方法，用于检测","#显示前10条数据","#显示数据结构","#查看两个数据集在类别上的差异","#统计总量","#计算某一列的描述信息","#设置数据比例将数据划分为两部分","#调用函数并起一个别名","#首先找到这些类，整理到一个列表","(in","*","+","...)。",".load(\"iris.csv\")","0.4])","02_dataframe介绍","1","1')","1，创建dataframe的步骤","2).show()","2.1","2.2","2.3","2、datafram","2，其他方式创建datafram","6)",":",":x[0]).collect()","<>","=","===========","==================从csv读取======================","==================基本统计功能","================交叉表","================直接创建==========================","================采样数据","===============增加一列(或者替换)","===============提取部分列","==========删除一列","[('ankit',25),('jalfaizy',22),('saurabh',20),('bala',26)]","\\","action：立即操作","age=int(x[1])))","api丰富","api实现","api或sql处理数据，会自动经过spark","api（如df.select())和sql(select","api：比spark","avg(),","case","check","cluster","count(),","count=====","countdistinct(),","createdataframe：panda","crosstab=============","datafram","dataframe、list、rdd","dataframe和dataset统一，dataframe只是dataset[row]的类型别名。由于python是弱类型语言，只能使用datafram","dataframe和普通的rdd的逻辑框架区别如下所示：","dataframe的抽象后，我们处理数据更加简单了，甚至可以用sql来处理数据了","dataframe相当于是一个带着schema的rdd","dataframe还引入了off","dataframe还配套了新的操作数据的方法，datafram","dataframe：分布式的row对象的集合，其提供了由列组成的详细模式信息，使得spark","def","describe================","df","df.agg(fn.count('sepalwidth').alias('width_count'),fn.countdistinct('cls').alias('distinct_cls_count')).show()","df.column","df.count()","df.crosstab('cls','sepallength').show()","df.describe('cls').show()","df.describe().show()","df.drop('cls').show()","df.groupby('cls').agg({'sepalwidth':'mean','sepallength':'max'}).show()","df.printschema()","df.randomsplit([0.6,","df.randomsplit([0.99,0.01])","df.sample(false,0.2,100)","df.select('cls').distinct().count()","df.select('sepallength','sepalwidth').show()","df.show(10)","df.withcolumn('newwidth',df.sepalwidth","diff_in_train_test","diff_in_train_test.distinct().count()","distinct","distributed：dataframe和rdd一样都是分布式的","drop=========================","evaluations:","evaluations：只有action才会触发transformation的执行","first(),","fn","function","groupby(colname).agg({'col':'fun','col2':'fun2'})","heap,意味着jvm堆以外的内存,","id,","immuatable：一旦rdd、dataframe被创建，就不能更改，只能通过transformation生成新的rdd、datafram","immutable：不可更改","import","jdbcdf","jsondf","kurtosis(),","l","lazi","max(),","mean(),","min(),","name","not_exist_cl","not_exist_cls:","option(\"header\",","panda","parallel：集群并行执行","parquetdf","peopl","person类的内部结构。","pyspark.sql","pyspark.sql.funct","pyspark.sql.typ","randomsplit","rdd","rdd.map(lambda","rdd是分布式的java对象的集合。dataframe是分布式的row对象的集合。dataframe除了提供了比rdd更丰富的算子以外，更重要的特点是提升执行效率、减少数据读取以及执行计划的优化。","rdd：分布式的对象的集合，spark并不知道对象的详细模式信息","resultdf","resultdf.show()","return","rich","row","row(name=x[0],","sample===========","sc","sc.parallelize(l)","schemapeopl","sdf","select==============","should_remove(x):","skewness(),","spark","spark.conf.set(\"spark.sql.shuffle.partitions\",","spark.createdataframe(people)","spark.read.format(\"csv\").","spark.read.format(\"jdbc\").option(\"url\",\"jdbc:mysql://localhost:3306/db_name\").option(\"dbtable\",\"table_name\").option(\"user\",\"xxx\").option(\"password\",\"xxx\").load()","spark.read.format('json').load('xxx.json')","spark.read.json(\"xxx.json\")","spark.read.parquet(\"xxx.parquet\")","spark.sparkcontext","sparksess","sparksession.builder.appname('test').getorcreate()","spark框架本身不了解","sql","sql可以进行某些形式的执行优化。","stddev(),","stddev_pop(),","stddev_samp(),","stringtyp","stringtype())","subtract，确保训练数据集覆盖了所有分类","sum(),","sumdistinct(),","testdf","testdf.select('cls').subtract(traindf.select('cls'))","traindf,","traindf,testdf","traindf.select('cls').subtract(testdf.select('cls')).distinct().rdd.map(lambda","traindf.select('cls').subtract(testdf.select('cls')).distinct().show()","traindf.withcolumn('new_cls',check(traindf['cls'])).filter('new_cl","transformation:延迟性操作","type","udf","udf(should_remove,stringtype())","udf================","udf：自定义函数","var_pop(),","var_samp()","variance()","vs","withcolumn===========","x","x:","xx_tabl","​","交叉表","介绍","从csv中读取数据","优化器（catalyst）的优化，即使你写的程序或sql不仅高效，也可以运行的很快。","分组统计","创建datafram","删除一列","只有action才会触发transformation的执行","右侧的dataframe提供了详细的结构信息（schema——每列的名称，类型）","在spark语义中，dataframe是一个分布式的行集合，可以想象为一个关系型数据库的表，或者一个带有列名的excel表格。它和rdd一样，有这样一些特点：","基于rdd创建","基本统计功能","增加一列","左侧的rdd","拆分数据集","提取部分列","数据源：rdd、csv、json、parquet、orc、jdbc","查看两个数据集在类别上的差异","测试数据集中有些类别在训练集中是不存在的，找到这些数据集做后续处理","统计信息","综合案例","自定义的汇总方法","调用方法例如：spark.read.xxx方法","这些内存直接受操作系统管理（而不是jvm）。","通过datafram","采样数据"],"day06_Spark_sql&Spark_streaming/s1.3.html":["\"\"\"{","\"01001\",","\"01002\",","\"agawam\",","\"city\"","\"cushman\",","\"id\"","\"ma\"","\"pop\"","\"state\"","#","#structtype：schema的整体结构，表示json的对象结构","#xxxstype:指的是某一列的数据类型","#使用内部的schema","#依照已有的dataframe，创建一个临时的表(相当于mysql数据库中的一个表)，这样就可以用纯sql语句进行数据操作","#只有被压缩后的json文件内容，才能被spark","#定义结构类型","#指定schema","*",",",".add(\"city\",",".add(\"id\",",".add(\"pop\"",".add(\"state\",stringtype())","03_spark","1.","15338,","1，get_json_object","1，通过反射自动推断，适合静态数据","2.","2，get_json","2，程序指定，适合程序运行中动态生成的数据","3.1","3.1节中的例子为通过反射自动推断schema，适合静态数据","3.2","36963,","3、json数据的处理","3，explod",":","=","==========================================","[","\\","]","])","arraytype(doubletype())),","automat","convers","convert","datafram","dataset","done","doubletype())","file","import","infer","json","jsondf","jsondf.createorreplacetempview(\"tmp_table\")","jsondf.filter(jsondf.pop>4000).show(10)","jsondf.printschema()","jsondf.show()","jsondf.show(2)","jsonrdd","jsonschema","jsonstr","json数据","load","longtype())","longtype(),","longtype(),true)","pop>4000\")","pyspark.sql","pyspark.sql.typ","rdd","reader","reader.json('data/nest.json')","reader.json(jsonrdd)","resultdf","resultdf.show(10)","sc","sc.parallelize(jsonstring)","schema","spark","spark.read.format('json').load('xxx.json')","spark.read.json(\"xxx.json\")","spark.read.json(jsonrdd)","spark.read.schema(jsonschema)","spark.read.schema(jsonschema).json('xxx.json')","spark.sparkcontext","spark.sql(\"select","sparksess","sparksession.builder.appname('json_demo').getorcreate()","sparksession.read.json","sql","sql正确读取，否则格式化后的数据读取会出现问题","sql能够自动将json数据集以结构化的形式加载为一个datafram","stringjsonrdd","stringtype())","stringtype(),","stringtype(),true)","structfield(\"city\",","structfield(\"id\",","structfield(\"loc\"","structfield(\"pop\",","structfield(\"state\",","structtype()","structtype([","tmp_tabl","true)","true),","us","}\"\"\"","}\"\"\",","下面我们来讲解如何进行程序指定schema","介绍","从json到datafram","从json字符串数组得到datafram","从json字符串数组得到rdd有两种方法","加载json数据","动态json数据的读取和操作","处理json数据","实践","嵌套结构的json","带有嵌套结构的json","指定dataframe的schema","无嵌套结构的json","无嵌套结构的json数据","没有嵌套结构的json","直接从文件生成datafram","直接利用spark.createdataframe()，见后面例子","读取一个json文件可以用sparksession.read.json方法","转换为rdd，再从rdd到datafram","重要的方法","静态json数据的读取和操作"],"day06_Spark_sql&Spark_streaming/s1.4.html":["!=","#","#无意义重复数据去重：数据中行与行完全重复","#有意义去重：删除除去无意义字段之外的完全重复的行数据","'''","'_missing')","'_o')","'age').show()","'age',","'age']","'age'])","'f'),","'f',","'gender',","'gender'])","'gender']).topandas().to_dict('records')[0]","'height',","'height':","'income'","'income'])","'m'),","'m',","'missing'","'weight').show()","'weight',","'weight':","(1,","(2,","(3,","(4,","(5,","(6,","(7,","(df_outliers[c]","(fn.count(c)",").alias(c","*","*[fn.mean(c).alias(c)","+",",","/","0),","0)]","0.05)","0.75],","04spark","1),","1.5","1.删除重复数据","1.计算每条记录的缺失值情况","1.首先删除完全一样的记录","100000),","11.0,","124.1,","129.2,","133.2,","143.5,","144.5,","154.2,","167.2,","191.7]}","1|","2),","2.1","2.2","2.3","2.其次，关键字段值完全一模一样的记录（在这个例子中，是指除了id之外的列一模一样）","2.处理缺失值","2.计算各列的缺失情况百分比","21),","23,","28),","28,'m',","2|","3.有意义的重复记录去重之后，再看某个无意义字段的值是否有重复（在这个例子中，是看id是否重复）","33),","33,","342.3,","342.3|","3|","3、删除缺失值过于严重的列","3、异常值处理","4),","4.对于id这种无意义的列重复，添加另外一列自增id","42),","42,","45),","45,","45,'m',","4|","4、按照缺失值删除行（threshold是根据一行记录中，缺失字段的百分比的定义）","4、数据清洗","5.1,","5.2,","5.3,","5.4,","5.5,","5.6,","5.7,","5.9,","54),","54,","5|","5、填充缺失值，可以用fillna来填充缺失值，","6.1000000000000005],","6|","76000),],","7|","93.0],","99),","99|","=",">>>","[","['id',","['weight',","[(","[(1,","[0.25,","[4.499999999999999,","[91.69999999999999,","[c","]","])","],","approxquantile方法接收三个参数：参数1，列名；参数2：想要计算的分位点，可以是一个点，也可以是一个列表（0和1之间的小数），第三个参数是能容忍的误差，如果是0，代表百分百精确计算。","bound","bounds[c][1])","bounds[col]","c","c!='id'])","col","cols:","df","df.dropduplicates()","df2","df2.column","df2.dropduplicates(subset","df3","df3.agg(fn.count('id').alias('id_count'),fn.countdistinct('id').alias('distinct_id_count')).collect()","df3.withcolumn('new_id',fn.monotonically_increasing_id()).show()","df_miss","df_miss.agg(*[(1","df_miss.column","df_miss.columns]).show()","df_miss.rdd.map(lambda","df_miss.select([","df_miss_no_incom","df_miss_no_income.agg(","df_miss_no_income.column","df_miss_no_income.dropna(thresh=3).show()","df_miss_no_income.fillna(means).show()","df_outlier","df_outliers.approxquantile(col,","df_outliers.filter('age_o').select('id',","df_outliers.filter('weight_o').select('id',","df_outliers.join(outliers,","df_outliers.select(*['id']","false|","false|false|","fillna可以接收两种类型的参数：","fn","fn.count('*'))).alias(c","groupby().count()：可以看到数据的重复情况","id|age|","id|weight_o|height_o|age_o|","id|weight|","import","iqr","iqr,","mean","means['gender']","none","none),","none,","on='id')","outlier","outliers.show()","pyspark.sql.funct","quantil","quantiles[0]","quantiles[1]","row:(row['id'],sum([c==non","row]))).collect()","spark.createdataframe([","sql案例数据清洗","true|","{'age':","{}","|","一个数字、字符串，这时整个dataset中所有的缺失值都会被填充为相同的值。","上述三种操作的核心都是：通过原始数据设定一个正常的范围，超过此范围的就是一个异常值","中位数绝对偏差去极值","为异常值字段打标志","也可以接收一个字典｛列名：值｝这样","先计算均值，并组织成一个字典","其实是先建一个df，不要缺失值的列","再回头看看这些异常值的值，重新和原始数据关联","分位数去极值","删除某些字段值完全一样的重复记录，subset参数定义这些字段","前面我们处理的数据实际上都是已经被处理好的规整数据，但是在大数据整个生产过程中，需要先对数据进行数据清洗，将杂乱无章的数据整理为符合后面处理要求的规整数据。","包含：缺失值，超过正常范围内的较大值或较小值","对于bool类型、或者分类类型，可以为缺失值单独设置一个类型，miss","对于数值类型，可以用均值或者中位数等填充","对缺失值对应的行或列进行标记","对缺失值进行删除操作(行，列)","对缺失值进行填充操作(列的均值)","异常值处理","异常值：不属于正常的值","数据去重","查看某一列是否有重复值","查看重复记录","正态分布去极值","然后添加其它的列","用边界值替换","缺失值处理","设定范围","超出这个范围的"],"day06_Spark_sql&Spark_streaming/ss1.1.html":["(离散流)","05_spark","1.1","1.2","1、sparkstreaming概述","batch方式（trident）","batch流式处理数据（spark","context","context上调用stop方法,","context处于活跃状态,","context对象(不关闭sparkcontext前提下),","context对象,设置stop()的可选参数为fals","context的start()),就不能有新的流算子(dstream)建立或者是添加到context中","context调用了stop方法之后","core和spark","dstream","dstreams中的每个rdd都包含确定时间间隔内的数据","dstream由一系列连续的rdd组成","filesystem","flume","kafka","socket","spark","sparkstreaming是什么","sparkstreaming的组件","sql都是处理属于离线批处理任务，数据一般都是在固定位置上，通常我们写好一个脚本，每天定时去处理数据，计算，保存数据结果。这类任务通常是t+1(一天一个任务)，对实时性要求不高。","start())","storm","stream","streaming优于storm","streaming差于storm","streaming简介","streaming）","tcp/ip","一个sparkcontext创建一个stream","一个sparkcontext对象可以重复利用去创建多个stream","一旦一个context已经停止,不能重新启动(stream","一旦一个context已经启动(调用了stream","之前我们接触的spark","也会关闭sparkcontext对象,","也支持micro","代表一个连续的数据流","以rdd为单位处理数据","以record为单位处理数据","任何对dstreams的操作都转换成了对dstreams隐含的rdd的操作","但在企业中存在很多实时性处理的需求，例如：双十一的京东阿里，通常会做一个实时的数据大屏，显示实时订单。这种情况下，对数据实时性要求较高，仅仅能够容忍到延迟1分钟或几秒钟。","但是需要关一个再开下一个","同一时间只能有一个stream","吞吐量：spark","吞吐量：单位时间内成功传输数据的数量","在jvm(java虚拟机)中,","在stream","在内部,","基本源","如果只想仅关闭stream","它是一个可扩展，高吞吐具有容错性的流式计算框架","实时计算框架对比","对比：","就不能再次调","延迟：spark","批处理计算框架","支持micro","数据源","流式计算框架","高级源"],"day06_Spark_sql&Spark_streaming/ss1.2.html":["\"))","\"/miniconda2/envs/py365/bin/python\"","\"/root/bigdata/spark\"","\"__main__\":","#","#参数2：指定执行计算的时间间隔","#启动streamingcontext","#将单词转换为(单词，1)的形式","#将数据按空格进行拆分为多个单词","#打印结果信息，会使得前面的transformation操作执行","#监听ip，端口上的上的数据","#等待计算结束","#统计单词个数","06_spark","1)","1，创建一个streamingcontext","1，需要安装一个nc工具：sudo","2、spark","2，从streamingcontext中创建一个数据对象","2，执行指令：nc","3，对数据对象进行transformations操作","4，输出结果","5，开始和停止","9999","=","==","__name__","driver和pyspark运行时，所使用的python解释器路径","import","instal","java_home='/root/bigdata/jdk'","line","line.split(\"","line:","lines.flatmap(lambda","lk","nc","os","os.environ[\"pyspark_driver_python\"]","os.environ[\"pyspark_python\"]","os.environ[\"spark_home\"]","os.environ['java_home']=java_hom","pair","pairs.reducebykey(lambda","pyspark","pyspark.stream","pyspark_python","sc","spark","spark_hom","sparkcontext","sparkcontext(\"local[2]\",appname=\"networkwordcount\")","ssc","ssc.awaittermination()","ssc.sockettextstream('localhost',9999)","ssc.start()","streamingcontext","streamingcontext(sc,","streaming实现wordcount","streaming编码实践","streaming编码步骤：","v","word","word:(word,1))","wordcount","wordcounts.pprint()","words.map(lambda","x,y:x+y)","y","yum","利用spark","可视化查看效果：http://192.168.19.137:4040","当存在多个版本时，不指定很可能会导致出错","点击streaming，查看效果","配置spark","需求：监听某个端口上的网络数据，实时统计出现的不同单词个数。"],"day06_Spark_sql&Spark_streaming/ss1.3.html":["\"))","\"/miniconda2/envs/py365/bin/python\"","\"/root/bigdata/spark\"","\"__main__\":","#","#定义state更新函数","#定义处理的时间间隔","#定义滑动时间间隔","#定义窗口长度","#开启检查点","#获取streamingcontext","#调用reducebykeyandwindow，来进行窗口函数的调用","#输出处理结果信息","#需要设置检查点","'aus':","'australia'","'ind':","'india'","'unknown'","'usa'","'usa':","(in","(last_sum","(output,","(word,","*","+",".map(lambda",".reducebykeyandwindow(addfunc,",".updatestatebykey(updatefunc=updatefunc)#应用updatestatebykey函数","0)","07_spark","1","1)","1))","3","3)","3.1","3.2","3、spark","6","9999)","=","==","\\","__name__","addfunc","base","batch_interv","batch_interval)","comput","count","country_nam","counts.pprint()","def","driver和pyspark运行时，所使用的python解释器路径","elif","else:","frequenc","frequency)","func:正向操作，类似于updatestatebykey","get_countryname(line):","import","invaddfunc","invaddfunc,","invfunc：反向操作","java_home='/root/bigdata/jdk'","lambda","last_sum):","line","line.split(\"","line.strip()","line:","lines.flatmap(lambda","lines.map(get_countryname)","os","os.environ[\"pyspark_driver_python\"]","os.environ[\"pyspark_python\"]","os.environ[\"spark_home\"]","os.environ['java_home']=java_hom","output","pyspark","pyspark.sql.sess","pyspark.stream","pyspark_python","reducebykeyandwindow(func,invfunc,windowlength,slideinterval,[num,tasks])","return","sc","seconds)","smart","spark","spark.sparkcontext","spark_hom","sparkcontext","sparksess","sparksession.builder.master(\"local[2]\").getorcreate()","sql来进行离线批处理","ssc","ssc.awaittermination()","ssc.checkpoint(\"checkpoint\")","ssc.sockettextstream(\"localhost\",","ssc.sockettextstream('localhost',","ssc.start()","steaming的状态操作","stream","streamingcontext","streamingcontext(sc,","streaming中存在两种状态操作","streaming中提供这种状态保护机制，即updatestatebykey","streaming实现的是一个实时批处理操作，每隔一段时间将数据进行打包，封装成rdd，是无状态的。","streaming的状态操作","sum(new_values)","time","unit","updatefunc(new_values,","updatestatebykey","window","window_count","window_counts.pprint()","window_length","window_length,","windows操作","window操作是基于窗口长度和滑动间隔来工作的","word:","x","x,","y","y:","一般超过一天都是用rdd或spark","举例：词统计。","代码","使用有状态的transformation，需要开启checkpoint","例如在热词时，在上一个窗口中可能是热词，这个一个窗口中可能不是热词，就需要在这个窗口中把该次剔除掉","其次，要定义state更新函数","典型案例：热点搜索词滑动统计，每隔10秒，统计最近60秒钟的搜索词的搜索频次，并打印出最靠前的3个搜索词出现次数。","创建sparkcontext","在spark","如果没有updatestatebykey，我们需要将每一秒的数据计算好放入mysql中取，再用mysql来进行统计计算","它将足够多的信息checkpoint到某些具备容错性的存储系统如hdfs上，以便出错时能够迅速恢复","对于每个batch，spark都会为每个之前已经存在的key去应用一次state更新函数，无论这个key在batch中是否有新的数据。如果state更新函数返回none，那么key对应的state就会被删除","对于每个新出现的key，也会执行state更新函数","对数据以空格进行拆分，分为多个单词","当存在多个版本时，不指定很可能会导致出错","指定一个函数如何使用之前的state和新值来更新st","操作细节","无状态：指的是每个时间片段的数据之间是没有关联的。","案例","案例：updatestatebykey","步骤：","每隔g秒，统计最近l秒的数据","滑动间隔g：控制每隔多长时间做一次运算","的容错机制","监听网络端口的数据，每隔3秒统计前6秒出现的单词数量","相关函数","窗口的长度控制考虑前几批次数据量","窗口长度l：运算的数据量","配置spark","需求：想要将一个大时间段（1天），即多个小时间段的数据内的数据持续进行累积操作","需求：监听网络端口的数据，获取到每个批次的出现的单词数量，并且需要把每个批次的信息保留下来","首先，要定义一个state，可以是任意的数据类型","默认为批处理的滑动间隔来确定计算结果的频率"],"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":["+","01_个性化电商广告推荐系统介绍","1.1","1.2","1.3","1.4","1234","1:男,2:女；","20170512），用第8天的做测试样本（20170513）","===>","==>","ad_feature.csv","adgroup_id：脱敏过的广告id；","adgroup_id：脱敏过的广告单元id；","age_level：年龄层次；","ali_display_ad_click是阿里巴巴提供的一个淘宝展示广告点击率预估数据集","behavior_log.csv","brand_id:","brand_id：脱敏过的品牌id；","btag：行为类型,","buy","campaign_id：脱敏过的广告计划id；","cart","cate/brand","cate/brand评分数据","cate_id：脱敏过的商品类目id；","click","clk：为0代表没有点击；为1代表点击；","cms_group_id：cms_group_id；","cms_segid：微群id；","ctr点击率预测模型","customer_id:","fav","final_gender_code：性别","flume：日志数据收集","hdfs：存储数据","kafka：实时日志数据处理队列","ml、redi","ml：模型训练","n","new_user_class_level：城市层级","noclk：为1代表没有点击；为0代表点击；","occupation：是否大学生","pid：资源位；","price:","pv","pvalue_level：消费档次，1:低档，2:中档，3:高档；","rate)概念","rate)预测来实现","raw_sample.csv","redis：缓存","regression)这样的机器学习算法，而推荐算法则是一些基于协同过滤推荐、基于内容的推荐等思想实现的算法","shopping_level：购物深度，1:浅层用户,2:中度用户,3:深度用户","spark","sql、spark","sql：离线处理","streming\\hdfs、spark","through","time_stamp为key，会有很多重复的记录；这是因为我们的不同的类型的行为数据是不同部门记录的，在打包到一起的时候，实际上会有小的偏差（即两个一样的time_stamp实际上是差异比较小的两个时间）","time_stamp：时间戳；","top","user","user_id：脱敏过的用户id；","user_profile.csv","userid：脱敏过的用户id；","user：脱敏过的用户id；","vs","|","​","“查询词(query)”，查询词和广告内容的匹配程度很大程度影响了点击概率，搜索广告的点击率普遍较高","一","一份广告基本信息数据ad_feature.csv：体现的是每个广告的类目(id)、品牌(id)、价格特征","一份广告点击的样本数据raw_sample.csv：体现的是用户对不同位置广告点击、没点击的情况","一份用户基本信息数据user_profile.csv：体现的是用户群组、性别、年龄、消费购物档次、所在城市级别等特征","一份用户行为日志数据behavior_log.csv：体现用户对商品类目(id)、品牌(id)的浏览、加购物车、收藏、购买等信息","个性化电商广告推荐系统介绍","主要包括","关联对应的广告完成召回","关联广告","其中一个广告id对应一个商品（宝贝），一个宝贝属于一个类目，一个宝贝属于一个品牌。","加入购物车","包括以下四种：","协同过滤","协同过滤召回","历史样本数据","原始样本骨架raw_sampl","召回","喜欢","在线处理业务流","字段说明如下：","宝贝的价格","实时商品类别/品牌","实时广告召回集","实时特征","实时行为日志","对应的召回集(缓存)","广告/用户特征(缓存)","广告基本信息表ad_featur","广告推荐结果","广告特征数据","我们是在对非搜索类型的广告进行点击率预测和推荐(没有搜索词、没有广告的内容特征信息)","排序","推荐业务处理主要流程：","推荐任务部分：","推荐算法","搜索中有很强的搜索信号","搜索和非搜索广告点击率预测的区别","数据处理部分：","数据集介绍","数据集来源：天池竞赛","本数据集涵盖了raw_sample中全部广告的基本信息(约80万条目)。字段说明如下：","本数据集涵盖了raw_sample中全部用户22天内的购物行为(共七亿条记录)。字段说明如下：","本数据集涵盖了raw_sample中全部用户的基本信息(约100多万用户)。字段说明如下：","浏览","涉及技术：flume、kafka、spark","淘宝网站中随机抽样了114万用户8天内的广告展示/点击日志（2600万条记录），构成原始的样本骨架。","点击率","点击率排序","点击率预测","点击率预测(ctr","点击率预测使用的算法通常是如逻辑回归(logic","点击率预测是对每次广告的点击情况做出预测，可以判定这次为点击或不点击，也可以给出点击或不点击的概率","点击率预测需要给出精准的点击概率，比如广告a点击率0.5%、广告b的点击率0.12%等；而推荐算法很多时候只需要得出一个最优的次序a>b>c即可。","用前面7天的做训练样本（20170506","用户基本信息表user_profil","用户特征数据","用户的行为日志behavior_log","电商广告推荐通常使用广告点击率(ctr","离线处理业务流","类型","缓存","脱敏过的品牌id；","脱敏过的广告主id；","评分数据","说明","购买","转化率","转化率指的是从状态a进入到状态b的概率，电商的转化率通常是指到达网站后，进而有成交记录的用户比率，如用户成交量/用户访问量","过滤","这里以user","非搜索广告（例如展示广告，信息流广告）的点击率的计算很多就来源于用户的兴趣和广告自身的特征，以及上下文环境。通常好位置能达到百分之几的点击率。对于很多底部的广告，点击率非常低，常常是千分之几，甚至更低","项目实现分析","项目效果展示","，1:是,0:否"],"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":["\"/miniconda2/envs/py365/bin/python\"","\"/root/bigdata/spark\"","\"192.168.19.137\"","\"4\"),","\"6g\"),","\"cateid\",","\"preprocessingbehaviorlog\"","\"rating\"])","\"spark://192.168.19.137:7077\"","\"userid\")","#","#mappartit","#注意：behavior_log_df.groupby(\"userid\").count()","'99999'),","(","(\"spark.app.name\",","(\"spark.dynamicallocation.enabled\",","(\"spark.dynamicallocation.initialexecutors\",","(\"spark.executor.cores\",","(\"spark.executor.memory\",","(\"spark.master\",","(\"spark.shuffle.service.enabled\",","('spark.sql.pivotmaxvalues',","(nullabl",")","*","+","+=","02_根据用户行为数据创建als模型并召回商品","0;","1","1),","10.246621...|","10.89842]...|","1043|","1043|110616|","1088|[[104,","11.699315...|","11.904813...|","11.968531...|","1101|","1101|365477|","110616|","1136340","113w","11800|","12.48068]...|","12.547271...|","12.652732...|","12.835257...|","1238|[[5631,","12968","13.665942...|","1342|[[5720,","14.51981]...|","148|[[3347,","1580|[[5731,","1591|[[1610,","1645|[[1610,","17.576496...|","1829|[[1610,","1959|[[1610,","1|[[1610,","1个执行器","2.0","2.1","2.2","2.3","2.3429515...|","20","2122|[[1610,","2142|[[1610,","2366|[[1610,","25.4989],...|","2659|[[5607,","274795|","2866|[[1610,","2|[[5579,","3)","3.641833]...|","301299|","3175|[[3347,","365477|","3749|[[1610,","385|","385|428950|","3|[[5607,","428950|","460561","463|[[1610,","46w","471|[[1610,","496|[[1610,","5.162216]...|","5.9051886...|","6.886987],...|","62353|","6250|","6379","7.752719]...|","723268134","7270|","7270|274795|","8.353473]...|","8.466453]...|","8237|","8237|301299|","833|[[5607,","9.065482]...|","9.250818]...|","91286|","=","==1:","==>","[i.cateid","[row(btag='buy',","[row(userid=1,","])","action操作","al","als(usercol='userid',","als.fit(brand_rating_df)","als.fit(cate_rating_df)","als_model","als_model.recommendforallusers(3)","alsmodel","alsmodel.load(\"hdfs://localhost:9000/models/userbrandratingmodel.obj\")","alsmodel.load(\"hdfs://localhost:9000/models/usercateratingalsmodel.obj\")","als模型","als的意思是交替最小二乘法（altern","api，2.2.2版本中无法使用","base","behavior_log_df","behavior_log_df.brandid).pivot(\"btag\",[\"pv\",\"fav\",\"cart\",\"buy\"]).count()","behavior_log_df.cateid).pivot(\"btag\",[\"pv\",\"fav\",\"cart\",\"buy\"]).count()","behavior_log_df.count()","behavior_log_df.count(),","behavior_log_df.dropna().count())","behavior_log_df.groupby(\"brandid\").count().count())","behavior_log_df.groupby(\"btag\").count().collect())","behavior_log_df.groupby(\"cateid\").count().count())","behavior_log_df.groupby(\"userid\").count().count())","behavior_log_df.groupby(behavior_log_df.userid,","behavior_log_df.show()","bigint,","brand:","brand_count_df","brand_count_df.show()","brand_count_df.write.csv(\"hdfs://localhost:9000/preprocessing_dataset/brand_count.csv\",","brandid:","brand|","btag:","buy:","buy=none)","cart:","cart=53,","cate","cate:","cate_count_df","cate_count_df.first()","cate_count_df.printschema()","cate_count_df.rdd.map(process_row).todf([\"userid\",","cate_count_df.write.csv(\"hdfs://localhost:9000/preprocessing_dataset/cate_count.csv\",","cate_rating_df","cate_rating_df.groupby(\"userid\").povit(\"cateid\").min(\"rating\")","cateid:","cateid=4520,","cate|","cf）的推荐系统算法。","cf）的推荐系统算法，也是目前spark内唯一一个推荐算法。","checkpointinterval=2)","checkpointinterval=5)","chunk","chunk.to_csv('test4.csv',index","client","client.hset(\"recall_cate\",","collect会将所有数据加载到内存，慎用","collect会把计算结果全部加载到内存，谨慎使用","conf","conf.setall(config)","config","config对象","count","count=15946033),","count=688904345)]","count=9115919),","count=9301837),","count>1","dataframe[userid:","dataset","dataset.withcolumnrenamed(\"_1\",","def","df","df.printschema()","df.show()","df，且值很多时，需要修改，默认是10000","double]","driver和pyspark运行时，所使用的python解释器路径","elif","executor使用的cpu核心数","false)","fav:","fav=none,","floattyp","foreachpartit","foreachrdd","header=true)","header=true,","host","import","integ","integertyp","integertype())","integertype()),","integertype,","item","itemcol='brandid',","itemcol='cateid',","item打分数据应该是通过一下方式进行处理转换为us","java_home='/root/bigdata/jdk'","long","longtyp","longtype()),","longtype,","m","m:","map","map返回的结果是rdd类型，需要调用todf方法转换为datafram","master的地址","matrix","matrix，所以这里可以不用运行","mllib：rdd","ml的模型训练是基于内存的，如果数据过大，内存空间小，迭代次数过多的化，可能会造成内存溢出，报错","ml：dataframe，","model","model.recommendforallusers(3)","model.recommendforallusers(3).show()","model.recommendforallusers(n)","model.recommendforusersubset","model.recommendforusersubset(dataset,","model.save(\"hdfs://localhost:9000/models/userbrandratingmodel.obj\")","model.save(\"hdfs://localhost:9000/models/usercateratingalsmodel.obj\")","model.transform","my_model","my_model.recommendforallusers(3).first()","n个物品","n的推荐","os","os.environ[\"pyspark_driver_python\"]","os.environ[\"pyspark_python\"]","os.environ[\"spark_home\"]","os.environ['java_home']=java_hom","panda","pandas的数据分批读取","partition:","pd","pd.read_csv('behavior_log.csv',chunksize=100,iterator=true)","pivot透视操作，把某列里的字段值转换成行并进行聚合运算(pyspark.sql.groupeddata.pivot)","pool","port","port=port)","print(\"判断数据是否有空值：\",","print(\"查看brandid的数据情况：\",","print(\"查看btag的数据情况：\",","print(\"查看cateid的数据情况：\",","print(\"查看userid的数据情况：\",","process_row(r):","pv:","pv=2326,","pv|","pv|11800|","pyspark","pyspark.ml.recommend","pyspark.sql","pyspark.sql.typ","pyspark_python","rate的数据","rating:","rating=10.35690689086914)]),","rating=11.770171165466309),","rating=13.665942192077637),","rating=20.736785888671875)]),","rating=24.901548385620117),","rating=25.498899459838867),","rating=5.2555742263793945)])]","rating=5.624575138092041),","rating=5.90518856048584),","ratingcol='rating',","reader","reader:","recall_cate_by_cf(partition):","recommendations=[row(cateid=1610,","recommendations=[row(cateid=5579,","recommendations=[row(cateid=5607,","recommendations|","redi","redis.connectionpool(host=host,","redis.redis(connection_pool=pool)","result","result.count()","result.foreachpartition(recall_cate_by_cf)","result.show()","ret","ret.collect()","ret.select(\"recommendations\").show()","ret.show()","root","row","row(btag='cart',","row(btag='fav',","row(btag='pv',","row(cateid=1610,","row(cateid=2447,","row(cateid=3347,","row(cateid=5690,","row(cateid=5737,","row(userid=1061650,","row(userid=2,","row(userid=3,","row.recommendations])","row.userid,","schema","schema=schema)","session","show","spark","spark.createdataframe([[1],[2],[3]])","spark.read.csv(\"hdfs://localhost:9000/data/behavior_log.csv\",","spark.read.csv(\"hdfs://localhost:9000/preprocessing_dataset/brand_count.csv\",","spark.read.csv(\"hdfs://localhost:9000/preprocessing_dataset/cate_count.csv\",","spark.sparkcontext.setcheckpointdir(\"hdfs://localhost:9000/checkpoint/\")","spark_app_nam","spark_app_name),","spark_hom","spark_url","spark_url),","sparkconf","sparkconf()","sparksess","sparksession.builder.config(conf=conf).getorcreate()","spark配置信息","squares），是spark2.*中加入的进行基于模型的协同过滤（model","squares），是spark中进行基于模型的协同过滤（model","string","stringtype()),","stringtype,","structfield(\"brandid\",","structfield(\"btag\",","structfield(\"buy\",","structfield(\"cart\",","structfield(\"cateid\",","structfield(\"fav\",","structfield(\"pv\",","structfield(\"timestamp\",","structfield(\"userid\",","structfield,","structtype([","structtype,","time_stamp:","timestamp:","timestamp|btag|cateid|brandid|","top","transform","transform中提供userid和cateid可以对打分进行预测，利用打分结果排序后","transform中提供userid和cateid可以对打分进行预测，利用打分结果排序后，同样可以实现top","true)","true),","user","user:","userid:","user|time_stamp|btag|","|","|332634|1493809895|","|467042|1493772641|","|467042|1493772644|","|558157|1493741625|","|558157|1493741626|","|558157|1493741627|","|619381|1493774638|","|728690|1493776998|","|857237|1493816945|","|991528|1493780633|","|991528|1493780710|","|991528|1493780712|","|991528|1493780714|","|991528|1493780764|","|991528|1493780765|","|[[104,","|[[1610,","|[[3347,","|[[5607,","|[[5631,","|[[5720,","|[[5731,","|userid|","二","从hdfs中加载csv文件为datafram","从hdfs加载csv文件","从hdfs加载csv文件为datafram","从hdfs加载之前存储的模型","从hdfs加载数据为dataframe，并设置结构","从hdfs加载模型","从hdfs加载预处理好的品牌的统计数据","以下三项配置，可以控制执行器数量","但好在我们训练als模型时，不需要转换为us","但由于cateid字段过多，这里运算量比很大，机器内存要求很高才能执行，否则无法完成任务","但该方法其实指标不治本，因为无法防止内存溢出，所以还是会报错","但这里我们将使用的spark的als模型进行cf推荐，因此注意这里数据输入不需要提前转换为矩阵，直接是","使用pyspark中的als矩阵分解方法实现cf评分预测","偏好打分数据集","偏好评分规则：","写入数据时才开始计算","分析数据集字段的类型和格式","创建spark","判断数据是否有空值：","利用config对象，创建spark","利用打分数据，训练als模型","厚厚的一块","参考：为什么spark中只有al","只有四种类型数据：pv、fav、cart、buy","只给部分用推荐，运算时间短","召回到redi","可通过该方法获得","同svd，它也是一种矩阵分解技术，但理论上，als在海量数据的处理上要优于svd。","同svd，它也是一种矩阵分解技术，对数据进行降维处理。","同上","基于spark的als隐因子模型进行cf评分预测","处理每一行数据：r表示row对象","大致查看一下数据类型","如果数据量大，应考虑的是增加内存、或限制迭代次数和训练数据量级等","如果透视的字段中的不同属性值超过10000个，则需要设置spark.sql.pivotmaxvalues，否则计算过程中会出现错误。文档介绍。","对每个分片的数据进行处理","将模型进行存储","建立redi","建立redis客户端","当存在多个版本时，不指定很可能会导致出错","当调用df.count()时才开始进行计算，这里的count计算的是dataframe的条目数，也就是共有多少个分组","当需要pivot","总的条目数，查看redis中总的条目数是否一致","打分规则","打印当前dataframe的结构","推荐结果存放在recommendations列中，","文档地址：https://spark.apache.org/docs/2.2.2/api/python/pyspark.ml.html?highlight=vectors#modul","文档地址：https://spark.apache.org/docs/latest/api/python/pyspark.ml.html?highlight=vectors#modul","方便练习可以对数据做拆分处理","显示效果:","显示结果:","更多了解：pyspark.ml.recommendation.","本数据集无空值条目，可放心处理","构建结构对象","查看brandid的数据情况：","查看btag的数据情况：","查看cateid的数据情况：","查看dataframe，默认显示前20条","查看user的数据情况：","查看是否有空值","查看更详细配置及说明：https://spark.apache.org/docs/latest/configuration.html","查看每列数据的类别情况","查看每列数据的类型","根据您统计的次数","根据用户对品牌偏好打分训练als模型","根据用户对类目偏好打分训练als模型","根据用户行为数据创建als模型并召回商品","模型训练好后，调用方法进行使用，具体api查看","此处训练时间较长","此时还没有开始计算","注意：","注意：recommendforusersubset","注意：todf不是每个rdd都有的方法，仅局限于此处的rdd","注意：由于数据量巨大，因此这里不考虑基于内存的cf算法","注意：由于数据量巨大，因此这里也不考虑基于内存的cf算法","注意：这里这是召回的是用户最感兴趣的n个类别","测试存储的模型","用户对商品类别的打分数据","用户对应的行为次数","用户对类别的偏好打分数据","用户行为数据拆分","由于是给所有用户进行推荐，此处运算时间也较长","由于运算时间比较长，所以这里先将结果存储起来，供后续其他操作使用","相当大的数量或部分","第一行数据","约113w用户","约12968类别id","约460561品牌id","约7亿条目723268134","给所有用户推荐top","给用户推荐top","给部分用户推荐top","统计每个用户对各个品牌的pv、fav、cart、buy数量","统计每个用户对各个品牌的pv、fav、cart、buy数量并保存结果","统计每个用户对各类商品的pv、fav、cart、buy数量","设置checkpoint的话，会把所有数据落盘，这样如果异常退出，下次重启后，可以接着上次的训练节点继续运行","设置spark","设置启动的spark的app名称，没有提供，将随机产生一个名称","设置该app启动时占用的内存用量，默认1g","该偏好权重比例，次数上限仅供参考，具体数值应根据产品业务场景权衡","详细使用方法：pyspark.ml.recommendation.","请谨慎使用","返回一个pythonrdd类型","返回一个pythonrdd类型，此时还没开始计算","返回的是一个dataframe，这里的count计算的是每一个分组的个数，但当前还没有进行计算","这里由于类型只有四个，所以直接使用collect，把数据全部加载出来","连接池","通常如果user","配置spark","预处理behavior_log数据集"],"day07_推荐系统案例/03_CTR预估数据准备.html":["\"","\")","\",","\"adgroupid\").\\","\"brandid\").\\","\"campaignid\").\\","\"cateid\").\\","\"customerid\").\\","\"nucl_onehot_value\"]).setoutputcol(\"features\").transform(user_profile_df3)","\"pl_onehot_value\",","\"timestamp\").\\","\"userid\").\\","\"上海\",","\"华为\",","\"女\"][0,1]","\"小米\",","\"广州\"][0,1,2]","\"微软\"][0,1,2,3]","\"空值占比：%0.2f%%\"%(nul_na_count/t_count*100))","\"空值占比：%0.2f%%\"%(pl_na_count/t_count*100))","#","#参数2","#参数3","#参数4","'''","'''特征处理'''","(4,[0],[1.0])|","(4,[1,3],[3.0,4.0])","(4,[1],[1.0])|","(4,[2],[1.0])|","(5,[0],[1.0])|","(5,[0],[1.0])|(10,[0,1,5],[4.0,...|","(5,[0],[1.0])|(10,[0,2,5],[2.0,...|","(5,[0],[1.0])|(10,[0,2,5],[4.0,...|","(5,[1],[1.0])|","(5,[1],[1.0])|(10,[0,1,6],[3.0,...|","(5,[1],[1.0])|(10,[0,2,6],[2.0,...|","(5,[1],[1.0])|(10,[0,2,6],[4.0,...|","(5,[1],[1.0])|(10,[0,2,6],[5.0,...|","(5,[1],[1.0])|(10,[0,2,6],[6.0,...|","(5,[1],[1.0])|(10,[0,3,6],[2.0,...|","(5,[2],[1.0])|","(5,[2],[1.0])|(10,[0,1,7],[5.0,...|","(5,[2],[1.0])|(10,[0,3,7],[2.0,...|","(5,[3],[1.0])|","(5,[3],[1.0])|(10,[0,1,8],[5.0,...|","(5,[3],[1.0])|(10,[0,3,8],[1.0,...|","(5,[3],[1.0])|(10,[0,3,8],[4.0,...|","(5,[4],[1.0])|","(5,[4],[1.0])|(10,[0,2,9],[5.0,...|","(negative)","(nullabl","(positive).",")","*********","+",",",",2.0",".withcolumn(\"new_user_class_level\",","0","0,","0,1,1]","0,1,2","0.","0.0,","0.01|","0.0])","0.0|","0.0|(2,[0],[1.0])|","0.]","03_ctr预估数据准备","05","0|","1","1\")","1)","1).where(\"new_user_class_level=","1).where(\"pvalue_level=","1,","1,0,0,1,0,0,1,0,0]","1.0","1.0,","1.0]","1.0e7|","1.0e8|","1.0|","1.0|(2,[1],[1.0])|","1.0}))","1.5e7|","10","10122|","102509|","10305|","10539|","10549|","10812|","10912|","1093|","109704|","10996|","10|","110847|","11115|","11256|","113068|","11310|","1136|","1141729","114532|","114w，略多余日志数据中用户数","11602|","11727|","11739|","11947|","11|","11|430539_1007|","11|430548_1007|","12","120847|","12195|","123242|","12549|","12620|","126746|","129079|","13","132721|","135256|","135610|","139744|","139747|","13|","13|430539_1007|","13|430548_1007|","140520|","14435|","14437|","14574|","145952|32.99|","148946|","149570|","149714|","14985|","14|","14|454237|249.0|","15155|","152414|","15347|","15455|","154623|","15783|","162394|","1670|","16749|","170121|","17054|1494691184|","172334|","174374|139.0|","176076|","17788|","179595|","179746|","182415|","182966|","186847|","19.0|","191036|","198424|","1:20","1|","1|154436|","1|174374|139.0|","1|249.0|","1|368.0|","1|428.0|","1|430548_1007|","1|5.5555556e7|","1|639.0|","1|8.8888888e7|","1来作为目标值","2","2,","2.0|","20","201060|","2017","202710|","20397|","205612|","206|","207754|","207800|199.0|","208458|","209959|","20|","211292|","211816|","213567|","217512|","218101|","218276|","218306|","218918|","221720|","2246|","23236|","23249291","234|","239302|","23:59:46","24*60*60))","240984|","243384|","24484|","248909|","2545|","255875","262215|","26557961","266086|","270027|","270719|","278301|","28145|","28589|","289563|","290675|","2],","2|","2|145952|32.99|","2|293656|","2|324420|","3","3,","3.","3.0])","3.0]))","3.0|","3.1","3.2","3.3","300556|","300681|","310408|","31183|","31239|","313401|","315371|","31899|","31|","32233|","326126|","33.0|","3308670","335413|","335495|","33756|","339334|","33|","344920","345870|","35156|","352666|","3644|","36|","37004|","375706|","375920|","37665|","37759|","38.0|","3800000.0|","383023|","387991|","3900000.0|","392038|","392|","395195|","3980000.0|","3],","3|","3|173047|","3|430548_1007|","4,","4.0","4.0,","4.0]))","4.0]).toarray())","4.0|","4.]","403318|","405447|","406125|","406713|","410958|","41289|","413653|","416333|","417722|","417898|","41925|","42055|","420769|430548_1007|","422260|","423436","427579|430548_1007|","4284|","431082|430548_1007|","4339|","43866|","43|","44251|","443295|","448651|","44|","451004|430539_1007|","4520|","454237|249.0|","46239|","468220|","4760000.0|","4824|","485749|","494312|430548_1007|","49911|","49|","4|","4|138833|","4|430548_1007|","5)","513942|","518883|","523|","527|","529913|","546930|","552638|","554311|","55|","561681|430548_1007|","575917","5762","5777|","58013|","582235|430548_1007|","588664|430548_1007|","5888888.0|","590965|","593001|430548_1007|","5953|","59774|","5|","5|430548_1007|","600195|430548_1007|","60214|","607788|","612|","6130|","618965|430548_1007|","6211|","624504|430539_1007|","6261|","63133|","6355|","639794|","6406|","6527","65726|","658722|","6636|","6703|","67558|","675674|430539_1007|","6769","6823|","684020|","685|","687854|430548_1007|","6972|","6|","6|207800|199.0|","7","70206|","7032|","7043|","70894|","7185|","7207|","7211|","7213|","72273|","72781|","735220|430548_1007|","739|","742741|430548_1007|","745|","756665|430548_1007|","77797|","782038|430548_1007|","794890|","79971|","80548|","817569|430548_1007|","820018|430548_1007|","83237|","83948|","8401|","846811","85373|","86243|","87331|199.0|","8888888.0|","88975|","89.9|","89831|","8|","8|430548_1007|","90141|","90351|","9086|","92241|","92560|","9293|","9510|","95471|170.0|","9600000.0|","97","99.0|","9900000.0|","9970|","99815","9994|","9995|","9996|","9|","9|186847|","9|430539_1007|","9|430548_1007|","=","=[0,1,1,0,0,1,0,0,0]","=[1,0,0]","[\"北京\",","[\"女“，”北京“，”苹果“]","[\"男\",","[\"男“，”上海“，”小米“]=[","[\"苹果\",","[0,","[0.","[1,","[1.","[1.0,","[3.0,","[r.cms_segid,","[row(clk='0',","[row(pid='430548_1007',","])","_1","_1)","_2","_2)","_3","_3)","_4","_4)","ad_feature_df","ad_feature_df.groupby(\"brandid\").count().count()","ad_feature_df.groupby(\"campaignid\").count().count()","ad_feature_df.groupby(\"cateid\").count().count()","ad_feature_df.groupby(\"customerid\").count().count()","ad_feature_df.printschema()","ad_feature_df.select(\"price\").filter(\"pric","ad_feature_df.select(\"price\").filter(\"price>10000\").count())","ad_feature_df.show()","ad_feature_df.sort(\"price\").show()","ad_feature_df.sort(\"price\",","adgroup_id:","adgroup_id总数：","adgroup_id总数：\",","adgroupid:","age_level,","age_level:","algorithms.","api.","ascending=false).show()","associ","binari","both","brand:","brandid:","brandid数值个数：","brandid：脱敏过的品牌id；","brand|price|","campaign_id:","campaignid:","campaignid数值个数：","campaignid：脱敏过的广告计划id；","cate_id:","cateid:","cateid数值个数：","cateid：脱敏过的商品类目id；","class","classification,","classification.","clk:","cms_group_id,","cms_group_id:","cms_segid:","count=10085063)]","count=1366056)]","count=16472898),","count=25191905),","count|","creat","ctr预估数据准备","customer:","customerid:","customerid:脱敏过的广告主id；","customerid数值个数：","dafaframe对象","datafram","dataframe来处理，因为方便，但注意如果数据量较大不推荐，因为这样会把全部数据加载到内存中","datetim","datetime.fromtimestamp(1494691186","datetime.fromtimestamp(1494691186)","def","dens","detail","df","df.\\","df.adgroup_id.cast(integertype())).withcolumnrenamed(\"adgroup_id\",","df.brand.cast(integertype())).withcolumnrenamed(\"brand\",","df.campaign_id.cast(integertype())).withcolumnrenamed(\"campaign_id\",","df.cate_id.cast(integertype())).withcolumnrenamed(\"cate_id\",","df.clk.cast(integertype()))","df.count())","df.customer.cast(integertype())).withcolumnrenamed(\"customer\",","df.groupby(\"adgroup_id\").count().count())","df.groupby(\"clk\").count().collect())","df.groupby(\"pid\").count().collect())","df.groupby(\"user\").count().count())","df.nonclk.cast(integertype())).\\","df.pid.cast(stringtype())).\\","df.price.cast(floattype()))","df.printschema()","df.replace(\"null\",","df.show()","df.time_stamp.cast(longtype())).withcolumnrenamed(\"time_stamp\",","df.user.cast(integertype())).withcolumnrenamed(\"user\",","df的基础上直接替换掉列数据","doc","doubl","encod","encoder])","false)","featur","feature_df","feature_df.select(\"features\").show()","feature_df.show()","features|","final_gender_code,","final_gender_code:","float","floattyp","floattype,","header=true)","header=true,","import","indic","inputcol='nucl_onehot_feature',","inputcol='pid_feature',","inputcol='pl_onehot_feature',","integ","integertype())","integertype()),","integertype,","label","label,","label/response.","labeledpoint","labeledpoint(0.0,","labeledpoint(1.0,","labeledpoint.","labeledpoint。","lambda","learn","local","long","longtype,","mllib,","model","model.predict([0.0,","model.predict(rdd)","model.predict(rdd2)","model2","model2.predict([0.0,","more","multiclass","neg","new_df","new_df.new_user_class_level.cast(stringtype()))","new_df.pvalue_level.cast(stringtype()))\\","new_df.show()","new_df.sort(\"timestamp\",","new_df.withcolumn(\"pvalue_level\",","new_user_class_level","new_user_class_level:","new_user_class_level的空值情况：","new_user_profile_df","new_user_profile_df.show()","nonclk:","nonclk和clk在这里是作为目标值，不做为特征","np","np.array(temp)","nul_na_count","nul_na_count,","nul_na_df","nul_na_df.rdd.map(row)","nul_na_df.show(10)","null|","null|249.0|","null|344920|","null|368.0|","null|428.0|","null|575917|","null|639.0|","numpi","occupation:","occupation作为特征值，pvalue_level作为目标值","onehotencod","onehotencoder(droplast=false,","onehotencoder：对特征列数据，进行热编码，通常需结合stringindexer一起使用","outputcol='nucl_onehot_feature')","outputcol='nucl_onehot_value')","outputcol='pid_feature')","outputcol='pid_value')","outputcol='pl_onehot_feature')","outputcol='pl_onehot_value')","pdf","pdf[\"pvalue_level\"]","pid","pid:","pid_value|","pid|nonclk|clk|","pid|nonclk|clk|pid_feature|","pipelin","pipeline(stages=[stringindexer,","pipeline.fit(raw_sample_df)","pipeline.fit(user_profile_df)","pipeline.fit(user_profile_df2)","pipeline_fit","pipeline_fit.transform(user_profile_df)","pipeline_fit.transform(user_profile_df2)","pipeline_model","pipeline_model.transform(raw_sample_df)","pipeline：让数据按顺序依次被处理，将前一次的处理结果作为下一次的输入","pl_na_count","pl_na_count,","pl_na_df","pl_na_df.rdd.map(row)","pl_na_df.show(10)","pl_na_df.topandas()","pl_onehot_feature:","pl_onehot_value:","pl_onehot_value列的值为稀疏向量，存储热独编码的结果","po","point","posit","predict","predicts.count())","predicts.map(lambda","predicts2","predicts2.take(20)","price","price:","price|","print(\"*********\")","print(\"age_level:","print(\"brandid数值个数：\",","print(\"campaignid数值个数：\",","print(\"cateid数值个数：\",","print(\"cms_group_id:","print(\"cms_segid:","print(\"customerid数值个数：\",","print(\"final_gender_code:","print(\"new_user_class_level的空值情况：\",","print(\"occupation:","print(\"pvalue_level的空值情况：\",","print(\"shopping_level:","print(\"价格低于1的条目个数\",","print(\"价格高于1w的条目个数：\",","print(\"分类特征值个数情况:","print(\"含缺失值的特征情况:","print(\"广告id","print(\"广告展示位pid情况：\",","print(\"广告点击数据情况clk：\",","print(\"总广告条数：\",df.count())","print(\"样本数据集总条目数：\",","print(\"测试样本个数：\")","print(\"用户user总数：\",","print(\"该时间之前的数据为训练样本，该时间以后的数据为测试样本：\",","print(\"预测值总数\",","print(new_df.select(\"pid_value\").first())","print(new_df.select(\"pid_value\").first().pid_value.toarray())","print(predicts.take(20))","print(sparsevector(4,","print(test_sample.count())","pvalue_level","pvalue_level:","pvalue_level的空值情况：","pyspark.ml","pyspark.ml.featur","pyspark.ml.linalg","pyspark.ml.linalg.sparsevector","pyspark.mllib.linalg","pyspark.mllib.regress","pyspark.mllib.tre","pyspark.sql.typ","python","r.age_level,","r.cms_group_id,","r.cms_segid,","r.final_gender_code,","r.occup","r.occupation])","r.shopping_level,","r:labeledpoint(r.new_user_class_level","r:labeledpoint(r.pvalue_level","randomforest","randomforest.trainclassifier(train_data,","randomforest.trainclassifier(train_data2,","raw_sample_df","raw_sample_df.filter(raw_sample_df.timestamp(1494691186","raw_sample_df.printschema()","raw_sample_df.show()","rdd","rdd2","refer","regress","repres","return","root","row","row(clk='1',","row(pid='430539_1007',","row(pid_value=sparsevector(2,","row(r):","schema","schema=schema)","schema=schema))","selection）","shopping_level,","shopping_level:","show","spark.read.csv(\"hdfs://localhost:9000/data/ad_feature.csv\",","spark.read.csv(\"hdfs://localhost:9000/data/raw_sample.csv\",","spark.read.csv(\"hdfs://localhost:9000/data/user_profile.csv\",","spark中使用热独编码","spars","sparse,","sparsevector","sparsevector(3,","start","store","string","stringindex","stringindexer(inputcol='new_user_class_level',","stringindexer(inputcol='pid',","stringindexer(inputcol='pvalue_level',","stringindexer对指定字符串列进行特征处理","stringindexer：对指定字符串列数据进行特征处理，如将性别数据“男”、“女”转化为0和1","stringtyp","stringtype,","structfield(\"age_level\",","structfield(\"cms_group_id\",","structfield(\"cms_segid\",","structfield(\"final_gender_code\",","structfield(\"new_user_class_level\",","structfield(\"occupation\",","structfield(\"pvalue_level\",","structfield(\"shopping_level\",","structfield(\"userid\",","structfield,","structtype([","structtype,","supervis","t_count","temp","time_stamp:","timestamp:","timestamp|adgroupid|","top","train_data","train_data2","train_sampl","true)","us","user:","user_profile_df","user_profile_df.count()","user_profile_df.dropna(subset=[\"new_user_class_level\"]).count()","user_profile_df.dropna(subset=[\"new_user_class_level\"]).rdd.map(","user_profile_df.dropna(subset=[\"pvalue_level\"]).count()","user_profile_df.dropna(subset=[\"pvalue_level\"]).rdd.map(","user_profile_df.dropna(subset=[\"pvalue_level\"]).unionall(spark.createdataframe(pdf,","user_profile_df.dropna(subset=[\"pvalue_level\"])：","user_profile_df.groupby(\"age_level\").count().count())","user_profile_df.groupby(\"cms_group_id\").count().count())","user_profile_df.groupby(\"cms_segid\").count().count())","user_profile_df.groupby(\"final_gender_code\").count().count())","user_profile_df.groupby(\"new_user_class_level\").count().show()","user_profile_df.groupby(\"occupation\").count().count())","user_profile_df.groupby(\"pvalue_level\").count().show()","user_profile_df.groupby(\"shopping_level\").count().count())","user_profile_df.na.fill(","user_profile_df.new_user_class_level.cast(stringtype()))","user_profile_df.printschema()","user_profile_df.pvalue_level.cast(stringtype()))\\","user_profile_df.show()","user_profile_df.withcolumn(\"pvalue_level\",","user_profile_df2","user_profile_df2.printschema()","user_profile_df2.show()","user_profile_df3","user_profile_df3.show()","userid:","user|time_stamp|adgroup_id|","vector","vector,","vector.","vectorassembl","vectorassembler().setinputcols([\"age_level\",","withcolumn(\"adgroup_id\",","withcolumn(\"brand\",","withcolumn(\"campaign_id\",","withcolumn(\"cate_id\",","withcolumn(\"clk\",","withcolumn(\"customer\",","withcolumn(\"nonclk\",","withcolumn(\"pid\",","withcolumn(\"price\",","withcolumn(\"time_stamp\",","withcolumn(\"user\",","x:int(x)).collect()","zero:","{0:","{2:2,3:7}","{2:2}","{},","|","|(10,[0,1,5],[4.0,...|","|(10,[0,1,6],[3.0,...|","|(10,[0,1,7],[5.0,...|","|(10,[0,1,8],[5.0,...|","|(10,[0,2,5],[2.0,...|","|(10,[0,2,5],[4.0,...|","|(10,[0,2,6],[2.0,...|","|(10,[0,2,6],[4.0,...|","|(10,[0,2,6],[5.0,...|","|(10,[0,2,6],[6.0,...|","|(10,[0,2,9],[5.0,...|","|(10,[0,3,6],[2.0,...|","|(10,[0,3,7],[2.0,...|","|(10,[0,3,8],[1.0,...|","|(10,[0,3,8],[4.0,...|","|117840|1494036743|","|177002|1494691186|","|243671|1494691186|","|286630|1494218579|","|286630|1494289247|","|298139|1494462593|","|322244|1494691179|","|399907|1494302958|","|421590|1494034144|","|449818|1494638778|","|488527|1494691184|","|530454|1494293746|","|555266|1494307136|","|581738|1494137644|","|623911|1494451608|","|623911|1494625301|","|627200|1494691179|","|628137|1494524935|","|628998|1494691180|","|674444|1494691179|","|704223|1494691183|","|707120|1494220810|","|738335|1494691179|","|739815|1494115387|","|771431|1494153867|","|775475|1494561036|","|839493|1494691183|","|914836|1494650879|","|914836|1494651029|","|976358|1494156949|","|adgroup_id|cate_id|campaign_id|customer|","|adgroupid|cateid|campaignid|customerid|brandid|","|adgroupid|cateid|campaignid|customerid|brandid|price|","|new_user_class_level|","|pvalue_level|","|userid|","|userid|cms_segid|cms_group_id|final_gender_code|age_level|pvalue_level|shopping_level|occupation|new_user_class_level","|userid|cms_segid|cms_group_id|final_gender_code|age_level|pvalue_level|shopping_level|occupation|new_user_class_level|","|userid|cms_segid|cms_group_id|final_gender_code|age_level|pvalue_level|shopping_level|occupation|new_user_class_level|pl_onehot_feature|pl_onehot_value|","|userid|cms_segid|cms_group_id|final_gender_code|age_level|pvalue_level|shopping_level|occupation|new_user_class_level|pl_onehot_feature|pl_onehot_value|nucl_onehot_feature|nucl_onehot_value|","….","…。","三","与非缺失数据进行拼接，完成pvalue_level的缺失值预测","且当前我们缺少对这些特征更加具体的信息，（如商品类目具体信息、品牌具体信息等），从而无法对这些特征的数据做聚类、降维处理","两个特征","从hdfs中加载广告基本信息数据，返回spark","从hdfs中加载样本数据信息","从hdfs加载用户基本信息数据","以上四个特征均属于分类特征，但由于分类值个数均过于庞大，如果去做热独编码处理，会导致数据过于稀疏","价格低于1的条目个数","价格高于1w的条目个数：","传统变化后的数据不是连续的，而是随机分配的，不容易应用在分类器中","传统变化：","但根据我们的经验，我们的广告推荐其实和用户的消费水平、用户所在城市等级都有比较大的关联，因此在这里pvalue_level、new_user_class_level都是比较重要的特征，我们不考虑舍弃","低维转高维方式","使用dataframe.withcolumn更改df列数据结构；使用dataframe.withcolumnrenamed更改列名称","使用热独编码转换pvalue_level的一维数据为多维，其中缺失值单独作为一个特征值","使用热编码转换new_user_class_level的一维数据为多维","假设有三组特征，分别表示年龄，城市，设备；","分析并预处理ad_feature数据集","分析并预处理raw_sample数据集","分析并预处理user_profile数据集","分析数据集字段的类型和格式","分类特征值个数情况:","利用schema从hdfs加载","利用管道对每一个数据进行热独编码处理","利用随机森林对new_user_class_level的缺失值进行预测","利用随机森林对pvalue_level的缺失值进行预测","前七天为训练数据、最后一天为测试数据","前面分析的以下几个分类特征值个数情况:","剔除掉缺失值数据，将余下的数据作为训练数据","参数1","参数：维度、索引列表、值列表","发现pvalue_level和new_user_class_level存在空值：（注意此处的null表示空值，而如果是null，则往往表示是一个字符串）","只有两种广告展示位，占比约为六比四","含缺失值的特征情况:","因此直接利用schema就可以加载进该数据，无需替换null值","因此这里不选取它们作为特征","因此这里经过map函数处理，将每一行数据转换为普通的列表数据","在panda","在特征中","填充方案","填充方案：结合用户的其他特征值，利用随机森林算法进行预测；但产生了大量人为构建的数据，一定程度上增加了数据的噪音","对pvalue_level进行热独编码，求值","对处理出来的特征处理列进行，热独编码","对每一组特征，使用枚举类型，从0开始；","将pvalue_level中的空值所在行数据剔除后的数据，作为训练样本","展示数据，默认前20条","广告id","广告展示位pid情况：","广告点击数据情况clk：","总广告条数：","总结：可以发现由于这两个字段的缺失过多，所以预测出来的值已经大大失真，但如果缺失率在10%以下，这种方法是比较有效的一种","我们使用cms_segid,","我们接下来采用将变量映射到高维空间的方法来处理数据，即将缺失项也当做一个单独的特征来对待，保证数据的原始性","打印df结构信息","把变量映射到高维空间：如pvalue_level的1维数据，转换成是否1、是否2、是否3、是否缺失的4维数据；这样保证了所有原始数据不变，同时能提高精确度，但这样会导致数据变得比较稀疏，如果样本量很小，反而会导致样本效果较差，因此也不能滥用","数据条数","显示内容:","显示特征情况","显示结果:","更改df表结构：更改列类型和列名称","更改表结构，转换为对应的数据类型","替换掉null字符串，替换掉","有两个分类","有关api的更多详细信息，请参阅labeledpointpython文档。","本样本数据集共计8天数据","构建表结构schema对象","查看前20条","查看各项数据的特征","查看是否有空值","查看最大时间","查看每列数据的类别情况","查看每列数据的类型","标记点是与标签/响应相关联的密集或稀疏的局部矢量。在mllib中，标记点用于监督学习算法。我们使用double来存储标签，因此我们可以在回归和分类中使用标记点。对于二分类情况，目标值应为0（负）或1（正）。对于多分类，标签应该是从零开始的类索引：0,","标记点表示为","树的棵数","样本数据集总条目数：","根据经验，以上几个分类特征都一定程度能体现用户在购物方面的特征，且类别都较少，都可以用来作为用户特征","根据经验，该数据集中，只有广告展示位pid对比较重要，且数据不同数据之间的占比约为6:4，因此pid可以作为一个关键特征","注意+1","注意用法：https://spark.apache.org/docs/latest/api/python/pyspark.mllib.html?highlight=tree%20random#pyspark.mllib.tree.randomforestmodel.predict","注意随机森林输入数据时，由于label的分类数是从0开始的，但pvalue_level的目前只分别是1，2，3，所以需要对应分别","注意，一般情况下：","注意：unionall的使用，两个df的表结构必须完全一样","注意：热编码只能对字符串类型的列数据进行处理","注意：由于本数据集中存在null字样的数据，无法直接设置schema，只能先将null类型的数据处理掉，然后进行类型转换","注意：还需要加入广告基本特征和用户基本特征才能做程一份完整的样本数据集","注意：这里的null会直接被pyspark识别为none数据，也就是na数据，所以这里可以直接利用schema导入数据","测试样本个数：","点和不点比率约：","热独编码","热独编码时，必须先将待处理字段转为字符串类型才可处理","热独编码是一种经典编码，是使用n位状态寄存器(如0和1)来对n个状态进行编码，每个状态都由他独立的寄存器位，并且在任意时候，其中只有一位有效。","特征中是否包含分类的特征","特征处理","特征处理，如1维转多维","特征选取","特征选取（featur","特征选择","特征选择就是选择那些靠谱的feature，去掉冗余的feature，对于搜索广告，query关键词和广告的匹配程度很重要；但对于展示广告，广告本身的历史表现，往往是最重要的feature。","用户user总数：","用户特征合并","由于该思想正好和热独编码实现方法一样，因此这里直接使用热独编码方式处理数据","目标值的分类个数","空值占比：32.49%","空值占比：54.24%","第二个特征是分类的:","筛选出缺失值条目","约","约2600w","约85w","缺失值处理","缺失值处理方案：","缺失率低于10%：可直接进行相应的填充，如默认值、均值、算法拟合等等；","而只选取price作为特征数据，因为价格本身是一个统计类型连续数值型数据，且能很好的体现广告的价值属性特征，通常也不需要做其他处理(离散化、归一化、标准化等)，所以这里直接将当做特征数据来使用","而经过热独编码，数据会变成稀疏的，方便分类器处理：","自然那么最终得出预测值后，需要对应+1才能还原回来","表示","训练分类模型","训练样本","训练样本个数：","训练样本：","训练的数据","该时间之前的数据为训练样本，该时间以后的数据为测试样本：","资源位。该特征属于分类特征，只有两类取值，因此考虑进行热编码处理即可，分为是否在资源位1、是否在资源位2","转换为panda","转换为普通的rdd类型","返回字段pid_value是一个稀疏向量类型数据","还原预测值","这样做保留了特征的多样性，但是也要注意如果数据过于稀疏(样本较少、维度过高)，其效果反而会变差","这里数据量比较小，直接转换为panda","这里注意predict参数，如果是预测多个，那么参数必须是直接有列表构成的rdd参数，而不能是dataframe.rdd类型","选出new_user_class_level全部的","除了前面处理的pvalue_level和new_user_class_level需要作为特征以外，(能体现出用户的购买力特征)，还有：","随机森林中","随机森林模型：pyspark.mllib.tree.randomforestmodel","随机森林：pyspark.mllib.tree.randomforest","需要先将缺失值全部替换为数值，与原有特征一起处理","预测值实际应该为2","预测值总数","预测全部的pvalue_level值:","预测单个数据","高于10%：往往会考虑舍弃该特征"],"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":["\"","\"adgroupid\").\\","\"age_level\",","\"brandid\").\\","\"campaignid\").\\","\"cateid\").\\","\"clk\",","\"cms_group_id\",","\"cms_segid\",","\"customerid\").\\","\"final_gender_code\",","\"nucl_onehot_value\"","\"occupation\",","\"outer\")","\"pid_value\",","\"pl_onehot_value\",","\"prediction\").sort(\"probability\").show(100)","\"price\",","\"probability\",","\"shopping_level\",","\"timestamp\").\\","\"timestamp\",","\"userid\").\\","#","'''","'''pid和特征的对应关系","'''从hdfs中加载样本数据信息'''","'''对缺失数据进行特征热编码'''","'outer')","'pid和特征的对应关系\\n430548_1007：0\\n430549_1007：1\\n'","(4,[0],[1.0])|","(4,[1],[1.0])|","(4,[2],[1.0])|","(5,[0],[1.0])|","(5,[1],[1.0])|","(5,[1],[1.0])|(18,[1,2,3,4,5,6,...|","(5,[1],[1.0])|(18,[1,2,4,5,6,7,...|","(5,[2],[1.0])|","(5,[3],[1.0])|","(5,[4],[1.0])|","(5,[4],[1.0])|(18,[1,2,3,4,5,6,...|","(nullabl","+",".withcolumn(\"new_user_class_level\",","0.0|","0.0|(2,[0],[1.0])|","0.4|[0.94045691149716...|","0.9248=0.0752，即点击概率约为7.52%","04_逻辑回归(lr)实现ctr预估","0|","0|(2,[1],[1.0])|","0|(2,[1],[1.0])|109.0|","0|(2,[1],[1.0])|176.0|","0|(2,[1],[1.0])|1880.0|","0|(2,[1],[1.0])|2200.0|","0|(2,[1],[1.0])|2360.0|","0|(2,[1],[1.0])|247.0|","0|(2,[1],[1.0])|5649.0|","0|(2,[1],[1.0])|697.0|","0|(2,[1],[1.0])|698.0|","1\")","1)","1.0e8|[0.86822033939259...|","1.0e8|[0.88410457194969...|","1.0e8|[0.89175497837562...|","1.0|","1.0|(2,[1],[1.0])|","1.5e7|[0.93741450446939...|","1.5e7|[0.93757135079959...|","1.5e7|[0.93834723093801...|","10.0|[0.94045690659874...|","100","10122|","1027.5|[0.94002127571285...|","10549|","108.0|","108.0|[0.94045685659402...|","10812|","109.0|[0.94045685608377...|","10912|","1099.0|[0.93972095713786...|","10996|","10|","110847|","11115|","11256|","11310|","118.0|[0.94045685149150...|","119.0|[0.93972146296721...|","11|","11|430539_1007|","11|430548_1007|","12.0|[0.93999396767518...|","122.5|[0.93999391088191...|","124.0|[0.94045684842999...|","125.0|[0.93972145987031...|","125.0|[0.94045684791973...|","127.0|[0.94045684689923...|","129.0|[0.94045684587872...|","135.0|[0.94045684281721...|","135256|","138.0|[0.93972145316035...|","138.0|[0.94021122658672...|","139744|","139747|","13|","13|430539_1007|","13|430548_1007|","145952|32.99|","149.0|[0.93999389726180...|","149.0|[0.94021122095226...|","14|","158.0|[0.93972144283734...|","158.0|[0.93999389263610...|","158.0|[0.94000390317214...|","158.0|[0.94045683108140...|","159.0|[0.94021121583003...|","16.9|[0.94045690307800...|","160.0|[0.94045683006090...|","1670|","168.0|[0.93972143767584...|","168.0|[0.94045682597887...|","1689.0|[0.94055856072019...|","174374|139.0|","176.0|[0.93972143354663...|","176.0|[0.93999388338470...|","176.0|[0.94045682189685...|","178.0|[0.94021120609778...|","18.0|[0.93972151509838...|","186847|","187.1|[0.94045681623305...|","188.0|[0.93972142735283...|","188.0|[0.94021120097554...|","188.0|[0.94031410659516...|","18|","19.0|","19.0|[0.94045690200647...|","19.8|[0.93999396366625...|","19.9|[0.93999396361485...|","195.0|[0.94021119738998...|","198.0|[0.94035413548387...|","198.0|[0.94045681067129...|","199.0|[0.94045681016104...|","19|","1|","1|249.0|","1|368.0|","1|428.0|","1|430548_1007|","1|5.5555556e7|[0.92481456486873...|","1|639.0|","1个虚拟变量，n为pvalue_level的取值范围","2.0|","20","207800|199.0|","208.0|[0.94039204931181...|","208458|","220.0|[0.93999386077017...|","220.0|[0.94028926340218...|","23.0|[0.94045689996546...|","23236|","234|","24*60*60))","24484|","248909|","25.0|[0.93980311449212...|","25.0|[0.94021128446795...|","25.98|[0.94045689844491...|","25029435","2545|","256.0|[0.94002167206744...|","258.0|[0.94002167103995...|","258.0|[0.94021116511987...|","259.0|[0.94021116460765...|","26557961","27.6|[0.93972151014334...|","273.0|[0.94002166333380...|","275.0|[0.94002166230631...|","278.0|[0.93972138089925...|","278.0|[0.94002166076508...|","278.0|[0.94021115487539...|","28.0|[0.93980311294563...|","28.0|[0.93999395945172...|","28145|","28589|","2890.0|[0.94028789742257...|","297.0|[0.94002165100394...|","298.0|[0.94002165049020...|","298.0|[0.94045675964600...|","299.0|[0.94002164997645...|","299.0|[0.94021114411869...|","2|","3.0|","3.5|[0.94045690991538...|","30.0|[0.93972150890458...|","30.0|[0.93980311191464...|","30.0|[0.93999395842379...|","300.0|[0.93972136954393...|","300556|","3088.0|[0.94055784801535...|","31.0|[0.94045689588345...|","311.0|[0.93972136386626...|","313401|","316.0|[0.94045675046144...|","31|","32.0|[0.94045689537319...|","32.8|[0.93999395698469...|","32233|","33.0|","33.0|[0.93972150735613...|","33.0|[0.93999395688189...|","335.0|[0.93999380166395...|","335413|","338.0|[0.93972134993018...|","339.0|[0.93999379960808...|","33|","348.0|[0.94002162480299...|","348.0|[0.94045673413334...|","349.0|[0.94045673362308...|","35.0|[0.93972150632383...|","35.0|[0.94002178560473...|","35.0|[0.94021127934572...|","35.5|[0.93999395559698...|","3644|","366.0|[0.94002161555560...|","368.0|[0.94002161452811...|","368.0|[0.94021110877521...|","369.0|[0.94002161401436...|","37004|","375706|","38.0|","38.0|[0.93972150477538...|","387991|","388.0|[0.94002160425322...|","39.6|[0.94045689149528...|","39.9|[0.94045689134220...|","392|","395195|","3960.0|[0.94055740378069...|","398.0|[0.94000377983931...|","398.0|[0.94021109340848...|","399.0|[0.94055921788912...|","3|","3|430548_1007|","4.0|","4.1","40.0|[0.94045689129118...|","4120.0|[0.94001968693052...|","4284|","43.98|[0.94045688926037...|","430.0|[0.94002158267595...|","430548_1007：0","430549_1007：1","43|","44.98|[0.93999395072458...|","440.0|[0.94002157753851...|","44|","4520|","454237|249.0|","459.0|[0.94055918732327...|","468.0|[0.94055918273839...|","478.0|[0.94045666780037...|","49.0|[0.94004219516957...|","49.0|[0.94021127217459...|","498.0|[0.94002154774131...|","499.0|[0.94055916694603...|","49|","4|","4|430548_1007|","5","500.0|[0.94002154671382...|","509.0|[0.94002154209012...|","519.0|[0.94045664687995...|","523|","529913|","546930|","55|","56.0|[0.93972149548468...|","5608.0|[0.94001892245145...|","563.0|[0.94002151434789...|","568.0|[0.94000369247841...|","568.0|[0.94021100633025...|","5718.0|[0.94001886593718...|","5777|","58.0|[0.93972149445238...|","58.0|[0.94021126756458...|","58.0|[0.94031417307687...|","59.98|[0.93972149343040...|","59.9|[0.93999394305620...|","590965|","5953|","598.0|[0.94002149636681...|","598.0|[0.94021099096349...|","599.0|[0.94055911600291...|","5|","5|430548_1007|","6.7|[0.93999397039920...|","60.0|[0.94045688108613...|","607788|","612|","6211|","6261|","629.0|[0.94055910071996...|","63133|","6355|","639794|","6406|","660.0|[0.94002146451460...|","672.0|[0.94002145834965...|","68.0|[0.93999393889308...|","68.0|[0.94031416796289...|","68.0|[0.94045687700412...|","6823|","684020|","688.0|[0.93999362023323...|","69.0|[0.94045687649386...|","6972|","6|","70206|","72.5|[0.94045687470798...|","7207|","7211|","7213|","737.0|[0.94055904570133...|","75.0|[0.93999393529532...|","77.0|[0.94045687241185...|","78.0|[0.93972148412937...|","788.0|[0.94055901972029...|","79.0|[0.93999393323945...|","79.0|[0.94045687139134...|","79.8|[0.94045687098314...|","794890|","83237|","87331|199.0|","88.0|[0.93999392861375...|","888.0|[0.94055896877705...|","8888.0|[0.94045237642030...|","89.9|","8|","8|430548_1007|","915.0|[0.94021082858784...|","92560|","9293|","9510|","95471|170.0|","98.0|[0.94045686169655...|","989.0|[0.94002129549211...|","99.0|","99.0|[0.93999392296012...|","99.0|[0.94045686118630...|","998.0|[0.94055891273943...|","9|","9|430539_1007|","9|430548_1007|","=","[","[_.userid==user_profile_df.userid]","[raw_sample_df.adgroupid==ad_feature_df.adgroupid]","]","])","_","_.join(user_profile_df,","_ad_feature_df","_ad_feature_df.\\","_ad_feature_df.adgroup_id.cast(integertype())).withcolumnrenamed(\"adgroup_id\",","_ad_feature_df.brand.cast(integertype())).withcolumnrenamed(\"brand\",","_ad_feature_df.campaign_id.cast(integertype())).withcolumnrenamed(\"campaign_id\",","_ad_feature_df.cate_id.cast(integertype())).withcolumnrenamed(\"cate_id\",","_ad_feature_df.customer.cast(integertype())).withcolumnrenamed(\"customer\",","_ad_feature_df.price.cast(floattype()))","_ad_feature_df.replace(\"null\",","_raw_sample_df1","_raw_sample_df1.\\","_raw_sample_df1.adgroup_id.cast(integertype())).withcolumnrenamed(\"adgroup_id\",","_raw_sample_df1.clk.cast(integertype()))","_raw_sample_df1.nonclk.cast(integertype())).\\","_raw_sample_df1.pid.cast(stringtype())).\\","_raw_sample_df1.show()","_raw_sample_df1.time_stamp.cast(longtype())).withcolumnrenamed(\"time_stamp\",","_raw_sample_df1.user.cast(integertype())).withcolumnrenamed(\"user\",","_raw_sample_df2","_raw_sample_df2.printschema()","_raw_sample_df2.show()","_user_profile_df1","_user_profile_df1.na.fill(","_user_profile_df2","_user_profile_df2.new_user_class_level.cast(stringtype()))","_user_profile_df2.pvalue_level.cast(stringtype()))\\","_user_profile_df2.show()","_user_profile_df2.withcolumn(\"pvalue_level\",","_user_profile_df3","_user_profile_df3.printschema()","_user_profile_df4","_user_profile_df4.printschema()","_user_profile_df4.show()","_和user_profile_df合并条件","ad_feature_df","ad_feature_df.printschema()","ad_feature_df.show()","adgroupid:","age_level:","brandid:","campaignid:","cateid:","clk:","cms_group_id:","cms_segid:","condit","condition,","condition2","condition2,","customerid:","dataframe数据合并：pyspark.sql.dataframe.join","dataset","datasets.printschema()","datasets.select(*useful_cols)","datasets_1","datasets_1.count())","datasets_1.dropna()","datasets_1.filter(datasets_1.timestamp(1494691186","doubl","encod","encoder])","features|","final_gender_code:","float","floattyp","floattype,","header=true)","header=true,","import","inputcol='nucl_onehot_feature',","inputcol='pid_feature',","inputcol='pl_onehot_feature',","integ","integertype())","integertype()),","integertype,","label目标值字段","logisticregress","logisticregression()","logisticregressionmodel","logisticregressionmodel.load(\"hdfs://localhost:9000/models/ctrmodel_normal.obj\")","long","longtype,","lr","lr.setlabelcol(\"clk\").setfeaturescol(\"features\").fit(train_datasets_1)","lr实现ctr预估","model","model.save(\"hdfs://localhost:9000/models/ctrmodel_normal.obj\")","model.transform(test_datasets_1)","new_user_class_level:","nonclk:","nucl_onehot_feature:","nucl_onehot_value:","occupation:","onehotencod","onehotencoder(droplast=false,","outputcol='nucl_onehot_feature')","outputcol='nucl_onehot_value')","outputcol='pid_feature')","outputcol='pid_value')","outputcol='pl_onehot_feature')","outputcol='pl_onehot_value')","pid:","pid_feature:","pid_value:","pid_value|","pid_value|price|cms_segid|cms_group_id|final_gender_code|age_level|shopping_level|occupation|pl_onehot_value|nucl_onehot_value|","pid|nonclk|clk|","pid|nonclk|clk|pid_feature|","pipelin","pipeline(stages=[stringindexer,","pipeline.fit(_raw_sample_df2)","pipeline.fit(_user_profile_df3)","pipeline.fit(_user_profile_df4)","pipeline_fit","pipeline_fit.transform(_raw_sample_df2)","pipeline_fit.transform(_user_profile_df3)","pipeline_fit.transform(_user_profile_df4)","pl_onehot_feature:","pl_onehot_value:","pl_onehot_value列的值为稀疏矩阵，存储热编码的结果","price:","price|","price|cms_segid|cms_group_id|final_gender_code|age_level|shopping_level|occupation|pl_onehot_value|nucl_onehot_value|","print(\"剔除空值数据后，还剩：\",","print(datasets.count())","probability|prediction|","pvalue_level:","pyspark.ml","pyspark.ml.classif","pyspark.ml.featur","pyspark.sql.typ","raw_sample_df","raw_sample_df.join(ad_feature_df,","raw_sample_df.show()","raw_sample_df和ad_feature_df合并条件","result_1","result_1.filter(result_1.clk==1).select(\"clk\",","result_1.select(\"clk\",","root","row","schema","schema=schema)","shopping_level:","show","spark.read.csv(\"hdfs://localhost:9000/data/raw_sample.csv\",","spark.read.csv(\"hdfs://localhost:9000/datasets/ad_feature.csv\",","spark.read.csv(\"hdfs://localhost:9000/datasets/user_profile.csv\",","spark逻辑回归(lr)训练点击率预测模型","string","stringindex","stringindexer(inputcol='new_user_class_level',","stringindexer(inputcol='pid',","stringindexer(inputcol='pvalue_level',","stringtyp","stringtype,","structfield(\"age_level\",","structfield(\"cms_group_id\",","structfield(\"cms_segid\",","structfield(\"final_gender_code\",","structfield(\"new_user_class_level\",","structfield(\"occupation\",","structfield(\"pvalue_level\",","structfield(\"shopping_level\",","structfield(\"userid\",","structfield,","structtype([","structtype,","test_datasets_1.show(5)","timestamp:","timestamp|adgroupid|","timestamp|clk|","top","train_datasets_1","train_datasets_1.show(5)","true)","useful_col","user_profile_df","user_profile_df.groupby(\"new_user_class_level\").min(\"nucl_onehot_feature\").show()","user_profile_df.groupby(\"pvalue_level\").min(\"pl_onehot_feature\").show()","user_profile_df.printschema()","user_profile_df.show()","userid:","vector","vectorassembl","vectorassembler().setinputcols(useful_cols[2:]).setoutputcol(\"features\").transform(datasets_1)","withcolumn(\"adgroup_id\",","withcolumn(\"brand\",","withcolumn(\"campaign_id\",","withcolumn(\"cate_id\",","withcolumn(\"clk\",","withcolumn(\"customer\",","withcolumn(\"nonclk\",","withcolumn(\"pid\",","withcolumn(\"price\",","withcolumn(\"time_stamp\",","withcolumn(\"user\",","|","|117840|1494036743|","|1494261938|","|1494436784|","|1494553913|","|1494677292|","|1494684007|","|286630|1494218579|","|286630|1494289247|","|298139|1494462593|","|399907|1494302958|","|421590|1494034144|","|449818|1494638778|","|530454|1494293746|","|555266|1494307136|","|581738|1494137644|","|623911|1494451608|","|623911|1494625301|","|628137|1494524935|","|707120|1494220810|","|739815|1494115387|","|771431|1494153867|","|775475|1494561036|","|914836|1494650879|","|914836|1494651029|","|976358|1494156949|","|adgroupid|cateid|campaignid|customerid|brandid|price|","|clk|","|new_user_class_level|min(nucl_onehot_feature)|","|new_user_class_level|nucl_onehot_featur","|pvalue_level|min(pl_onehot_feature)|","|pvalue_level|pl_onehot_featur","|userid|","|userid|cms_segid|cms_group_id|final_gender_code|age_level|pvalue_level|shopping_level|occupation|new_user_class_level|pl_onehot_feature|pl_onehot_value|nucl_onehot_feature|nucl_onehot_value|","“new_user_class_level”的特征对应关系","从hdfs中加载广告基本信息数据","从hdfs加载用户基本信息数据","使用热编码转换new_user_class_level的一维数据为多维","使用热编码转换pvalue_level的一维数据为多维，增加n","创建逻辑回归训练器，并训练模型：logisticregression、","利用schema从hdfs加载","剔除冗余、不需要的字段","剔除空值数据后，还剩：","四","因为前面提到广告的点击率一般都比较低，所以预测值通常都是0，因此通常需要反减得出点击的概率","如果预测值是0，其概率是0.9248，那么反之可推出1的可能性就是1","对pvalue_level进行热编码，求值","对模型进行存储","展示数据，默认前20条","年龄等级，1","广告价格","所有的特征的特征向量已经汇总到在features字段中","按probability升序排列数据，probability表示预测结果的概率","时间字段，划分训练集和测试集","显示结果:","更改df表结构：更改列类型和列名称","更改表结构，转换为对应的数据类型","替换掉null字符串","本小节主要根据广告点击样本数据集(raw_sample)、广告基本特征数据集(ad_feature)、用户基本信息数据集(user_profile)构建出了一个完整的样本数据集，并按日期划分为了训练集(前七天)和测试集(最后一天)，利用逻辑回归进行训练。","构建表结构schema对象","查看datasets条目数","查看datasets的结构","查看样本中点击的被实际点击的条目的预测情况","样本数据pid特征处理","根据测试数据进行预测","根据特征字段计算出特征向量，并划分出训练数据集和测试数据集","根据特征字段计算特征向量","热编码中：\"pvalue_level\"特征对应关系:","热编码时，必须先将待处理字段转为字符串类型才可处理","特征值字段","用户微群id","用户性别特征，[1,2]","用户组id","由于前面使用的是outer方式合并的数据，产生了部分空值数据，这里必须先剔除掉","筛选指定字段数据，构建新的数据集","约7天的数据","训练ctrmodel_normal：直接将对应的特征的特征值组合成对应的特征向量进行训练","训练数据集:","训练模型时，通过对类别特征数据进行处理，一定程度达到提高了模型的效果","设置目标字段、特征值字段并训练","资源位的特征向量","载入训练好的模型","运行过程是先将pvalue_level转换为一列新的特征数据，然后对该特征数据求出的热编码值，存在了新的一列数据中，类型为一个稀疏矩阵","需要先将缺失值全部替换为数值，便于处理，否则会抛出异常"],"day07_推荐系统案例/05_离线推荐处理.html":["\"","\"adgroupid\").\\","\"age_level\",","\"age_level\":","\"brandid\").\\","\"campaignid\").\\","\"cateid\")","\"cateid\").\\","\"cms_group_id\",","\"cms_group_id\":","\"customerid\").\\","\"final_gender_code\",","\"final_gender_code\":","\"new_user_class_level\"","\"new_user_class_level\":","\"occupation\",","\"occupation\":","\"pid\",","\"price\"","\"price\":","\"pvalue_level\",","\"pvalue_level\":","\"shopping_level\",","\"shopping_level\":","\"userid\",","#","#准备","*ret)","+","...","0","05_离线推荐处理","1","1\")","10856|","11050|","12276|","12948","12955","12960","138953.0","140008.0","1661","1666","1669","1670","2","20","200)","201|","20个","237471.0","238761.0","238772.0","3","313","314","352273.0","4","4.8523283|","4.979195|","4267|","4610|","467512.0","4766|","5","5.0776596|","5.12694|","5.139578|","5.1701374|","5.1离线数据缓存之离线召回集","5.2","5.245261|","5.2992325|","5.6804466|","5.6882005|","5.838009|","5.9155636|","500","500:","5392|","6","6.0623236|","6.2145095|","6.6835284|","6261|","6306|","6580|","6766","6767","6768","6769","7","7.149538|","7.3395424|","7.4762917|","7.479664|","7214|","7266|","7267|","7270|","7282|","731,","8","8071|","818681.0","838953.0","843456","845337.0","846728","846729","846810","8655|","877|","8|","9.917084|","932|","=",">=","[","]","])","_","_.topandas()","_ad_feature_df","_ad_feature_df.\\","_ad_feature_df.adgroup_id.cast(integertype())).withcolumnrenamed(\"adgroup_id\",","_ad_feature_df.brand.cast(integertype())).withcolumnrenamed(\"brand\",","_ad_feature_df.campaign_id.cast(integertype())).withcolumnrenamed(\"campaign_id\",","_ad_feature_df.cate_id.cast(integertype())).withcolumnrenamed(\"cate_id\",","_ad_feature_df.customer.cast(integertype())).withcolumnrenamed(\"customer\",","_ad_feature_df.price.cast(floattype()))","_ad_feature_df.replace(\"null\",","ad_feature_df","ad_feature_df.foreachpartition(foreachpartition)","ad_feature_df.select(\"adgroupid\",","adgroupid,","age_level:","als_model","als_model.transform(spark.createdataframe(cateid_df)).sort(\"prediction\",","als_model.userfactor","als_model.userfactors.select(\"id\").collect():","alsmodel","alsmodel.load(\"hdfs://localhost:9000/models/usercateratingalsmodel.obj\")","array]","ascending=false).na.drop()","ascending=false).na.drop().show()","break","cateid","cateid)，这里控制了userid一样，所以相当于是在求某用户对所有分类的兴趣程度","cateid_df","cateid_df.insert(0,","cateid_list","cateid_list.head(20):","client","client.hset(\"ad_features\",","client.hset(\"user_features1\",","client.sadd(userid,","cms_group_id:","cms_segid:","column","dafaframe对象","data","dataframe[id:","dataframe[userid:","dataframe来处理，把数据载入内存","db=10)","db=9)","def","del","df","df.\\","df.adgroup_id.cast(integertype())).withcolumnrenamed(\"adgroup_id\",","df.brand.cast(integertype())).withcolumnrenamed(\"brand\",","df.campaign_id.cast(integertype())).withcolumnrenamed(\"campaign_id\",","df.cate_id.cast(integertype())).withcolumnrenamed(\"cate_id\",","df.customer.cast(integertype())).withcolumnrenamed(\"customer\",","df.price.cast(floattype()))","df.replace(\"null\",","df:","dtype:","featur","feature_cols_from_ad","feature_cols_from_us","features:","final_gender_code:","float64","floattyp","foreachpartition(partition):","foreachpartition2(partition):","gc","gc.collect()","header=true)","header=true,","id","import","int,","int]","integertype())","integertype()),","integertype,","json","json.dumps(data))","len(ret)","length:","longtype,","name:","need","need))","new_user_class_level:","np","np.array([8","np.array([userid","np.random.choice(pdf.where(pdf.cateid==11156).dropna().adgroupid.astype(np.int64),","numpi","occupation:","panda","partition:","pd","pd.dataframe(pdf.cateid.unique(),columns=[\"cateid\"])","pdf","pdf.where(pdf.cateid==11156).dropna().adgroupid","port=6379,","pvalue_level:","pyspark.ml.recommend","pyspark.sql.typ","r","r.adgroupid,","r.age_level,","r.cms_group_id,","r.final_gender_code,","r.id","r.new_user_class_level","r.occupation,","r.price","r.pvalue_level,","r.shopping_level,","r.userid,","range(6769)]))","redi","redis.strictredis(host=\"192.168.19.137\",","redis.strictredis(host=\"192.168.199.188\",","ret","ret.union(np.random.choice(pdf.where(pdf.cateid==i.cateid).adgroupid.dropna().astype(np.int64),","row","schema","schema=schema)","set()","shopping_level:","show","spark.read.csv(\"hdfs://localhost:8020/csv/user_profile.csv\",","spark.read.csv(\"hdfs://localhost:9000/data/ad_feature.csv\",","stringtype,","structfield(\"age_level\",","structfield(\"cms_group_id\",","structfield(\"cms_segid\",","structfield(\"final_gender_code\",","structfield(\"new_user_class_level\",","structfield(\"occupation\",","structfield(\"pvalue_level\",","structfield(\"shopping_level\",","structfield(\"userid\",","structfield,","structtype([","structtype,","top","user_profile_df","user_profile_df.foreachpartition(foreachpartition2)","userid","userid、cataid的df，对应预测值进行排序","withcolumn(\"adgroup_id\",","withcolumn(\"brand\",","withcolumn(\"campaign_id\",","withcolumn(\"cate_id\",","withcolumn(\"customer\",","withcolumn(\"price\",","{","|","|userid|cateid|prediction|","}","×","一一对应的datafram","中","五","从hdfs中加载广告基本信息数据","从hdfs中加载广告基本信息数据，返回spark","从hdfs加载之前存储的模型","从hdfs加载用户基本信息数据","从前20个分类中选出500个进行召回","传入","利用als模型进行类别的召回","利用schema从hdfs加载","利用模型，传入datasets(userid,","加载als模型，注意必须先有spark上下文管理器，即sparkcontext，但这里sparksession创建后，自动创建了sparkcontext","和","因此这里对于pid，应该是由广告系统发起推荐请求时，向推荐系统明确要推荐的用户是谁，以及对应的资源位，或者说有哪些","如果redis所在机器，内存不足，会抛出异常","如果不足500个，那么随机选出need个广告","如果达到500个则退出","存储用户召回，使用redis第9号数据库，类型：sets类型","广告资源位，属于场景特征，也就是说，每一种广告通常是可以防止在多种资源外下的","当前用户","所以这里我们除了需要我们训练的als模型以外，还需要有一个广告和类别的对应关系。","所有用户的id","所有类别","手动释放一些内存","找到前","显示结果:","更改df表结构：更改列类型和列名称","更改表结构，转换为对应的数据类型","替换掉null字符串","替换掉null字符串，替换掉","最感兴趣的类别","来自广告基本信息中","构建表结构schema对象","根据指定的类别找到对应的广告","注意：由于本数据集中存在null字样的数据，无法直接设置schema，只能先将null类型的数据处理掉，然后进行类型转换","用户特征","由于这里数据集其实很少，所以我们再直接转成panda","离线推荐数据缓存","离线数据缓存之离线特征","转成json字符串再保存，能保证数据再次倒出来时，能有效的转换成python类型","返回模型中关于用户的所有属性","这样如果有多个资源位，那么每个资源位都会对应相应的一个推荐列表","这里主要是利用我们前面训练的als模型进行协同过滤召回，但是注意，我们als模型召回的是用户最感兴趣的类别，而我们需要的是用户可能感兴趣的广告的集合，因此我们还需要根据召回的类别匹配出对应的广告。","这里我们只需要adgroupid、和cateid","遍历als_model","需要进行缓存的特征值"],"day07_推荐系统案例/06_实时推荐.html":["\"430548_1007\")","\"430548_1007\"),","\"adgroupid\",","\"age_level\",","\"cms_group_id\",","\"features\"])","\"final_gender_code\",","\"new_user_class_level\"","\"occupation\",","\"pid\",","\"price\",","\"pvalue_level\",","\"shopping_level\",","#","'''","+","0.0|","06_实时推荐","1","129682|[8.5,1.0,0.0,1.0,...|","133457,","133457|[169.0,1.0,0.0,1....|","133457|[169.0,1.0,0.0,1....|[2.69019485098454...|[0.93644557925748...|","13维","164807,","164807|[228.0,1.0,0.0,1....|[2.69019430490611...|[0.93644554675747...|","169717|[2.20000004768371...|","173327,","173327|[356.0,1.0,0.0,1....|[2.69019312019358...|[0.93644547624893...|","186334|[106.0,1.0,0.0,1....|","199445|[5.0,1.0,0.0,1.0,...|","20","201867,","201867|[179.0,1.0,0.0,1....|[2.69019475842887...|[0.93644557374900...|","221585|[18.5,1.0,0.0,1.0...|","221714|[4.80000019073486...|","227731,","227731|[199.0,1.0,0.0,1....|[2.69019457331754...|[0.93644556273205...|","229827,","229827|[238.0,1.0,0.0,1....|[2.69019421235044...|[0.93644554124900...|","241402,","241402|[269.0,1.0,0.0,1....|[2.69019392542787...|[0.93644552417271...|","25542,","25542|[176.0,1.0,0.0,1....|[2.69019478619557...|[0.93644557540155...|","258252|[7.59999990463256...|","265403,","265403|[198.0,1.0,0.0,1....|[2.69019458257311...|[0.93644556328290...|","275819,","275819|[3280.0,1.0,0.0,1...|[2.69016605691669...|[0.93644386554961...|","277335,","277335|[181.5,1.0,0.0,1....|[2.69019473528996...|[0.93644557237189...|","290950|[6.5,1.0,0.0,1.0,...|","292027|[16.0,1.0,0.0,1.0...|","29466,","29466|[640.0,1.0,0.0,1....|[2.69019049161265...|[0.93644531980785...|","2维","31314|[15.8000001907348...|","339382]","339382|[163.0,1.0,0.0,1....|[2.69019490651794...|[0.93644558256256...|","351366,","351366|[246.0,1.0,0.0,1....|[2.69019413830591...|[0.93644553684221...|","3维","401433,","401433|[1200.0,1.0,0.0,1...|[2.69018530849532...|[0.93644501133142...|","430023|[34.2000007629394...|","445914|[9.89999961853027...|","494224,","494224|[169.0,1.0,0.0,1....|[2.69019485098454...|[0.93644557925748...|","569939,","569939|[188.0,1.0,0.0,1....|[2.69019467512877...|[0.93644556879138...|","575633,","575633|[180.0,1.0,0.0,1....|[2.69019474917331...|[0.93644557319816...|","583215,","583215|[3750.0,1.0,0.0,1...|[2.69016170680037...|[0.93644360664433...|","6.1","631204|[19888.0,1.0,0.0,...|[2.69001234046578...|[0.93643471623189...|","692672|[47.0,1.0,0.0,1.0...|","746178|[16.7999992370605...|","763027|[68.0,1.0,0.0,1.0...|","797982|[33.0,1.0,0.0,1.0...|","815219|[2.40000009536743...|","815312|[2.29999995231628...|","816999|[5.0,1.0,0.0,1.0,...|","8|","=","==>","==>7维","[","[0","[631204,","[]","[i.adgroupid","[row(adgroupid=631204),","]","ad_featur","adgroupid","adgroupid))","adgroupid,","age_level_valu","age_level_value[age_level_rela[int(features[\"age_level\"])]]","client_of_featur","client_of_recal","client_of_recall.smembers(userid)","cms_group_id_valu","cms_group_id_value[cms_group_id_rela[int(features[\"cms_group_id\"])]]","columns=[\"userid\",","create_datasets(88,","create_datasets(userid,","ctr_model","ctr_model.transform(datasets).sort(\"probability\")","ctr预测模型","dataset","datasets.show()","db=10)","db=9)","def","densevector","densevector([price]","featur","features.items():","features.update(ad_feature)","features.update(user_feature)","features[k]","features_col","features|","final_gender_code_valu","final_gender_code_value[final_gender_code_rela[int(features[\"final_gender_code\"])]]","final_gender_code_value\\","float(features[\"price\"])","import","int(adgroupid)","json","json.loads(client_of_features.hget(\"ad_features\",","json.loads(client_of_features.hget(\"user_features\",","k,v","logisticregressionmodel","logisticregressionmodel.load(\"hdfs://localhost:9000/models/ctrmodel_allonehot.obj\")","new_user_class_level_valu","new_user_class_level_value)","new_user_class_level_value[new_user_class_level_rela[int(features[\"new_user_class_level\"])]]","none:","n列表","occupation_valu","occupation_value[occupation_rela[int(features[\"occupation\"])]]","panda","pd","pd.dataframe(create_datasets(8,","pdf","pid):","pid_valu","pid_value[pid_rela[pid]]","port=6379,","predict","prediction.select(\"adgroupid\").head(20)","prediction.select(\"adgroupid\").head(20)]","prediction.show()","price","print(age_level_value)","print(cms_group_id_value)","print(final_gender_code_value)","print(new_user_class_level_value)","print(occupation_value)","print(pid_value)","print(pvalue_level_value)","print(shopping_level_value)","probability|prediction|","pvalue_level_valu","pvalue_level_value[pvalue_level_rela[int(features[\"pvalue_level\"])]]","pyspark.ml.classif","pyspark.ml.linalg","range(13)]","range(2)]","range(2)]#[0,0]","range(3)]","range(4)]","range(5)]","range(7)]","rawprediction|","recall_set","recall_sets:","redi","redis.strictredis(host=\"192.168.19.137\",","result","result.append((userid,","return","row","row(adgroupid=133457),","row(adgroupid=164807),","row(adgroupid=173327),","row(adgroupid=201867),","row(adgroupid=227731),","row(adgroupid=229827),","row(adgroupid=241402),","row(adgroupid=25542),","row(adgroupid=265403),","row(adgroupid=275819),","row(adgroupid=277335),","row(adgroupid=29466),","row(adgroupid=339382)]","row(adgroupid=351366),","row(adgroupid=401433),","row(adgroupid=494224),","row(adgroupid=569939),","row(adgroupid=575633),","row(adgroupid=583215),","shopping_level_valu","shopping_level_value[shopping_level_rela[int(features[\"shopping_level\"])]]","show","spark.createdataframe(pdf)","top","user_featur","userid))","v","vector","vector))","{}","|","|userid|adgroupid|","六","实时产生推荐结果","推荐任务处理","显示结果:","特征","特征值","特征获取","类别型特征，2个分类","类别型特征，3个分类","类别型特征，7个分类","类别型特征，约13个分类","获取用户召回集","获取用户特征","获取该广告的特征值","载入训练好的模型","遍历召回集","预测结果"]},"length":59},"tokenStore":{"root":{"0":{"0":{"docs":{},":":{"0":{"4":{"docs":{},":":{"1":{"2":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}}},"1":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.005668934240362812}},"_":{"docs":{},"什":{"docs":{},"么":{"docs":{},"是":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":10}}}}}}}}}}},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},"的":{"docs":{},"使":{"docs":{},"用":{"docs":{"day03_Hadoop/ha2.1.html":{"ref":"day03_Hadoop/ha2.1.html","tf":10}}}}}}}},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},"生":{"docs":{},"态":{"docs":{},"系":{"docs":{},"统":{"docs":{"day03_Hadoop/ha4.1.html":{"ref":"day03_Hadoop/ha4.1.html","tf":10}}}}}}}}}}},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{},"基":{"docs":{},"本":{"docs":{},"概":{"docs":{},"念":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":10}}}}}}}}},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"简":{"docs":{},"介":{"docs":{},"与":{"docs":{},"环":{"docs":{},"境":{"docs":{},"部":{"docs":{},"署":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":10}}}}}}}}}}}}}},"资":{"docs":{},"源":{"docs":{},"调":{"docs":{},"度":{"docs":{},"框":{"docs":{},"架":{"docs":{},"y":{"docs":{},"a":{"docs":{},"r":{"docs":{},"n":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":10}}}}}}}}}}}},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":5}},"入":{"docs":{},"门":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":10}}}}}}}}},"个":{"docs":{},"性":{"docs":{},"化":{"docs":{},"电":{"docs":{},"商":{"docs":{},"广":{"docs":{},"告":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"系":{"docs":{},"统":{"docs":{},"介":{"docs":{},"绍":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":10}}}}}}}}}}}}}}}},"'":{"docs":{},")":{"docs":{},";":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.007407407407407408}}}}}},"2":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0022675736961451248}},"_":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},"核":{"docs":{},"心":{"docs":{},"组":{"docs":{},"件":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":10}}}}}}}}}}},"d":{"docs":{},"f":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":5}},"s":{"docs":{},"读":{"docs":{},"写":{"docs":{},"流":{"docs":{},"程":{"docs":{},"&":{"docs":{},"高":{"docs":{},"可":{"docs":{},"用":{"docs":{"day03_Hadoop/ha4.2.html":{"ref":"day03_Hadoop/ha4.2.html","tf":10}}}}}}}}}}}}},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{},"的":{"docs":{},"s":{"docs":{},"h":{"docs":{},"e":{"docs":{},"l":{"docs":{},"l":{"docs":{},"操":{"docs":{},"作":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":10}}}}}}}}}}}}},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"数":{"docs":{},"据":{"docs":{},"模":{"docs":{},"型":{"docs":{"Hive&HBase/06_hbase数据模型.html":{"ref":"Hive&HBase/06_hbase数据模型.html","tf":10}}}}}}}}}}},"分":{"docs":{},"布":{"docs":{},"式":{"docs":{},"计":{"docs":{},"算":{"docs":{},"框":{"docs":{},"架":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{"day03_Hadoop/ha3.2.html":{"ref":"day03_Hadoop/ha3.2.html","tf":10}}}}}}}}}}}}}}}}},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"概":{"docs":{},"念":{"docs":{},"介":{"docs":{},"绍":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":10}}}}}}}}},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"介":{"docs":{},"绍":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":10}}}}}}}}}}}}},"根":{"docs":{},"据":{"docs":{},"用":{"docs":{},"户":{"docs":{},"行":{"docs":{},"为":{"docs":{},"数":{"docs":{},"据":{"docs":{},"创":{"docs":{},"建":{"docs":{},"a":{"docs":{},"l":{"docs":{},"s":{"docs":{},"模":{"docs":{},"型":{"docs":{},"并":{"docs":{},"召":{"docs":{},"回":{"docs":{},"商":{"docs":{},"品":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":10}}}}}}}}}}}}}}}}}}}}}}}},"3":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.003401360544217687}},"_":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},"优":{"docs":{},"势":{"docs":{"day03_Hadoop/ha1.3.html":{"ref":"day03_Hadoop/ha1.3.html","tf":10}}}},"发":{"docs":{},"行":{"docs":{},"版":{"docs":{},"选":{"docs":{},"择":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":10}}}}}}}}}}}},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},"设":{"docs":{},"计":{"docs":{},"思":{"docs":{},"路":{"docs":{"day03_Hadoop/ha2.3.html":{"ref":"day03_Hadoop/ha2.3.html","tf":10}}}}}}}}},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{},"的":{"docs":{},"函":{"docs":{},"数":{"docs":{},"和":{"docs":{},"自":{"docs":{},"定":{"docs":{},"义":{"docs":{},"函":{"docs":{},"数":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":10}}}}}}}}}}}}}},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"的":{"docs":{},"安":{"docs":{},"装":{"docs":{},"与":{"docs":{},"s":{"docs":{},"h":{"docs":{},"e":{"docs":{},"l":{"docs":{},"l":{"docs":{},"操":{"docs":{},"作":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":10}}}}}}}}}}}}}}}}}},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{},"e":{"docs":{},"实":{"docs":{},"战":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":10}}}}}}}}}}}}},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"常":{"docs":{},"用":{"docs":{},"算":{"docs":{},"子":{"docs":{},"练":{"docs":{},"习":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":10}}}}}}}}}}},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":3.333333333333333}}}}}}},"c":{"docs":{},"t":{"docs":{},"r":{"docs":{},"预":{"docs":{},"估":{"docs":{},"数":{"docs":{},"据":{"docs":{},"准":{"docs":{},"备":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":10}}}}}}}}}}}}},"4":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704},"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0022675736961451248}},"_":{"docs":{},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},"架":{"docs":{},"构":{"docs":{"day03_Hadoop/ha2.4.html":{"ref":"day03_Hadoop/ha2.4.html","tf":10}}}}}}},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{},"综":{"docs":{},"合":{"docs":{},"案":{"docs":{},"例":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":10}}}}}}}}},"a":{"docs":{},"p":{"docs":{},"p":{"docs":{},"y":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"操":{"docs":{},"作":{"docs":{},"h":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":10}}}}}}}}}}}}}}}}},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{},"e":{"docs":{},"原":{"docs":{},"理":{"docs":{"day03_Hadoop/ha3.5.html":{"ref":"day03_Hadoop/ha3.5.html","tf":10}}}}}}}}}}}}},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":5}}}}}}},"逻":{"docs":{},"辑":{"docs":{},"回":{"docs":{},"归":{"docs":{},"(":{"docs":{},"l":{"docs":{},"r":{"docs":{},")":{"docs":{},"实":{"docs":{},"现":{"docs":{},"c":{"docs":{},"t":{"docs":{},"r":{"docs":{},"预":{"docs":{},"估":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":10}}}}}}}}}}}}}}}}}},"'":{"docs":{},")":{"docs":{},";":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}},"/":{"docs":{},"e":{"docs":{},"m":{"docs":{},"p":{"docs":{},"l":{"docs":{},"o":{"docs":{},"y":{"docs":{},"e":{"docs":{},"e":{"docs":{},".":{"docs":{},"t":{"docs":{},"x":{"docs":{},"t":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}}}}}}}}},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":5}}}}}}}},"5":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.005668934240362812},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},"_":{"docs":{},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},"环":{"docs":{},"境":{"docs":{},"搭":{"docs":{},"建":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":10}}}}}}}}},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"组":{"docs":{},"件":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":10}}}}}}}}},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":5},"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":5}}}}}}},"离":{"docs":{},"线":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"处":{"docs":{},"理":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":10}}}}}}}}}},"6":{"docs":{},":":{"0":{"1":{"docs":{},":":{"1":{"0":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"_":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":5}},"安":{"docs":{},"装":{"docs":{},"部":{"docs":{},"署":{"docs":{},"&":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"a":{"docs":{},"l":{"docs":{},"o":{"docs":{},"n":{"docs":{},"e":{"docs":{},"模":{"docs":{},"式":{"docs":{},"介":{"docs":{},"绍":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":10}}}}}}}}}}}}}}}}}}}}}}}}}},"实":{"docs":{},"时":{"docs":{},"推":{"docs":{},"荐":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":10}}}}}}}},"7":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.022026431718061675}},":":{"0":{"9":{"docs":{},":":{"1":{"2":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}},"docs":{}},"docs":{}}},"docs":{}},"2":{"8":{"docs":{},":":{"1":{"2":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}},"docs":{}},"docs":{}}},"docs":{}},"5":{"0":{"docs":{},":":{"1":{"4":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"_":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":5}}}}}}}}},"9":{"docs":{},":":{"0":{"7":{"docs":{},":":{"1":{"2":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}},"docs":{}},"docs":{}}},"8":{"docs":{},":":{"1":{"2":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}},"docs":{}},"docs":{}}},"docs":{}},"2":{"1":{"docs":{},":":{"1":{"2":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}}},"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.012987012987012988},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.011435105774728416},"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907},"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.010067114093959731},"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289}},".":{"0":{"0":{"0":{"0":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147}}},"docs":{}},"docs":{}},"1":{"2":{"0":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}},"docs":{}},"docs":{},"*":{"docs":{},"v":{"docs":{},"_":{"docs":{},"p":{"docs":{},"u":{"docs":{},"k":{"docs":{},")":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}},"q":{"docs":{},"i":{"docs":{},"k":{"docs":{},")":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}}}},",":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.003795066413662239},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.009259259259259259}}},"正":{"docs":{},"则":{"docs":{},"化":{"docs":{},"系":{"docs":{},"数":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.004079135223332654}}}},"2":{"docs":{},"*":{"docs":{},"(":{"docs":{},"e":{"docs":{},"r":{"docs":{},"r":{"docs":{},"o":{"docs":{},"r":{"docs":{},"*":{"docs":{},"v":{"docs":{},"_":{"docs":{},"p":{"docs":{},"u":{"docs":{},"k":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}},"q":{"docs":{},"i":{"docs":{},"k":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}}}}}}}}}},"学":{"docs":{},"习":{"docs":{},"率":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}},"5":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}},"docs":{},".":{"0":{"docs":{},".":{"0":{"docs":{},":":{"docs":{"day03_Hadoop/ha2.1.html":{"ref":"day03_Hadoop/ha2.1.html","tf":0.0136986301369863}}}},"docs":{}}},"docs":{}},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0016316540893330613}}},"]":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}}},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.006730573118498878},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.08635097493036212},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.042283298097251586}},"(":{"2":{"docs":{},",":{"docs":{},"[":{"0":{"docs":{},"]":{"docs":{},",":{"docs":{},"[":{"1":{"docs":{},".":{"0":{"docs":{},"]":{"docs":{},")":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.006526616357332245},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.005968961400716275}}}}}},"docs":{}}},"docs":{}}}}},"docs":{}}}},"docs":{}}}},"1":{"2":{"3":{"1":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147}}},"docs":{}},"docs":{}},"6":{"1":{"2":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147}}},"docs":{}},"docs":{}},"docs":{},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683}}}},"2":{"4":{"docs":{},".":{"2":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}},"docs":{}}},"docs":{}},"3":{"1":{"0":{"1":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147}}},"docs":{}},"docs":{}},"3":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}},"docs":{}},"4":{"2":{"7":{"6":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147}}},"docs":{}},"docs":{}},"6":{"6":{"6":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147}}},"docs":{}},"7":{"7":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147}}},"docs":{}},"docs":{}},"7":{"6":{"7":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147}}},"docs":{}},"8":{"1":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147}}},"docs":{}},"docs":{}},"9":{"0":{"0":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147}}},"docs":{}},"docs":{}},"docs":{},"]":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"4":{"5":{"6":{"9":{"1":{"1":{"4":{"9":{"7":{"1":{"6":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"5":{"3":{"2":{"2":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147}}},"docs":{}},"docs":{}},"8":{"1":{"7":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147}}},"docs":{}},"docs":{}},"docs":{},")":{"docs":{},"+":{"1":{"docs":{},".":{"2":{"docs":{},"​":{"docs":{},"，":{"docs":{},"也":{"docs":{},"就":{"docs":{},"是":{"4":{"docs":{},".":{"2":{"docs":{},"分":{"docs":{},"。":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}},"docs":{}}},"docs":{}}}}}}},"docs":{}}},"docs":{}}},"；":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}},"6":{"4":{"1":{"5":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147}}},"docs":{}},"5":{"5":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147}}},"docs":{}},"docs":{}},"docs":{}},"7":{"0":{"7":{"1":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147}}},"docs":{}},"docs":{}},"2":{"0":{"6":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147}}},"docs":{}},"docs":{}},"5":{"docs":{},"]":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}},"9":{"2":{"1":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147}}},"docs":{}},"docs":{}},"docs":{}},"8":{"5":{"2":{"8":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147}}},"docs":{}},"docs":{}},"docs":{},"版":{"docs":{},"本":{"docs":{},"后":{"docs":{},"加":{"docs":{},"入":{"docs":{},"位":{"docs":{},"图":{"docs":{},"索":{"docs":{},"引":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}}},"9":{"0":{"0":{"1":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147}}},"docs":{}},"docs":{}},"2":{"4":{"8":{"docs":{},"=":{"0":{"docs":{},".":{"0":{"7":{"5":{"2":{"docs":{},"，":{"docs":{},"即":{"docs":{},"点":{"docs":{},"击":{"docs":{},"概":{"docs":{},"率":{"docs":{},"约":{"docs":{},"为":{"7":{"docs":{},".":{"5":{"2":{"docs":{},"%":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}},"docs":{}}},"docs":{}}}}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}},"docs":{}},"docs":{}},"6":{"9":{"5":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147}}},"docs":{}},"docs":{}},"docs":{},"x":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}},"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},"]":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"，":{"docs":{},"其":{"docs":{},"复":{"docs":{},"制":{"docs":{},"备":{"docs":{},"份":{"docs":{},"数":{"docs":{},"设":{"docs":{},"置":{"docs":{},"为":{"2":{"docs":{},",":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282}}}},"docs":{}}}}}}}}}}},"~":{"2":{"5":{"5":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}},"docs":{}},"docs":{}},"docs":{}},")":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}},"]":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}},";":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}},",":{"1":{"docs":{},",":{"1":{"docs":{},"]":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"2":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}},"docs":{}}},"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.06873342851315521},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0692399522483088}},"(":{"2":{"docs":{},",":{"docs":{},"[":{"1":{"docs":{},"]":{"docs":{},",":{"docs":{},"[":{"1":{"docs":{},".":{"0":{"docs":{},"]":{"docs":{},")":{"docs":{},"|":{"1":{"0":{"9":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}}},"docs":{}},"7":{"6":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}}},"docs":{}},"8":{"8":{"0":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"2":{"2":{"0":{"0":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}}},"docs":{}},"docs":{}},"3":{"6":{"0":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}}},"docs":{}},"docs":{}},"4":{"7":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}}},"docs":{}},"docs":{}},"5":{"6":{"4":{"9":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"6":{"9":{"7":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}}},"8":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}},"docs":{}}},"docs":{}}}}},"docs":{}}}},"docs":{}}}},"1":{"0":{"0":{"0":{"0":{"0":{"docs":{},")":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}},"docs":{}},"docs":{}},"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}},",":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}},"%":{"docs":{"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}}},")":{"docs":{},".":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"(":{"docs":{},"l":{"docs":{},"a":{"docs":{},"m":{"docs":{},"b":{"docs":{},"d":{"docs":{},"a":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}}}}}}}}}},"1":{"2":{"2":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0016316540893330613},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}},"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.005668934240362812}},",":{"docs":{},"h":{"docs":{},"t":{"docs":{},"t":{"docs":{},"p":{"docs":{},":":{"docs":{},"/":{"docs":{},"/":{"docs":{},"w":{"docs":{},"w":{"docs":{},"w":{"docs":{},".":{"docs":{},"i":{"docs":{},"t":{"docs":{},"c":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},".":{"docs":{},"c":{"docs":{},"n":{"docs":{},"/":{"1":{"docs":{},".":{"docs":{},"h":{"docs":{},"t":{"docs":{},"m":{"docs":{},"l":{"docs":{},",":{"docs":{},"k":{"docs":{},"w":{"8":{"docs":{},"|":{"docs":{},"k":{"docs":{},"w":{"1":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}},"docs":{}}}}},"docs":{}}}}}}}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}},".":{"2":{"2":{"6":{"docs":{},".":{"6":{"8":{"docs":{},".":{"1":{"3":{"7":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.006711409395973154}}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}},"2":{"4":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}},"5":{"0":{"9":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}}},"docs":{}},"docs":{}},"7":{"docs":{},".":{"5":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"0":{"2":{"1":{"2":{"7":{"5":{"7":{"1":{"2":{"8":{"5":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.005668934240362812}},",":{"docs":{},"h":{"docs":{},"t":{"docs":{},"t":{"docs":{},"p":{"docs":{},":":{"docs":{},"/":{"docs":{},"/":{"docs":{},"w":{"docs":{},"w":{"docs":{},"w":{"docs":{},".":{"docs":{},"i":{"docs":{},"t":{"docs":{},"c":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},".":{"docs":{},"c":{"docs":{},"n":{"docs":{},"/":{"2":{"docs":{},".":{"docs":{},"h":{"docs":{},"t":{"docs":{},"m":{"docs":{},"l":{"docs":{},",":{"docs":{},"k":{"docs":{},"w":{"6":{"docs":{},"|":{"docs":{},"k":{"docs":{},"w":{"3":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}},"docs":{}}}}},"docs":{}}}}}}}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}},"3":{"0":{"5":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.003401360544217687}},",":{"docs":{},"h":{"docs":{},"t":{"docs":{},"t":{"docs":{},"p":{"docs":{},":":{"docs":{},"/":{"docs":{},"/":{"docs":{},"w":{"docs":{},"w":{"docs":{},"w":{"docs":{},".":{"docs":{},"i":{"docs":{},"t":{"docs":{},"c":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},".":{"docs":{},"c":{"docs":{},"n":{"docs":{},"/":{"3":{"docs":{},".":{"docs":{},"h":{"docs":{},"t":{"docs":{},"m":{"docs":{},"l":{"docs":{},",":{"docs":{},"k":{"docs":{},"w":{"7":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}},"docs":{}}}}}}}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}},"4":{"3":{"docs":{},"|":{"1":{"1":{"0":{"6":{"1":{"6":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}},"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.01020408163265306}},",":{"docs":{},"h":{"docs":{},"t":{"docs":{},"t":{"docs":{},"p":{"docs":{},":":{"docs":{},"/":{"docs":{},"/":{"docs":{},"w":{"docs":{},"w":{"docs":{},"w":{"docs":{},".":{"docs":{},"i":{"docs":{},"t":{"docs":{},"c":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},".":{"docs":{},"c":{"docs":{},"n":{"docs":{},"/":{"4":{"docs":{},".":{"docs":{},"h":{"docs":{},"t":{"docs":{},"m":{"docs":{},"l":{"docs":{},",":{"docs":{},"k":{"docs":{},"w":{"5":{"docs":{},"|":{"docs":{},"k":{"docs":{},"w":{"1":{"docs":{},"|":{"docs":{},"k":{"docs":{},"w":{"4":{"docs":{},"|":{"docs":{},"k":{"docs":{},"w":{"9":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}},"docs":{}}}}},"docs":{}}}}},"docs":{}}}}},"docs":{}}}}}}}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}},"5":{"3":{"9":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981}}}},"docs":{}},"4":{"9":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.001835610850499694},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}},"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0022675736961451248}},",":{"docs":{},"h":{"docs":{},"t":{"docs":{},"t":{"docs":{},"p":{"docs":{},":":{"docs":{},"/":{"docs":{},"/":{"docs":{},"w":{"docs":{},"w":{"docs":{},"w":{"docs":{},".":{"docs":{},"i":{"docs":{},"t":{"docs":{},"c":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},".":{"docs":{},"c":{"docs":{},"n":{"docs":{},"/":{"5":{"docs":{},".":{"docs":{},"h":{"docs":{},"t":{"docs":{},"m":{"docs":{},"l":{"docs":{},",":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}},"8":{"1":{"2":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.001835610850499694},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}},"5":{"6":{"docs":{},"|":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"docs":{}},"8":{"docs":{},"|":{"docs":{},"[":{"docs":{},"[":{"1":{"0":{"4":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}},"docs":{}},"docs":{}},"docs":{}}}}},"docs":{},".":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"4":{"5":{"6":{"8":{"5":{"6":{"5":{"9":{"4":{"0":{"2":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"9":{"1":{"2":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.001835610850499694},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}},"3":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0010197838058331635}}}},"7":{"0":{"4":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"9":{"6":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0016316540893330613},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"9":{"7":{"2":{"0":{"9":{"5":{"7":{"1":{"3":{"7":{"8":{"6":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"4":{"5":{"6":{"8":{"5":{"6":{"0":{"8":{"3":{"7":{"7":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.011013215859030838},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}},",":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147},"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676},"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}},"]":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.004545454545454545}},")":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}},"分":{"docs":{},"钟":{"docs":{},"没":{"docs":{},"有":{"docs":{},"收":{"docs":{},"到":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"n":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"报":{"docs":{},"告":{"docs":{},"认":{"docs":{},"为":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"n":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"死":{"docs":{},"掉":{"docs":{},"了":{"docs":{"day03_Hadoop/ha2.4.html":{"ref":"day03_Hadoop/ha2.4.html","tf":0.03333333333333333}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"'":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.011013215859030838}}},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"4":{"5":{"6":{"9":{"0":{"6":{"5":{"9":{"8":{"7":{"4":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"2":{"4":{"6":{"6":{"2":{"1":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0024469820554649264}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"8":{"9":{"8":{"4":{"2":{"docs":{},"]":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0024469820554649264}}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.002039567611666327},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"1":{"0":{"1":{"docs":{},"|":{"3":{"6":{"5":{"4":{"7":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}},"5":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"docs":{}},"6":{"1":{"6":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}},"docs":{}},"docs":{}},"8":{"4":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}},"docs":{}},"docs":{}},"1":{"1":{"0":{"0":{"1":{"1":{"docs":{},"‬":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}},"docs":{}},"docs":{}},"docs":{}},"5":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}},"docs":{}},"2":{"5":{"6":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0016316540893330613},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}},"docs":{}},"3":{"0":{"6":{"8":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"1":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0016316540893330613},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}},"6":{"3":{"4":{"0":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}},"docs":{}},"docs":{}},"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{},"w":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}},"4":{"1":{"7":{"2":{"9":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}},"docs":{}},"docs":{}},"docs":{}},"5":{"3":{"2":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{},"w":{"docs":{},"，":{"docs":{},"略":{"docs":{},"多":{"docs":{},"余":{"docs":{},"日":{"docs":{},"志":{"docs":{},"数":{"docs":{},"据":{"docs":{},"中":{"docs":{},"用":{"docs":{},"户":{"docs":{},"数":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}},"6":{"0":{"2":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"7":{"2":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"3":{"9":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"8":{"0":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}},"docs":{}},"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"4":{"5":{"6":{"8":{"5":{"1":{"4":{"9":{"1":{"5":{"0":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"9":{"4":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"9":{"7":{"2":{"1":{"4":{"6":{"2":{"9":{"6":{"7":{"2":{"1":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.036281179138321996}},",":{"1":{"0":{"1":{"docs":{},",":{"2":{"0":{"1":{"8":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.003401360544217687}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"4":{"docs":{},",":{"2":{"0":{"1":{"8":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}},":":{"0":{"0":{"docs":{},":":{"1":{"2":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},".":{"0":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}},"6":{"9":{"9":{"3":{"1":{"5":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0024469820554649264}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"9":{"0":{"4":{"8":{"1":{"3":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0024469820554649264}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"6":{"8":{"5":{"3":{"1":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0024469820554649264}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"|":{"4":{"3":{"0":{"5":{"3":{"9":{"docs":{},"_":{"1":{"0":{"0":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0012237405669997961},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0015917230401910067}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"4":{"8":{"docs":{},"_":{"1":{"0":{"0":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0024474811339995923},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0031834460803820135}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}},"2":{"0":{"8":{"4":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"1":{"9":{"5":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"2":{"7":{"6":{"docs":{},"|":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"docs":{}},"docs":{},".":{"5":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"9":{"9":{"9":{"3":{"9":{"1":{"0":{"8":{"8":{"1":{"9":{"1":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"3":{"2":{"4":{"2":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"4":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}},"8":{"docs":{},"|":{"docs":{},"[":{"docs":{},"[":{"5":{"6":{"3":{"1":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}},"docs":{}},"4":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"4":{"5":{"6":{"8":{"4":{"8":{"4":{"2":{"9":{"9":{"9":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"1":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.0091324200913242}}}},"docs":{}}},"5":{"4":{"9":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"9":{"7":{"2":{"1":{"4":{"5":{"9":{"8":{"7":{"0":{"3":{"1":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"4":{"0":{"4":{"5":{"6":{"8":{"4":{"7":{"9":{"1":{"9":{"7":{"3":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"6":{"2":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"7":{"4":{"6":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"7":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"4":{"5":{"6":{"8":{"4":{"6":{"8":{"9":{"9":{"2":{"3":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"9":{"0":{"7":{"9":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"4":{"8":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289}}},"docs":{}},"5":{"5":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289}}},"docs":{}},"6":{"0":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289}}},"8":{"2":{"docs":{},"|":{"docs":{},"[":{"8":{"docs":{},".":{"5":{"docs":{},",":{"1":{"docs":{},".":{"0":{"docs":{},",":{"0":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"0":{"docs":{},",":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}}},"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}},"docs":{}},"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"4":{"5":{"6":{"8":{"4":{"5":{"8":{"7":{"8":{"7":{"2":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"2":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00684931506849315}}}},"docs":{}}},"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.018518518518518517},"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.01927437641723356},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},":":{"1":{"1":{"docs":{},":":{"1":{"2":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}},"docs":{}},"docs":{}}},"9":{"docs":{},":":{"5":{"5":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"9":{"9":{"9":{"3":{"9":{"6":{"7":{"6":{"7":{"5":{"1":{"8":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"4":{"8":{"0":{"6":{"8":{"docs":{},"]":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0024469820554649264}}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"5":{"4":{"7":{"2":{"7":{"1":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0024469820554649264}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"6":{"5":{"2":{"7":{"3":{"2":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0024469820554649264}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"8":{"3":{"5":{"2":{"5":{"7":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0024469820554649264}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"3":{"2":{"7":{"2":{"1":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"3":{"4":{"5":{"7":{"docs":{},",":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}},"|":{"docs":{},"[":{"1":{"6":{"9":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"0":{"docs":{},",":{"0":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}},"[":{"2":{"docs":{},".":{"6":{"9":{"0":{"1":{"9":{"4":{"8":{"5":{"0":{"9":{"8":{"4":{"5":{"4":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"6":{"4":{"4":{"5":{"5":{"7":{"9":{"2":{"5":{"7":{"4":{"8":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}}}}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}}},"docs":{}},"docs":{}},"docs":{},".":{"2":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00684931506849315}}}},"docs":{}}},"4":{"2":{"docs":{},"|":{"docs":{},"[":{"docs":{},"[":{"5":{"7":{"2":{"0":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}},"docs":{}},"5":{"2":{"5":{"6":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}},"docs":{}},"6":{"1":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"4":{"5":{"6":{"8":{"4":{"2":{"8":{"1":{"7":{"2":{"1":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"8":{"9":{"5":{"3":{"docs":{},".":{"0":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"9":{"7":{"2":{"1":{"4":{"5":{"3":{"1":{"6":{"0":{"3":{"5":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"4":{"0":{"2":{"1":{"1":{"2":{"2":{"6":{"5":{"8":{"6":{"7":{"2":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"9":{"7":{"4":{"4":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00851063829787234},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}},":":{"3":{"7":{"docs":{},":":{"1":{"2":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},".":{"6":{"6":{"5":{"9":{"4":{"2":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"|":{"4":{"3":{"0":{"5":{"3":{"9":{"docs":{},"_":{"1":{"0":{"0":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0012237405669997961},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0015917230401910067}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"4":{"8":{"docs":{},"_":{"1":{"0":{"0":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0024474811339995923},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0031834460803820135}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0016316540893330613},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0015917230401910067}}},"维":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}},"4":{"0":{"0":{"0":{"8":{"docs":{},".":{"0":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}},"docs":{}}},"docs":{}},"docs":{}},"5":{"2":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"3":{"docs":{},".":{"5":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.0045662100456621}}}},"docs":{}}},"4":{"3":{"5":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{},".":{"5":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.0091324200913242}}}},"docs":{}}},"5":{"7":{"4":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"9":{"5":{"2":{"docs":{},"|":{"3":{"2":{"docs":{},".":{"9":{"9":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"8":{"9":{"4":{"6":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0008158270446665307}}}},"docs":{}},"docs":{}},"docs":{},"|":{"docs":{},"[":{"docs":{},"[":{"3":{"3":{"4":{"7":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}},"9":{"5":{"7":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"7":{"1":{"4":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"8":{"5":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"9":{"9":{"9":{"3":{"8":{"9":{"7":{"2":{"6":{"1":{"8":{"0":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"4":{"0":{"2":{"1":{"1":{"2":{"2":{"0":{"9":{"5":{"2":{"2":{"6":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"docs":{},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}},".":{"5":{"1":{"9":{"8":{"1":{"docs":{},"]":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0024469820554649264}}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"|":{"4":{"5":{"4":{"2":{"3":{"7":{"docs":{},"|":{"2":{"4":{"9":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0024474811339995923},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.002785515320334262}}}},"5":{"1":{"5":{"5":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"2":{"4":{"1":{"4":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"3":{"3":{"8":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.007692307692307693}}}},"docs":{}},"4":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"4":{"5":{"5":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"6":{"2":{"3":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{},".":{"2":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}},"docs":{}}},"5":{"8":{"3":{"2":{"3":{"1":{"3":{"9":{"8":{"6":{"6":{"docs":{},")":{"docs":{},"'":{"docs":{},"}":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}},"]":{"docs":{},"}":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"9":{"1":{"8":{"9":{"5":{"4":{"docs":{},")":{"docs":{},"'":{"docs":{},"}":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}},"]":{"docs":{},"}":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"7":{"8":{"3":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"8":{"0":{"docs":{},"|":{"docs":{},"[":{"docs":{},"[":{"5":{"7":{"3":{"1":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}},"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"9":{"7":{"2":{"1":{"4":{"4":{"2":{"8":{"3":{"7":{"3":{"4":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"9":{"9":{"3":{"8":{"9":{"2":{"6":{"3":{"6":{"1":{"0":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"4":{"0":{"0":{"0":{"3":{"9":{"0":{"3":{"1":{"7":{"2":{"1":{"4":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"4":{"5":{"6":{"8":{"3":{"1":{"0":{"8":{"1":{"4":{"0":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"9":{"1":{"docs":{},"|":{"docs":{},"[":{"docs":{},"[":{"1":{"6":{"1":{"0":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}},"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"2":{"1":{"1":{"2":{"1":{"5":{"8":{"3":{"0":{"0":{"3":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.010526315789473684},"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.008403361344537815},"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617},"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416},"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}},":":{"4":{"3":{"docs":{},":":{"5":{"3":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00851063829787234}}}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}}},"6":{"0":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"4":{"5":{"6":{"8":{"3":{"0":{"0":{"6":{"0":{"9":{"0":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"2":{"3":{"9":{"4":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"3":{"docs":{},".":{"1":{"7":{"7":{"docs":{},".":{"7":{"1":{"docs":{},".":{"1":{"2":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.006711409395973154}}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}},"4":{"5":{"docs":{},"|":{"docs":{},"[":{"docs":{},"[":{"1":{"6":{"1":{"0":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}},"8":{"0":{"7":{"docs":{},",":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}},"|":{"docs":{},"[":{"2":{"2":{"8":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"0":{"docs":{},",":{"0":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{},"[":{"2":{"docs":{},".":{"6":{"9":{"0":{"1":{"9":{"4":{"3":{"0":{"4":{"9":{"0":{"6":{"1":{"1":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"6":{"4":{"4":{"5":{"5":{"4":{"6":{"7":{"5":{"7":{"4":{"7":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}}}}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}}},"docs":{}},"docs":{}},"docs":{}},"6":{"1":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}},"6":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}},"9":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}},"docs":{}},"7":{"0":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.001835610850499694},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"4":{"9":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{},".":{"2":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.0045662100456621}}}},"docs":{}}},"8":{"9":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"5":{"5":{"8":{"5":{"6":{"0":{"7":{"2":{"0":{"1":{"9":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"9":{"7":{"2":{"1":{"4":{"3":{"7":{"6":{"7":{"5":{"8":{"4":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"4":{"0":{"4":{"5":{"6":{"8":{"2":{"5":{"9":{"7":{"8":{"8":{"7":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"9":{"7":{"1":{"7":{"docs":{},"|":{"docs":{},"[":{"2":{"docs":{},".":{"2":{"0":{"0":{"0":{"0":{"0":{"0":{"4":{"7":{"6":{"8":{"3":{"7":{"1":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{},",":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876},"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}},":":{"4":{"2":{"docs":{},":":{"1":{"2":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},".":{"9":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"4":{"5":{"6":{"9":{"0":{"3":{"0":{"7":{"8":{"0":{"0":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"7":{"0":{"1":{"2":{"1":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981}}}},"docs":{}},"docs":{}},"5":{"4":{"docs":{},"|":{"1":{"4":{"9":{"4":{"6":{"9":{"1":{"1":{"8":{"4":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"2":{"3":{"3":{"4":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"3":{"3":{"2":{"7":{"docs":{},",":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}},"|":{"docs":{},"[":{"3":{"5":{"6":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"0":{"docs":{},",":{"0":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{},"[":{"2":{"docs":{},".":{"6":{"9":{"0":{"1":{"9":{"3":{"1":{"2":{"0":{"1":{"9":{"3":{"5":{"8":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"6":{"4":{"4":{"5":{"4":{"7":{"6":{"2":{"4":{"8":{"9":{"3":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}}}}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}}},"docs":{}},"docs":{}},"docs":{}},"4":{"3":{"7":{"4":{"docs":{},"|":{"1":{"3":{"9":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"6":{"0":{"7":{"6":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"9":{"7":{"2":{"1":{"4":{"3":{"3":{"5":{"4":{"6":{"6":{"3":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"9":{"9":{"3":{"8":{"8":{"3":{"3":{"8":{"4":{"7":{"0":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"4":{"0":{"4":{"5":{"6":{"8":{"2":{"1":{"8":{"9":{"6":{"8":{"5":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"7":{"8":{"8":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"8":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"2":{"1":{"1":{"2":{"0":{"6":{"0":{"9":{"7":{"7":{"8":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"9":{"5":{"9":{"5":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"7":{"4":{"6":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}},".":{"5":{"7":{"6":{"4":{"9":{"6":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0024469820554649264}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"8":{"2":{"4":{"1":{"5":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"9":{"6":{"6":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{},"|":{"docs":{},"[":{"docs":{},"[":{"1":{"6":{"1":{"0":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}},"docs":{}},"3":{"docs":{},".":{"4":{"9":{"docs":{},".":{"4":{"6":{"docs":{},".":{"2":{"2":{"8":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}}},"5":{"5":{"2":{"4":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}},"docs":{}},"docs":{}},"docs":{}},"6":{"3":{"3":{"4":{"docs":{},"|":{"docs":{},"[":{"1":{"0":{"6":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"0":{"docs":{},",":{"0":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}}},"docs":{}},"docs":{}},"8":{"4":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}},"docs":{}},"docs":{}},"docs":{}},"7":{"docs":{},".":{"1":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"4":{"5":{"6":{"8":{"1":{"6":{"2":{"3":{"3":{"0":{"5":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"8":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"9":{"7":{"2":{"1":{"4":{"2":{"7":{"3":{"5":{"2":{"8":{"3":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"4":{"0":{"2":{"1":{"1":{"2":{"0":{"0":{"9":{"7":{"5":{"5":{"4":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"3":{"1":{"4":{"1":{"0":{"6":{"5":{"9":{"5":{"1":{"6":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}},":":{"0":{"2":{"docs":{},":":{"0":{"2":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}},"docs":{}},"docs":{}}},"docs":{}},"3":{"1":{"docs":{},":":{"1":{"2":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}},"docs":{}},"docs":{}}},"docs":{}},"4":{"2":{"docs":{},":":{"1":{"2":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"]":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"9":{"7":{"2":{"1":{"5":{"1":{"5":{"0":{"9":{"8":{"3":{"8":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}},"9":{"1":{"0":{"3":{"6":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{},".":{"7":{"docs":{},"]":{"docs":{},"}":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}},"docs":{}}},"2":{"docs":{},".":{"1":{"6":{"8":{"docs":{},".":{"1":{"9":{"docs":{},".":{"1":{"3":{"7":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}},":":{"4":{"0":{"4":{"0":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}},"docs":{}},"docs":{}},"docs":{}},"5":{"0":{"0":{"7":{"0":{"docs":{"day03_Hadoop/ha2.1.html":{"ref":"day03_Hadoop/ha2.1.html","tf":0.0136986301369863}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}},"4":{"docs":{},".":{"2":{"3":{"7":{"docs":{},".":{"1":{"4":{"2":{"docs":{},".":{"2":{"1":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}},"5":{"9":{"docs":{},"|":{"docs":{},"[":{"docs":{},"[":{"1":{"6":{"1":{"0":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}},"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"2":{"1":{"1":{"1":{"9":{"7":{"3":{"8":{"9":{"9":{"8":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"8":{"4":{"2":{"4":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"3":{"5":{"4":{"1":{"3":{"5":{"4":{"8":{"3":{"8":{"7":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"4":{"5":{"6":{"8":{"1":{"0":{"6":{"7":{"1":{"2":{"9":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"9":{"3":{"9":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}},"docs":{}},"4":{"4":{"5":{"docs":{},"|":{"docs":{},"[":{"5":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"0":{"docs":{},",":{"0":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"0":{"docs":{},",":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}}},"docs":{}},"docs":{}},"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"4":{"5":{"6":{"8":{"1":{"0":{"1":{"6":{"1":{"0":{"4":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"docs":{},"]":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}},":":{"1":{"0":{"docs":{},":":{"1":{"2":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"/":{"0":{"3":{"docs":{},"/":{"0":{"8":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},".":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"4":{"5":{"6":{"9":{"0":{"2":{"0":{"0":{"6":{"4":{"7":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"8":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"9":{"9":{"9":{"3":{"9":{"6":{"3":{"6":{"6":{"6":{"2":{"5":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"9":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"9":{"9":{"9":{"3":{"9":{"6":{"3":{"6":{"1":{"4":{"8":{"5":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.00238758456028651}}}},"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775},"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863},"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.009887005649717515},"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.0016233766233766235},"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008},"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html":{"ref":"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html","tf":0.024390243902439025},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.003430531732418525},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428},"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196},"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704},"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112},"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.06235827664399093},"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.02608695652173913},"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.004405286343612335},"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678},"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012},"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.008710801393728223},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.019027484143763214}},".":{"0":{"0":{"0":{"0":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.014124293785310734}}},"docs":{}},"docs":{}},"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938},"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.006322659596165613}}},"]":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}},"e":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"8":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0014276973281664286}},"[":{"0":{"docs":{},".":{"8":{"6":{"8":{"2":{"2":{"0":{"3":{"3":{"9":{"3":{"9":{"2":{"5":{"9":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"8":{"4":{"1":{"0":{"4":{"5":{"7":{"1":{"9":{"4":{"9":{"6":{"9":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"9":{"1":{"7":{"5":{"4":{"9":{"7":{"8":{"3":{"7":{"5":{"6":{"2":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.008362227207831939},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.007958615200955034}},"(":{"2":{"docs":{},",":{"docs":{},"[":{"1":{"docs":{},"]":{"docs":{},",":{"docs":{},"[":{"1":{"docs":{},".":{"0":{"docs":{},"]":{"docs":{},")":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0016316540893330613},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0019896538002387586}}}}}},"docs":{}}},"docs":{}}}}},"docs":{}}}},"docs":{}}},"}":{"docs":{},")":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}},"1":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775},"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009},"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102},"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625},"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}},"_":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"系":{"docs":{},"统":{"docs":{},"简":{"docs":{},"介":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":10}}}}}}}}},".":{"0":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196},"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.01606425702811245}}},"docs":{}}},"2":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863},"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282},"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102},"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625},"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}},"_":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"系":{"docs":{},"统":{"docs":{},"架":{"docs":{},"构":{"docs":{},"设":{"docs":{},"计":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":10}}}}}}}}}}},".":{"0":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}},"docs":{}}},"3":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734},"day03_Hadoop/ha1.3.html":{"ref":"day03_Hadoop/ha1.3.html","tf":0.07692307692307693},"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}},"_":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"算":{"docs":{},"法":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":10}}}}}}}},"4":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.0016233766233766235},"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}},"_":{"docs":{},"案":{"docs":{},"例":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":5}}}}}},"5":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008},"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.0045662100456621}},"_":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"系":{"docs":{},"统":{"docs":{},"评":{"docs":{},"估":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":10}}}}}}}}},"e":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},"[":{"0":{"docs":{},".":{"9":{"3":{"7":{"4":{"1":{"4":{"5":{"0":{"4":{"4":{"6":{"9":{"3":{"9":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"5":{"7":{"1":{"3":{"5":{"0":{"7":{"9":{"9":{"5":{"9":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"8":{"3":{"4":{"7":{"2":{"3":{"0":{"9":{"3":{"8":{"0":{"1":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"6":{"docs":{"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html":{"ref":"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html","tf":0.024390243902439025}},"_":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"系":{"docs":{},"统":{"docs":{},"的":{"docs":{},"冷":{"docs":{},"启":{"docs":{},"动":{"docs":{},"问":{"docs":{},"题":{"docs":{"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html":{"ref":"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html","tf":10}}}}}}}}}}}}}},"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.006493506493506494},"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842},"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}},"收":{"docs":{},"集":{"docs":{},"⽤":{"docs":{},"户":{"docs":{},"特":{"docs":{},"征":{"docs":{"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html":{"ref":"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html","tf":0.024390243902439025}}}}}}}},"x":{"docs":{"day03_Hadoop/ha3.5.html":{"ref":"day03_Hadoop/ha3.5.html","tf":0.030303030303030304}}},"删":{"docs":{},"除":{"docs":{},"重":{"docs":{},"复":{"docs":{},"数":{"docs":{},"据":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}},"计":{"docs":{},"算":{"docs":{},"每":{"docs":{},"条":{"docs":{},"记":{"docs":{},"录":{"docs":{},"的":{"docs":{},"缺":{"docs":{},"失":{"docs":{},"值":{"docs":{},"情":{"docs":{},"况":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}}}}},"首":{"docs":{},"先":{"docs":{},"删":{"docs":{},"除":{"docs":{},"完":{"docs":{},"全":{"docs":{},"一":{"docs":{},"样":{"docs":{},"的":{"docs":{},"记":{"docs":{},"录":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}}}}},",":{"0":{"docs":{},",":{"0":{"docs":{},",":{"1":{"docs":{},",":{"0":{"docs":{},",":{"0":{"docs":{},",":{"1":{"docs":{},",":{"0":{"docs":{},",":{"0":{"docs":{},"]":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"1":{"docs":{},"]":{"docs":{},"或":{"docs":{},"[":{"0":{"docs":{},",":{"1":{"docs":{},"]":{"docs":{},"之":{"docs":{},"间":{"docs":{},",":{"docs":{},"一":{"docs":{},"般":{"docs":{},"可":{"docs":{},"以":{"docs":{},"使":{"docs":{},"用":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}},"docs":{}}},"docs":{}}},"来":{"docs":{},"计":{"docs":{},"算":{"docs":{},"，":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}},"之":{"docs":{},"间":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}},"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.0016233766233766235},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0008158270446665307}},"k":{"docs":{},"w":{"3":{"docs":{},":":{"1":{"docs":{},",":{"docs":{},"k":{"docs":{},"w":{"6":{"docs":{},":":{"1":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0022675736961451248}}},"docs":{}}},"docs":{}}}}},"docs":{}}},"docs":{}}}},"/":{"3":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}},"docs":{}},"表":{"docs":{},"示":{"docs":{},"强":{"docs":{},"负":{"docs":{},"相":{"docs":{},"关":{"docs":{},"，":{"docs":{},"+":{"1":{"docs":{},"表":{"docs":{},"示":{"docs":{},"强":{"docs":{},"正":{"docs":{},"相":{"docs":{},"关":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}},"docs":{}}}}}}},"正":{"docs":{},"相":{"docs":{},"关":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}},"负":{"docs":{},"相":{"docs":{},"关":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}},"用":{"docs":{},"户":{"docs":{},"对":{"docs":{},"相":{"docs":{},"似":{"docs":{},"物":{"docs":{},"品":{"docs":{},"物":{"docs":{},"品":{"docs":{},"的":{"docs":{},"评":{"docs":{},"分":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.0016233766233766235}}}}}}}}}}}}}},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208},"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577},"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391},"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}},"指":{"docs":{},"定":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},"任":{"docs":{},"务":{"docs":{},"调":{"docs":{},"度":{"docs":{},"优":{"docs":{},"先":{"docs":{},"级":{"docs":{},"(":{"docs":{},"v":{"docs":{},"e":{"docs":{},"r":{"docs":{},"y":{"docs":{},"_":{"docs":{},"h":{"docs":{},"i":{"docs":{},"g":{"docs":{},"h":{"docs":{},"|":{"docs":{},"h":{"docs":{},"i":{"docs":{},"g":{"docs":{},"h":{"docs":{},")":{"docs":{},",":{"docs":{},"如":{"docs":{},"：":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},")":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102},"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}},",":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.09090909090909091},"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.022727272727272728},"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00684931506849315},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}},"]":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}},")":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}}},"w":{"docs":{},"h":{"docs":{},"e":{"docs":{},"r":{"docs":{},"e":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"n":{"docs":{},"e":{"docs":{},"w":{"docs":{},"_":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"c":{"docs":{},"l":{"docs":{},"a":{"docs":{},"s":{"docs":{},"s":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"=":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}},"p":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"=":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}},":":{"2":{"0":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}},"docs":{}},"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}},"男":{"docs":{},",":{"2":{"docs":{},":":{"docs":{},"女":{"docs":{},"；":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}},"docs":{}}}},"：":{"docs":{},"梯":{"docs":{},"度":{"docs":{},"下":{"docs":{},"降":{"docs":{},"法":{"docs":{},"推":{"docs":{},"导":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}},"、":{"2":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}},"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}},"内":{"docs":{},"嵌":{"docs":{},"(":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}},"什":{"docs":{},"么":{"docs":{},"是":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}}}}}}},"速":{"docs":{},"度":{"docs":{},"快":{"docs":{},"（":{"docs":{},"比":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{},"e":{"docs":{},"在":{"docs":{},"内":{"docs":{},"存":{"docs":{},"中":{"docs":{},"快":{"1":{"0":{"0":{"docs":{},"倍":{"docs":{},"，":{"docs":{},"在":{"docs":{},"磁":{"docs":{},"盘":{"docs":{},"中":{"docs":{},"快":{"1":{"0":{"docs":{},"倍":{"docs":{},"）":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}},"docs":{}},"docs":{}}}}}}}}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}}}}}}}}}},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}},"s":{"docs":{},"t":{"docs":{},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"m":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"概":{"docs":{},"述":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}}}}}}}}}}}}}}}},"易":{"docs":{},"整":{"docs":{},"合":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}}}}},"解":{"docs":{},"决":{"docs":{},"了":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"的":{"docs":{},"缺":{"docs":{},"点":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}}}}}}}}}}}},"。":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.025}}},"个":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"n":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"/":{"docs":{},"n":{"docs":{},"n":{"docs":{},"(":{"docs":{},"m":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},")":{"docs":{"day03_Hadoop/ha2.4.html":{"ref":"day03_Hadoop/ha2.4.html","tf":0.03333333333333333}}}}}}}}}}}}}}}}}}}}},"文":{"docs":{},"件":{"docs":{},"会":{"docs":{},"被":{"docs":{},"拆":{"docs":{},"分":{"docs":{},"成":{"docs":{},"多":{"docs":{},"个":{"docs":{},"b":{"docs":{},"l":{"docs":{},"o":{"docs":{},"c":{"docs":{},"k":{"docs":{"day03_Hadoop/ha2.4.html":{"ref":"day03_Hadoop/ha2.4.html","tf":0.03333333333333333}}}}}}}}}}}}}}}},"执":{"docs":{},"行":{"docs":{},"器":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}},"虚":{"docs":{},"拟":{"docs":{},"变":{"docs":{},"量":{"docs":{},"，":{"docs":{},"n":{"docs":{},"为":{"docs":{},"p":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"的":{"docs":{},"取":{"docs":{},"值":{"docs":{},"范":{"docs":{},"围":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}}}}}}},"）":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"r":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}}},"，":{"docs":{},"c":{"docs":{},"l":{"docs":{},"i":{"docs":{},"e":{"docs":{},"n":{"docs":{},"t":{"docs":{},"提":{"docs":{},"交":{"docs":{},"作":{"docs":{},"业":{"docs":{},"请":{"docs":{},"求":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}}}}}}}}}},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"结":{"docs":{},"果":{"docs":{},"写":{"docs":{},"磁":{"docs":{},"盘":{"docs":{},"，":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{},"e":{"docs":{},"写":{"docs":{},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},"，":{"docs":{},"多":{"docs":{},"个":{"docs":{},"m":{"docs":{},"r":{"docs":{},"之":{"docs":{},"间":{"docs":{},"通":{"docs":{},"过":{"docs":{},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},"交":{"docs":{},"换":{"docs":{},"数":{"docs":{},"据":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"创":{"docs":{},"建":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"的":{"docs":{},"步":{"docs":{},"骤":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}},"一":{"docs":{},"个":{"docs":{},"s":{"docs":{},"t":{"docs":{},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"m":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"t":{"docs":{},"e":{"docs":{},"x":{"docs":{},"t":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099}}}}}}}}}}}}}}}}}}}}}},"g":{"docs":{},"e":{"docs":{},"t":{"docs":{},"_":{"docs":{},"j":{"docs":{},"s":{"docs":{},"o":{"docs":{},"n":{"docs":{},"_":{"docs":{},"o":{"docs":{},"b":{"docs":{},"j":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}}}}}}}}}}}}},"通":{"docs":{},"过":{"docs":{},"反":{"docs":{},"射":{"docs":{},"自":{"docs":{},"动":{"docs":{},"推":{"docs":{},"断":{"docs":{},"，":{"docs":{},"适":{"docs":{},"合":{"docs":{},"静":{"docs":{},"态":{"docs":{},"数":{"docs":{},"据":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}}}}}}}}}}}}},"需":{"docs":{},"要":{"docs":{},"安":{"docs":{},"装":{"docs":{},"一":{"docs":{},"个":{"docs":{},"n":{"docs":{},"c":{"docs":{},"工":{"docs":{},"具":{"docs":{},"：":{"docs":{},"s":{"docs":{},"u":{"docs":{},"d":{"docs":{},"o":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099}}}}}}}}}}}}}}}}}},"'":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}},"|":{"1":{"5":{"4":{"4":{"3":{"6":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"7":{"4":{"3":{"7":{"4":{"docs":{},"|":{"1":{"3":{"9":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"2":{"4":{"9":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.001193792280143255}}}},"docs":{}}},"docs":{}},"docs":{}},"3":{"6":{"8":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}},"docs":{}}},"docs":{}},"docs":{}},"4":{"2":{"8":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}}},"docs":{}},"3":{"0":{"5":{"4":{"8":{"docs":{},"_":{"1":{"0":{"0":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"5":{"docs":{},".":{"5":{"5":{"5":{"5":{"5":{"5":{"6":{"docs":{},"e":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},"[":{"0":{"docs":{},".":{"9":{"2":{"4":{"8":{"1":{"4":{"5":{"6":{"4":{"8":{"6":{"8":{"7":{"3":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"6":{"3":{"9":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}}},"docs":{}},"docs":{}},"8":{"docs":{},".":{"8":{"8":{"8":{"8":{"8":{"8":{"8":{"docs":{},"e":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.06587803385682235},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.07401512136888182}},"[":{"docs":{},"[":{"1":{"6":{"1":{"0":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}},"\"":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289}}}},"来":{"docs":{},"作":{"docs":{},"为":{"docs":{},"目":{"docs":{},"标":{"docs":{},"值":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}},"2":{"0":{"0":{"3":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}},"4":{"docs":{},"年":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}},"6":{"docs":{},"年":{"2":{"docs":{},"月":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},"成":{"docs":{},"为":{"docs":{},"a":{"docs":{},"p":{"docs":{},"a":{"docs":{},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},"的":{"docs":{},"独":{"docs":{},"立":{"docs":{},"开":{"docs":{},"源":{"docs":{},"项":{"docs":{},"目":{"docs":{},"(":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}}}}}}}}}}}}}}}}}}}}}}},"4":{"docs":{},"月":{"docs":{},"—":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}},"docs":{}}},"8":{"docs":{},"年":{"4":{"docs":{},"月":{"docs":{},"—":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}},"docs":{},"—":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}},"9":{"docs":{},"年":{"3":{"docs":{},"月":{"docs":{},"—":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}},"5":{"docs":{},"月":{"docs":{},"—":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}},"7":{"docs":{},"月":{"docs":{},"—":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.018018018018018018}}}}},"docs":{}}},"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.020134228187919462}},")":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"1":{"0":{"6":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"2":{"docs":{},"年":{"1":{"1":{"docs":{},"月":{"docs":{},"—":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}},"docs":{}},"docs":{}}},"4":{"docs":{},"年":{"6":{"docs":{},"月":{"1":{"docs":{},"日":{"docs":{},"的":{"docs":{},"时":{"docs":{},"候":{"docs":{},"，":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"宣":{"docs":{},"布":{"docs":{},"了":{"docs":{},"不":{"docs":{},"再":{"docs":{},"开":{"docs":{},"发":{"docs":{},"s":{"docs":{},"h":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"，":{"docs":{},"全":{"docs":{},"面":{"docs":{},"转":{"docs":{},"向":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{}}},"docs":{}}},"5":{"0":{"6":{"2":{"3":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"7":{"0":{"5":{"1":{"2":{"docs":{},"）":{"docs":{},"，":{"docs":{},"用":{"docs":{},"第":{"8":{"docs":{},"天":{"docs":{},"的":{"docs":{},"做":{"docs":{},"测":{"docs":{},"试":{"docs":{},"样":{"docs":{},"本":{"docs":{},"（":{"2":{"0":{"1":{"7":{"0":{"5":{"1":{"3":{"docs":{},"）":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}},"docs":{}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}},"8":{"6":{"7":{"docs":{},",":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}},"|":{"docs":{},"[":{"1":{"7":{"9":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"0":{"docs":{},",":{"0":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{},"[":{"2":{"docs":{},".":{"6":{"9":{"0":{"1":{"9":{"4":{"7":{"5":{"8":{"4":{"2":{"8":{"8":{"7":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"6":{"4":{"4":{"5":{"5":{"7":{"3":{"7":{"4":{"9":{"0":{"0":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}}}}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}}},"docs":{}},"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}},"年":{"4":{"docs":{},"月":{"docs":{},"—":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}},"docs":{}},",":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}},"docs":{},"|":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"2":{"7":{"1":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"3":{"9":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"5":{"6":{"1":{"2":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"6":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"7":{"7":{"5":{"4":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981}}}},"docs":{}},"docs":{}},"8":{"0":{"0":{"docs":{},"|":{"1":{"9":{"9":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"8":{"4":{"5":{"8":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}},"docs":{}},"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"3":{"9":{"2":{"0":{"4":{"9":{"3":{"1":{"1":{"8":{"1":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"9":{"9":{"5":{"9":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.013422818791946308},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.004078303425774877},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0032633081786661226},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0015917230401910067},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.008456659619450317}},")":{"docs":{"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}},":":{"0":{"9":{"docs":{},":":{"1":{"1":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}},"个":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"1":{"1":{"2":{"9":{"2":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"8":{"1":{"6":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"2":{"2":{"docs":{},"|":{"docs":{},"[":{"docs":{},"[":{"1":{"6":{"1":{"0":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}},"docs":{}},"3":{"5":{"6":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"4":{"2":{"docs":{},"|":{"docs":{},"[":{"docs":{},"[":{"1":{"6":{"1":{"0":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}},"docs":{}},"7":{"5":{"1":{"2":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"8":{"1":{"0":{"1":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}},"2":{"7":{"6":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"3":{"0":{"6":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"9":{"1":{"8":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{},")":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}},"2":{"0":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"9":{"9":{"9":{"3":{"8":{"6":{"0":{"7":{"7":{"0":{"1":{"7":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"4":{"0":{"2":{"8":{"9":{"2":{"6":{"3":{"4":{"0":{"2":{"1":{"8":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"1":{"5":{"8":{"5":{"docs":{},"|":{"docs":{},"[":{"1":{"8":{"docs":{},".":{"5":{"docs":{},",":{"1":{"docs":{},".":{"0":{"docs":{},",":{"0":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"0":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}},"docs":{}}}},"docs":{}},"docs":{}},"7":{"1":{"4":{"docs":{},"|":{"docs":{},"[":{"4":{"docs":{},".":{"8":{"0":{"0":{"0":{"0":{"0":{"1":{"9":{"0":{"7":{"3":{"4":{"8":{"6":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}},"2":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"2":{"docs":{},".":{"6":{"8":{"docs":{},".":{"1":{"7":{"2":{"docs":{},".":{"1":{"9":{"0":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.006711409395973154}}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}}},"3":{"docs":{},".":{"2":{"4":{"3":{"docs":{},".":{"0":{"docs":{},".":{"0":{"docs":{},"|":{"2":{"2":{"3":{"docs":{},".":{"2":{"4":{"3":{"docs":{},".":{"1":{"9":{"1":{"docs":{},".":{"2":{"5":{"5":{"docs":{},"|":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}},"4":{"6":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"7":{"7":{"3":{"1":{"docs":{},",":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}},"|":{"docs":{},"[":{"1":{"9":{"9":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"0":{"docs":{},",":{"0":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{},"[":{"2":{"docs":{},".":{"6":{"9":{"0":{"1":{"9":{"4":{"5":{"7":{"3":{"3":{"1":{"7":{"5":{"4":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"6":{"4":{"4":{"5":{"5":{"6":{"2":{"7":{"3":{"2":{"0":{"5":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}}}}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}}},"docs":{}},"docs":{}},"docs":{}},"9":{"8":{"2":{"7":{"docs":{},",":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}},"|":{"docs":{},"[":{"2":{"3":{"8":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"0":{"docs":{},",":{"0":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{},"[":{"2":{"docs":{},".":{"6":{"9":{"0":{"1":{"9":{"4":{"2":{"1":{"2":{"3":{"5":{"0":{"4":{"4":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"6":{"4":{"4":{"5":{"5":{"4":{"1":{"2":{"4":{"9":{"0":{"0":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}}}}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.04081632653061224}},",":{"1":{"0":{"2":{"docs":{},",":{"2":{"0":{"1":{"8":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"3":{"docs":{},",":{"2":{"0":{"1":{"8":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0022675736961451248}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"4":{"docs":{},",":{"2":{"0":{"1":{"8":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}},"3":{"2":{"3":{"6":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}},"4":{"9":{"2":{"9":{"1":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"4":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0016316540893330613},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"6":{"6":{"docs":{},"|":{"docs":{},"[":{"docs":{},"[":{"1":{"6":{"1":{"0":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}},"docs":{}},"7":{"4":{"7":{"1":{"docs":{},".":{"0":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"8":{"7":{"6":{"1":{"docs":{},".":{"0":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}},"docs":{}}},"docs":{}},"7":{"2":{"docs":{},".":{"0":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"9":{"3":{"0":{"2":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.0045662100456621}}},":":{"5":{"9":{"docs":{},":":{"4":{"6":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"4":{"5":{"6":{"8":{"9":{"9":{"9":{"6":{"5":{"4":{"6":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"4":{"0":{"9":{"8":{"4":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"1":{"4":{"0":{"2":{"docs":{},",":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}},"|":{"docs":{},"[":{"2":{"6":{"9":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"0":{"docs":{},",":{"0":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{},"[":{"2":{"docs":{},".":{"6":{"9":{"0":{"1":{"9":{"3":{"9":{"2":{"5":{"4":{"2":{"7":{"8":{"7":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"6":{"4":{"4":{"5":{"5":{"2":{"4":{"1":{"7":{"2":{"7":{"1":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}}}}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}}},"docs":{}},"docs":{}},"docs":{}},"3":{"3":{"8":{"4":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"4":{"8":{"4":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}},"docs":{}},"8":{"9":{"0":{"9":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{},"*":{"6":{"0":{"docs":{},"*":{"6":{"0":{"docs":{},")":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}}},"5":{"0":{"2":{"9":{"4":{"3":{"5":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"4":{"5":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.001835610850499694},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}},"5":{"4":{"2":{"docs":{},",":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}},"|":{"docs":{},"[":{"1":{"7":{"6":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"0":{"docs":{},",":{"0":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{},"[":{"2":{"docs":{},".":{"6":{"9":{"0":{"1":{"9":{"4":{"7":{"8":{"6":{"1":{"9":{"5":{"5":{"7":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"6":{"4":{"4":{"5":{"5":{"7":{"5":{"4":{"0":{"1":{"5":{"5":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}}}}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}}},"docs":{}},"8":{"7":{"5":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}},"docs":{}},"docs":{}},"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}},".":{"2":{"5":{"5":{"docs":{},".":{"2":{"5":{"5":{"docs":{},".":{"2":{"5":{"5":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}},"6":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"0":{"2":{"1":{"6":{"7":{"2":{"0":{"6":{"7":{"4":{"4":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"8":{"2":{"5":{"2":{"docs":{},"|":{"docs":{},"[":{"7":{"docs":{},".":{"5":{"9":{"9":{"9":{"9":{"9":{"9":{"0":{"4":{"6":{"3":{"2":{"5":{"6":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}},"docs":{}},"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"0":{"2":{"1":{"6":{"7":{"1":{"0":{"3":{"9":{"9":{"5":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"2":{"1":{"1":{"1":{"6":{"5":{"1":{"1":{"9":{"8":{"7":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.001193792280143255}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"9":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"2":{"1":{"1":{"1":{"6":{"4":{"6":{"0":{"7":{"6":{"5":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"docs":{},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}},"]":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876}}},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"9":{"8":{"0":{"3":{"1":{"1":{"4":{"4":{"9":{"2":{"1":{"2":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"4":{"0":{"2":{"1":{"1":{"2":{"8":{"4":{"4":{"6":{"7":{"9":{"5":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"4":{"9":{"8":{"9":{"docs":{},"]":{"docs":{},",":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}},"docs":{}},"docs":{}},"docs":{}},"9":{"8":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"4":{"5":{"6":{"8":{"9":{"8":{"4":{"4":{"4":{"9":{"1":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}},"docs":{}}},"6":{"2":{"2":{"1":{"5":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"5":{"4":{"0":{"3":{"docs":{},",":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}},"|":{"docs":{},"[":{"1":{"9":{"8":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"0":{"docs":{},",":{"0":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{},"[":{"2":{"docs":{},".":{"6":{"9":{"0":{"1":{"9":{"4":{"5":{"8":{"2":{"5":{"7":{"3":{"1":{"1":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"6":{"4":{"4":{"5":{"5":{"6":{"3":{"2":{"8":{"2":{"9":{"0":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}}}}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}}},"docs":{}},"docs":{}},"5":{"7":{"9":{"6":{"1":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"9":{"docs":{},"|":{"docs":{},"[":{"docs":{},"[":{"5":{"6":{"0":{"7":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}},"docs":{}},"6":{"0":{"8":{"6":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"7":{"0":{"0":{"2":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"7":{"1":{"9":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"3":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}},"docs":{}},"docs":{}},"1":{"5":{"1":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}},"docs":{}},"docs":{}},"3":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"0":{"2":{"1":{"6":{"6":{"3":{"3":{"3":{"3":{"8":{"0":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"4":{"7":{"9":{"5":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.00897226753670473}}}},"docs":{}},"docs":{}},"docs":{}},"5":{"8":{"1":{"9":{"docs":{},",":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}},"|":{"docs":{},"[":{"3":{"2":{"8":{"0":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"0":{"docs":{},",":{"0":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{},"[":{"2":{"docs":{},".":{"6":{"9":{"0":{"1":{"6":{"6":{"0":{"5":{"6":{"9":{"1":{"6":{"6":{"9":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"6":{"4":{"4":{"3":{"8":{"6":{"5":{"5":{"4":{"9":{"6":{"1":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}}}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}},"docs":{}},"docs":{}},"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"0":{"2":{"1":{"6":{"6":{"2":{"3":{"0":{"6":{"3":{"1":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.001193792280143255}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"7":{"3":{"3":{"5":{"docs":{},",":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}},"|":{"docs":{},"[":{"1":{"8":{"1":{"docs":{},".":{"5":{"docs":{},",":{"1":{"docs":{},".":{"0":{"docs":{},",":{"0":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{},"[":{"2":{"docs":{},".":{"6":{"9":{"0":{"1":{"9":{"4":{"7":{"3":{"5":{"2":{"8":{"9":{"9":{"6":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"6":{"4":{"4":{"5":{"5":{"7":{"2":{"3":{"7":{"1":{"8":{"9":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}}}}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}}},"docs":{}},"docs":{}},"docs":{}},"8":{"3":{"0":{"1":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0008158270446665307}}}},"docs":{}},"docs":{}},"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"9":{"7":{"2":{"1":{"3":{"8":{"0":{"8":{"9":{"9":{"2":{"5":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"4":{"0":{"0":{"2":{"1":{"6":{"6":{"0":{"7":{"6":{"5":{"0":{"8":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"2":{"1":{"1":{"1":{"5":{"4":{"8":{"7":{"5":{"3":{"9":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"docs":{},".":{"6":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"9":{"7":{"2":{"1":{"5":{"1":{"0":{"1":{"4":{"3":{"3":{"4":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"8":{"1":{"4":{"5":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0012237405669997961},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.001193792280143255}}}},"docs":{}},"docs":{}},"5":{"8":{"9":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}},"docs":{}},"6":{"6":{"docs":{},"|":{"docs":{},"[":{"docs":{},"[":{"1":{"6":{"1":{"0":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}},"docs":{}},"9":{"0":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"2":{"8":{"7":{"8":{"9":{"7":{"4":{"2":{"2":{"5":{"7":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"5":{"6":{"3":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{},")":{"docs":{},"]":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}},",":{"docs":{},"'":{"docs":{},"m":{"docs":{},"'":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"9":{"8":{"0":{"3":{"1":{"1":{"2":{"9":{"4":{"5":{"6":{"3":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"9":{"9":{"3":{"9":{"5":{"9":{"4":{"5":{"1":{"7":{"2":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"9":{"0":{"6":{"7":{"5":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"9":{"5":{"0":{"docs":{},"|":{"docs":{},"[":{"6":{"docs":{},".":{"5":{"docs":{},",":{"1":{"docs":{},".":{"0":{"docs":{},",":{"0":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"0":{"docs":{},",":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}}},"docs":{}},"docs":{}},"docs":{}},"2":{"0":{"2":{"7":{"docs":{},"|":{"docs":{},"[":{"1":{"6":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"0":{"docs":{},",":{"0":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"0":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}},"docs":{}}}},"docs":{}},"docs":{}},"docs":{}},"4":{"6":{"6":{"docs":{},",":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}},"|":{"docs":{},"[":{"6":{"4":{"0":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"0":{"docs":{},",":{"0":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{},"[":{"2":{"docs":{},".":{"6":{"9":{"0":{"1":{"9":{"0":{"4":{"9":{"1":{"6":{"1":{"2":{"6":{"5":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"6":{"4":{"4":{"5":{"3":{"1":{"9":{"8":{"0":{"7":{"8":{"5":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}}}}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}}},"docs":{}},"docs":{}},"7":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"0":{"2":{"1":{"6":{"5":{"1":{"0":{"0":{"3":{"9":{"4":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"8":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"0":{"2":{"1":{"6":{"5":{"0":{"4":{"9":{"0":{"2":{"0":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"4":{"5":{"6":{"7":{"5":{"9":{"6":{"4":{"6":{"0":{"0":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"9":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"0":{"2":{"1":{"6":{"4":{"9":{"9":{"7":{"6":{"4":{"5":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.001193792280143255}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"2":{"1":{"1":{"1":{"4":{"4":{"1":{"1":{"8":{"6":{"9":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"docs":{}},"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775},"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863},"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734},"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.0016233766233766235},"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008},"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html":{"ref":"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html","tf":0.04878048780487805},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683},"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938},"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196},"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704},"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112},"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0022675736961451248},"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.02608695652173913},"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676},"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678},"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726},"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0008158270446665307},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.008710801393728223}},"/":{"4":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}},"docs":{}},")":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"及":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{},"e":{"docs":{},"任":{"docs":{},"务":{"docs":{},"个":{"docs":{},"数":{"docs":{},"限":{"docs":{},"制":{"docs":{},"，":{"docs":{},"如":{"docs":{},"：":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}}}}}}}}}}}}}}},",":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.06060606060606061},"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.013636363636363636},"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}}},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}},"]":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}},".":{"0":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0032633081786661226},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.003979307600477517}}}},"1":{"docs":{"day03_Hadoop/ha2.1.html":{"ref":"day03_Hadoop/ha2.1.html","tf":0.0136986301369863},"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617},"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965},"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}},"_":{"docs":{},"基":{"docs":{},"于":{"docs":{},"模":{"docs":{},"型":{"docs":{},"的":{"docs":{},"协":{"docs":{},"同":{"docs":{},"过":{"docs":{},"滤":{"docs":{},"推":{"docs":{},"荐":{"docs":{"day02_推荐算法/01_基于模型的协同过滤推荐.html":{"ref":"day02_推荐算法/01_基于模型的协同过滤推荐.html","tf":10}}}}}}}}}}}}}}},"2":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625},"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617},"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965},"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}},"_":{"docs":{},"基":{"docs":{},"于":{"docs":{},"回":{"docs":{},"归":{"docs":{},"模":{"docs":{},"型":{"docs":{},"的":{"docs":{},"协":{"docs":{},"同":{"docs":{},"过":{"docs":{},"滤":{"docs":{},"推":{"docs":{},"荐":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":10}}}}}}}}}}}}}}}}},"3":{"4":{"2":{"9":{"5":{"1":{"5":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0024469820554649264}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{"day03_Hadoop/ha2.3.html":{"ref":"day03_Hadoop/ha2.3.html","tf":0.1111111111111111},"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965},"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}},"_":{"docs":{},"基":{"docs":{},"于":{"docs":{},"矩":{"docs":{},"阵":{"docs":{},"分":{"docs":{},"解":{"docs":{},"的":{"docs":{},"协":{"docs":{},"同":{"docs":{},"过":{"docs":{},"滤":{"docs":{},"推":{"docs":{},"荐":{"docs":{"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html":{"ref":"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html","tf":10}}}}}}}}}}}}}}}},".":{"0":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}},"docs":{}}},"4":{"docs":{"day03_Hadoop/ha2.4.html":{"ref":"day03_Hadoop/ha2.4.html","tf":0.03333333333333333}},"_":{"docs":{},"l":{"docs":{},"f":{"docs":{},"m":{"docs":{},"算":{"docs":{},"法":{"docs":{},"实":{"docs":{},"现":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":10}}}}}}}}}},".":{"1":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625}}},"docs":{}}},"5":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428}},"_":{"docs":{},"b":{"docs":{},"i":{"docs":{},"a":{"docs":{},"s":{"docs":{},"s":{"docs":{},"v":{"docs":{},"d":{"docs":{},"算":{"docs":{},"法":{"docs":{},"实":{"docs":{},"现":{"docs":{"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":10}}}}}}}}}}}}}}},"6":{"docs":{},"_":{"docs":{},"基":{"docs":{},"于":{"docs":{},"内":{"docs":{},"容":{"docs":{},"的":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"算":{"docs":{},"法":{"docs":{"day02_推荐算法/07_基于内容的推荐算法.html":{"ref":"day02_推荐算法/07_基于内容的推荐算法.html","tf":10}}}}}}}}}}}},".":{"0":{"docs":{"day03_Hadoop/ha2.1.html":{"ref":"day03_Hadoop/ha2.1.html","tf":0.0410958904109589},"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}},"docs":{}}},"7":{"docs":{},"_":{"docs":{},"电":{"docs":{},"影":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"(":{"docs":{},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"t":{"docs":{},"e":{"docs":{},"n":{"docs":{},"t":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"d":{"docs":{},")":{"docs":{},"物":{"docs":{},"品":{"docs":{},"画":{"docs":{},"像":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":10}}}}}}}}}}}}}}}}}}}}}}}}}},"8":{"docs":{},"_":{"docs":{},"电":{"docs":{},"影":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"(":{"docs":{},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"t":{"docs":{},"e":{"docs":{},"n":{"docs":{},"t":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"d":{"docs":{},")":{"docs":{},"用":{"docs":{},"户":{"docs":{},"画":{"docs":{},"像":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":10}}}}}}}}}}}}}}}}}}}}}}}}}},"9":{"docs":{},"_":{"docs":{},"电":{"docs":{},"影":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"(":{"docs":{},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"t":{"docs":{},"e":{"docs":{},"n":{"docs":{},"t":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"d":{"docs":{},")":{"docs":{},"t":{"docs":{},"o":{"docs":{},"p":{"docs":{"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":5}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.006493506493506494},"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842},"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}},"x":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}},"其":{"docs":{},"次":{"docs":{},"，":{"docs":{},"关":{"docs":{},"键":{"docs":{},"字":{"docs":{},"段":{"docs":{},"值":{"docs":{},"完":{"docs":{},"全":{"docs":{},"一":{"docs":{},"模":{"docs":{},"一":{"docs":{},"样":{"docs":{},"的":{"docs":{},"记":{"docs":{},"录":{"docs":{},"（":{"docs":{},"在":{"docs":{},"这":{"docs":{},"个":{"docs":{},"例":{"docs":{},"子":{"docs":{},"中":{"docs":{},"，":{"docs":{},"是":{"docs":{},"指":{"docs":{},"除":{"docs":{},"了":{"docs":{},"i":{"docs":{},"d":{"docs":{},"之":{"docs":{},"外":{"docs":{},"的":{"docs":{},"列":{"docs":{},"一":{"docs":{},"模":{"docs":{},"一":{"docs":{},"样":{"docs":{},"）":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"处":{"docs":{},"理":{"docs":{},"缺":{"docs":{},"失":{"docs":{},"值":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}},"计":{"docs":{},"算":{"docs":{},"各":{"docs":{},"列":{"docs":{},"的":{"docs":{},"缺":{"docs":{},"失":{"docs":{},"情":{"docs":{},"况":{"docs":{},"百":{"docs":{},"分":{"docs":{},"比":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}}}}}},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208},"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876},"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.01276595744680851},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}},":":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208},"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}},"\\":{"docs":{},"l":{"docs":{},"a":{"docs":{},"m":{"docs":{},"b":{"docs":{},"d":{"docs":{},"a":{"docs":{},"*":{"docs":{},"b":{"docs":{},"_":{"docs":{},"u":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}},"{":{"docs":{},"b":{"docs":{},"_":{"docs":{},"u":{"docs":{},"}":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}},"s":{"docs":{},"u":{"docs":{},"m":{"docs":{},"_":{"docs":{},"{":{"docs":{},"u":{"docs":{},",":{"docs":{},"i":{"docs":{},"\\":{"docs":{},"i":{"docs":{},"n":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}},"：":{"docs":{},"随":{"docs":{},"机":{"docs":{},"梯":{"docs":{},"度":{"docs":{},"下":{"docs":{},"降":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}},"）":{"docs":{},"y":{"docs":{},"a":{"docs":{},"r":{"docs":{},"n":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}}},"，":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"o":{"docs":{},"u":{"docs":{},"r":{"docs":{},"c":{"docs":{},"e":{"docs":{},"m":{"docs":{},"a":{"docs":{},"n":{"docs":{},"a":{"docs":{},"g":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}}}}}}}}}}},"任":{"docs":{},"务":{"docs":{},"调":{"docs":{},"度":{"docs":{},"和":{"docs":{},"启":{"docs":{},"动":{"docs":{},"开":{"docs":{},"销":{"docs":{},"大":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}}}}}}}}},"其":{"docs":{},"他":{"docs":{},"方":{"docs":{},"式":{"docs":{},"创":{"docs":{},"建":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}},"g":{"docs":{},"e":{"docs":{},"t":{"docs":{},"_":{"docs":{},"j":{"docs":{},"s":{"docs":{},"o":{"docs":{},"n":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}}}}}},"程":{"docs":{},"序":{"docs":{},"指":{"docs":{},"定":{"docs":{},"，":{"docs":{},"适":{"docs":{},"合":{"docs":{},"程":{"docs":{},"序":{"docs":{},"运":{"docs":{},"行":{"docs":{},"中":{"docs":{},"动":{"docs":{},"态":{"docs":{},"生":{"docs":{},"成":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}}}}}}}}}}}}}}}}},"从":{"docs":{},"s":{"docs":{},"t":{"docs":{},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"m":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"t":{"docs":{},"e":{"docs":{},"x":{"docs":{},"t":{"docs":{},"中":{"docs":{},"创":{"docs":{},"建":{"docs":{},"一":{"docs":{},"个":{"docs":{},"数":{"docs":{},"据":{"docs":{},"对":{"docs":{},"象":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099}}}}}}}}}}}}}}}}}}}}}}}}}}}},"执":{"docs":{},"行":{"docs":{},"指":{"docs":{},"令":{"docs":{},"：":{"docs":{},"n":{"docs":{},"c":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099}}}}}}}}}},"、":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}},"本":{"docs":{},"地":{"docs":{},"(":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}},"为":{"docs":{},"什":{"docs":{},"么":{"docs":{},"要":{"docs":{},"学":{"docs":{},"习":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}}}}}}}}}},"易":{"docs":{},"用":{"docs":{},"性":{"docs":{},"（":{"docs":{},"可":{"docs":{},"以":{"docs":{},"通":{"docs":{},"过":{"docs":{},"j":{"docs":{},"a":{"docs":{},"v":{"docs":{},"a":{"docs":{},"/":{"docs":{},"s":{"docs":{},"c":{"docs":{},"a":{"docs":{},"l":{"docs":{},"a":{"docs":{},"/":{"docs":{},"p":{"docs":{},"y":{"docs":{},"t":{"docs":{},"h":{"docs":{},"o":{"docs":{},"n":{"docs":{},"/":{"docs":{},"r":{"docs":{},"开":{"docs":{},"发":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"应":{"docs":{},"用":{"docs":{},"程":{"docs":{},"序":{"docs":{},"）":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"丢":{"docs":{},"失":{"docs":{},"了":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"的":{"docs":{},"优":{"docs":{},"点":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}}}}}}}}}}},"统":{"docs":{},"一":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"源":{"docs":{},"访":{"docs":{},"问":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}}}}}}}}}},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099}}}}}}}},"^":{"8":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.013245033112582781}}},"docs":{}},"|":{"1":{"4":{"5":{"9":{"5":{"2":{"docs":{},"|":{"3":{"2":{"docs":{},".":{"9":{"9":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"2":{"9":{"3":{"6":{"5":{"6":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"3":{"2":{"4":{"4":{"2":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.07791148276565368},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.022284122562674095}},"[":{"docs":{},"[":{"5":{"5":{"7":{"9":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}},"]":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"维":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.004228329809725159}}}},"3":{"0":{"0":{"5":{"5":{"6":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}},"docs":{}},"6":{"8":{"1":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"9":{"7":{"2":{"1":{"3":{"6":{"9":{"5":{"4":{"3":{"9":{"3":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"1":{"2":{"9":{"9":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}},"docs":{}},"docs":{}},"docs":{}},"4":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}},"8":{"8":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"5":{"5":{"7":{"8":{"4":{"8":{"0":{"1":{"5":{"3":{"5":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"docs":{}},"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"9":{"7":{"2":{"1":{"5":{"0":{"8":{"9":{"0":{"4":{"5":{"8":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"8":{"0":{"3":{"1":{"1":{"1":{"9":{"1":{"4":{"6":{"4":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"9":{"9":{"3":{"9":{"5":{"8":{"4":{"2":{"3":{"7":{"9":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"1":{"0":{"4":{"0":{"8":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"1":{"8":{"3":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"9":{"7":{"2":{"1":{"3":{"6":{"3":{"8":{"6":{"6":{"2":{"6":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"2":{"3":{"9":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}}},"docs":{}},"docs":{}},"3":{"1":{"4":{"docs":{},"|":{"docs":{},"[":{"1":{"5":{"docs":{},".":{"8":{"0":{"0":{"0":{"0":{"0":{"1":{"9":{"0":{"7":{"3":{"4":{"8":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}}}},"docs":{}},"4":{"0":{"1":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}},"docs":{}},"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}},"4":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}},"5":{"3":{"7":{"1":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"6":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"4":{"5":{"6":{"7":{"5":{"0":{"4":{"6":{"1":{"4":{"4":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"7":{"5":{"docs":{},"|":{"docs":{},"[":{"docs":{},"[":{"3":{"3":{"4":{"7":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}},"docs":{}},"8":{"9":{"9":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0016316540893330613},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"4":{"5":{"6":{"8":{"9":{"5":{"8":{"8":{"3":{"4":{"5":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"2":{"2":{"3":{"3":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}},"docs":{}},"6":{"1":{"2":{"6":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0008158270446665307}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{},"位":{"2":{"docs":{},"进":{"docs":{},"制":{"docs":{},"数":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}},"docs":{},"二":{"docs":{},"进":{"docs":{},"制":{"docs":{},"数":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}}},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"4":{"5":{"6":{"8":{"9":{"5":{"3":{"7":{"3":{"1":{"9":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"8":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"9":{"9":{"9":{"3":{"9":{"5":{"6":{"9":{"8":{"4":{"6":{"9":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"3":{"0":{"8":{"6":{"7":{"0":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"5":{"4":{"1":{"3":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}},"9":{"5":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"9":{"9":{"9":{"3":{"8":{"0":{"1":{"6":{"6":{"3":{"9":{"5":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"7":{"5":{"6":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"8":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"9":{"7":{"2":{"1":{"3":{"4":{"9":{"9":{"3":{"0":{"1":{"8":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"9":{"3":{"3":{"4":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"8":{"2":{"docs":{},"]":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}},"|":{"docs":{},"[":{"1":{"6":{"3":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"0":{"docs":{},",":{"0":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{},"[":{"2":{"docs":{},".":{"6":{"9":{"0":{"1":{"9":{"4":{"9":{"0":{"6":{"5":{"1":{"7":{"9":{"4":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"6":{"4":{"4":{"5":{"5":{"8":{"2":{"5":{"6":{"2":{"5":{"6":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}}}}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}}},"docs":{}},"docs":{}},"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"9":{"9":{"9":{"3":{"7":{"9":{"9":{"6":{"0":{"8":{"0":{"8":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.030612244897959183}},",":{"1":{"0":{"1":{"docs":{},",":{"2":{"0":{"1":{"8":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"2":{"docs":{},",":{"2":{"0":{"1":{"8":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"3":{"docs":{},",":{"2":{"0":{"1":{"8":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00684931506849315}}},")":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}},".":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}},"[":{"0":{"docs":{},".":{"9":{"3":{"9":{"7":{"2":{"1":{"5":{"0":{"7":{"3":{"5":{"6":{"1":{"3":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"9":{"9":{"3":{"9":{"5":{"6":{"8":{"8":{"1":{"8":{"9":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.001835610850499694},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"4":{"2":{"docs":{},".":{"3":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}},"|":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}},"docs":{}}},"4":{"9":{"2":{"0":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}},"docs":{}},"docs":{}},"docs":{}},"5":{"8":{"7":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"8":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"0":{"2":{"1":{"6":{"2":{"4":{"8":{"0":{"2":{"9":{"9":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"4":{"5":{"6":{"7":{"3":{"4":{"1":{"3":{"3":{"3":{"4":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"9":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"4":{"5":{"6":{"7":{"3":{"3":{"6":{"2":{"3":{"0":{"8":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"docs":{}},"5":{"1":{"3":{"6":{"6":{"docs":{},",":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}},"|":{"docs":{},"[":{"2":{"4":{"6":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"0":{"docs":{},",":{"0":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{},"[":{"2":{"docs":{},".":{"6":{"9":{"0":{"1":{"9":{"4":{"1":{"3":{"8":{"3":{"0":{"5":{"9":{"1":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"6":{"4":{"4":{"5":{"5":{"3":{"6":{"8":{"4":{"2":{"2":{"1":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}}}}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}}},"docs":{}},"docs":{}},"5":{"6":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"2":{"2":{"7":{"3":{"docs":{},".":{"0":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}},"docs":{}}},"docs":{}},"docs":{}},"6":{"6":{"6":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.021541950113378686}},",":{"1":{"0":{"2":{"docs":{},",":{"2":{"0":{"1":{"8":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"5":{"docs":{},",":{"2":{"0":{"1":{"8":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"9":{"7":{"2":{"1":{"5":{"0":{"6":{"3":{"2":{"3":{"8":{"3":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"4":{"0":{"0":{"2":{"1":{"7":{"8":{"5":{"6":{"0":{"4":{"7":{"3":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"2":{"1":{"1":{"2":{"7":{"9":{"3":{"4":{"5":{"7":{"2":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"5":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"9":{"9":{"9":{"3":{"9":{"5":{"5":{"5":{"9":{"6":{"9":{"8":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"6":{"4":{"4":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0016316540893330613},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}},"5":{"4":{"7":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}},"docs":{}},"docs":{}},"docs":{}},"6":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"0":{"2":{"1":{"6":{"1":{"5":{"5":{"5":{"5":{"6":{"0":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"8":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"0":{"2":{"1":{"6":{"1":{"4":{"5":{"2":{"8":{"1":{"1":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0015917230401910067}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"2":{"1":{"1":{"1":{"0":{"8":{"7":{"7":{"5":{"2":{"1":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"9":{"6":{"3":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.007692307692307693}}}},"docs":{}},"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"0":{"2":{"1":{"6":{"1":{"4":{"0":{"1":{"4":{"3":{"6":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"7":{"0":{"0":{"4":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0008158270446665307},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}},"docs":{}},"docs":{}},"4":{"9":{"docs":{},"|":{"docs":{},"[":{"docs":{},"[":{"1":{"6":{"1":{"0":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}},"docs":{}},"5":{"7":{"0":{"6":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}},"docs":{}},"9":{"2":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"6":{"6":{"5":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"7":{"5":{"9":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"8":{"0":{"0":{"0":{"0":{"0":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"3":{"0":{"2":{"3":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"5":{"docs":{},"|":{"4":{"2":{"8":{"9":{"5":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}},"7":{"9":{"9":{"1":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0008158270446665307},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}},"docs":{}},"docs":{}},"docs":{}},"8":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"0":{"2":{"1":{"6":{"0":{"4":{"2":{"5":{"3":{"2":{"2":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"docs":{},".":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}},"[":{"0":{"docs":{},".":{"9":{"3":{"9":{"7":{"2":{"1":{"5":{"0":{"4":{"7":{"7":{"5":{"3":{"8":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"9":{"0":{"0":{"0":{"0":{"0":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"2":{"0":{"3":{"8":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0008158270446665307},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}},"5":{"1":{"9":{"5":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0016316540893330613},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0015917230401910067}}}},"docs":{}},"docs":{}},"docs":{}},"6":{"0":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"5":{"5":{"7":{"4":{"0":{"3":{"7":{"8":{"0":{"6":{"9":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"docs":{}},"8":{"0":{"0":{"0":{"0":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"0":{"0":{"3":{"7":{"7":{"9":{"8":{"3":{"9":{"3":{"1":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"2":{"1":{"1":{"0":{"9":{"3":{"4":{"0":{"8":{"4":{"8":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"9":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"5":{"5":{"9":{"2":{"1":{"7":{"8":{"8":{"9":{"1":{"2":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"docs":{},".":{"6":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"4":{"5":{"6":{"8":{"9":{"1":{"4":{"9":{"5":{"2":{"8":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"9":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"4":{"5":{"6":{"8":{"9":{"1":{"3":{"4":{"2":{"2":{"0":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775},"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734},"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.0016233766233766235},"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html":{"ref":"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html","tf":0.024390243902439025},"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196},"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704},"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112},"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0022675736961451248},"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044},"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726},"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.006968641114982578}},".":{"0":{"docs":{},"]":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0016316540893330613},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0031834460803820135}}}},"1":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009},"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726},"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.007692307692307693},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},".":{"1":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}},"2":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}},"3":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}},"5":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}},"docs":{}},"节":{"docs":{},"中":{"docs":{},"的":{"docs":{},"例":{"docs":{},"子":{"docs":{},"为":{"docs":{},"通":{"docs":{},"过":{"docs":{},"反":{"docs":{},"射":{"docs":{},"自":{"docs":{},"动":{"docs":{},"推":{"docs":{},"断":{"docs":{},"s":{"docs":{},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},"m":{"docs":{},"a":{"docs":{},"，":{"docs":{},"适":{"docs":{},"合":{"docs":{},"静":{"docs":{},"态":{"docs":{},"数":{"docs":{},"据":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"2":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726},"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.007692307692307693},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},".":{"1":{"docs":{"day03_Hadoop/ha3.2.html":{"ref":"day03_Hadoop/ha3.2.html","tf":0.02040816326530612}}},"2":{"docs":{"day03_Hadoop/ha3.2.html":{"ref":"day03_Hadoop/ha3.2.html","tf":0.02040816326530612}}},"docs":{}}},"3":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},".":{"1":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}},"2":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}},"3":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}},"docs":{}}},"4":{"docs":{"day03_Hadoop/ha3.5.html":{"ref":"day03_Hadoop/ha3.5.html","tf":0.030303030303030304},"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}},".":{"1":{"4":{"docs":{},"/":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"d":{"docs":{},"i":{"docs":{},"r":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}},"docs":{}},"docs":{}}},"5":{"docs":{},".":{"0":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00851063829787234}}},"docs":{}},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"4":{"5":{"6":{"9":{"0":{"9":{"9":{"1":{"5":{"3":{"8":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"6":{"4":{"1":{"8":{"3":{"3":{"docs":{},"]":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0024469820554649264}}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"9":{"1":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}},"docs":{}},"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.006493506493506494},"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},"x":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}},"有":{"docs":{},"意":{"docs":{},"义":{"docs":{},"的":{"docs":{},"重":{"docs":{},"复":{"docs":{},"记":{"docs":{},"录":{"docs":{},"去":{"docs":{},"重":{"docs":{},"之":{"docs":{},"后":{"docs":{},"，":{"docs":{},"再":{"docs":{},"看":{"docs":{},"某":{"docs":{},"个":{"docs":{},"无":{"docs":{},"意":{"docs":{},"义":{"docs":{},"字":{"docs":{},"段":{"docs":{},"的":{"docs":{},"值":{"docs":{},"是":{"docs":{},"否":{"docs":{},"有":{"docs":{},"重":{"docs":{},"复":{"docs":{},"（":{"docs":{},"在":{"docs":{},"这":{"docs":{},"个":{"docs":{},"例":{"docs":{},"子":{"docs":{},"中":{"docs":{},"，":{"docs":{},"是":{"docs":{},"看":{"docs":{},"i":{"docs":{},"d":{"docs":{},"是":{"docs":{},"否":{"docs":{},"重":{"docs":{},"复":{"docs":{},"）":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},":":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}},"]":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208},"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.004545454545454545}},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}}},"：":{"docs":{},"算":{"docs":{},"法":{"docs":{},"实":{"docs":{},"现":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}},")":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548},"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}},")":{"docs":{},".":{"docs":{},"d":{"docs":{},"r":{"docs":{},"o":{"docs":{},"p":{"docs":{},"n":{"docs":{},"a":{"docs":{},"(":{"docs":{},")":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.011363636363636364}}},"]":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.004545454545454545}}}},",":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876},"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.01276595744680851},"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.01818181818181818},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}},"，":{"docs":{},"在":{"docs":{},"启":{"docs":{},"动":{"docs":{},"的":{"docs":{},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"t":{"docs":{},"a":{"docs":{},"i":{"docs":{},"n":{"docs":{},"e":{"docs":{},"r":{"docs":{},"中":{"docs":{},"创":{"docs":{},"建":{"docs":{},"a":{"docs":{},"p":{"docs":{},"p":{"docs":{},"l":{"docs":{},"i":{"docs":{},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"m":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"无":{"docs":{},"法":{"docs":{},"充":{"docs":{},"分":{"docs":{},"利":{"docs":{},"用":{"docs":{},"内":{"docs":{},"存":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}}}}}}},"e":{"docs":{},"x":{"docs":{},"p":{"docs":{},"l":{"docs":{},"o":{"docs":{},"d":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}}}},"对":{"docs":{},"数":{"docs":{},"据":{"docs":{},"对":{"docs":{},"象":{"docs":{},"进":{"docs":{},"行":{"docs":{},"t":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"s":{"docs":{},"f":{"docs":{},"o":{"docs":{},"r":{"docs":{},"m":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"s":{"docs":{},"操":{"docs":{},"作":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099}}}}}}}}}}}}}}}}}}}}}}}}}}},"、":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},"(":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}}},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}},"特":{"docs":{},"点":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}}}}}},"通":{"docs":{},"用":{"docs":{},"性":{"docs":{},"（":{"docs":{},"可":{"docs":{},"以":{"docs":{},"使":{"docs":{},"用":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}}}}}}}}}}}},"兼":{"docs":{},"容":{"docs":{},"h":{"docs":{},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}}}}}}}},"j":{"docs":{},"s":{"docs":{},"o":{"docs":{},"n":{"docs":{},"数":{"docs":{},"据":{"docs":{},"的":{"docs":{},"处":{"docs":{},"理":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}}}}}}},"删":{"docs":{},"除":{"docs":{},"缺":{"docs":{},"失":{"docs":{},"值":{"docs":{},"过":{"docs":{},"于":{"docs":{},"严":{"docs":{},"重":{"docs":{},"的":{"docs":{},"列":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}}}},"异":{"docs":{},"常":{"docs":{},"值":{"docs":{},"处":{"docs":{},"理":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}},"日":{"docs":{},"留":{"docs":{},"存":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}},"|":{"1":{"7":{"3":{"0":{"4":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"4":{"3":{"0":{"5":{"4":{"8":{"docs":{},"_":{"1":{"0":{"0":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00684931506849315},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.03732408729349378},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.019896538002387585}},"[":{"docs":{},"[":{"5":{"6":{"0":{"7":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}},"维":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}},"4":{"0":{"0":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.006711409395973154}}},"1":{"4":{"3":{"3":{"docs":{},",":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}},"|":{"docs":{},"[":{"1":{"2":{"0":{"0":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"0":{"docs":{},",":{"0":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{},"[":{"2":{"docs":{},".":{"6":{"9":{"0":{"1":{"8":{"5":{"3":{"0":{"8":{"4":{"9":{"5":{"3":{"2":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"6":{"4":{"4":{"5":{"0":{"1":{"1":{"3":{"3":{"1":{"4":{"2":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}}}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}},"docs":{}},"docs":{}},"docs":{}},"3":{"3":{"1":{"8":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}}},"docs":{}},"docs":{}},"docs":{}},"5":{"4":{"4":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"6":{"1":{"2":{"5":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"7":{"1":{"3":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"4":{"5":{"6":{"8":{"9":{"1":{"2":{"9":{"1":{"1":{"8":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"1":{"0":{"9":{"5":{"8":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"2":{"0":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"0":{"1":{"9":{"6":{"8":{"6":{"9":{"3":{"0":{"5":{"2":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"8":{"9":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"3":{"6":{"5":{"3":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"6":{"3":{"3":{"3":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"7":{"7":{"2":{"2":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"8":{"9":{"8":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"9":{"2":{"5":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"2":{"0":{"5":{"5":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"7":{"6":{"9":{"docs":{},"|":{"4":{"3":{"0":{"5":{"4":{"8":{"docs":{},"_":{"1":{"0":{"0":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"2":{"2":{"6":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"3":{"4":{"3":{"6":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}},"docs":{}},"docs":{}},"docs":{}},"5":{"1":{"docs":{"day03_Hadoop/ha2.1.html":{"ref":"day03_Hadoop/ha2.1.html","tf":0.0136986301369863}}},"docs":{}},"6":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"docs":{}},"7":{"5":{"7":{"9":{"docs":{},"|":{"4":{"3":{"0":{"5":{"4":{"8":{"docs":{},"_":{"1":{"0":{"0":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"8":{"4":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.002039567611666327},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0019896538002387586}}}},"9":{"5":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{},")":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.0045662100456621}}}},"3":{"0":{"0":{"2":{"3":{"docs":{},"|":{"docs":{},"[":{"3":{"4":{"docs":{},".":{"2":{"0":{"0":{"0":{"0":{"0":{"7":{"6":{"2":{"9":{"3":{"9":{"4":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}}}},"docs":{}},"docs":{}},"5":{"4":{"8":{"docs":{},"_":{"1":{"0":{"0":{"7":{"docs":{},"：":{"0":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"9":{"docs":{},"_":{"1":{"0":{"0":{"7":{"docs":{},"：":{"1":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"0":{"2":{"1":{"5":{"8":{"2":{"6":{"7":{"5":{"9":{"5":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"1":{"0":{"8":{"2":{"docs":{},"|":{"4":{"3":{"0":{"5":{"4":{"8":{"docs":{},"_":{"1":{"0":{"0":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"3":{"9":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"8":{"6":{"6":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0016316540893330613},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}},".":{"9":{"8":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"4":{"5":{"6":{"8":{"8":{"9":{"2":{"6":{"0":{"3":{"7":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}},"docs":{}}},"4":{"0":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"0":{"2":{"1":{"5":{"7":{"7":{"5":{"3":{"8":{"5":{"1":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"1":{"6":{"docs":{"day03_Hadoop/ha2.1.html":{"ref":"day03_Hadoop/ha2.1.html","tf":0.0136986301369863}}},"docs":{}},"2":{"5":{"1":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"3":{"2":{"9":{"5":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"5":{"9":{"1":{"4":{"docs":{},"|":{"docs":{},"[":{"9":{"docs":{},".":{"8":{"9":{"9":{"9":{"9":{"9":{"6":{"1":{"8":{"5":{"3":{"0":{"2":{"7":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}},"docs":{}},"docs":{}},"8":{"6":{"5":{"1":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0016316540893330613},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}},".":{"9":{"8":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"9":{"9":{"9":{"3":{"9":{"5":{"0":{"7":{"2":{"4":{"5":{"8":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}},"docs":{}}},"5":{"1":{"0":{"0":{"4":{"docs":{},"|":{"4":{"3":{"0":{"5":{"3":{"9":{"docs":{},"_":{"1":{"0":{"0":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"2":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}},"4":{"2":{"3":{"7":{"docs":{},"|":{"2":{"4":{"9":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"9":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"5":{"5":{"9":{"1":{"8":{"7":{"3":{"2":{"3":{"2":{"7":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"docs":{},")":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}},"'":{"docs":{},"m":{"docs":{},"'":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}},"6":{"0":{"5":{"6":{"1":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}},"docs":{}},"docs":{}},"docs":{}},"1":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"docs":{}},"2":{"3":{"9":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"3":{"1":{"docs":{"day03_Hadoop/ha2.1.html":{"ref":"day03_Hadoop/ha2.1.html","tf":0.0136986301369863}}},"docs":{},"|":{"docs":{},"[":{"docs":{},"[":{"1":{"6":{"1":{"0":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}},"7":{"5":{"1":{"2":{"docs":{},".":{"0":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"8":{"2":{"2":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"5":{"5":{"9":{"1":{"8":{"2":{"7":{"3":{"8":{"3":{"9":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"docs":{},"w":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}},"7":{"1":{"docs":{},"|":{"docs":{},"[":{"docs":{},"[":{"1":{"6":{"1":{"0":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}},"6":{"0":{"0":{"0":{"0":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"6":{"docs":{},"|":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"docs":{}},"7":{"0":{"docs":{"day03_Hadoop/ha2.1.html":{"ref":"day03_Hadoop/ha2.1.html","tf":0.0136986301369863}}},"docs":{}},"8":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"4":{"5":{"6":{"6":{"6":{"7":{"8":{"0":{"0":{"3":{"7":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"docs":{}},"8":{"2":{"4":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"5":{"7":{"4":{"9":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"9":{"4":{"2":{"2":{"4":{"docs":{},",":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}},"|":{"docs":{},"[":{"1":{"6":{"9":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"0":{"docs":{},",":{"0":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{},"[":{"2":{"docs":{},".":{"6":{"9":{"0":{"1":{"9":{"4":{"8":{"5":{"0":{"9":{"8":{"4":{"5":{"4":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"6":{"4":{"4":{"5":{"5":{"7":{"9":{"2":{"5":{"7":{"4":{"8":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}}}}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}}},"docs":{}},"docs":{}},"3":{"1":{"2":{"docs":{},"|":{"4":{"3":{"0":{"5":{"4":{"8":{"docs":{},"_":{"1":{"0":{"0":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"6":{"docs":{},"|":{"docs":{},"[":{"docs":{},"[":{"1":{"6":{"1":{"0":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}},"8":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"0":{"2":{"1":{"5":{"4":{"7":{"7":{"4":{"1":{"3":{"1":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"9":{"1":{"1":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"5":{"5":{"9":{"1":{"6":{"6":{"9":{"4":{"6":{"0":{"3":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0016316540893330613},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"0":{"4":{"2":{"1":{"9":{"5":{"1":{"6":{"9":{"5":{"7":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"2":{"1":{"1":{"2":{"7":{"2":{"1":{"7":{"4":{"5":{"9":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734},"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.0016233766233766235},"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html":{"ref":"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html","tf":0.024390243902439025},"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196},"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704},"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.003401360544217687},"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.006968641114982578}},".":{"0":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}},"]":{"docs":{},")":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}},".":{"docs":{},"t":{"docs":{},"o":{"docs":{},"a":{"docs":{},"r":{"docs":{},"r":{"docs":{},"a":{"docs":{},"y":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.001193792280143255}}}},"1":{"docs":{"day03_Hadoop/ha4.1.html":{"ref":"day03_Hadoop/ha4.1.html","tf":0.023809523809523808},"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}},"利":{"docs":{},"用":{"docs":{},"p":{"docs":{},"y":{"docs":{},"c":{"docs":{},"h":{"docs":{},"a":{"docs":{},"r":{"docs":{},"m":{"docs":{},"编":{"docs":{},"写":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}}}}}}}}}}}}}}}},"2":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704},"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{"day03_Hadoop/ha4.2.html":{"ref":"day03_Hadoop/ha4.2.html","tf":0.0196078431372549}}}}}},"3":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907},"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}},"4":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907},"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}},"5":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}},"6":{"3":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}},"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}},"8":{"5":{"2":{"3":{"2":{"8":{"3":{"docs":{},"|":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{},".":{"5":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00851063829787234}}},"docs":{}}},"9":{"7":{"9":{"1":{"9":{"5":{"docs":{},"|":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{},"对":{"docs":{},"于":{"docs":{},"i":{"docs":{},"d":{"docs":{},"这":{"docs":{},"种":{"docs":{},"无":{"docs":{},"意":{"docs":{},"义":{"docs":{},"的":{"docs":{},"列":{"docs":{},"重":{"docs":{},"复":{"docs":{},"，":{"docs":{},"添":{"docs":{},"加":{"docs":{},"另":{"docs":{},"外":{"docs":{},"一":{"docs":{},"列":{"docs":{},"自":{"docs":{},"增":{"docs":{},"i":{"docs":{},"d":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}}}}}}}}}}}}}}}}},"]":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.003430531732418525}},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683},"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102},"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.004545454545454545},"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}},")":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}},"]":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.004545454545454545}}}},":":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}},",":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.007782101167315175},"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.01276595744680851},"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.011363636363636364},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}},"）":{"docs":{},"验":{"docs":{},"证":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}},"，":{"docs":{},"a":{"docs":{},"p":{"docs":{},"p":{"docs":{},"l":{"docs":{},"i":{"docs":{},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"m":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"启":{"docs":{},"动":{"docs":{},"后":{"docs":{},"向":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"o":{"docs":{},"u":{"docs":{},"r":{"docs":{},"c":{"docs":{},"e":{"docs":{},"m":{"docs":{},"a":{"docs":{},"n":{"docs":{},"a":{"docs":{},"g":{"docs":{},"e":{"docs":{},"r":{"docs":{},"注":{"docs":{},"册":{"docs":{},"进":{"docs":{},"程":{"docs":{},",":{"docs":{},"申":{"docs":{},"请":{"docs":{},"资":{"docs":{},"源":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"不":{"docs":{},"适":{"docs":{},"合":{"docs":{},"迭":{"docs":{},"代":{"docs":{},"计":{"docs":{},"算":{"docs":{},"（":{"docs":{},"如":{"docs":{},"机":{"docs":{},"器":{"docs":{},"学":{"docs":{},"习":{"docs":{},"、":{"docs":{},"图":{"docs":{},"计":{"docs":{},"算":{"docs":{},"等":{"docs":{},"等":{"docs":{},"）":{"docs":{},"，":{"docs":{},"交":{"docs":{},"互":{"docs":{},"式":{"docs":{},"处":{"docs":{},"理":{"docs":{},"（":{"docs":{},"数":{"docs":{},"据":{"docs":{},"挖":{"docs":{},"掘":{"docs":{},"）":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"对":{"docs":{},"相":{"docs":{},"同":{"docs":{},"的":{"docs":{},"经":{"docs":{},"度":{"docs":{},"和":{"docs":{},"纬":{"docs":{},"度":{"docs":{},"做":{"docs":{},"累":{"docs":{},"计":{"docs":{},"求":{"docs":{},"和":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}}}}}}}}}}}},"输":{"docs":{},"出":{"docs":{},"结":{"docs":{},"果":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099}}}}}}},"、":{"docs":{},"兼":{"docs":{},"容":{"docs":{},"性":{"docs":{},"（":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"程":{"docs":{},"序":{"docs":{},"可":{"docs":{},"以":{"docs":{},"运":{"docs":{},"行":{"docs":{},"在":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"a":{"docs":{},"l":{"docs":{},"o":{"docs":{},"n":{"docs":{},"e":{"docs":{},"/":{"docs":{},"y":{"docs":{},"a":{"docs":{},"r":{"docs":{},"n":{"docs":{},"/":{"docs":{},"m":{"docs":{},"e":{"docs":{},"s":{"docs":{},"o":{"docs":{},"s":{"docs":{},"）":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"提":{"docs":{},"供":{"docs":{},"了":{"docs":{},"标":{"docs":{},"准":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"库":{"docs":{},"连":{"docs":{},"接":{"docs":{},"（":{"docs":{},"j":{"docs":{},"d":{"docs":{},"b":{"docs":{},"c":{"docs":{},"/":{"docs":{},"o":{"docs":{},"d":{"docs":{},"b":{"docs":{},"c":{"docs":{},"）":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}}}}}}}}}}}}}}}}}}}}}}}},"按":{"docs":{},"照":{"docs":{},"缺":{"docs":{},"失":{"docs":{},"值":{"docs":{},"删":{"docs":{},"除":{"docs":{},"行":{"docs":{},"（":{"docs":{},"t":{"docs":{},"h":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"l":{"docs":{},"d":{"docs":{},"是":{"docs":{},"根":{"docs":{},"据":{"docs":{},"一":{"docs":{},"行":{"docs":{},"记":{"docs":{},"录":{"docs":{},"中":{"docs":{},"，":{"docs":{},"缺":{"docs":{},"失":{"docs":{},"字":{"docs":{},"段":{"docs":{},"的":{"docs":{},"百":{"docs":{},"分":{"docs":{},"比":{"docs":{},"的":{"docs":{},"定":{"docs":{},"义":{"docs":{},"）":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"数":{"docs":{},"据":{"docs":{},"清":{"docs":{},"洗":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}},"个":{"docs":{},"分":{"docs":{},"区":{"docs":{},"）":{"docs":{},"如":{"docs":{},"未":{"docs":{},"指":{"docs":{},"定":{"docs":{},"分":{"docs":{},"区":{"docs":{},"数":{"docs":{},"量":{"docs":{},"，":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"会":{"docs":{},"自":{"docs":{},"动":{"docs":{},"设":{"docs":{},"置":{"docs":{},"分":{"docs":{},"区":{"docs":{},"数":{"docs":{},"。":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"]":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}},"f":{"docs":{},"i":{"docs":{},"r":{"docs":{},"s":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}}}},"|":{"1":{"3":{"8":{"8":{"3":{"3":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"4":{"3":{"0":{"5":{"4":{"8":{"docs":{},"_":{"1":{"0":{"0":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.031409341219661435},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.007560684440907282}}}},"5":{"0":{"0":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"0":{"2":{"1":{"5":{"4":{"6":{"7":{"1":{"3":{"8":{"2":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}},":":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"3":{"2":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}},"docs":{}},"9":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"0":{"2":{"1":{"5":{"4":{"2":{"0":{"9":{"0":{"1":{"2":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"docs":{},"%":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}},"1":{"3":{"9":{"4":{"2":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"8":{"8":{"8":{"3":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"9":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"4":{"5":{"6":{"6":{"4":{"6":{"8":{"7":{"9":{"9":{"5":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"docs":{}},"2":{"3":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0016316540893330613},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0010197838058331635}}}},"9":{"9":{"1":{"3":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"3":{"9":{"2":{"docs":{},"|":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"docs":{}},"docs":{}},"4":{"6":{"9":{"3":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{},")":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.0045662100456621}}}},"5":{"2":{"6":{"3":{"8":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"4":{"3":{"1":{"1":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0016316540893330613},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"6":{"0":{"8":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"0":{"1":{"8":{"9":{"2":{"2":{"4":{"5":{"1":{"4":{"5":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"docs":{}},"1":{"6":{"8":{"1":{"docs":{},"|":{"4":{"3":{"0":{"5":{"4":{"8":{"docs":{},"_":{"1":{"0":{"0":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"3":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"0":{"2":{"1":{"5":{"1":{"4":{"3":{"4":{"7":{"8":{"9":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"8":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"0":{"0":{"3":{"6":{"9":{"2":{"4":{"7":{"8":{"4":{"1":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"2":{"1":{"1":{"0":{"0":{"6":{"3":{"3":{"0":{"2":{"5":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"9":{"9":{"3":{"9":{"docs":{},",":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}},"|":{"docs":{},"[":{"1":{"8":{"8":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"0":{"docs":{},",":{"0":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{},"[":{"2":{"docs":{},".":{"6":{"9":{"0":{"1":{"9":{"4":{"6":{"7":{"5":{"1":{"2":{"8":{"7":{"7":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"6":{"4":{"4":{"5":{"5":{"6":{"8":{"7":{"9":{"1":{"3":{"8":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}}}}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"9":{"7":{"2":{"1":{"4":{"9":{"5":{"4":{"8":{"4":{"6":{"8":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"7":{"1":{"8":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"0":{"1":{"8":{"8":{"6":{"5":{"9":{"3":{"7":{"1":{"8":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"docs":{}},"5":{"6":{"3":{"3":{"docs":{},",":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}},"|":{"docs":{},"[":{"1":{"8":{"0":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"0":{"docs":{},",":{"0":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{},"[":{"2":{"docs":{},".":{"6":{"9":{"0":{"1":{"9":{"4":{"7":{"4":{"9":{"1":{"7":{"3":{"3":{"1":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"6":{"4":{"4":{"5":{"5":{"7":{"3":{"1":{"9":{"8":{"1":{"6":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}}}}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}}},"docs":{}},"docs":{}},"9":{"1":{"7":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}},"docs":{}},"docs":{}},"docs":{}},"6":{"2":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}},"docs":{}},"7":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0016316540893330613},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}},"docs":{}},"8":{"0":{"1":{"3":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}}},"docs":{}},"docs":{}},"2":{"2":{"3":{"5":{"docs":{},"|":{"4":{"3":{"0":{"5":{"4":{"8":{"docs":{},"_":{"1":{"0":{"0":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"3":{"2":{"1":{"5":{"docs":{},",":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}},"|":{"docs":{},"[":{"3":{"7":{"5":{"0":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"0":{"docs":{},",":{"0":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{},"[":{"2":{"docs":{},".":{"6":{"9":{"0":{"1":{"6":{"1":{"7":{"0":{"6":{"8":{"0":{"0":{"3":{"7":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"6":{"4":{"4":{"3":{"6":{"0":{"6":{"6":{"4":{"4":{"3":{"3":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}}}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}},"docs":{}},"docs":{}},"docs":{}},"8":{"6":{"6":{"4":{"docs":{},"|":{"4":{"3":{"0":{"5":{"4":{"8":{"docs":{},"_":{"1":{"0":{"0":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"8":{"8":{"8":{"8":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"9":{"7":{"2":{"1":{"4":{"9":{"4":{"4":{"5":{"2":{"3":{"8":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"4":{"0":{"2":{"1":{"1":{"2":{"6":{"7":{"5":{"6":{"4":{"5":{"8":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"3":{"1":{"4":{"1":{"7":{"3":{"0":{"7":{"6":{"8":{"7":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"9":{"0":{"9":{"6":{"5":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}},"docs":{}},"docs":{}},"3":{"0":{"0":{"1":{"docs":{},"|":{"4":{"3":{"0":{"5":{"4":{"8":{"docs":{},"_":{"1":{"0":{"0":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"5":{"3":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0016316540893330613},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0015917230401910067}}}},"docs":{}},"7":{"7":{"4":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"8":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"0":{"2":{"1":{"4":{"9":{"6":{"3":{"6":{"6":{"8":{"1":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"2":{"1":{"0":{"9":{"9":{"0":{"9":{"6":{"3":{"4":{"9":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"9":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"5":{"5":{"9":{"1":{"1":{"6":{"0":{"0":{"2":{"9":{"1":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.001193792280143255}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"docs":{},".":{"9":{"8":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"9":{"7":{"2":{"1":{"4":{"9":{"3":{"4":{"3":{"0":{"4":{"0":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"9":{"9":{"9":{"3":{"9":{"4":{"3":{"0":{"5":{"6":{"2":{"0":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196},"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.005226480836236934}},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416},"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.004545454545454545}},".":{"docs":{},".":{"docs":{},".":{"docs":{},"(":{"docs":{},"'":{"docs":{},"w":{"docs":{},"h":{"docs":{},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"'":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}}}}}}},"]":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.004545454545454545}}}},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208},"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938},"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.011363636363636364}}},"]":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.01276595744680851}},")":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}},"）":{"docs":{},"停":{"docs":{},"止":{"docs":{},"y":{"docs":{},"a":{"docs":{},"r":{"docs":{},"n":{"docs":{},"相":{"docs":{},"关":{"docs":{},"的":{"docs":{},"进":{"docs":{},"程":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}}}}}}}}}},"，":{"docs":{},"a":{"docs":{},"p":{"docs":{},"p":{"docs":{},"l":{"docs":{},"i":{"docs":{},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"m":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"申":{"docs":{},"请":{"docs":{},"到":{"docs":{},"资":{"docs":{},"源":{"docs":{},"后":{"docs":{},"，":{"docs":{},"向":{"docs":{},"对":{"docs":{},"应":{"docs":{},"的":{"docs":{},"n":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"m":{"docs":{},"a":{"docs":{},"n":{"docs":{},"a":{"docs":{},"g":{"docs":{},"e":{"docs":{},"r":{"docs":{},"申":{"docs":{},"请":{"docs":{},"启":{"docs":{},"动":{"docs":{},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"t":{"docs":{},"a":{"docs":{},"i":{"docs":{},"n":{"docs":{},"e":{"docs":{},"r":{"docs":{},",":{"docs":{},"将":{"docs":{},"要":{"docs":{},"执":{"docs":{},"行":{"docs":{},"的":{"docs":{},"程":{"docs":{},"序":{"docs":{},"分":{"docs":{},"发":{"docs":{},"到":{"docs":{},"n":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"m":{"docs":{},"a":{"docs":{},"n":{"docs":{},"a":{"docs":{},"g":{"docs":{},"e":{"docs":{},"r":{"docs":{},"上":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"不":{"docs":{},"适":{"docs":{},"合":{"docs":{},"流":{"docs":{},"式":{"docs":{},"处":{"docs":{},"理":{"docs":{},"（":{"docs":{},"点":{"docs":{},"击":{"docs":{},"日":{"docs":{},"志":{"docs":{},"分":{"docs":{},"析":{"docs":{},"）":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}}}}}}}}}}}}}},"开":{"docs":{},"始":{"docs":{},"和":{"docs":{},"停":{"docs":{},"止":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099}}}}}}}},".":{"0":{"7":{"7":{"6":{"5":{"9":{"6":{"docs":{},"|":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"1":{"2":{"6":{"9":{"4":{"docs":{},"|":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"docs":{}},"docs":{}},"docs":{}},"3":{"9":{"5":{"7":{"8":{"docs":{},"|":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"6":{"2":{"2":{"1":{"6":{"docs":{},"]":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0024469820554649264}}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"7":{"0":{"1":{"3":{"7":{"4":{"docs":{},"|":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}},"通":{"docs":{},"过":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"实":{"docs":{},"现":{"docs":{},"i":{"docs":{},"p":{"docs":{},"地":{"docs":{},"址":{"docs":{},"查":{"docs":{},"询":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}}}}}}}}}}}}},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.0045662100456621}}},"离":{"docs":{},"线":{"docs":{},"数":{"docs":{},"据":{"docs":{},"缓":{"docs":{},"存":{"docs":{},"之":{"docs":{},"离":{"docs":{},"线":{"docs":{},"召":{"docs":{},"回":{"docs":{},"集":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}},"2":{"4":{"5":{"2":{"6":{"1":{"docs":{},"|":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"9":{"9":{"2":{"3":{"2":{"5":{"docs":{},"|":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{"Hive&HBase/06_hbase数据模型.html":{"ref":"Hive&HBase/06_hbase数据模型.html","tf":0.020833333333333332},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.0091324200913242}}}},"3":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.0091324200913242}}}},"4":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00684931506849315}}}},"5":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.0045662100456621}}}},"6":{"8":{"0":{"4":{"4":{"6":{"6":{"docs":{},"|":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"8":{"2":{"0":{"0":{"5":{"docs":{},"|":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}},"7":{"docs":{},".":{"0":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}},"docs":{}},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.0045662100456621}}}},"8":{"3":{"8":{"0":{"0":{"9":{"docs":{},"|":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"9":{"0":{"5":{"1":{"8":{"8":{"6":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"1":{"5":{"5":{"6":{"3":{"6":{"docs":{},"|":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00684931506849315}}}},"docs":{},"*":{"docs":{},".":{"docs":{},"j":{"docs":{},"a":{"docs":{},"r":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}},"日":{"docs":{},"留":{"docs":{},"存":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}},"|":{"4":{"3":{"0":{"5":{"4":{"8":{"docs":{},"_":{"1":{"0":{"0":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.021211503161329796},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.005571030640668524}}},"、":{"docs":{},"填":{"docs":{},"充":{"docs":{},"缺":{"docs":{},"失":{"docs":{},"值":{"docs":{},"，":{"docs":{},"可":{"docs":{},"以":{"docs":{},"用":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"l":{"docs":{},"n":{"docs":{},"a":{"docs":{},"来":{"docs":{},"填":{"docs":{},"充":{"docs":{},"缺":{"docs":{},"失":{"docs":{},"值":{"docs":{},"，":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}}}}}}}}}}}}}}}}},"6":{"0":{"0":{"1":{"9":{"5":{"docs":{},"|":{"4":{"3":{"0":{"5":{"4":{"8":{"docs":{},"_":{"1":{"0":{"0":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"2":{"1":{"4":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"7":{"7":{"8":{"8":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"4":{"5":{"6":{"8":{"8":{"1":{"0":{"8":{"6":{"1":{"3":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"2":{"0":{"8":{"docs":{},".":{"6":{"docs":{},".":{"1":{"5":{"6":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}},"1":{"0":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}},"*":{"docs":{},"k":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}},"2":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.001835610850499694},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"3":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"8":{"9":{"6":{"5":{"docs":{},"|":{"4":{"3":{"0":{"5":{"4":{"8":{"docs":{},"_":{"1":{"0":{"0":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"2":{"1":{"1":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0016316540893330613},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}},"3":{"5":{"3":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}},"docs":{}},"docs":{}},"4":{"5":{"0":{"4":{"docs":{},"|":{"4":{"3":{"0":{"5":{"3":{"9":{"docs":{},"_":{"1":{"0":{"0":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"5":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.004893964110929853}}}},"docs":{}},"6":{"1":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0012237405669997961},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.001193792280143255},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"docs":{}},"9":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"5":{"5":{"9":{"1":{"0":{"0":{"7":{"1":{"9":{"9":{"6":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"docs":{}},"3":{"0":{"6":{"docs":{},"|":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"docs":{}},"1":{"2":{"0":{"4":{"docs":{},"|":{"docs":{},"[":{"1":{"9":{"8":{"8":{"8":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"0":{"docs":{},",":{"0":{"docs":{},".":{"0":{"docs":{},",":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{},"[":{"2":{"docs":{},".":{"6":{"9":{"0":{"0":{"1":{"2":{"3":{"4":{"0":{"4":{"6":{"5":{"7":{"8":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"6":{"4":{"3":{"4":{"7":{"1":{"6":{"2":{"3":{"1":{"8":{"9":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}}}}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}},"docs":{}},"docs":{}},"3":{"3":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}},"docs":{}},"5":{"5":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0016316540893330613},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}},"7":{"9":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}},"docs":{}},"9":{"7":{"9":{"4":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"4":{"0":{"6":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0008158270446665307},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}},"docs":{}},"docs":{}},"5":{"2":{"7":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}},"docs":{}},"7":{"2":{"6":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"8":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"7":{"2":{"2":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"6":{"0":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"0":{"2":{"1":{"4":{"6":{"4":{"5":{"1":{"4":{"6":{"0":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"3":{"6":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"7":{"0":{"3":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"2":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"0":{"2":{"1":{"4":{"5":{"8":{"3":{"4":{"9":{"6":{"5":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"5":{"5":{"8":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"6":{"7":{"4":{"docs":{},"|":{"4":{"3":{"0":{"5":{"3":{"9":{"docs":{},"_":{"1":{"0":{"0":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"6":{"6":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289}}},"7":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289}}},"8":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289}}},"9":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289}}},"docs":{}},"docs":{}},"8":{"2":{"3":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0016316540893330613},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}},"4":{"0":{"2":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}},"docs":{}},"docs":{}},"5":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981}}}},"7":{"8":{"5":{"4":{"docs":{},"|":{"4":{"3":{"0":{"5":{"4":{"8":{"docs":{},"_":{"1":{"0":{"0":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"8":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"9":{"9":{"9":{"3":{"6":{"2":{"0":{"2":{"3":{"3":{"2":{"3":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"9":{"9":{"9":{"3":{"9":{"3":{"8":{"8":{"9":{"3":{"0":{"8":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"4":{"0":{"3":{"1":{"4":{"1":{"6":{"7":{"9":{"6":{"2":{"8":{"9":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"4":{"5":{"6":{"8":{"7":{"7":{"0":{"0":{"4":{"1":{"2":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"9":{"2":{"6":{"7":{"2":{"docs":{},"|":{"docs":{},"[":{"4":{"7":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"0":{"docs":{},",":{"0":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"0":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}},"docs":{}}}},"docs":{}},"docs":{}},"docs":{}},"7":{"2":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0016316540893330613},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}},"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"4":{"5":{"6":{"8":{"7":{"6":{"4":{"9":{"3":{"8":{"6":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289}},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}},"]":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416},"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}},"]":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208},"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}},")":{"docs":{},".":{"docs":{},"t":{"docs":{},"a":{"docs":{},"k":{"docs":{},"e":{"docs":{},"(":{"1":{"0":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}},"docs":{}},"2":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}},"docs":{}}}}}}}}},",":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938},"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.004545454545454545}}},"，":{"docs":{},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"t":{"docs":{},"a":{"docs":{},"i":{"docs":{},"n":{"docs":{},"e":{"docs":{},"r":{"docs":{},"启":{"docs":{},"动":{"docs":{},"后":{"docs":{},"，":{"docs":{},"执":{"docs":{},"行":{"docs":{},"对":{"docs":{},"应":{"docs":{},"的":{"docs":{},"任":{"docs":{},"务":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}}}}}}}}}}}}}}}}}},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{},"e":{"docs":{},"编":{"docs":{},"程":{"docs":{},"不":{"docs":{},"够":{"docs":{},"灵":{"docs":{},"活":{"docs":{},"，":{"docs":{},"仅":{"docs":{},"支":{"docs":{},"持":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"和":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{},"e":{"docs":{},"两":{"docs":{},"种":{"docs":{},"操":{"docs":{},"作":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},".":{"0":{"6":{"2":{"3":{"2":{"3":{"6":{"docs":{},"|":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"1":{"0":{"0":{"0":{"0":{"0":{"0":{"0":{"0":{"0":{"0":{"0":{"0":{"0":{"0":{"5":{"docs":{},"]":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}},")":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.006711409395973154}}}},"2":{"1":{"4":{"5":{"0":{"9":{"5":{"docs":{},"|":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"6":{"8":{"3":{"5":{"2":{"8":{"4":{"docs":{},"|":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"7":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"9":{"9":{"9":{"3":{"9":{"7":{"0":{"3":{"9":{"9":{"2":{"0":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"8":{"8":{"6":{"9":{"8":{"7":{"docs":{},"]":{"docs":{},",":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0024469820554649264}}}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"|":{"2":{"0":{"7":{"8":{"0":{"0":{"docs":{},"|":{"1":{"9":{"9":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0038751784621660207},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0015917230401910067}}}},"7":{"0":{"2":{"0":{"6":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0016316540893330613},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0015917230401910067}}}},"docs":{}},"docs":{}},"3":{"2":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}}},"docs":{}},"4":{"3":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"8":{"9":{"4":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"1":{"8":{"5":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"2":{"0":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}},"1":{"1":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"3":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"4":{"docs":{},"|":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"docs":{}},"2":{"7":{"3":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"3":{"2":{"6":{"8":{"1":{"3":{"4":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0024469820554649264}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"6":{"6":{"docs":{},"|":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"docs":{}},"7":{"0":{"docs":{},"|":{"2":{"7":{"4":{"7":{"9":{"5":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.00897226753670473}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.00897226753670473},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"8":{"1":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"8":{"2":{"docs":{},"|":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"docs":{}},"docs":{},".":{"5":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"4":{"5":{"6":{"8":{"7":{"4":{"7":{"0":{"7":{"9":{"8":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"3":{"1":{"docs":{},",":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"5":{"2":{"2":{"0":{"docs":{},"|":{"4":{"3":{"0":{"5":{"4":{"8":{"docs":{},"_":{"1":{"0":{"0":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"7":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"5":{"5":{"9":{"0":{"4":{"5":{"7":{"0":{"1":{"3":{"3":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"9":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981}}}},"docs":{}},"4":{"2":{"7":{"4":{"1":{"docs":{},"|":{"4":{"3":{"0":{"5":{"4":{"8":{"docs":{},"_":{"1":{"0":{"0":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"5":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}}},"6":{"1":{"7":{"8":{"docs":{},"|":{"docs":{},"[":{"1":{"6":{"docs":{},".":{"7":{"9":{"9":{"9":{"9":{"9":{"2":{"3":{"7":{"0":{"6":{"0":{"5":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"5":{"6":{"6":{"6":{"5":{"docs":{},"|":{"4":{"3":{"0":{"5":{"4":{"8":{"docs":{},"_":{"1":{"0":{"0":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"9":{"9":{"9":{"3":{"9":{"3":{"5":{"2":{"9":{"5":{"3":{"2":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"6":{"0":{"0":{"0":{"docs":{},")":{"docs":{},",":{"docs":{},"]":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}},"docs":{}},"docs":{}},"3":{"0":{"2":{"7":{"docs":{},"|":{"docs":{},"[":{"6":{"8":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"0":{"docs":{},",":{"0":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"0":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}},"docs":{}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"7":{"7":{"9":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.030612244897959183}},",":{"1":{"0":{"3":{"docs":{},",":{"2":{"0":{"1":{"8":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"4":{"docs":{},",":{"2":{"0":{"1":{"8":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"4":{"5":{"6":{"8":{"7":{"2":{"4":{"1":{"1":{"8":{"5":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"8":{"2":{"0":{"3":{"8":{"docs":{},"|":{"4":{"3":{"0":{"5":{"4":{"8":{"docs":{},"_":{"1":{"0":{"0":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"8":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"5":{"5":{"9":{"0":{"1":{"9":{"7":{"2":{"0":{"2":{"9":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"9":{"7":{"2":{"1":{"4":{"8":{"4":{"1":{"2":{"9":{"3":{"7":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"9":{"4":{"8":{"9":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}},"docs":{}},"docs":{}},"7":{"9":{"8":{"2":{"docs":{},"|":{"docs":{},"[":{"3":{"3":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"0":{"docs":{},",":{"0":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"0":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}},"docs":{}}}},"docs":{}},"docs":{}},"docs":{}},"9":{"7":{"1":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}}},"docs":{}},"docs":{}},"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"9":{"9":{"9":{"3":{"9":{"3":{"3":{"2":{"3":{"9":{"4":{"5":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"4":{"0":{"4":{"5":{"6":{"8":{"7":{"1":{"3":{"9":{"1":{"3":{"4":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"8":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"4":{"5":{"6":{"8":{"7":{"0":{"9":{"8":{"3":{"1":{"4":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}},",":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876},"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.004545454545454545}}},"，":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"执":{"docs":{},"行":{"docs":{},"完":{"docs":{},"毕":{"docs":{},"之":{"docs":{},"后":{"docs":{},"，":{"docs":{},"向":{"docs":{},"a":{"docs":{},"p":{"docs":{},"p":{"docs":{},"l":{"docs":{},"i":{"docs":{},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"m":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"返":{"docs":{},"回":{"docs":{},"结":{"docs":{},"果":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"日":{"docs":{},"留":{"docs":{},"存":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}},")":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.004545454545454545}}}},"|":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}},".":{"1":{"4":{"9":{"5":{"3":{"8":{"docs":{},"|":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"3":{"3":{"9":{"5":{"4":{"2":{"4":{"docs":{},"|":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"4":{"7":{"6":{"2":{"9":{"1":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"9":{"6":{"6":{"4":{"docs":{},"|":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"7":{"5":{"2":{"7":{"1":{"9":{"docs":{},"]":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0024469820554649264}}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"8":{"0":{"5":{"4":{"8":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"7":{"1":{"docs":{},"|":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"docs":{}},"docs":{}},"1":{"5":{"2":{"1":{"9":{"docs":{},"|":{"docs":{},"[":{"2":{"docs":{},".":{"4":{"0":{"0":{"0":{"0":{"0":{"0":{"9":{"5":{"3":{"6":{"7":{"4":{"3":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}},"docs":{}},"3":{"1":{"2":{"docs":{},"|":{"docs":{},"[":{"2":{"docs":{},".":{"2":{"9":{"9":{"9":{"9":{"9":{"9":{"5":{"2":{"3":{"1":{"6":{"2":{"8":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}},"docs":{}},"docs":{}},"6":{"9":{"9":{"9":{"docs":{},"|":{"docs":{},"[":{"5":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"0":{"docs":{},",":{"0":{"docs":{},".":{"0":{"docs":{},",":{"1":{"docs":{},".":{"0":{"docs":{},",":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}}},"docs":{}},"docs":{}},"docs":{}},"7":{"5":{"6":{"9":{"docs":{},"|":{"4":{"3":{"0":{"5":{"4":{"8":{"docs":{},"_":{"1":{"0":{"0":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"8":{"6":{"8":{"1":{"docs":{},".":{"0":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"2":{"0":{"0":{"1":{"8":{"docs":{},"|":{"4":{"3":{"0":{"5":{"4":{"8":{"docs":{},"_":{"1":{"0":{"0":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"3":{"7":{"docs":{},"|":{"3":{"0":{"1":{"2":{"9":{"9":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}},"docs":{}},"docs":{}},"3":{"2":{"3":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0016316540893330613},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0015917230401910067}}}},"docs":{}},"docs":{}},"3":{"docs":{},"|":{"docs":{},"[":{"docs":{},"[":{"5":{"6":{"0":{"7":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}},"8":{"9":{"5":{"3":{"docs":{},".":{"0":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"9":{"4":{"8":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}}},"docs":{}},"docs":{}},"docs":{}},"4":{"0":{"1":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}}},"docs":{}},"3":{"4":{"5":{"6":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}},"docs":{}},"docs":{}},"docs":{}},"5":{"3":{"3":{"7":{"docs":{},".":{"0":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"6":{"7":{"2":{"8":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}},"9":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}},"docs":{}},"docs":{}},"8":{"1":{"0":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}},"1":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"5":{"3":{"7":{"3":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"6":{"2":{"4":{"3":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}}},"docs":{}},"docs":{}},"5":{"5":{"docs":{},"|":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"docs":{}},"docs":{}},"7":{"3":{"3":{"1":{"docs":{},"|":{"1":{"9":{"9":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"docs":{}},"8":{"8":{"8":{"8":{"8":{"8":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"4":{"5":{"2":{"3":{"7":{"6":{"4":{"2":{"0":{"3":{"0":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"5":{"5":{"8":{"9":{"6":{"8":{"7":{"7":{"7":{"0":{"5":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"9":{"7":{"5":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"3":{"9":{"9":{"9":{"3":{"9":{"2":{"8":{"6":{"1":{"3":{"7":{"5":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"9":{"8":{"3":{"1":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{},".":{"9":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}}},"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.01680672268907563},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.013937282229965157}},"》":{"docs":{},"\"":{"docs":{},"等":{"docs":{},"，":{"docs":{},"我":{"docs":{},"们":{"docs":{},"是":{"docs":{},"不":{"docs":{},"是":{"docs":{},"就":{"docs":{},"可":{"docs":{},"以":{"docs":{},"分":{"docs":{},"析":{"docs":{},"出":{"docs":{},"该":{"docs":{},"用":{"docs":{},"户":{"docs":{},"的":{"docs":{},"一":{"docs":{},"些":{"docs":{},"兴":{"docs":{},"趣":{"docs":{},"特":{"docs":{},"征":{"docs":{},"如":{"docs":{},"：":{"docs":{},"\"":{"docs":{},"爱":{"docs":{},"国":{"docs":{},"\"":{"docs":{},"、":{"docs":{},"\"":{"docs":{},"战":{"docs":{},"争":{"docs":{},"\"":{"docs":{},"、":{"docs":{},"\"":{"docs":{},"赛":{"docs":{},"车":{"docs":{},"\"":{"docs":{},"、":{"docs":{},"\"":{"docs":{},"动":{"docs":{},"作":{"docs":{},"\"":{"docs":{},"、":{"docs":{},"\"":{"docs":{},"军":{"docs":{},"事":{"docs":{},"\"":{"docs":{},"、":{"docs":{},"\"":{"docs":{},"吴":{"docs":{},"京":{"docs":{},"\"":{"docs":{},"、":{"docs":{},"\"":{"docs":{},"韩":{"docs":{},"三":{"docs":{},"平":{"docs":{},"\"":{"docs":{},"等":{"docs":{},"标":{"docs":{},"签":{"docs":{},"。":{"docs":{"day02_推荐算法/07_基于内容的推荐算法.html":{"ref":"day02_推荐算法/07_基于内容的推荐算法.html","tf":0.03125}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},",":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938},"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.006818181818181818}}},"，":{"docs":{},"a":{"docs":{},"p":{"docs":{},"p":{"docs":{},"l":{"docs":{},"i":{"docs":{},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"m":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"向":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"o":{"docs":{},"u":{"docs":{},"r":{"docs":{},"c":{"docs":{},"e":{"docs":{},"m":{"docs":{},"a":{"docs":{},"n":{"docs":{},"a":{"docs":{},"g":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},")":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}},"位":{"2":{"docs":{},"进":{"docs":{},"制":{"docs":{},"数":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}},"docs":{}},".":{"3":{"5":{"3":{"4":{"7":{"3":{"docs":{},"]":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0024469820554649264}}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"4":{"6":{"6":{"4":{"5":{"3":{"docs":{},"]":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0024469820554649264}}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"|":{"4":{"3":{"0":{"5":{"4":{"8":{"docs":{},"_":{"1":{"0":{"0":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.005302875790332449},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.001193792280143255},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.03484320557491289},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.08456659619450317}}}},"9":{"0":{"1":{"4":{"1":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}}},"docs":{}},"docs":{}},"3":{"5":{"1":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"8":{"6":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{},")":{"docs":{},".":{"docs":{},"t":{"docs":{},"a":{"docs":{},"k":{"docs":{},"e":{"docs":{},"(":{"3":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}},"docs":{}}}}}}}}},"1":{"2":{"8":{"6":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.004893964110929853}}}},"docs":{}},"docs":{}},"5":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"2":{"1":{"0":{"8":{"2":{"8":{"5":{"8":{"7":{"8":{"4":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"docs":{}},"2":{"2":{"4":{"1":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"5":{"6":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}},"docs":{}},"9":{"3":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0016316540893330613},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}},"docs":{},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}},"3":{"2":{"docs":{},"|":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"docs":{},"]":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}},".":{"0":{"docs":{},"]":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}},"docs":{}}},"5":{"1":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0016316540893330613},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}},"4":{"7":{"1":{"docs":{},"|":{"1":{"7":{"0":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"6":{"0":{"0":{"0":{"0":{"0":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"7":{"0":{"0":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}},"*":{"docs":{},"k":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}},"docs":{}},"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}},"8":{"9":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"0":{"2":{"1":{"2":{"9":{"5":{"4":{"9":{"2":{"1":{"1":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"4":{"5":{"6":{"8":{"6":{"1":{"6":{"9":{"6":{"5":{"5":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"9":{"0":{"0":{"0":{"0":{"0":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"7":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"8":{"1":{"5":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}},"docs":{}},"docs":{},".":{"0":{"docs":{},"|":{"docs":{},"[":{"0":{"docs":{},".":{"9":{"4":{"0":{"5":{"5":{"8":{"9":{"1":{"2":{"7":{"3":{"9":{"4":{"3":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"9":{"4":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}}},"5":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}}},"6":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}}},"9":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099}},")":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.007633587786259542}}}},"docs":{}},"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.021541950113378686}},",":{"1":{"0":{"2":{"docs":{},",":{"2":{"0":{"1":{"8":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"5":{"docs":{},",":{"2":{"0":{"1":{"8":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},")":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}},"|":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}},".":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0008158270446665307},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}},"[":{"0":{"docs":{},".":{"9":{"3":{"9":{"9":{"9":{"3":{"9":{"2":{"2":{"9":{"6":{"0":{"1":{"2":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"4":{"0":{"4":{"5":{"6":{"8":{"6":{"1":{"1":{"8":{"6":{"3":{"0":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"docs":{},",":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876},"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.004545454545454545}}},"]":{"docs":{},",":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}},")":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}},"]":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}},".":{"0":{"6":{"5":{"4":{"8":{"2":{"docs":{},"]":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0024469820554649264}}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"2":{"5":{"0":{"8":{"1":{"8":{"docs":{},"]":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0024469820554649264}}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"9":{"1":{"7":{"0":{"8":{"4":{"docs":{},"|":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"|":{"1":{"8":{"6":{"8":{"4":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"4":{"3":{"0":{"5":{"3":{"9":{"docs":{},"_":{"1":{"0":{"0":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"4":{"8":{"docs":{},"_":{"1":{"0":{"0":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0012237405669997961},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0015917230401910067}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.002039567611666327},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.001193792280143255}}}},"docs":{},"c":{"0":{"1":{"docs":{},",":{"docs":{},"n":{"0":{"1":{"0":{"1":{"docs":{},",":{"8":{"2":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}},"docs":{}},"docs":{}}},"2":{"docs":{},",":{"5":{"9":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}},"docs":{}},"docs":{}}},"3":{"docs":{},",":{"6":{"5":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}},"2":{"docs":{},",":{"docs":{},"n":{"0":{"2":{"0":{"1":{"docs":{},",":{"8":{"1":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}},"docs":{}},"docs":{}}},"2":{"docs":{},",":{"8":{"2":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}},"docs":{}},"docs":{}}},"3":{"docs":{},",":{"7":{"9":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}},"3":{"docs":{},",":{"docs":{},"n":{"0":{"3":{"0":{"1":{"docs":{},",":{"5":{"6":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}},"docs":{}},"docs":{}}},"2":{"docs":{},",":{"9":{"2":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}},"docs":{}},"docs":{}}},"6":{"docs":{},",":{"7":{"2":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}},"docs":{}},"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208},"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428},"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196},"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.02054794520547945}},"o":{"docs":{},"r":{"docs":{},"e":{"docs":{"./":{"ref":"./","tf":0.011235955056179775},"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.017857142857142856},"day03_Hadoop/ha4.1.html":{"ref":"day03_Hadoop/ha4.1.html","tf":0.023809523809523808},"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726},"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}},"项":{"docs":{},"目":{"docs":{},"更":{"docs":{},"名":{"docs":{},"为":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}}}}}}}}}},"实":{"docs":{},"战":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}},"案":{"docs":{},"例":{"docs":{},"_":{"docs":{},"p":{"docs":{},"v":{"docs":{},"&":{"docs":{},"u":{"docs":{},"v":{"docs":{},"统":{"docs":{},"计":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":5}}}}}}}}}}}},"_":{"docs":{},"i":{"docs":{},"p":{"docs":{},"统":{"docs":{},"计":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":5}}}}}}}}},"编":{"docs":{},"写":{"docs":{},"的":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"，":{"docs":{},"不":{"docs":{},"同":{"docs":{},"的":{"docs":{},"语":{"docs":{},"言":{"docs":{},"生":{"docs":{},"成":{"docs":{},"不":{"docs":{},"同":{"docs":{},"的":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}}}}}}}}}}}}}}}}}}}}}},"和":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}}}}}}},"p":{"docs":{},"u":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876}}}}},"l":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00684931506849315}},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147},"Hive&HBase/06_hbase数据模型.html":{"ref":"Hive&HBase/06_hbase数据模型.html","tf":0.041666666666666664},"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.00881057268722467},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289}},"s":{"docs":{},"=":{"docs":{},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"m":{"docs":{},"s":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.00847457627118644}}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"s":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147}}}}}}}},"[":{"docs":{},"\"":{"docs":{},"m":{"docs":{},"o":{"docs":{},"v":{"docs":{},"i":{"docs":{},"e":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},"]":{"docs":{},",":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"s":{"docs":{},"=":{"docs":{},"\"":{"docs":{},"r":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"\"":{"docs":{},")":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}}}}}}}}}}}}}}}}}}}},",":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876}}}}}}}}}}},"u":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}}}}}}}},"+":{"docs":{},"c":{"docs":{},"e":{"docs":{},"l":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.006607929515418502}}}}}},"=":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"_":{"docs":{},"i":{"docs":{},"n":{"docs":{},"f":{"docs":{},"o":{"docs":{},":":{"docs":{},"a":{"docs":{},"d":{"docs":{},"d":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"s":{"docs":{},",":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.011013215859030838}}}}}}}}}},"b":{"docs":{},"i":{"docs":{},"r":{"docs":{},"t":{"docs":{},"h":{"docs":{},"d":{"docs":{},"a":{"docs":{},"y":{"docs":{},",":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.011013215859030838}}}}}}}}}}},"s":{"docs":{},"e":{"docs":{},"x":{"docs":{},",":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.011013215859030838}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},",":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.006607929515418502}}}}}}}}}}}}}}}}}}}}}}}}},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.010526315789473684},"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0022675736961451248},"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"s":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"(":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{},"e":{"docs":{},"(":{"docs":{},"l":{"docs":{},"a":{"docs":{},"m":{"docs":{},"b":{"docs":{},"d":{"docs":{},"a":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}}}}}}}}}}}}}}}}}}}}}},"方":{"docs":{},"式":{"docs":{},"创":{"docs":{},"建":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}}}}}}}},"_":{"docs":{},"l":{"docs":{},"i":{"docs":{},"s":{"docs":{},"t":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}},"不":{"docs":{},"去":{"docs":{},"重":{"docs":{},"而":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"_":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"去":{"docs":{},"重":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}}}}}}}}}}}}}}}}}}},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}},"/":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"_":{"docs":{},"l":{"docs":{},"i":{"docs":{},"s":{"docs":{},"t":{"docs":{},"作":{"docs":{},"用":{"docs":{},":":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}}}}}}}}}}}}}}}}}},"会":{"docs":{},"将":{"docs":{},"所":{"docs":{},"有":{"docs":{},"数":{"docs":{},"据":{"docs":{},"加":{"docs":{},"载":{"docs":{},"到":{"docs":{},"内":{"docs":{},"存":{"docs":{},"，":{"docs":{},"慎":{"docs":{},"用":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}},"把":{"docs":{},"计":{"docs":{},"算":{"docs":{},"结":{"docs":{},"果":{"docs":{},"全":{"docs":{},"部":{"docs":{},"加":{"docs":{},"载":{"docs":{},"到":{"docs":{},"内":{"docs":{},"存":{"docs":{},"，":{"docs":{},"谨":{"docs":{},"慎":{"docs":{},"使":{"docs":{},"用":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}},"s":{"docs":{},":":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}},"m":{"docs":{},"m":{"docs":{},"e":{"docs":{},"n":{"docs":{},"t":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}},"o":{"docs":{},"n":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282}},";":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}},":":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282}}}}}},"b":{"docs":{},"i":{"docs":{},"n":{"docs":{},"e":{"docs":{},"r":{"docs":{},"(":{"docs":{},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"f":{"docs":{},",":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}},"=":{"docs":{},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"f":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"m":{"docs":{},"b":{"docs":{},"i":{"docs":{},"n":{"docs":{},"e":{"docs":{},"r":{"docs":{},",":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}}}}}}}}}}}}}}}},".":{"docs":{},"m":{"docs":{},"y":{"docs":{},"s":{"docs":{},"q":{"docs":{},"l":{"docs":{},".":{"docs":{},"j":{"docs":{},"d":{"docs":{},"b":{"docs":{},"c":{"docs":{},".":{"docs":{},"d":{"docs":{},"r":{"docs":{},"i":{"docs":{},"v":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}}}}}}}}},"p":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"s":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}},"u":{"docs":{},"t":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}},"s":{"docs":{},"t":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.003795066413662239},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.004629629629629629}},"=":{"docs":{},"\\":{"docs":{},"s":{"docs":{},"u":{"docs":{},"m":{"docs":{},"_":{"docs":{},"{":{"docs":{},"u":{"docs":{},",":{"docs":{},"i":{"docs":{},"\\":{"docs":{},"i":{"docs":{},"n":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}}}}},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009},"day03_Hadoop/ha3.2.html":{"ref":"day03_Hadoop/ha3.2.html","tf":0.02040816326530612},"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704},"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676},"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726},"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0032626427406199023}},"e":{"docs":{},"r":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.010526315789473684}},"(":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"s":{"docs":{},")":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}}}}}}},".":{"docs":{},"m":{"docs":{},"o":{"docs":{},"s":{"docs":{},"t":{"docs":{},"_":{"docs":{},"c":{"docs":{},"o":{"docs":{},"m":{"docs":{},"m":{"docs":{},"o":{"docs":{},"n":{"docs":{},"(":{"5":{"0":{"docs":{},")":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}},"docs":{}},"docs":{}}}}}}}}}}}}}}}},"s":{"docs":{},")":{"docs":{},":":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.017543859649122806}}}},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}}}}}}}},"p":{"docs":{},"p":{"docs":{},"r":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}}}},")":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}},")":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}},"案":{"docs":{},"例":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}},"(":{"docs":{},")":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}},"=":{"1":{"0":{"0":{"8":{"5":{"0":{"6":{"3":{"docs":{},")":{"docs":{},"]":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"3":{"6":{"6":{"0":{"5":{"6":{"docs":{},")":{"docs":{},"]":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"5":{"9":{"4":{"6":{"0":{"3":{"3":{"docs":{},")":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"6":{"4":{"7":{"2":{"8":{"9":{"8":{"docs":{},")":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"2":{"5":{"1":{"9":{"1":{"9":{"0":{"5":{"docs":{},")":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"6":{"8":{"8":{"9":{"0":{"4":{"3":{"4":{"5":{"docs":{},")":{"docs":{},"]":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"9":{"1":{"1":{"5":{"9":{"1":{"9":{"docs":{},")":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"3":{"0":{"1":{"8":{"3":{"7":{"docs":{},")":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}},"d":{"docs":{},"i":{"docs":{},"s":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}},"r":{"docs":{},"y":{"docs":{},"_":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.015267175572519083}}}}}}}},">":{"1":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}},"docs":{}},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}}}}},"n":{"docs":{},"f":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}},"i":{"docs":{},"g":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}},"u":{"docs":{},"r":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428}},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"节":{"docs":{},"点":{"docs":{},"中":{"docs":{},"添":{"docs":{},"加":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428}}}}}}}}}}}}}},"对":{"docs":{},"象":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}},"/":{"docs":{},"h":{"docs":{},"i":{"docs":{},"v":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}},"目":{"docs":{},"录":{"docs":{},",":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}},".":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"a":{"docs":{},"l":{"docs":{},"l":{"docs":{},"(":{"docs":{},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"f":{"docs":{},"i":{"docs":{},"g":{"docs":{},")":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}},"t":{"docs":{},"a":{"docs":{},"i":{"docs":{},"n":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}},"r":{"docs":{},"i":{"docs":{},"b":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.020080321285140562}}}},"o":{"docs":{},"l":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}},"e":{"docs":{},"n":{"docs":{},"t":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}},"/":{"docs":{},"u":{"docs":{},"p":{"docs":{},"l":{"docs":{},"o":{"docs":{},"a":{"docs":{},"d":{"docs":{},"s":{"docs":{},"/":{"2":{"0":{"1":{"3":{"docs":{},"/":{"0":{"7":{"docs":{},"/":{"docs":{},"r":{"docs":{},"c":{"docs":{},"a":{"docs":{},"s":{"docs":{},"s":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"r":{"docs":{},"a":{"docs":{},".":{"docs":{},"p":{"docs":{},"n":{"docs":{},"g":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}}}}}}}}}}}},"s":{"docs":{},"t":{"docs":{},"u":{"docs":{},"d":{"docs":{},"i":{"docs":{},"o":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}}}}}}}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}},"x":{"docs":{},"t":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.03125}},"上":{"docs":{},"调":{"docs":{},"用":{"docs":{},"s":{"docs":{},"t":{"docs":{},"o":{"docs":{},"p":{"docs":{},"方":{"docs":{},"法":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}}}}}}}}}},"处":{"docs":{},"于":{"docs":{},"活":{"docs":{},"跃":{"docs":{},"状":{"docs":{},"态":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}}}}}}},"对":{"docs":{},"象":{"docs":{},"(":{"docs":{},"不":{"docs":{},"关":{"docs":{},"闭":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"t":{"docs":{},"e":{"docs":{},"x":{"docs":{},"t":{"docs":{},"前":{"docs":{},"提":{"docs":{},"下":{"docs":{},")":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}}}}}}}}}}}}}}}}}}}}},",":{"docs":{},"设":{"docs":{},"置":{"docs":{},"s":{"docs":{},"t":{"docs":{},"o":{"docs":{},"p":{"docs":{},"(":{"docs":{},")":{"docs":{},"的":{"docs":{},"可":{"docs":{},"选":{"docs":{},"参":{"docs":{},"数":{"docs":{},"为":{"docs":{},"f":{"docs":{},"a":{"docs":{},"l":{"docs":{},"s":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}}}}}}}}}}}}}}}}}}}}},"的":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{},",":{"docs":{},"就":{"docs":{},"不":{"docs":{},"能":{"docs":{},"有":{"docs":{},"新":{"docs":{},"的":{"docs":{},"流":{"docs":{},"算":{"docs":{},"子":{"docs":{},"(":{"docs":{},"d":{"docs":{},"s":{"docs":{},"t":{"docs":{},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"m":{"docs":{},")":{"docs":{},"建":{"docs":{},"立":{"docs":{},"或":{"docs":{},"者":{"docs":{},"是":{"docs":{},"添":{"docs":{},"加":{"docs":{},"到":{"docs":{},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"t":{"docs":{},"e":{"docs":{},"x":{"docs":{},"t":{"docs":{},"中":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"调":{"docs":{},"用":{"docs":{},"了":{"docs":{},"s":{"docs":{},"t":{"docs":{},"o":{"docs":{},"p":{"docs":{},"方":{"docs":{},"法":{"docs":{},"之":{"docs":{},"后":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}}}}}}}}}}}}}}},"n":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.042492917847025496}},"o":{"docs":{},"r":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}},"h":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{},":":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}}}}}},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},".":{"docs":{},"c":{"docs":{},"l":{"docs":{},"o":{"docs":{},"s":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.031161473087818695}}}}}}}},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"_":{"docs":{},"t":{"docs":{},"a":{"docs":{},"b":{"docs":{},"l":{"docs":{},"e":{"docs":{},"(":{"docs":{},"'":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"s":{"docs":{},"'":{"docs":{},",":{"docs":{},"{":{"docs":{},"'":{"docs":{},"c":{"docs":{},"f":{"1":{"docs":{},"'":{"docs":{},":":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0056657223796034}}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}},"d":{"docs":{},"e":{"docs":{},"l":{"docs":{},"e":{"docs":{},"t":{"docs":{},"e":{"docs":{},"_":{"docs":{},"t":{"docs":{},"a":{"docs":{},"b":{"docs":{},"l":{"docs":{},"e":{"docs":{},"(":{"docs":{},"'":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"s":{"docs":{},"'":{"docs":{},",":{"docs":{},"d":{"docs":{},"i":{"docs":{},"s":{"docs":{},"a":{"docs":{},"b":{"docs":{},"l":{"docs":{},"e":{"docs":{},"=":{"docs":{},"t":{"docs":{},"r":{"docs":{},"u":{"docs":{},"e":{"docs":{},")":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"o":{"docs":{},"p":{"docs":{},"e":{"docs":{},"n":{"docs":{},"(":{"docs":{},")":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}},":":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}}}}},"t":{"docs":{},"a":{"docs":{},"b":{"docs":{},"l":{"docs":{},"e":{"docs":{},"(":{"docs":{},"'":{"docs":{},"m":{"docs":{},"y":{"docs":{},"t":{"docs":{},"a":{"docs":{},"b":{"docs":{},"l":{"docs":{},"e":{"docs":{},"'":{"docs":{},")":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"'":{"docs":{},")":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0113314447592068}}}},"s":{"docs":{},"'":{"docs":{},")":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0113314447592068}}}}}}}}}}},"s":{"docs":{},"(":{"docs":{},")":{"docs":{},":":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}}}}}}}}}}}}}},".":{"docs":{},"d":{"docs":{},"e":{"docs":{},"l":{"docs":{},"e":{"docs":{},"t":{"docs":{},"e":{"docs":{},"_":{"docs":{},"t":{"docs":{},"a":{"docs":{},"b":{"docs":{},"l":{"docs":{},"e":{"docs":{},"(":{"docs":{},"t":{"docs":{},"a":{"docs":{},"b":{"docs":{},"l":{"docs":{},"e":{"docs":{},"_":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},",":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0056657223796034}}}}}}}}}}}}}}}}}}}}}}}}}}}},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}},"(":{"docs":{},"s":{"docs":{},"t":{"docs":{},"r":{"1":{"docs":{},",":{"docs":{},"s":{"docs":{},"t":{"docs":{},"r":{"2":{"docs":{},",":{"docs":{},"…":{"docs":{},")":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}},"docs":{}}}}}},"docs":{}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},",":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{},"i":{"docs":{},"c":{"docs":{},"l":{"docs":{},"e":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},")":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}}}}}}}}}}}}}}}}}},"_":{"docs":{},"w":{"docs":{},"s":{"docs":{},"(":{"docs":{},"'":{"docs":{},":":{"docs":{},"'":{"docs":{},",":{"docs":{},"b":{"docs":{},".":{"docs":{},"k":{"docs":{},"w":{"docs":{},",":{"docs":{},"c":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0045351473922902496}}}}}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},",":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{},"i":{"docs":{},"c":{"docs":{},"l":{"docs":{},"e":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},")":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}}}}}}}}}}}}}}}}}}}}},")":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}},":":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}},"：":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}},"s":{"docs":{},"i":{"docs":{},"s":{"docs":{},"t":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}},"v":{"docs":{},"e":{"docs":{},"r":{"docs":{},"s":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}},"t":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}},"d":{"docs":{},"i":{"docs":{},"t":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}},"i":{"docs":{},"o":{"docs":{},"n":{"2":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}},",":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{},",":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}},"p":{"docs":{},"y":{"docs":{},"f":{"docs":{},"r":{"docs":{},"o":{"docs":{},"m":{"docs":{},"l":{"docs":{},"o":{"docs":{},"c":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}}}}},"d":{"docs":{},"e":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.031746031746031744}}}}},"t":{"docs":{},"r":{"docs":{},"预":{"docs":{},"估":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}},"数":{"docs":{},"据":{"docs":{},"准":{"docs":{},"备":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}},"测":{"docs":{},"模":{"docs":{},"型":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}},"点":{"docs":{},"击":{"docs":{},"率":{"docs":{},"预":{"docs":{},"测":{"docs":{},"模":{"docs":{},"型":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.010526315789473684}}}}}}}}},"_":{"docs":{},"m":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"l":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}},".":{"docs":{},"t":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"s":{"docs":{},"f":{"docs":{},"o":{"docs":{},"r":{"docs":{},"m":{"docs":{},"(":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"s":{"docs":{},")":{"docs":{},".":{"docs":{},"s":{"docs":{},"o":{"docs":{},"r":{"docs":{},"t":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"p":{"docs":{},"r":{"docs":{},"o":{"docs":{},"b":{"docs":{},"a":{"docs":{},"b":{"docs":{},"i":{"docs":{},"l":{"docs":{},"i":{"docs":{},"t":{"docs":{},"y":{"docs":{},"\"":{"docs":{},")":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"\"":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.007062146892655367}},"\"":{"docs":{},"d":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.004545454545454545}}}}}},"a":{"docs":{},"l":{"docs":{},"c":{"docs":{},"u":{"docs":{},"l":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},")":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}},"l":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}},"r":{"docs":{},"t":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734},"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}},":":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}},"=":{"5":{"3":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}},"docs":{}},"docs":{}}}},"t":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625}},"a":{"docs":{},"l":{"docs":{},"y":{"docs":{},"s":{"docs":{},"t":{"docs":{},"o":{"docs":{},"p":{"docs":{},"t":{"docs":{},"i":{"docs":{},"m":{"docs":{},"i":{"docs":{},"z":{"docs":{},"e":{"docs":{},"r":{"docs":{},"：":{"docs":{},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"l":{"docs":{},"y":{"docs":{},"s":{"docs":{},"t":{"docs":{},"优":{"docs":{},"化":{"docs":{},"器":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}}}}}}}}}}}}}}}}}}}}}}}}}}}},"e":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}},"/":{"docs":{},"b":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.010526315789473684}},"评":{"docs":{},"分":{"docs":{},"数":{"docs":{},"据":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}}}}}},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},"：":{"docs":{},"脱":{"docs":{},"敏":{"docs":{},"过":{"docs":{},"的":{"docs":{},"商":{"docs":{},"品":{"docs":{},"类":{"docs":{},"目":{"docs":{},"i":{"docs":{},"d":{"docs":{},"；":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.010526315789473684}}}}}}}}}}}}}},":":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"_":{"docs":{},"d":{"docs":{},"f":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}},".":{"docs":{},"f":{"docs":{},"i":{"docs":{},"r":{"docs":{},"s":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}},"p":{"docs":{},"r":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"s":{"docs":{},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},"m":{"docs":{},"a":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}}}}}}}}}}}}},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},".":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"(":{"docs":{},"p":{"docs":{},"r":{"docs":{},"o":{"docs":{},"c":{"docs":{},"e":{"docs":{},"s":{"docs":{},"s":{"docs":{},"_":{"docs":{},"r":{"docs":{},"o":{"docs":{},"w":{"docs":{},")":{"docs":{},".":{"docs":{},"t":{"docs":{},"o":{"docs":{},"d":{"docs":{},"f":{"docs":{},"(":{"docs":{},"[":{"docs":{},"\"":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"w":{"docs":{},"r":{"docs":{},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},".":{"docs":{},"c":{"docs":{},"s":{"docs":{},"v":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},":":{"docs":{},"/":{"docs":{},"/":{"docs":{},"l":{"docs":{},"o":{"docs":{},"c":{"docs":{},"a":{"docs":{},"l":{"docs":{},"h":{"docs":{},"o":{"docs":{},"s":{"docs":{},"t":{"docs":{},":":{"9":{"0":{"0":{"0":{"docs":{},"/":{"docs":{},"p":{"docs":{},"r":{"docs":{},"e":{"docs":{},"p":{"docs":{},"r":{"docs":{},"o":{"docs":{},"c":{"docs":{},"e":{"docs":{},"s":{"docs":{},"s":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"_":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"/":{"docs":{},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"_":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},".":{"docs":{},"c":{"docs":{},"s":{"docs":{},"v":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"r":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"_":{"docs":{},"d":{"docs":{},"f":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}},".":{"docs":{},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"b":{"docs":{},"y":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"p":{"docs":{},"o":{"docs":{},"v":{"docs":{},"i":{"docs":{},"t":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"m":{"docs":{},"i":{"docs":{},"n":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"r":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"\"":{"docs":{},")":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},":":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}},"i":{"docs":{},"d":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289}},":":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.004078303425774877},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}},"=":{"4":{"5":{"2":{"0":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"数":{"docs":{},"值":{"docs":{},"个":{"docs":{},"数":{"docs":{},"：":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}},"：":{"docs":{},"脱":{"docs":{},"敏":{"docs":{},"过":{"docs":{},"的":{"docs":{},"商":{"docs":{},"品":{"docs":{},"类":{"docs":{},"目":{"docs":{},"i":{"docs":{},"d":{"docs":{},"；":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}},")":{"docs":{},"，":{"docs":{},"这":{"docs":{},"里":{"docs":{},"控":{"docs":{},"制":{"docs":{},"了":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"i":{"docs":{},"d":{"docs":{},"一":{"docs":{},"样":{"docs":{},"，":{"docs":{},"所":{"docs":{},"以":{"docs":{},"相":{"docs":{},"当":{"docs":{},"于":{"docs":{},"是":{"docs":{},"在":{"docs":{},"求":{"docs":{},"某":{"docs":{},"用":{"docs":{},"户":{"docs":{},"对":{"docs":{},"所":{"docs":{},"有":{"docs":{},"分":{"docs":{},"类":{"docs":{},"的":{"docs":{},"兴":{"docs":{},"趣":{"docs":{},"程":{"docs":{},"度":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"_":{"docs":{},"d":{"docs":{},"f":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.006968641114982578}},".":{"docs":{},"i":{"docs":{},"n":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"t":{"docs":{},"(":{"0":{"docs":{},",":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289}}}},"docs":{}}}}}}}}}}},"l":{"docs":{},"i":{"docs":{},"s":{"docs":{},"t":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}},".":{"docs":{},"h":{"docs":{},"e":{"docs":{},"a":{"docs":{},"d":{"docs":{},"(":{"2":{"0":{"docs":{},")":{"docs":{},":":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}},"docs":{}},"docs":{}}}}}}}}}}}}}},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}},"p":{"docs":{},"定":{"docs":{},"理":{"docs":{"Hive&HBase/06_hbase数据模型.html":{"ref":"Hive&HBase/06_hbase数据模型.html","tf":0.020833333333333332}}}}},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},"_":{"docs":{},"b":{"docs":{},"l":{"docs":{},"o":{"docs":{},"o":{"docs":{},"m":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{},"_":{"docs":{},"o":{"docs":{},"n":{"docs":{},"_":{"docs":{},"w":{"docs":{},"r":{"docs":{},"i":{"docs":{},"t":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}}}}}}}}}},"s":{"docs":{},"e":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}},"m":{"docs":{},"p":{"docs":{},"a":{"docs":{},"i":{"docs":{},"g":{"docs":{},"n":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},"：":{"docs":{},"脱":{"docs":{},"敏":{"docs":{},"过":{"docs":{},"的":{"docs":{},"广":{"docs":{},"告":{"docs":{},"计":{"docs":{},"划":{"docs":{},"i":{"docs":{},"d":{"docs":{},"；":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}}}}}}}},":":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}},"i":{"docs":{},"d":{"docs":{},":":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}},"数":{"docs":{},"值":{"docs":{},"个":{"docs":{},"数":{"docs":{},"：":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}},"：":{"docs":{},"脱":{"docs":{},"敏":{"docs":{},"过":{"docs":{},"的":{"docs":{},"广":{"docs":{},"告":{"docs":{},"计":{"docs":{},"划":{"docs":{},"i":{"docs":{},"d":{"docs":{},"；":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}},"f":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.00847457627118644},"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}},"的":{"docs":{},"评":{"docs":{},"分":{"docs":{},"结":{"docs":{},"果":{"docs":{},"也":{"docs":{},"是":{"docs":{},"存":{"docs":{},"在":{"docs":{},"差":{"docs":{},"异":{"docs":{},"的":{"docs":{},"，":{"docs":{},"因":{"docs":{},"为":{"docs":{},"严":{"docs":{},"格":{"docs":{},"意":{"docs":{},"义":{"docs":{},"上":{"docs":{},"他":{"docs":{},"们":{"docs":{},"其":{"docs":{},"实":{"docs":{},"应":{"docs":{},"当":{"docs":{},"属":{"docs":{},"于":{"docs":{},"两":{"docs":{},"种":{"docs":{},"不":{"docs":{},"同":{"docs":{},"的":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"算":{"docs":{},"法":{"docs":{},"，":{"docs":{},"各":{"docs":{},"自":{"docs":{},"在":{"docs":{},"不":{"docs":{},"同":{"docs":{},"的":{"docs":{},"领":{"docs":{},"域":{"docs":{},"不":{"docs":{},"同":{"docs":{},"场":{"docs":{},"景":{"docs":{},"下":{"docs":{},"，":{"docs":{},"都":{"docs":{},"会":{"docs":{},"比":{"docs":{},"另":{"docs":{},"一":{"docs":{},"种":{"docs":{},"的":{"docs":{},"效":{"docs":{},"果":{"docs":{},"更":{"docs":{},"佳":{"docs":{},"，":{"docs":{},"但":{"docs":{},"具":{"docs":{},"体":{"docs":{},"哪":{"docs":{},"一":{"docs":{},"种":{"docs":{},"更":{"docs":{},"佳":{"docs":{},"，":{"docs":{},"必":{"docs":{},"须":{"docs":{},"经":{"docs":{},"过":{"docs":{},"合":{"docs":{},"理":{"docs":{},"的":{"docs":{},"效":{"docs":{},"果":{"docs":{},"评":{"docs":{},"估":{"docs":{},"，":{"docs":{},"因":{"docs":{},"此":{"docs":{},"在":{"docs":{},"实":{"docs":{},"现":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"系":{"docs":{},"统":{"docs":{},"时":{"docs":{},"这":{"docs":{},"两":{"docs":{},"种":{"docs":{},"算":{"docs":{},"法":{"docs":{},"往":{"docs":{},"往":{"docs":{},"都":{"docs":{},"是":{"docs":{},"需":{"docs":{},"要":{"docs":{},"去":{"docs":{},"实":{"docs":{},"现":{"docs":{},"的":{"docs":{},"，":{"docs":{},"然":{"docs":{},"后":{"docs":{},"对":{"docs":{},"产":{"docs":{},"生":{"docs":{},"的":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"效":{"docs":{},"果":{"docs":{},"进":{"docs":{},"行":{"docs":{},"评":{"docs":{},"估":{"docs":{},"分":{"docs":{},"析":{"docs":{},"选":{"docs":{},"出":{"docs":{},"更":{"docs":{},"优":{"docs":{},"方":{"docs":{},"案":{"docs":{},"。":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"预":{"docs":{},"测":{"docs":{},"评":{"docs":{},"分":{"docs":{},"和":{"docs":{},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"m":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}},"）":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147}},"的":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"系":{"docs":{},"统":{"docs":{},"算":{"docs":{},"法":{"docs":{},"。":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}},"，":{"docs":{},"也":{"docs":{},"是":{"docs":{},"目":{"docs":{},"前":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"内":{"docs":{},"唯":{"docs":{},"一":{"docs":{},"一":{"docs":{},"个":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"算":{"docs":{},"法":{"docs":{},"。":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"算":{"docs":{},"法":{"docs":{},"做":{"docs":{},"一":{"docs":{},"个":{"docs":{},"大":{"docs":{},"致":{"docs":{},"的":{"docs":{},"分":{"docs":{},"类":{"docs":{},"：":{"docs":{"day02_推荐算法/01_基于模型的协同过滤推荐.html":{"ref":"day02_推荐算法/01_基于模型的协同过滤推荐.html","tf":0.07692307692307693}}}}}}}}}}}}},"_":{"docs":{},"l":{"docs":{},"i":{"docs":{},"s":{"docs":{},"t":{"docs":{},")":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}}}}},"l":{"docs":{},"i":{"docs":{},"c":{"docs":{},"k":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734},"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.010526315789473684}}}},"e":{"docs":{},"n":{"docs":{},"t":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.005226480836236934}},":":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}},"将":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"n":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"返":{"docs":{},"回":{"docs":{},"的":{"docs":{},"分":{"docs":{},"配":{"docs":{},"的":{"docs":{},"可":{"docs":{},"写":{"docs":{},"的":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"n":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"列":{"docs":{},"表":{"docs":{},"和":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"数":{"docs":{},"据":{"docs":{},"一":{"docs":{},"同":{"docs":{},"发":{"docs":{},"送":{"docs":{},"给":{"docs":{},"最":{"docs":{},"近":{"docs":{},"的":{"docs":{},"第":{"docs":{},"一":{"docs":{},"个":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"n":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"节":{"docs":{},"点":{"docs":{},"，":{"docs":{},"此":{"docs":{},"后":{"docs":{},"c":{"docs":{},"l":{"docs":{},"i":{"docs":{},"e":{"docs":{},"n":{"docs":{},"t":{"docs":{},"端":{"docs":{},"和":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"n":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"分":{"docs":{},"配":{"docs":{},"的":{"docs":{},"多":{"docs":{},"个":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"n":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"构":{"docs":{},"成":{"docs":{},"p":{"docs":{},"i":{"docs":{},"p":{"docs":{},"e":{"docs":{},"l":{"docs":{},"i":{"docs":{},"n":{"docs":{},"e":{"docs":{},"管":{"docs":{},"道":{"docs":{},"，":{"docs":{},"c":{"docs":{},"l":{"docs":{},"i":{"docs":{},"e":{"docs":{},"n":{"docs":{},"t":{"docs":{},"端":{"docs":{},"向":{"docs":{},"输":{"docs":{},"出":{"docs":{},"流":{"docs":{},"对":{"docs":{},"象":{"docs":{},"中":{"docs":{},"写":{"docs":{},"数":{"docs":{},"据":{"docs":{},"。":{"docs":{},"c":{"docs":{},"l":{"docs":{},"i":{"docs":{},"e":{"docs":{},"n":{"docs":{},"t":{"docs":{},"每":{"docs":{},"向":{"docs":{},"第":{"docs":{},"一":{"docs":{},"个":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"n":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"写":{"docs":{},"入":{"docs":{},"一":{"docs":{},"个":{"docs":{},"p":{"docs":{},"a":{"docs":{},"c":{"docs":{},"k":{"docs":{},"e":{"docs":{},"t":{"docs":{},"，":{"docs":{},"这":{"docs":{},"个":{"docs":{},"p":{"docs":{},"a":{"docs":{},"c":{"docs":{},"k":{"docs":{},"e":{"docs":{},"t":{"docs":{},"便":{"docs":{},"会":{"docs":{},"直":{"docs":{},"接":{"docs":{},"在":{"docs":{},"p":{"docs":{},"i":{"docs":{},"p":{"docs":{},"e":{"docs":{},"l":{"docs":{},"i":{"docs":{},"n":{"docs":{},"e":{"docs":{},"里":{"docs":{},"传":{"docs":{},"给":{"docs":{},"第":{"docs":{},"二":{"docs":{},"个":{"docs":{},"、":{"docs":{},"第":{"docs":{},"三":{"docs":{},"个":{"docs":{},"…":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"n":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"。":{"docs":{"day03_Hadoop/ha4.2.html":{"ref":"day03_Hadoop/ha4.2.html","tf":0.0196078431372549}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"端":{"docs":{},"按":{"1":{"2":{"8":{"docs":{},"m":{"docs":{},"b":{"docs":{},"的":{"docs":{},"块":{"docs":{},"切":{"docs":{},"分":{"docs":{},"文":{"docs":{},"件":{"docs":{},"。":{"docs":{"day03_Hadoop/ha4.2.html":{"ref":"day03_Hadoop/ha4.2.html","tf":0.0196078431372549}}}}}}}}}}}},"docs":{}},"docs":{}},"docs":{}}},"访":{"docs":{},"问":{"docs":{},"r":{"docs":{},"e":{"docs":{},"g":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"v":{"docs":{},"e":{"docs":{},"r":{"docs":{},"写":{"docs":{},"入":{"docs":{},"数":{"docs":{},"据":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}}}}}}}}}}}}}},"：":{"docs":{},"客":{"docs":{},"户":{"docs":{},"端":{"docs":{},"进":{"docs":{},"程":{"docs":{},"，":{"docs":{},"负":{"docs":{},"责":{"docs":{},"提":{"docs":{},"交":{"docs":{},"作":{"docs":{},"业":{"docs":{},"到":{"docs":{},"m":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"。":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}}}}}}}}}}}}}}}}}}}}},".":{"docs":{},"h":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"r":{"docs":{},"e":{"docs":{},"c":{"docs":{},"a":{"docs":{},"l":{"docs":{},"l":{"docs":{},"_":{"docs":{},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}},"a":{"docs":{},"d":{"docs":{},"_":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"1":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}},"docs":{}}}}}}}}}}}}}}}}}}}},"s":{"docs":{},"a":{"docs":{},"d":{"docs":{},"d":{"docs":{},"(":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"i":{"docs":{},"d":{"docs":{},",":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}},"_":{"docs":{},"o":{"docs":{},"f":{"docs":{},"_":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}},"r":{"docs":{},"e":{"docs":{},"c":{"docs":{},"a":{"docs":{},"l":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}},"l":{"docs":{},".":{"docs":{},"s":{"docs":{},"m":{"docs":{},"e":{"docs":{},"m":{"docs":{},"b":{"docs":{},"e":{"docs":{},"r":{"docs":{},"s":{"docs":{},"(":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"i":{"docs":{},"d":{"docs":{},")":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"(":{"docs":{},"c":{"docs":{},"o":{"docs":{},"m":{"docs":{},"m":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}},"、":{"docs":{},"j":{"docs":{},"d":{"docs":{},"b":{"docs":{},"c":{"docs":{},"/":{"docs":{},"o":{"docs":{},"d":{"docs":{},"b":{"docs":{},"c":{"docs":{},"、":{"docs":{},"w":{"docs":{},"e":{"docs":{},"b":{"docs":{},"g":{"docs":{},"u":{"docs":{},"i":{"docs":{},"。":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}}}}}}}}}}}},"a":{"docs":{},"s":{"docs":{},"s":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147},"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.011695906432748537},"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},"n":{"docs":{},"o":{"docs":{},",":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},"s":{"docs":{},"c":{"docs":{},"o":{"docs":{},"r":{"docs":{},"e":{"docs":{},")":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}}}}}}}}},";":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}},"i":{"docs":{},"f":{"docs":{},"i":{"docs":{},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}},".":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}},"o":{"docs":{},"u":{"docs":{},"d":{"docs":{},"e":{"docs":{},"r":{"docs":{},"a":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.008403361344537815}},"推":{"docs":{},"出":{"docs":{},"c":{"docs":{},"d":{"docs":{},"h":{"docs":{},"（":{"docs":{},"c":{"docs":{},"l":{"docs":{},"o":{"docs":{},"u":{"docs":{},"d":{"docs":{},"e":{"docs":{},"r":{"docs":{},"a":{"docs":{},"’":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}}}}}}}}}}}}}}}}}}}},"u":{"docs":{},"s":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009},"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282},"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112},"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}},"k":{"docs":{},"：":{"docs":{},"为":{"0":{"docs":{},"代":{"docs":{},"表":{"docs":{},"没":{"docs":{},"有":{"docs":{},"点":{"docs":{},"击":{"docs":{},"；":{"docs":{},"为":{"1":{"docs":{},"代":{"docs":{},"表":{"docs":{},"点":{"docs":{},"击":{"docs":{},"；":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}},"docs":{}}}}}}}}}},"docs":{}}},":":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}}},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.018518518518518517},"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.012048192771084338},"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.003401360544217687},"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.006607929515418502},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}},"e":{"docs":{},"_":{"docs":{},"i":{"docs":{},"n":{"docs":{},"v":{"docs":{},"e":{"docs":{},"r":{"docs":{},"t":{"docs":{},"e":{"docs":{},"d":{"docs":{},"_":{"docs":{},"t":{"docs":{},"a":{"docs":{},"b":{"docs":{},"l":{"docs":{},"e":{"docs":{},"(":{"docs":{},"m":{"docs":{},"o":{"docs":{},"v":{"docs":{},"i":{"docs":{},"e":{"docs":{},"_":{"docs":{},"p":{"docs":{},"r":{"docs":{},"o":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"e":{"docs":{},")":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}},":":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"m":{"docs":{},"o":{"docs":{},"v":{"docs":{},"i":{"docs":{},"e":{"docs":{},"_":{"docs":{},"p":{"docs":{},"r":{"docs":{},"o":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"e":{"docs":{},"(":{"docs":{},"m":{"docs":{},"o":{"docs":{},"v":{"docs":{},"i":{"docs":{},"e":{"docs":{},"_":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},")":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}},":":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"p":{"docs":{},"r":{"docs":{},"o":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842},"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}},":":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}}}}}}}}}}}}}}},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"c":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"s":{"docs":{},"(":{"8":{"8":{"docs":{},",":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}},"docs":{}},"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"i":{"docs":{},"d":{"docs":{},",":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}}}}}}}}}}}}},"t":{"docs":{},"a":{"docs":{},"b":{"docs":{},"l":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{},":":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}}}}}},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"：":{"docs":{},"p":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"a":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}},"o":{"docs":{},"r":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}},":":{"docs":{},"j":{"docs":{},"e":{"docs":{},"r":{"docs":{},"r":{"docs":{},"i":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}},"t":{"docs":{},"o":{"docs":{},"m":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}}}}}},"o":{"docs":{},"s":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"b":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}}}}}},"u":{"docs":{},"t":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"等":{"docs":{},"人":{"docs":{},"实":{"docs":{},"现":{"docs":{},"了":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},"和":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{},"e":{"docs":{},"机":{"docs":{},"制":{"docs":{},")":{"docs":{},"。":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"s":{"docs":{},"t":{"docs":{},"o":{"docs":{},"m":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.008032128514056224}},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},":":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}},":":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}},"i":{"docs":{},"d":{"docs":{},":":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}},"脱":{"docs":{},"敏":{"docs":{},"过":{"docs":{},"的":{"docs":{},"广":{"docs":{},"告":{"docs":{},"主":{"docs":{},"i":{"docs":{},"d":{"docs":{},"；":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}},"数":{"docs":{},"值":{"docs":{},"个":{"docs":{},"数":{"docs":{},"：":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}},"d":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428}},"h":{"5":{"docs":{},".":{"7":{"docs":{},".":{"0":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}},"/":{"docs":{},"l":{"docs":{},"o":{"docs":{},"g":{"docs":{},"s":{"docs":{},"/":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{"day03_Hadoop/ha2.1.html":{"ref":"day03_Hadoop/ha2.1.html","tf":0.0410958904109589}}}}}}}}}}}}}},".":{"docs":{},"t":{"docs":{},"a":{"docs":{},"r":{"docs":{},".":{"docs":{},"g":{"docs":{},"z":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196},"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}},"j":{"docs":{},"a":{"docs":{},"r":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.008032128514056224}},"'":{"docs":{},";":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}},";":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}}},"docs":{}}},"docs":{}}},"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}},":":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}},"版":{"docs":{},"本":{"docs":{},"一":{"docs":{},"致":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}},"的":{"docs":{},"这":{"docs":{},"些":{"docs":{},"组":{"docs":{},"件":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}}},"p":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428},"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}},"n":{"docs":{},"t":{"docs":{},",":{"docs":{},"w":{"docs":{},"o":{"docs":{},"r":{"docs":{},"d":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}}},"h":{"docs":{},"a":{"docs":{},"r":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}},"r":{"docs":{},"o":{"docs":{},"m":{"docs":{},"e":{"docs":{},"/":{"2":{"9":{"docs":{},".":{"0":{"docs":{},".":{"1":{"5":{"4":{"7":{"docs":{},".":{"6":{"6":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.006711409395973154}}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}},"docs":{}},"docs":{}}}}}},"e":{"docs":{},"c":{"docs":{},"k":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}},"p":{"docs":{},"o":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"=":{"2":{"docs":{},")":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}},"5":{"docs":{},")":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}},"docs":{}}}}}}}}}}}}}}}}}},"u":{"docs":{},"n":{"docs":{},"k":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}},".":{"docs":{},"t":{"docs":{},"o":{"docs":{},"_":{"docs":{},"c":{"docs":{},"s":{"docs":{},"v":{"docs":{},"(":{"docs":{},"'":{"docs":{},"t":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"4":{"docs":{},".":{"docs":{},"c":{"docs":{},"s":{"docs":{},"v":{"docs":{},"'":{"docs":{},",":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}},"docs":{}}}}}}}}}}}}}}}}}},"c":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.003401360544217687}},".":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},",":{"docs":{},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{},"_":{"docs":{},"w":{"docs":{},"s":{"docs":{},"(":{"docs":{},"'":{"docs":{},",":{"docs":{},"'":{"docs":{},",":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"_":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"(":{"docs":{},"c":{"docs":{},"c":{"docs":{},".":{"docs":{},"k":{"docs":{},"w":{"docs":{},"_":{"docs":{},"w":{"docs":{},")":{"docs":{},")":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"s":{"docs":{},"t":{"docs":{},"r":{"docs":{},"_":{"docs":{},"t":{"docs":{},"o":{"docs":{},"_":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"(":{"docs":{},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{},"_":{"docs":{},"w":{"docs":{},"s":{"docs":{},"(":{"docs":{},"'":{"docs":{},",":{"docs":{},"'":{"docs":{},",":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"_":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"(":{"docs":{},"c":{"docs":{},"c":{"docs":{},".":{"docs":{},"k":{"docs":{},"w":{"docs":{},"_":{"docs":{},"w":{"docs":{},")":{"docs":{},")":{"docs":{},")":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0022675736961451248}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},";":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.003401360544217687}}}}}}}}}}}},"e":{"docs":{},"l":{"docs":{},"l":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.006607929515418502}}}}},"i":{"docs":{},"t":{"docs":{},"y":{"docs":{},"_":{"docs":{},"b":{"docs":{},"r":{"docs":{},"o":{"docs":{},"a":{"docs":{},"d":{"docs":{},"c":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}},".":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}}},"_":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}},"e":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}},"[":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{},"]":{"docs":{},"[":{"3":{"docs":{},"]":{"docs":{},")":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}},"i":{"docs":{},"d":{"docs":{},"_":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}}}}}}}},"!":{"docs":{},"=":{"docs":{},"'":{"docs":{},"i":{"docs":{},"d":{"docs":{},"'":{"docs":{},"]":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}},"m":{"docs":{},"s":{"docs":{},"_":{"docs":{},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},"：":{"docs":{},"c":{"docs":{},"m":{"docs":{},"s":{"docs":{},"_":{"docs":{},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},"；":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}}}}}}}}}},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}},":":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0010197838058331635},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}},"_":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.004228329809725159}},"e":{"docs":{},"[":{"docs":{},"c":{"docs":{},"m":{"docs":{},"s":{"docs":{},"_":{"docs":{},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},"_":{"docs":{},"r":{"docs":{},"e":{"docs":{},"l":{"docs":{},"a":{"docs":{},"[":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"[":{"docs":{},"\"":{"docs":{},"c":{"docs":{},"m":{"docs":{},"s":{"docs":{},"_":{"docs":{},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},"]":{"docs":{},")":{"docs":{},"]":{"docs":{},"]":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"s":{"docs":{},"e":{"docs":{},"g":{"docs":{},"i":{"docs":{},"d":{"docs":{},"：":{"docs":{},"微":{"docs":{},"群":{"docs":{},"i":{"docs":{},"d":{"docs":{},"；":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}},":":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0010197838058331635},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}},"h":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"0":{"0":{"1":{"docs":{},"/":{"docs":{},"t":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625}},"/":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625}},"t":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{},".":{"docs":{},"t":{"docs":{},"x":{"docs":{},"t":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625}},"文":{"docs":{},"件":{"docs":{},"下":{"docs":{},"载":{"docs":{},"到":{"docs":{},"c":{"docs":{},"e":{"docs":{},"n":{"docs":{},"t":{"docs":{},"o":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625}}}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{},".":{"docs":{},"o":{"docs":{},"u":{"docs":{},"t":{"docs":{"day03_Hadoop/ha2.1.html":{"ref":"day03_Hadoop/ha2.1.html","tf":0.0410958904109589}}}}}},":":{"docs":{"day03_Hadoop/ha2.1.html":{"ref":"day03_Hadoop/ha2.1.html","tf":0.0136986301369863}}}},"docs":{}},"1":{"docs":{},".":{"docs":{},"x":{"docs":{},"时":{"docs":{},"并":{"docs":{},"没":{"docs":{},"有":{"docs":{},"y":{"docs":{},"a":{"docs":{},"r":{"docs":{},"n":{"docs":{},"，":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}}}}}}}}}}}}}}}}}},"docs":{"./":{"ref":"./","tf":0.011235955056179775},"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863},"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.02702702702702703},"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.0641025641025641},"day03_Hadoop/ha2.1.html":{"ref":"day03_Hadoop/ha2.1.html","tf":0.0410958904109589},"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.08125},"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.017857142857142856},"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269},"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.01680672268907563},"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.009259259259259259},"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.007407407407407408},"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.012048192771084338},"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0022675736961451248},"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.004405286343612335},"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00851063829787234},"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}},"概":{"docs":{},"念":{"docs":{},"扩":{"docs":{},"展":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}},"述":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}},"、":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}}}}}}},"®":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}},"发":{"docs":{},"展":{"docs":{},"史":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}},"布":{"docs":{},"的":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}},"行":{"docs":{},"版":{"docs":{},"的":{"docs":{},"选":{"docs":{},"择":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}},"名":{"docs":{},"字":{"docs":{},"的":{"docs":{},"由":{"docs":{},"来":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}}}},"的":{"docs":{},"概":{"docs":{},"念":{"docs":{},":":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{},"e":{"docs":{},"是":{"docs":{},"g":{"docs":{},"o":{"docs":{},"o":{"docs":{},"g":{"docs":{},"l":{"docs":{},"e":{"docs":{},"论":{"docs":{},"文":{"docs":{},"的":{"docs":{},"开":{"docs":{},"源":{"docs":{},"实":{"docs":{},"现":{"docs":{"day03_Hadoop/ha3.2.html":{"ref":"day03_Hadoop/ha3.2.html","tf":0.02040816326530612}}}}}}}}}}}}}}}}}}}}}}}}}},"能":{"docs":{},"做":{"docs":{},"什":{"docs":{},"么":{"docs":{},"?":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}}}},"项":{"docs":{},"目":{"docs":{},"作":{"docs":{},"者":{"docs":{},"的":{"docs":{},"孩":{"docs":{},"子":{"docs":{},"给":{"docs":{},"一":{"docs":{},"个":{"docs":{},"棕":{"docs":{},"黄":{"docs":{},"色":{"docs":{},"的":{"docs":{},"大":{"docs":{},"象":{"docs":{},"样":{"docs":{},"子":{"docs":{},"的":{"docs":{},"填":{"docs":{},"充":{"docs":{},"玩":{"docs":{},"具":{"docs":{},"的":{"docs":{},"命":{"docs":{},"名":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}}}}}}}}}}}}}}}}}}}}}}}}},"）":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}},"是":{"docs":{},"所":{"docs":{},"有":{"docs":{},"搜":{"docs":{},"索":{"docs":{},"引":{"docs":{},"擎":{"docs":{},"的":{"docs":{},"共":{"docs":{},"性":{"docs":{},"问":{"docs":{},"题":{"docs":{},"的":{"docs":{},"廉":{"docs":{},"价":{"docs":{},"解":{"docs":{},"决":{"docs":{},"方":{"docs":{},"案":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282}}}}}}}}}}}}}}}}}}}}},"核":{"docs":{},"心":{"docs":{},"组":{"docs":{},"件":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282}}}}}},"优":{"docs":{},"势":{"docs":{"day03_Hadoop/ha1.3.html":{"ref":"day03_Hadoop/ha1.3.html","tf":0.07692307692307693}}}},"生":{"docs":{},"态":{"docs":{},"系":{"docs":{},"统":{"docs":{"day03_Hadoop/ha4.1.html":{"ref":"day03_Hadoop/ha4.1.html","tf":0.023809523809523808}},"成":{"docs":{},"熟":{"docs":{"day03_Hadoop/ha1.3.html":{"ref":"day03_Hadoop/ha1.3.html","tf":0.07692307692307693}}}},"的":{"docs":{},"特":{"docs":{},"点":{"docs":{"day03_Hadoop/ha4.1.html":{"ref":"day03_Hadoop/ha4.1.html","tf":0.023809523809523808}}}}}}},"圈":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}},".":{"docs":{},"t":{"docs":{},"m":{"docs":{},"p":{"docs":{},".":{"docs":{},"d":{"docs":{},"i":{"docs":{},"r":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428}}}}}}}}}},"_":{"docs":{},"h":{"docs":{},"o":{"docs":{},"m":{"docs":{},"e":{"docs":{},"=":{"docs":{},"/":{"docs":{},"r":{"docs":{},"o":{"docs":{},"o":{"docs":{},"t":{"docs":{},"/":{"docs":{},"b":{"docs":{},"i":{"docs":{},"g":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"/":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428},"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}}}}}}}}}}}}}}}}}}},"数":{"docs":{},"据":{"docs":{},"分":{"docs":{},"布":{"docs":{},"式":{"docs":{},"存":{"docs":{},"储":{"docs":{},"（":{"docs":{},"数":{"docs":{},"据":{"docs":{},"分":{"docs":{},"块":{"docs":{},"，":{"docs":{},"冗":{"docs":{},"余":{"docs":{},"存":{"docs":{},"储":{"docs":{},"）":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}}}}}}}}}}}}}}}},"早":{"docs":{},"期":{"docs":{},",":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}},")":{"docs":{},"方":{"docs":{},"式":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}},"计":{"docs":{},"算":{"docs":{},"流":{"docs":{},"程":{"docs":{"day03_Hadoop/ha3.5.html":{"ref":"day03_Hadoop/ha3.5.html","tf":0.030303030303030304}}}}}},"企":{"docs":{},"业":{"docs":{},"应":{"docs":{},"用":{"docs":{},"案":{"docs":{},"例":{"docs":{},"之":{"docs":{},"消":{"docs":{},"费":{"docs":{},"大":{"docs":{},"数":{"docs":{},"据":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}}}},"案":{"docs":{},"例":{"docs":{},"之":{"docs":{},"商":{"docs":{},"业":{"docs":{},"零":{"docs":{},"售":{"docs":{},"大":{"docs":{},"数":{"docs":{},"据":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}}}}}},"。":{"docs":{},"配":{"docs":{},"置":{"docs":{},"好":{"docs":{},"环":{"docs":{},"境":{"docs":{},"变":{"docs":{},"量":{"docs":{},"。":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}}}}}},"r":{"docs":{},"d":{"docs":{},"w":{"docs":{},"a":{"docs":{},"r":{"docs":{},"e":{"docs":{},")":{"docs":{},"上":{"docs":{},"的":{"docs":{},"分":{"docs":{},"布":{"docs":{},"式":{"docs":{},"文":{"docs":{},"件":{"docs":{},"系":{"docs":{},"统":{"docs":{"day03_Hadoop/ha2.3.html":{"ref":"day03_Hadoop/ha2.3.html","tf":0.1111111111111111}}}}}}}}}}}}}}}}}},"s":{"docs":{},"h":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}},"n":{"docs":{},"d":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}},"p":{"docs":{},"p":{"docs":{},"y":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.019830028328611898}},"e":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"n":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"(":{"docs":{},"'":{"1":{"9":{"2":{"docs":{},".":{"1":{"6":{"8":{"docs":{},".":{"1":{"9":{"docs":{},".":{"1":{"3":{"7":{"docs":{},"'":{"docs":{},")":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.031161473087818695}}}}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{},"s":{"docs":{},"o":{"docs":{},"m":{"docs":{},"e":{"docs":{},"h":{"docs":{},"o":{"docs":{},"s":{"docs":{},"t":{"docs":{},"'":{"docs":{},")":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}},",":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}}}}}}}}}}}}}}}}}}}}},"操":{"docs":{},"作":{"docs":{},"h":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}}}}}}}}}}},"t":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{"./":{"ref":"./","tf":0.011235955056179775},"day03_Hadoop/ha4.1.html":{"ref":"day03_Hadoop/ha4.1.html","tf":0.023809523809523808},"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.017391304347826087},"Hive&HBase/06_hbase数据模型.html":{"ref":"Hive&HBase/06_hbase数据模型.html","tf":0.020833333333333332},"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.011013215859030838},"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.014164305949008499},"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}},"不":{"docs":{},"同":{"docs":{},"于":{"docs":{},"一":{"docs":{},"般":{"docs":{},"的":{"docs":{},"关":{"docs":{},"系":{"docs":{},"数":{"docs":{},"据":{"docs":{},"库":{"docs":{},",":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}}}}}}}},"适":{"docs":{},"合":{"docs":{},"有":{"docs":{},"j":{"docs":{},"o":{"docs":{},"i":{"docs":{},"n":{"docs":{},",":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}}}}}},"与":{"docs":{},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}},"使":{"docs":{},"用":{"docs":{},"场":{"docs":{},"景":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}},"内":{"docs":{},"部":{"docs":{},"使":{"docs":{},"用":{"docs":{},"哈":{"docs":{},"希":{"docs":{},"表":{"docs":{},",":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}}}}},"可":{"docs":{},"以":{"docs":{},"存":{"docs":{},"储":{"docs":{},"超":{"docs":{},"大":{"docs":{},"数":{"docs":{},"据":{"docs":{},"并":{"docs":{},"适":{"docs":{},"合":{"docs":{},"用":{"docs":{},"来":{"docs":{},"进":{"docs":{},"行":{"docs":{},"大":{"docs":{},"数":{"docs":{},"据":{"docs":{},"的":{"docs":{},"实":{"docs":{},"时":{"docs":{},"查":{"docs":{},"询":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}}}}}}}}}}}}}}}}}}}},"在":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},"生":{"docs":{},"态":{"docs":{},"中":{"docs":{},"的":{"docs":{},"地":{"docs":{},"位":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}}}}}}}}}},"基":{"docs":{},"于":{"docs":{},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},"进":{"docs":{},"行":{"docs":{},"数":{"docs":{},"据":{"docs":{},"存":{"docs":{},"储":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}}}}}}}}},"建":{"docs":{},"立":{"docs":{},"在":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},"文":{"docs":{},"件":{"docs":{},"系":{"docs":{},"统":{"docs":{},"上":{"docs":{},",":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}}}}}}}}}}}},"提":{"docs":{},"供":{"docs":{},"对":{"docs":{},"数":{"docs":{},"据":{"docs":{},"的":{"docs":{},"随":{"docs":{},"机":{"docs":{},"实":{"docs":{},"时":{"docs":{},"读":{"docs":{},"/":{"docs":{},"写":{"docs":{},"访":{"docs":{},"问":{"docs":{},"功":{"docs":{},"能":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}}}}}}}}}}}}}},"是":{"docs":{},"a":{"docs":{},"p":{"docs":{},"a":{"docs":{},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},"基":{"docs":{},"金":{"docs":{},"会":{"docs":{},"顶":{"docs":{},"级":{"docs":{},"项":{"docs":{},"目":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}}}}}}}}}},"g":{"docs":{},"o":{"docs":{},"o":{"docs":{},"g":{"docs":{},"l":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}},"一":{"docs":{},"个":{"docs":{},"分":{"docs":{},"布":{"docs":{},"式":{"docs":{},"的":{"docs":{},"、":{"docs":{},"面":{"docs":{},"向":{"docs":{},"列":{"docs":{},"的":{"docs":{},"开":{"docs":{},"源":{"docs":{},"数":{"docs":{},"据":{"docs":{},"库":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}}}}}}}}}}}}},"c":{"docs":{},"a":{"docs":{},"p":{"docs":{},"中":{"docs":{},"的":{"docs":{},"c":{"docs":{},"p":{"docs":{},"系":{"docs":{},"统":{"docs":{},",":{"docs":{},"即":{"docs":{},"h":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"是":{"docs":{},"强":{"docs":{},"一":{"docs":{},"致":{"docs":{},"性":{"docs":{},"的":{"docs":{"Hive&HBase/06_hbase数据模型.html":{"ref":"Hive&HBase/06_hbase数据模型.html","tf":0.020833333333333332}}}}}}}}}}}}}}}}}}}}}}}}},"简":{"docs":{},"介":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}},"的":{"docs":{},"列":{"docs":{},"由":{"docs":{"Hive&HBase/06_hbase数据模型.html":{"ref":"Hive&HBase/06_hbase数据模型.html","tf":0.020833333333333332}}}},"数":{"docs":{},"据":{"docs":{},"模":{"docs":{},"型":{"docs":{"Hive&HBase/06_hbase数据模型.html":{"ref":"Hive&HBase/06_hbase数据模型.html","tf":0.020833333333333332}}}}}},"安":{"docs":{},"装":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}},".":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0056657223796034}},"c":{"docs":{},"l":{"docs":{},"u":{"docs":{},"s":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},".":{"docs":{},"d":{"docs":{},"i":{"docs":{},"s":{"docs":{},"t":{"docs":{},"r":{"docs":{},"i":{"docs":{},"b":{"docs":{},"u":{"docs":{},"t":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}}}}}}}}}},"m":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}},"r":{"docs":{},"o":{"docs":{},"o":{"docs":{},"t":{"docs":{},"d":{"docs":{},"i":{"docs":{},"r":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}},"s":{"docs":{},"h":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}},"u":{"docs":{},"n":{"docs":{},"s":{"docs":{},"a":{"docs":{},"f":{"docs":{},"e":{"docs":{},".":{"docs":{},"s":{"docs":{},"t":{"docs":{},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"m":{"docs":{},".":{"docs":{},"c":{"docs":{},"a":{"docs":{},"p":{"docs":{},"a":{"docs":{},"b":{"docs":{},"i":{"docs":{},"l":{"docs":{},"i":{"docs":{},"t":{"docs":{},"y":{"docs":{},".":{"docs":{},"e":{"docs":{},"n":{"docs":{},"f":{"docs":{},"o":{"docs":{},"r":{"docs":{},"c":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"z":{"docs":{},"o":{"docs":{},"o":{"docs":{},"k":{"docs":{},"e":{"docs":{},"e":{"docs":{},"p":{"docs":{},"e":{"docs":{},"r":{"docs":{},".":{"docs":{},"p":{"docs":{},"r":{"docs":{},"o":{"docs":{},"p":{"docs":{},"e":{"docs":{},"r":{"docs":{},"t":{"docs":{},"y":{"docs":{},".":{"docs":{},"c":{"docs":{},"l":{"docs":{},"i":{"docs":{},"e":{"docs":{},"n":{"docs":{},"t":{"docs":{},"p":{"docs":{},"o":{"docs":{},"r":{"docs":{},"t":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}}},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"d":{"docs":{},"i":{"docs":{},"r":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}}}}}}}}},"q":{"docs":{},"u":{"docs":{},"o":{"docs":{},"r":{"docs":{},"u":{"docs":{},"m":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}}}}}}}}}},"_":{"docs":{},"h":{"docs":{},"o":{"docs":{},"m":{"docs":{},"e":{"docs":{},"=":{"docs":{},"/":{"docs":{},"r":{"docs":{},"o":{"docs":{},"o":{"docs":{},"t":{"docs":{},"/":{"docs":{},"b":{"docs":{},"i":{"docs":{},"g":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"/":{"docs":{},"h":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}}}}}}}}}}}}}}}},"m":{"docs":{},"a":{"docs":{},"n":{"docs":{},"a":{"docs":{},"g":{"docs":{},"e":{"docs":{},"s":{"docs":{},"_":{"docs":{},"z":{"docs":{},"k":{"docs":{},"=":{"docs":{},"f":{"docs":{},"a":{"docs":{},"l":{"docs":{},"s":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}}}}}}}}},"中":{"docs":{},"最":{"docs":{},"核":{"docs":{},"心":{"docs":{},"的":{"docs":{},"模":{"docs":{},"块":{"docs":{},"，":{"docs":{},"主":{"docs":{},"要":{"docs":{},"负":{"docs":{},"责":{"docs":{},"响":{"docs":{},"应":{"docs":{},"用":{"docs":{},"户":{"docs":{},"i":{"docs":{},"/":{"docs":{},"o":{"docs":{},"请":{"docs":{},"求":{"docs":{},"，":{"docs":{},"向":{"docs":{},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},"文":{"docs":{},"件":{"docs":{},"系":{"docs":{},"统":{"docs":{},"中":{"docs":{},"读":{"docs":{},"写":{"docs":{},"数":{"docs":{},"据":{"docs":{},"。":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"启":{"docs":{},"动":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}},"存":{"docs":{},"储":{"docs":{},"的":{"docs":{},"核":{"docs":{},"心":{"docs":{},"，":{"docs":{},"由":{"docs":{},"m":{"docs":{},"e":{"docs":{},"m":{"docs":{},"s":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{},"e":{"docs":{},"和":{"docs":{},"s":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{},"e":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"e":{"docs":{},"组":{"docs":{},"成":{"docs":{},"。":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"模":{"docs":{},"块":{"docs":{},"协":{"docs":{},"作":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}},"组":{"docs":{},"件":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}},"d":{"docs":{},"f":{"docs":{"./":{"ref":"./","tf":0.011235955056179775},"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282},"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.0125},"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428},"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907},"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.037037037037037035}},"s":{"docs":{},"是":{"docs":{},"g":{"docs":{},"f":{"docs":{},"s":{"docs":{},"的":{"docs":{},"开":{"docs":{},"源":{"docs":{},"实":{"docs":{},"现":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282}}}}}}}}}}},"的":{"docs":{},"特":{"docs":{},"点":{"docs":{},":":{"docs":{},"扩":{"docs":{},"展":{"docs":{},"性":{"docs":{},"&":{"docs":{},"容":{"docs":{},"错":{"docs":{},"性":{"docs":{},"&":{"docs":{},"海":{"docs":{},"量":{"docs":{},"数":{"docs":{},"量":{"docs":{},"存":{"docs":{},"储":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282}}}}}}}}}}}}}}}}}}},"使":{"docs":{},"用":{"docs":{"day03_Hadoop/ha2.1.html":{"ref":"day03_Hadoop/ha2.1.html","tf":0.0136986301369863}}}},"设":{"docs":{},"计":{"docs":{},"目":{"docs":{},"标":{"docs":{"day03_Hadoop/ha2.3.html":{"ref":"day03_Hadoop/ha2.3.html","tf":0.1111111111111111}}}}}}},":":{"docs":{},"/":{"docs":{},"/":{"docs":{},"h":{"docs":{},"o":{"docs":{},"s":{"docs":{},"t":{"docs":{},":":{"docs":{},"p":{"docs":{},"o":{"docs":{},"r":{"docs":{},"t":{"docs":{},"/":{"docs":{},"d":{"docs":{},"i":{"docs":{},"r":{"1":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625}}},"docs":{}}}},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625}},"e":{"1":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625}}},"2":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625}}},"3":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625}}},"docs":{}}}}},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},"/":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.0125}}}}}}}}}}}}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"/":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},"/":{"docs":{},"d":{"docs":{},"i":{"docs":{},"r":{"1":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428},"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}},"/":{"docs":{},"o":{"docs":{},"u":{"docs":{},"t":{"docs":{},"p":{"docs":{},"u":{"docs":{},"t":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}},"t":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{},".":{"docs":{},"t":{"docs":{},"x":{"docs":{},"t":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"/":{"docs":{},"h":{"docs":{},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{},"/":{"docs":{},"l":{"docs":{},"i":{"docs":{},"b":{"docs":{},"/":{"docs":{},"h":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}},"u":{"docs":{},"d":{"docs":{},"f":{"docs":{},".":{"docs":{},"p":{"docs":{},"y":{"docs":{},";":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}}}}}}}}}}}}}}}}}}}}}},"能":{"docs":{},"提":{"docs":{},"供":{"docs":{},"高":{"docs":{},"吞":{"docs":{},"吐":{"docs":{},"量":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"访":{"docs":{},"问":{"docs":{},"，":{"docs":{},"非":{"docs":{},"常":{"docs":{},"适":{"docs":{},"合":{"docs":{},"大":{"docs":{},"规":{"docs":{},"模":{"docs":{},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{},"上":{"docs":{},"的":{"docs":{},"应":{"docs":{},"用":{"docs":{"day03_Hadoop/ha2.3.html":{"ref":"day03_Hadoop/ha2.3.html","tf":0.1111111111111111}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"设":{"docs":{},"计":{"docs":{},"思":{"docs":{},"路":{"docs":{"day03_Hadoop/ha2.3.html":{"ref":"day03_Hadoop/ha2.3.html","tf":0.1111111111111111}}}}}},"优":{"docs":{},"缺":{"docs":{},"点":{"docs":{"day03_Hadoop/ha2.4.html":{"ref":"day03_Hadoop/ha2.4.html","tf":0.03333333333333333}}}}},"架":{"docs":{},"构":{"docs":{"day03_Hadoop/ha2.4.html":{"ref":"day03_Hadoop/ha2.4.html","tf":0.03333333333333333}}}},"环":{"docs":{},"境":{"docs":{},"搭":{"docs":{},"建":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428}}}}}},"如":{"docs":{},"何":{"docs":{},"实":{"docs":{},"现":{"docs":{},"高":{"docs":{},"可":{"docs":{},"用":{"docs":{},"(":{"docs":{},"h":{"docs":{},"a":{"docs":{},")":{"docs":{"day03_Hadoop/ha4.2.html":{"ref":"day03_Hadoop/ha4.2.html","tf":0.0196078431372549}}}}}}}}}}}}},"读":{"docs":{},"写":{"docs":{},"流":{"docs":{},"程":{"docs":{"day03_Hadoop/ha4.2.html":{"ref":"day03_Hadoop/ha4.2.html","tf":0.0196078431372549}}}}}},"需":{"docs":{},"要":{"docs":{},"把":{"docs":{},"数":{"docs":{},"据":{"docs":{},"导":{"docs":{},"出":{"docs":{},"交":{"docs":{},"给":{"docs":{},"应":{"docs":{},"用":{"docs":{},"程":{"docs":{},"序":{"docs":{},",":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}}}}}}}},"中":{"docs":{},"任":{"docs":{},"意":{"docs":{},"位":{"docs":{},"置":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}},"：":{"docs":{},"存":{"docs":{},"储":{"docs":{},"数":{"docs":{},"据":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}}},"p":{"docs":{},":":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{"./":{"ref":"./","tf":0.011235955056179775},"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.012605042016806723},"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.08641975308641975},"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.007407407407407408},"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.024096385542168676}},"可":{"docs":{},"以":{"docs":{},"在":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},"上":{"docs":{},"运":{"docs":{},"行":{"docs":{},"s":{"docs":{},"q":{"docs":{},"l":{"docs":{},"操":{"docs":{},"作":{"docs":{},",":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}}}}}}}}}}}}}}}}},":":{"docs":{},"数":{"docs":{},"据":{"docs":{},"仓":{"docs":{},"库":{"docs":{"day03_Hadoop/ha4.1.html":{"ref":"day03_Hadoop/ha4.1.html","tf":0.023809523809523808}}}}}}},".":{"docs":{},"e":{"docs":{},"x":{"docs":{},"e":{"docs":{},"c":{"docs":{},".":{"docs":{},"s":{"docs":{},"c":{"docs":{},"r":{"docs":{},"i":{"docs":{},"p":{"docs":{},"t":{"docs":{},".":{"docs":{},"w":{"docs":{},"r":{"docs":{},"a":{"docs":{},"p":{"docs":{},"p":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}}}}},"d":{"docs":{},"y":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"i":{"docs":{},"c":{"docs":{},".":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{},"i":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},".":{"docs":{},"m":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"=":{"docs":{},"n":{"docs":{},"o":{"docs":{},"n":{"docs":{},"s":{"docs":{},"t":{"docs":{},"r":{"docs":{},"i":{"docs":{},"c":{"docs":{},"t":{"docs":{},";":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"m":{"docs":{},"e":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{},"e":{"docs":{},".":{"docs":{},"w":{"docs":{},"a":{"docs":{},"r":{"docs":{},"e":{"docs":{},"h":{"docs":{},"o":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},".":{"docs":{},"d":{"docs":{},"i":{"docs":{},"r":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196},"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}}}}}}}}}}}}}}}}}}}},"_":{"docs":{},"h":{"docs":{},"o":{"docs":{},"m":{"docs":{},"e":{"docs":{},"=":{"docs":{},"/":{"docs":{},"r":{"docs":{},"o":{"docs":{},"o":{"docs":{},"t":{"docs":{},"/":{"docs":{},"b":{"docs":{},"i":{"docs":{},"g":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"/":{"docs":{},"h":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}}}}}}}}}}}}}},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"v":{"docs":{},"e":{"docs":{},"r":{"2":{"docs":{},"基":{"docs":{},"于":{"docs":{},"t":{"docs":{},"h":{"docs":{},"r":{"docs":{},"i":{"docs":{},"f":{"docs":{},"t":{"docs":{},",":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}}},"docs":{}}}}}}},"。":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}},"中":{"docs":{},"表":{"docs":{},"的":{"docs":{},"类":{"docs":{},"型":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}},"分":{"docs":{},"区":{"docs":{},"表":{"docs":{},"实":{"docs":{},"际":{"docs":{},"就":{"docs":{},"是":{"docs":{},"对":{"docs":{},"应":{"docs":{},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},"文":{"docs":{},"件":{"docs":{},"系":{"docs":{},"统":{"docs":{},"上":{"docs":{},"独":{"docs":{},"立":{"docs":{},"的":{"docs":{},"文":{"docs":{},"件":{"docs":{},"夹":{"docs":{},"，":{"docs":{},"该":{"docs":{},"文":{"docs":{},"件":{"docs":{},"夹":{"docs":{},"内":{"docs":{},"的":{"docs":{},"文":{"docs":{},"件":{"docs":{},"是":{"docs":{},"该":{"docs":{},"分":{"docs":{},"区":{"docs":{},"所":{"docs":{},"有":{"docs":{},"数":{"docs":{},"据":{"docs":{},"文":{"docs":{},"件":{"docs":{},"。":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"启":{"docs":{},"动":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}},"基":{"docs":{},"本":{"docs":{},"概":{"docs":{},"念":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}},"安":{"docs":{},"装":{"docs":{},"目":{"docs":{},"录":{"docs":{},"的":{"docs":{},"l":{"docs":{},"i":{"docs":{},"b":{"docs":{},"目":{"docs":{},"录":{"docs":{},"下":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}}}},"支":{"docs":{},"持":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"类":{"docs":{},"型":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}},"是":{"docs":{},"数":{"docs":{},"据":{"docs":{},"仓":{"docs":{},"库":{"docs":{},"工":{"docs":{},"具":{"docs":{},"，":{"docs":{},"没":{"docs":{},"有":{"docs":{},"集":{"docs":{},"群":{"docs":{},"的":{"docs":{},"概":{"docs":{},"念":{"docs":{},"，":{"docs":{},"如":{"docs":{},"果":{"docs":{},"想":{"docs":{},"提":{"docs":{},"交":{"docs":{},"h":{"docs":{},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{},"作":{"docs":{},"业":{"docs":{},"只":{"docs":{},"需":{"docs":{},"要":{"docs":{},"在":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},"集":{"docs":{},"群":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"目":{"docs":{},"前":{"docs":{},"大":{"docs":{},"数":{"docs":{},"据":{"docs":{},"领":{"docs":{},"域":{"docs":{},"，":{"docs":{},"事":{"docs":{},"实":{"docs":{},"上":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"仓":{"docs":{},"库":{"docs":{},"标":{"docs":{},"准":{"docs":{},"。":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}}}}}}}}}}}}}}}}}}}}}},"的":{"docs":{},"m":{"docs":{},"e":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{},"e":{"docs":{},"元":{"docs":{},"数":{"docs":{},"据":{"docs":{},"服":{"docs":{},"务":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}}}}}}},"内":{"docs":{},"部":{"docs":{},"表":{"docs":{},"和":{"docs":{},"外":{"docs":{},"部":{"docs":{},"表":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}}}},"简":{"docs":{},"介":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}},"，":{"docs":{},"每":{"docs":{},"一":{"docs":{},"个":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}},">":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.008032128514056224}},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.007407407407407408}}}}}}}}},"不":{"docs":{},"会":{"docs":{},"对":{"docs":{},"s":{"docs":{},"t":{"docs":{},"u":{"docs":{},"d":{"docs":{},"e":{"docs":{},"n":{"docs":{},"t":{"docs":{},".":{"docs":{},"t":{"docs":{},"x":{"docs":{},"t":{"docs":{},"做":{"docs":{},"任":{"docs":{},"何":{"docs":{},"格":{"docs":{},"式":{"docs":{},"处":{"docs":{},"理":{"docs":{},"，":{"docs":{},"因":{"docs":{},"为":{"docs":{},"h":{"docs":{},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{},"本":{"docs":{},"身":{"docs":{},"并":{"docs":{},"不":{"docs":{},"强":{"docs":{},"调":{"docs":{},"数":{"docs":{},"据":{"docs":{},"的":{"docs":{},"存":{"docs":{},"储":{"docs":{},"格":{"docs":{},"式":{"docs":{},"。":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"会":{"docs":{},"自":{"docs":{},"动":{"docs":{},"添":{"docs":{},"加":{"docs":{},"分":{"docs":{},"区":{"docs":{},"列":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}}}},"把":{"docs":{},"查":{"docs":{},"询":{"docs":{},"的":{"docs":{},"结":{"docs":{},"果":{"docs":{},"变":{"docs":{},"成":{"docs":{},"了":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{},"e":{"docs":{},"作":{"docs":{},"业":{"docs":{},"通":{"docs":{},"过":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},"执":{"docs":{},"行":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"q":{"docs":{},"l":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}},"从":{"docs":{},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},"中":{"docs":{},"加":{"docs":{},"载":{"docs":{},"p":{"docs":{},"y":{"docs":{},"t":{"docs":{},"h":{"docs":{},"o":{"docs":{},"n":{"docs":{},"脚":{"docs":{},"本":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}}}}}}}}}}}}},"目":{"docs":{},"录":{"docs":{},"下":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}},"综":{"docs":{},"合":{"docs":{},"案":{"docs":{},"例":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}}}},"s":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{},"i":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}},"g":{"docs":{},"h":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282}}}}},"t":{"docs":{},"t":{"docs":{},"p":{"docs":{},":":{"docs":{},"/":{"docs":{},"/":{"1":{"9":{"2":{"docs":{},",":{"1":{"6":{"8":{"docs":{},".":{"1":{"9":{"docs":{},".":{"1":{"3":{"7":{"docs":{},":":{"8":{"0":{"8":{"8":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},".":{"1":{"6":{"8":{"docs":{},".":{"1":{"9":{"docs":{},".":{"1":{"3":{"7":{"docs":{},":":{"4":{"0":{"4":{"0":{"docs":{},"/":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}},"docs":{}},"docs":{}},"docs":{}},"8":{"0":{"8":{"0":{"docs":{},"/":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{},"p":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"a":{"docs":{},"s":{"docs":{},".":{"docs":{},"p":{"docs":{},"y":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},".":{"docs":{},"o":{"docs":{},"r":{"docs":{},"g":{"docs":{},"/":{"docs":{},"p":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"a":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}}}}}}}}}}}}}}}}}}}},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},".":{"docs":{},"a":{"docs":{},"p":{"docs":{},"a":{"docs":{},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},".":{"docs":{},"o":{"docs":{},"r":{"docs":{},"g":{"docs":{},"/":{"docs":{},"d":{"docs":{},"o":{"docs":{},"c":{"docs":{},"s":{"docs":{},"/":{"docs":{},"r":{"1":{"docs":{},".":{"0":{"docs":{},".":{"4":{"docs":{},"/":{"docs":{},"c":{"docs":{},"n":{"docs":{},"/":{"docs":{},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},"_":{"docs":{},"s":{"docs":{},"h":{"docs":{},"e":{"docs":{},"l":{"docs":{},"l":{"docs":{},".":{"docs":{},"h":{"docs":{},"t":{"docs":{},"m":{"docs":{},"l":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625}}}}}}}}}}}}}}}}}}}}}},"docs":{}}},"docs":{}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}},"a":{"docs":{},"r":{"docs":{},"c":{"docs":{},"h":{"docs":{},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{},".":{"docs":{},"c":{"docs":{},"l":{"docs":{},"o":{"docs":{},"u":{"docs":{},"d":{"docs":{},"e":{"docs":{},"r":{"docs":{},"a":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"m":{"docs":{},"/":{"docs":{},"c":{"docs":{},"d":{"docs":{},"h":{"5":{"docs":{},"/":{"docs":{},"c":{"docs":{},"d":{"docs":{},"h":{"docs":{},"/":{"5":{"docs":{},"/":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907},"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}},"h":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}},"docs":{}}}}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}},"w":{"docs":{},"w":{"docs":{},"w":{"docs":{},".":{"docs":{},"i":{"docs":{},"t":{"docs":{},"c":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},".":{"docs":{},"c":{"docs":{},"n":{"docs":{},"/":{"1":{"docs":{},".":{"docs":{},"h":{"docs":{},"t":{"docs":{},"m":{"docs":{},"l":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}}}},"2":{"docs":{},".":{"docs":{},"h":{"docs":{},"t":{"docs":{},"m":{"docs":{},"l":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}}}},"3":{"docs":{},".":{"docs":{},"h":{"docs":{},"t":{"docs":{},"m":{"docs":{},"l":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}}}},"4":{"docs":{},".":{"docs":{},"h":{"docs":{},"t":{"docs":{},"m":{"docs":{},"l":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}}}},"5":{"docs":{},".":{"docs":{},"h":{"docs":{},"t":{"docs":{},"m":{"docs":{},"l":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}}}},"docs":{}}}}}}}}}}}}}}}}}},"s":{"docs":{},":":{"docs":{},"/":{"docs":{},"/":{"docs":{},"c":{"docs":{},"w":{"docs":{},"i":{"docs":{},"k":{"docs":{},"i":{"docs":{},".":{"docs":{},"a":{"docs":{},"p":{"docs":{},"a":{"docs":{},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},".":{"docs":{},"o":{"docs":{},"r":{"docs":{},"g":{"docs":{},"/":{"docs":{},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"f":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"n":{"docs":{},"c":{"docs":{},"e":{"docs":{},"/":{"docs":{},"d":{"docs":{},"i":{"docs":{},"s":{"docs":{},"p":{"docs":{},"l":{"docs":{},"a":{"docs":{},"y":{"docs":{},"/":{"docs":{},"h":{"docs":{},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{},"/":{"docs":{},"l":{"docs":{},"a":{"docs":{},"n":{"docs":{},"g":{"docs":{},"u":{"docs":{},"a":{"docs":{},"g":{"docs":{},"e":{"docs":{},"m":{"docs":{},"a":{"docs":{},"n":{"docs":{},"u":{"docs":{},"a":{"docs":{},"l":{"docs":{},"+":{"docs":{},"u":{"docs":{},"d":{"docs":{},"f":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"/":{"1":{"docs":{},".":{"0":{"docs":{},"\"":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}},"1":{"docs":{},"\"":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.020134228187919462}}}},"docs":{}}},"docs":{}}}},"m":{"docs":{},"l":{"docs":{},"、":{"docs":{},"各":{"docs":{},"类":{"docs":{},"报":{"docs":{},"表":{"docs":{},"、":{"docs":{},"图":{"docs":{},"像":{"docs":{},"和":{"docs":{},"音":{"docs":{},"频":{"docs":{},"/":{"docs":{},"视":{"docs":{},"频":{"docs":{},"信":{"docs":{},"息":{"docs":{},"等":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}}}}}}}}}}}}}}}}},"o":{"docs":{},"m":{"docs":{},"e":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428}}}},"r":{"docs":{},"t":{"docs":{},"o":{"docs":{},"n":{"docs":{},"w":{"docs":{},"o":{"docs":{},"r":{"docs":{},"k":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}},"s":{"docs":{},"t":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}},"e":{"docs":{},"a":{"docs":{},"p":{"docs":{},"q":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}},".":{"docs":{},"n":{"docs":{},"l":{"docs":{},"a":{"docs":{},"r":{"docs":{},"g":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{},"(":{"2":{"docs":{},",":{"docs":{},"w":{"docs":{},"o":{"docs":{},"r":{"docs":{},"d":{"docs":{},"_":{"docs":{},"c":{"docs":{},"n":{"docs":{},"t":{"docs":{},"s":{"docs":{},")":{"docs":{},":":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}}}}}}}}},"docs":{}}}}}}}}}}}},"(":{"docs":{},"使":{"docs":{},"用":{"docs":{},"操":{"docs":{},"作":{"docs":{},"系":{"docs":{},"统":{"docs":{},"层":{"docs":{},"面":{"docs":{},"上":{"docs":{},"的":{"docs":{},"内":{"docs":{},"存":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}}}}}}}}}}}}}}}},",":{"docs":{},"意":{"docs":{},"味":{"docs":{},"着":{"docs":{},"j":{"docs":{},"v":{"docs":{},"m":{"docs":{},"堆":{"docs":{},"以":{"docs":{},"外":{"docs":{},"的":{"docs":{},"内":{"docs":{},"存":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}},"d":{"docs":{},"e":{"docs":{},"r":{"docs":{},"=":{"docs":{},"t":{"docs":{},"r":{"docs":{},"u":{"docs":{},"e":{"docs":{},")":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0024469820554649264},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289}}},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0024469820554649264},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}},"_":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"_":{"docs":{},"o":{"docs":{},"n":{"docs":{},"_":{"docs":{},"w":{"docs":{},"r":{"docs":{},"i":{"docs":{},"t":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}}}}}}},"q":{"docs":{},"l":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.009259259259259259}},"(":{"docs":{},"h":{"docs":{},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}},"操":{"docs":{},"作":{"docs":{},"初":{"docs":{},"体":{"docs":{},"验":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}}},"l":{"docs":{},"o":{"docs":{},"g":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}},"m":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}},"启":{"docs":{},"动":{"docs":{},",":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}},"失":{"docs":{},"效":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}},"将":{"docs":{},"失":{"docs":{},"效":{"docs":{},"r":{"docs":{},"e":{"docs":{},"g":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"v":{"docs":{},"e":{"docs":{},"r":{"docs":{},"上":{"docs":{},"的":{"docs":{},"r":{"docs":{},"e":{"docs":{},"g":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"分":{"docs":{},"配":{"docs":{},"到":{"docs":{},"其":{"docs":{},"他":{"docs":{},"节":{"docs":{},"点":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"更":{"docs":{},"新":{"docs":{},"h":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},":":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}}}}}}}}}},"r":{"docs":{},"e":{"docs":{},"g":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"v":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}}}}}},"s":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{},"e":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}},"m":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}},"l":{"docs":{"day03_Hadoop/ha4.1.html":{"ref":"day03_Hadoop/ha4.1.html","tf":0.023809523809523808}},"l":{"docs":{},"i":{"docs":{},"b":{"docs":{"day03_Hadoop/ha4.1.html":{"ref":"day03_Hadoop/ha4.1.html","tf":0.023809523809523808}},"的":{"docs":{},"区":{"docs":{},"别":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}},"：":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}},"、":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"i":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}},"：":{"docs":{},"模":{"docs":{},"型":{"docs":{},"训":{"docs":{},"练":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"，":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}},"的":{"docs":{},"模":{"docs":{},"型":{"docs":{},"训":{"docs":{},"练":{"docs":{},"是":{"docs":{},"基":{"docs":{},"于":{"docs":{},"内":{"docs":{},"存":{"docs":{},"的":{"docs":{},"，":{"docs":{},"如":{"docs":{},"果":{"docs":{},"数":{"docs":{},"据":{"docs":{},"过":{"docs":{},"大":{"docs":{},"，":{"docs":{},"内":{"docs":{},"存":{"docs":{},"空":{"docs":{},"间":{"docs":{},"小":{"docs":{},"，":{"docs":{},"迭":{"docs":{},"代":{"docs":{},"次":{"docs":{},"数":{"docs":{},"过":{"docs":{},"多":{"docs":{},"的":{"docs":{},"化":{"docs":{},"，":{"docs":{},"可":{"docs":{},"能":{"docs":{},"会":{"docs":{},"造":{"docs":{},"成":{"docs":{},"内":{"docs":{},"存":{"docs":{},"溢":{"docs":{},"出":{"docs":{},"，":{"docs":{},"报":{"docs":{},"错":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"e":{"docs":{},"r":{"docs":{},"c":{"docs":{},"h":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"i":{"docs":{},"s":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775},"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}},"g":{"docs":{"day03_Hadoop/ha3.5.html":{"ref":"day03_Hadoop/ha3.5.html","tf":0.030303030303030304}}}},"t":{"docs":{},"r":{"docs":{},"i":{"docs":{},"c":{"docs":{},"=":{"docs":{},"\"":{"docs":{},"j":{"docs":{},"a":{"docs":{},"c":{"docs":{},"c":{"docs":{},"a":{"docs":{},"r":{"docs":{},"d":{"docs":{},"\"":{"docs":{},")":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.005649717514124294}}}}}}}}}}}}}}}},"h":{"docs":{},"o":{"docs":{},"d":{"docs":{},".":{"docs":{},"l":{"docs":{},"o":{"docs":{},"w":{"docs":{},"e":{"docs":{},"r":{"docs":{},"(":{"docs":{},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683}}}}}}}}}},":":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}},"=":{"docs":{},"\"":{"docs":{},"a":{"docs":{},"l":{"docs":{},"l":{"docs":{},"\"":{"docs":{},")":{"docs":{},":":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}}}}}}},"a":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{"day03_Hadoop/ha2.4.html":{"ref":"day03_Hadoop/ha2.4.html","tf":0.03333333333333333}}}}}},"s":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}},"s":{"docs":{},"o":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}},"m":{"docs":{},"o":{"docs":{},"r":{"docs":{},"y":{"docs":{},"：":{"docs":{},"达":{"docs":{},"到":{"8":{"0":{"docs":{},"%":{"docs":{},"数":{"docs":{},"据":{"docs":{},"时":{"docs":{},"，":{"docs":{},"将":{"docs":{},"数":{"docs":{},"据":{"docs":{},"锁":{"docs":{},"在":{"docs":{},"内":{"docs":{},"存":{"docs":{},"上":{"docs":{},"，":{"docs":{},"将":{"docs":{},"这":{"docs":{},"部":{"docs":{},"分":{"docs":{},"输":{"docs":{},"出":{"docs":{},"到":{"docs":{},"磁":{"docs":{},"盘":{"docs":{},"上":{"docs":{"day03_Hadoop/ha3.5.html":{"ref":"day03_Hadoop/ha3.5.html","tf":0.030303030303030304}}}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{}},"docs":{}}}}}}}},"a":{"docs":{},"n":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.008032128514056224},"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}},"(":{"docs":{},")":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}},"s":{"docs":{},"[":{"docs":{},"'":{"docs":{},"g":{"docs":{},"e":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"r":{"docs":{},"'":{"docs":{},"]":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}}}}}}},"a":{"docs":{},"r":{"docs":{},"z":{"docs":{},"提":{"docs":{},"出":{"docs":{},"的":{"docs":{},"一":{"docs":{},"个":{"docs":{},"实":{"docs":{},"时":{"docs":{},"大":{"docs":{},"数":{"docs":{},"据":{"docs":{},"处":{"docs":{},"理":{"docs":{},"框":{"docs":{},"架":{"docs":{},"。":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}}}}}}}}}}}}}}}}}},"e":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.003430531732418525}},"(":{"docs":{},"p":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"i":{"docs":{},"c":{"docs":{},"t":{"docs":{},"_":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"u":{"docs":{},"l":{"docs":{},"t":{"docs":{},"s":{"docs":{},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}},":":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}}}}}}}}}}}}}},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}},"评":{"docs":{},"估":{"docs":{},"指":{"docs":{},"标":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}},"t":{"docs":{},"h":{"docs":{"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}},"r":{"docs":{},"i":{"docs":{},"x":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}},"，":{"docs":{},"所":{"docs":{},"以":{"docs":{},"这":{"docs":{},"里":{"docs":{},"可":{"docs":{},"以":{"docs":{},"不":{"docs":{},"用":{"docs":{},"运":{"docs":{},"行":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}},"p":{"docs":{"day03_Hadoop/ha3.2.html":{"ref":"day03_Hadoop/ha3.2.html","tf":0.02040816326530612},"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196},"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}},"(":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}},")":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876}}},"f":{"docs":{},"u":{"docs":{},"n":{"docs":{},",":{"docs":{},"可":{"docs":{},"迭":{"docs":{},"代":{"docs":{},"对":{"docs":{},"象":{"docs":{},")":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}},"c":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},",":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}},")":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}},"l":{"docs":{},"a":{"docs":{},"m":{"docs":{},"b":{"docs":{},"d":{"docs":{},"a":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876},"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}}}}}}}},"s":{"docs":{},"q":{"docs":{},"u":{"docs":{},"a":{"docs":{},"r":{"docs":{},"e":{"docs":{},",":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}},"i":{"docs":{},"n":{"docs":{},"_":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{},",":{"docs":{},"i":{"docs":{},"n":{"docs":{},"_":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},")":{"docs":{"day03_Hadoop/ha3.2.html":{"ref":"day03_Hadoop/ha3.2.html","tf":0.02040816326530612}}}}}}}}}}}}}}}}}},"t":{"docs":{},"u":{"docs":{},"p":{"docs":{},"l":{"docs":{},"e":{"docs":{},",":{"docs":{},"[":{"docs":{},"g":{"docs":{},"e":{"docs":{},"t":{"docs":{},"_":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"u":{"docs":{},"l":{"docs":{},"t":{"docs":{},"(":{"docs":{},"i":{"docs":{},"p":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}}}}}}}}}}}}}}}}}}}},"函":{"docs":{},"数":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}},"r":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.03571428571428571}},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282},"day03_Hadoop/ha3.2.html":{"ref":"day03_Hadoop/ha3.2.html","tf":0.02040816326530612},"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.012605042016806723},"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.027777777777777776}},"e":{"2":{"docs":{},".":{"docs":{},"x":{"docs":{},"架":{"docs":{},"构":{"docs":{"day03_Hadoop/ha3.5.html":{"ref":"day03_Hadoop/ha3.5.html","tf":0.030303030303030304}}}}}}},"docs":{},":":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009},"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282}}},"和":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}}}}}},"是":{"docs":{},"g":{"docs":{},"o":{"docs":{},"o":{"docs":{},"g":{"docs":{},"l":{"docs":{},"e":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{},"e":{"docs":{},"的":{"docs":{},"开":{"docs":{},"源":{"docs":{},"实":{"docs":{},"现":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282}}}}}}}}}}}}}}}}}}}}}}},"特":{"docs":{},"点":{"docs":{},":":{"docs":{},"扩":{"docs":{},"展":{"docs":{},"性":{"docs":{},"&":{"docs":{},"容":{"docs":{},"错":{"docs":{},"性":{"docs":{},"&":{"docs":{},"海":{"docs":{},"量":{"docs":{},"数":{"docs":{},"据":{"docs":{},"离":{"docs":{},"线":{"docs":{},"处":{"docs":{},"理":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282}}}}}}}}}}}}}}}}}}}}},".":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"w":{"docs":{},"o":{"docs":{},"r":{"docs":{},"k":{"docs":{},".":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428},"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}}}}}}}}}}},"j":{"docs":{},"o":{"docs":{},"b":{"docs":{},".":{"docs":{},"p":{"docs":{},"r":{"docs":{},"i":{"docs":{},"o":{"docs":{},"r":{"docs":{},"i":{"docs":{},"t":{"docs":{},"y":{"docs":{},"=":{"docs":{},"v":{"docs":{},"e":{"docs":{},"r":{"docs":{},"y":{"docs":{},"_":{"docs":{},"h":{"docs":{},"i":{"docs":{},"g":{"docs":{},"h":{"docs":{},"。":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}}}}}}}}}}}}}}}}}}},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},".":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"k":{"docs":{},"s":{"docs":{},"=":{"2":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}},"docs":{}}}}}}}}}}},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{},"e":{"docs":{},".":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"k":{"docs":{},"s":{"docs":{},"=":{"5":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}},"docs":{}}}}}}}}}}}}}}},"_":{"docs":{},"s":{"docs":{},"h":{"docs":{},"u":{"docs":{},"f":{"docs":{},"f":{"docs":{},"l":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428},"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}}}}},"优":{"docs":{},"点":{"docs":{},":":{"docs":{"day03_Hadoop/ha3.2.html":{"ref":"day03_Hadoop/ha3.2.html","tf":0.02040816326530612}}}}},"分":{"docs":{},"而":{"docs":{},"治":{"docs":{},"之":{"docs":{},"的":{"docs":{},"思":{"docs":{},"想":{"docs":{"day03_Hadoop/ha3.2.html":{"ref":"day03_Hadoop/ha3.2.html","tf":0.02040816326530612}}}}}}}}},"编":{"docs":{},"程":{"docs":{},"分":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"和":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{},"e":{"docs":{},"阶":{"docs":{},"段":{"docs":{"day03_Hadoop/ha3.2.html":{"ref":"day03_Hadoop/ha3.2.html","tf":0.02040816326530612}}}}}}}}}}}}}}},"执":{"docs":{},"行":{"docs":{},"步":{"docs":{},"骤":{"docs":{"day03_Hadoop/ha3.2.html":{"ref":"day03_Hadoop/ha3.2.html","tf":0.02040816326530612}}}}}},"模":{"docs":{},"型":{"docs":{"day03_Hadoop/ha3.2.html":{"ref":"day03_Hadoop/ha3.2.html","tf":0.02040816326530612}}}}}},"缺":{"docs":{},"点":{"docs":{},":":{"docs":{"day03_Hadoop/ha3.2.html":{"ref":"day03_Hadoop/ha3.2.html","tf":0.02040816326530612}}}}},"实":{"docs":{},"战":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}},"原":{"docs":{},"理":{"docs":{},"详":{"docs":{},"解":{"docs":{"day03_Hadoop/ha3.5.html":{"ref":"day03_Hadoop/ha3.5.html","tf":0.030303030303030304}}}}}},"架":{"docs":{},"构":{"docs":{"day03_Hadoop/ha3.5.html":{"ref":"day03_Hadoop/ha3.5.html","tf":0.06060606060606061}}}},"，":{"docs":{},"减":{"docs":{},"少":{"docs":{},"开":{"docs":{},"发":{"docs":{},"人":{"docs":{},"员":{"docs":{},"的":{"docs":{},"学":{"docs":{},"习":{"docs":{},"成":{"docs":{},"本":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}}}}},"中":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"和":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{},"e":{"docs":{},"任":{"docs":{},"务":{"docs":{},"都":{"docs":{},"是":{"docs":{},"以":{"docs":{},"进":{"docs":{},"程":{"docs":{},"的":{"docs":{},"方":{"docs":{},"式":{"docs":{},"运":{"docs":{},"行":{"docs":{},"着":{"docs":{},"，":{"docs":{},"而":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"中":{"docs":{},"的":{"docs":{},"j":{"docs":{},"o":{"docs":{},"b":{"docs":{},"是":{"docs":{},"以":{"docs":{},"线":{"docs":{},"程":{"docs":{},"方":{"docs":{},"式":{"docs":{},"运":{"docs":{},"行":{"docs":{},"在":{"docs":{},"进":{"docs":{},"程":{"docs":{},"中":{"docs":{},"。":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"框":{"docs":{},"架":{"docs":{},"局":{"docs":{},"限":{"docs":{},"性":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}}}}}}}}}},"阶":{"docs":{},"段":{"docs":{"day03_Hadoop/ha3.2.html":{"ref":"day03_Hadoop/ha3.2.html","tf":0.02040816326530612}}}},"p":{"docs":{},"e":{"docs":{},"r":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.012048192771084338}},"(":{"docs":{},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"f":{"docs":{},",":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.011695906432748537}}}}}}}}}}},"：":{"docs":{},"将":{"docs":{},"前":{"docs":{},"面":{"docs":{},"切":{"docs":{},"分":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"做":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"处":{"docs":{},"理":{"docs":{},"(":{"docs":{},"将":{"docs":{},"数":{"docs":{},"据":{"docs":{},"进":{"docs":{},"行":{"docs":{},"分":{"docs":{},"类":{"docs":{},"，":{"docs":{},"输":{"docs":{},"出":{"docs":{},"(":{"docs":{},"k":{"docs":{},",":{"docs":{},"v":{"docs":{},")":{"docs":{},"键":{"docs":{},"值":{"docs":{},"对":{"docs":{},"数":{"docs":{},"据":{"docs":{},")":{"docs":{"day03_Hadoop/ha3.5.html":{"ref":"day03_Hadoop/ha3.5.html","tf":0.030303030303030304}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"数":{"docs":{},"据":{"docs":{},"进":{"docs":{},"行":{"docs":{},"处":{"docs":{},"理":{"docs":{"day03_Hadoop/ha3.5.html":{"ref":"day03_Hadoop/ha3.5.html","tf":0.030303030303030304}}}}}}}}}},":":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}},"就":{"docs":{},"是":{"docs":{},"一":{"docs":{},"个":{"docs":{},"t":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"s":{"docs":{},"f":{"docs":{},"o":{"docs":{},"r":{"docs":{},"m":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}}}}}}},"返":{"docs":{},"回":{"docs":{},"的":{"docs":{},"结":{"docs":{},"果":{"docs":{},"是":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"类":{"docs":{},"型":{"docs":{},"，":{"docs":{},"需":{"docs":{},"要":{"docs":{},"调":{"docs":{},"用":{"docs":{},"t":{"docs":{},"o":{"docs":{},"d":{"docs":{},"f":{"docs":{},"方":{"docs":{},"法":{"docs":{},"转":{"docs":{},"换":{"docs":{},"为":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"x":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}}}}},"(":{"docs":{},")":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}},"n":{"docs":{},"a":{"docs":{},"g":{"docs":{},"e":{"docs":{},"m":{"docs":{},"e":{"docs":{},"n":{"docs":{},"t":{"docs":{},".":{"docs":{},"(":{"docs":{},"资":{"docs":{},"源":{"docs":{},"调":{"docs":{},"度":{"docs":{},"系":{"docs":{},"统":{"docs":{},")":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282}}}}}}}}}}}}}}}}}}},"s":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577},"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.03225806451612903}},":":{"2":{"1":{"8":{"1":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}},"docs":{}},"docs":{}},"docs":{}},"6":{"0":{"0":{"0":{"0":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"9":{"0":{"0":{"0":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428}},"/":{"docs":{},"h":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"节":{"docs":{},"点":{"docs":{},"选":{"docs":{},"举":{"docs":{"day03_Hadoop/ha4.2.html":{"ref":"day03_Hadoop/ha4.2.html","tf":0.0196078431372549}}}},"上":{"docs":{},"装":{"docs":{},"h":{"docs":{},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{},"就":{"docs":{},"可":{"docs":{},"以":{"docs":{},"了":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}}}}},"，":{"docs":{},"避":{"docs":{},"免":{"docs":{},"单":{"docs":{},"点":{"docs":{},"问":{"docs":{},"题":{"docs":{},"；":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}}}},".":{"docs":{},"s":{"docs":{},"h":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}}},"和":{"docs":{},"w":{"docs":{},"o":{"docs":{},"r":{"docs":{},"k":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}}}}},"：":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"a":{"docs":{},"l":{"docs":{},"o":{"docs":{},"n":{"docs":{},"e":{"docs":{},"模":{"docs":{},"式":{"docs":{},"中":{"docs":{},"主":{"docs":{},"控":{"docs":{},"节":{"docs":{},"点":{"docs":{},"，":{"docs":{},"负":{"docs":{},"责":{"docs":{},"接":{"docs":{},"收":{"docs":{},"c":{"docs":{},"l":{"docs":{},"i":{"docs":{},"e":{"docs":{},"n":{"docs":{},"t":{"docs":{},"提":{"docs":{},"交":{"docs":{},"的":{"docs":{},"作":{"docs":{},"业":{"docs":{},"，":{"docs":{},"管":{"docs":{},"理":{"docs":{},"w":{"docs":{},"o":{"docs":{},"r":{"docs":{},"k":{"docs":{},"e":{"docs":{},"r":{"docs":{},"，":{"docs":{},"并":{"docs":{},"命":{"docs":{},"令":{"docs":{},"w":{"docs":{},"o":{"docs":{},"r":{"docs":{},"k":{"docs":{},"e":{"docs":{},"r":{"docs":{},"启":{"docs":{},"动":{"docs":{},"d":{"docs":{},"r":{"docs":{},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{},"r":{"docs":{},"和":{"docs":{},"e":{"docs":{},"x":{"docs":{},"e":{"docs":{},"c":{"docs":{},"u":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{},"。":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"的":{"docs":{},"地":{"docs":{},"址":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}},"i":{"docs":{},"n":{"docs":{},"(":{"docs":{},")":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269},"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017},"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}},":":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269},"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017},"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}}},"h":{"docs":{},"o":{"docs":{},"u":{"docs":{},"t":{"docs":{},":":{"docs":{},"机":{"docs":{},"器":{"docs":{},"学":{"docs":{},"习":{"docs":{},"库":{"docs":{"day03_Hadoop/ha4.1.html":{"ref":"day03_Hadoop/ha4.1.html","tf":0.023809523809523808}}}}}}}}}}}}},"y":{"docs":{},"s":{"docs":{},"q":{"docs":{},"l":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863},"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.018518518518518517}},"/":{"docs":{},"d":{"docs":{},"e":{"docs":{},"r":{"docs":{},"b":{"docs":{},"i":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}},"驱":{"docs":{},"动":{"docs":{},"到":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}},"_":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"e":{"docs":{},".":{"docs":{},"t":{"docs":{},"x":{"docs":{},"t":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}}}},"m":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"l":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}},".":{"docs":{},"r":{"docs":{},"e":{"docs":{},"c":{"docs":{},"o":{"docs":{},"m":{"docs":{},"m":{"docs":{},"e":{"docs":{},"n":{"docs":{},"d":{"docs":{},"f":{"docs":{},"o":{"docs":{},"r":{"docs":{},"a":{"docs":{},"l":{"docs":{},"l":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"s":{"docs":{},"(":{"3":{"docs":{},")":{"docs":{},".":{"docs":{},"f":{"docs":{},"i":{"docs":{},"r":{"docs":{},"s":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"o":{"docs":{},"v":{"docs":{},"i":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}},"e":{"docs":{},"的":{"docs":{},"评":{"docs":{},"分":{"docs":{},"矩":{"docs":{},"阵":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}}}}}}},"_":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.009727626459143969},"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}},".":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"_":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"m":{"docs":{},"o":{"docs":{},"v":{"docs":{},"i":{"docs":{},"e":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},",":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}}}}}}}},":":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876}}},"[":{"docs":{},"\"":{"docs":{},"t":{"docs":{},"a":{"docs":{},"g":{"docs":{},"s":{"docs":{},"\"":{"docs":{},"]":{"docs":{},".":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876}}}}}}}}}}}}}}}}}}}}}},"p":{"docs":{},"r":{"docs":{},"o":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.007782101167315175},"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}},"e":{"docs":{},".":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"_":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"m":{"docs":{},"o":{"docs":{},"v":{"docs":{},"i":{"docs":{},"e":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},",":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}}}}}}},"l":{"docs":{},"o":{"docs":{},"c":{"docs":{},"[":{"docs":{},"l":{"docs":{},"i":{"docs":{},"s":{"docs":{},"t":{"docs":{},"(":{"docs":{},"m":{"docs":{},"i":{"docs":{},"d":{"docs":{},"s":{"docs":{},")":{"docs":{},"]":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}}}}}}}}}}}}}}}},"[":{"docs":{},"\"":{"docs":{},"w":{"docs":{},"e":{"docs":{},"i":{"docs":{},"g":{"docs":{},"h":{"docs":{},"t":{"docs":{},"s":{"docs":{},"\"":{"docs":{},"]":{"docs":{},".":{"docs":{},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"m":{"docs":{},"s":{"docs":{},"(":{"docs":{},")":{"docs":{},":":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}}}}}}}}}},"m":{"docs":{},"i":{"docs":{},"d":{"docs":{},"]":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}},"t":{"docs":{},"a":{"docs":{},"g":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876}},"s":{"docs":{},")":{"docs":{},")":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876}}}}}}}}},"s":{"docs":{},".":{"docs":{},"j":{"docs":{},"o":{"docs":{},"i":{"docs":{},"n":{"docs":{},"(":{"docs":{},"n":{"docs":{},"e":{"docs":{},"w":{"docs":{},"_":{"docs":{},"t":{"docs":{},"a":{"docs":{},"g":{"docs":{},"s":{"docs":{},")":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}},"[":{"docs":{},"\"":{"docs":{},"g":{"docs":{},"e":{"docs":{},"n":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"\"":{"docs":{},"]":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}},".":{"docs":{},"a":{"docs":{},"p":{"docs":{},"p":{"docs":{},"l":{"docs":{},"y":{"docs":{},"(":{"docs":{},"l":{"docs":{},"a":{"docs":{},"m":{"docs":{},"b":{"docs":{},"d":{"docs":{},"a":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}}}}}}}}}},"_":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}},"d":{"docs":{},"e":{"docs":{},"l":{"2":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},".":{"docs":{},"p":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"i":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},"[":{"0":{"docs":{},".":{"0":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}}},"docs":{}}}}}}}}}}}},"docs":{"day02_推荐算法/01_基于模型的协同过滤推荐.html":{"ref":"day02_推荐算法/01_基于模型的协同过滤推荐.html","tf":0.07692307692307693},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147},"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}},")":{"docs":{},"隐":{"docs":{},"语":{"docs":{},"义":{"docs":{},"模":{"docs":{},"型":{"docs":{},"核":{"docs":{},"心":{"docs":{},"思":{"docs":{},"想":{"docs":{},"是":{"docs":{},"通":{"docs":{},"过":{"docs":{},"隐":{"docs":{},"含":{"docs":{},"特":{"docs":{},"征":{"docs":{},"联":{"docs":{},"系":{"docs":{},"用":{"docs":{},"户":{"docs":{},"和":{"docs":{},"物":{"docs":{},"品":{"docs":{},"，":{"docs":{},"如":{"docs":{},"下":{"docs":{},"图":{"docs":{},"：":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"[":{"docs":{},"c":{"docs":{},"o":{"docs":{},"r":{"docs":{},"p":{"docs":{},"u":{"docs":{},"s":{"docs":{},"[":{"docs":{},"i":{"docs":{},"]":{"docs":{},"]":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876}}}}}}}}}}}}},".":{"docs":{},"r":{"docs":{},"e":{"docs":{},"c":{"docs":{},"o":{"docs":{},"m":{"docs":{},"m":{"docs":{},"e":{"docs":{},"n":{"docs":{},"d":{"docs":{},"f":{"docs":{},"o":{"docs":{},"r":{"docs":{},"a":{"docs":{},"l":{"docs":{},"l":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"s":{"docs":{},"(":{"3":{"docs":{},")":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}},".":{"docs":{},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}},"docs":{},"n":{"docs":{},")":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0032626427406199023}}}}}}}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"s":{"docs":{},"u":{"docs":{},"b":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}},"(":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"s":{"docs":{},"a":{"docs":{},"v":{"docs":{},"e":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},":":{"docs":{},"/":{"docs":{},"/":{"docs":{},"l":{"docs":{},"o":{"docs":{},"c":{"docs":{},"a":{"docs":{},"l":{"docs":{},"h":{"docs":{},"o":{"docs":{},"s":{"docs":{},"t":{"docs":{},":":{"9":{"0":{"0":{"0":{"docs":{},"/":{"docs":{},"m":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"l":{"docs":{},"s":{"docs":{},"/":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"b":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"r":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"m":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"l":{"docs":{},".":{"docs":{},"o":{"docs":{},"b":{"docs":{},"j":{"docs":{},"\"":{"docs":{},")":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"a":{"docs":{},"l":{"docs":{},"s":{"docs":{},"m":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"l":{"docs":{},".":{"docs":{},"o":{"docs":{},"b":{"docs":{},"j":{"docs":{},"\"":{"docs":{},")":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"c":{"docs":{},"t":{"docs":{},"r":{"docs":{},"m":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"l":{"docs":{},"_":{"docs":{},"n":{"docs":{},"o":{"docs":{},"r":{"docs":{},"m":{"docs":{},"a":{"docs":{},"l":{"docs":{},".":{"docs":{},"o":{"docs":{},"b":{"docs":{},"j":{"docs":{},"\"":{"docs":{},")":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}},"t":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"s":{"docs":{},"f":{"docs":{},"o":{"docs":{},"r":{"docs":{},"m":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}},"(":{"docs":{},"t":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{},"_":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"s":{"docs":{},"_":{"1":{"docs":{},")":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}},"p":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"i":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},"[":{"0":{"docs":{},".":{"0":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}}},"docs":{}},"r":{"docs":{},"d":{"docs":{},"d":{"2":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}},"u":{"docs":{},"l":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}},"e":{"docs":{},"s":{"docs":{},".":{"docs":{},"(":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},"的":{"docs":{},"核":{"docs":{},"心":{"docs":{},"组":{"docs":{},"件":{"docs":{},")":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282}}}}}}}}}}}}}}}}}}}}},"r":{"docs":{},"e":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112},"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"n":{"docs":{},"i":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{},"/":{"1":{"docs":{},".":{"0":{"docs":{},"\"":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.013422818791946308}}}},"docs":{}}},"docs":{}}}}}}}},"i":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}},"d":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876},"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842},"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}},",":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938},"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}}}},"k":{"docs":{},"e":{"docs":{},",":{"1":{"3":{"0":{"0":{"0":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}},"n":{"docs":{},"(":{"docs":{},")":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}},"k":{"docs":{},"d":{"docs":{},"i":{"docs":{},"r":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625},"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704},"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112},"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0022675736961451248}}}}}},"v":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.025}}},"r":{"docs":{},"_":{"docs":{},"w":{"docs":{},"o":{"docs":{},"r":{"docs":{},"d":{"docs":{},"_":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},".":{"docs":{},"p":{"docs":{},"i":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}}}}}}}}}},"j":{"docs":{},"o":{"docs":{},"b":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.029239766081871343}},",":{"docs":{},"m":{"docs":{},"r":{"docs":{},"s":{"docs":{},"t":{"docs":{},"e":{"docs":{},"p":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}}},".":{"docs":{},"j":{"docs":{},"o":{"docs":{},"b":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.011695906432748537}}}}}},"实":{"docs":{},"现":{"docs":{},"w":{"docs":{},"o":{"docs":{},"r":{"docs":{},"d":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}}}}}}},"是":{"docs":{},"最":{"docs":{},"简":{"docs":{},"单":{"docs":{},"的":{"docs":{},"方":{"docs":{},"式":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}}},"程":{"docs":{},"序":{"docs":{},"可":{"docs":{},"以":{"docs":{},"在":{"docs":{},"本":{"docs":{},"地":{"docs":{},"测":{"docs":{},"试":{"docs":{},"运":{"docs":{},"行":{"docs":{},"也":{"docs":{},"可":{"docs":{},"以":{"docs":{},"部":{"docs":{},"署":{"docs":{},"到":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},"集":{"docs":{},"群":{"docs":{},"上":{"docs":{},"运":{"docs":{},"行":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"s":{"docs":{},"t":{"docs":{},"e":{"docs":{},"p":{"docs":{},"(":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"p":{"docs":{},"e":{"docs":{},"r":{"docs":{},"=":{"docs":{},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"f":{"docs":{},".":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"p":{"docs":{},"e":{"docs":{},"r":{"docs":{},",":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}}}}}}}}}}}}}}},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{},"e":{"docs":{},"r":{"docs":{},"=":{"docs":{},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"f":{"docs":{},".":{"docs":{},"t":{"docs":{},"o":{"docs":{},"p":{"docs":{},"_":{"docs":{},"n":{"docs":{},"_":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{},"e":{"docs":{},"r":{"docs":{},")":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"w":{"docs":{},"o":{"docs":{},"r":{"docs":{},"d":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},"m":{"docs":{},"r":{"docs":{},"j":{"docs":{},"o":{"docs":{},"b":{"docs":{},")":{"docs":{},":":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}}}},".":{"docs":{},"r":{"docs":{},"u":{"docs":{},"n":{"docs":{},"(":{"docs":{},")":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}}}}}}}}}}}},":":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}},"u":{"docs":{},"l":{"docs":{},"t":{"docs":{},"i":{"docs":{},"c":{"docs":{},"l":{"docs":{},"a":{"docs":{},"s":{"docs":{},"s":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}},"s":{"3":{"docs":{},"等":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}},"docs":{},"h":{"docs":{},"e":{"docs":{},"l":{"docs":{},"l":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196},"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.004405286343612335}},"的":{"docs":{},"优":{"docs":{},"缺":{"docs":{},"点":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}},"基":{"docs":{},"本":{"docs":{},"使":{"docs":{},"用":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}}}},"操":{"docs":{},"作":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":5.00625}},"练":{"docs":{},"习":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625}}}}}},"（":{"docs":{},"进":{"docs":{},"入":{"docs":{},"s":{"docs":{},"h":{"docs":{},"e":{"docs":{},"l":{"docs":{},"l":{"docs":{},"命":{"docs":{},"令":{"docs":{},"行":{"docs":{},"）":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}}}}},"中":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}},"u":{"docs":{},"f":{"docs":{},"f":{"docs":{},"l":{"docs":{"day03_Hadoop/ha3.2.html":{"ref":"day03_Hadoop/ha3.2.html","tf":0.02040816326530612}},"e":{"docs":{},"方":{"docs":{},"法":{"docs":{},"作":{"docs":{},"用":{"docs":{},"，":{"docs":{},"所":{"docs":{},"以":{"docs":{},"需":{"docs":{},"要":{"docs":{},"强":{"docs":{},"行":{"docs":{},"转":{"docs":{},"换":{"docs":{},"为":{"docs":{},"列":{"docs":{},"表":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}}}}}}}}}}}},"&":{"docs":{},"s":{"docs":{},"o":{"docs":{},"r":{"docs":{},"t":{"docs":{},":":{"docs":{},"将":{"docs":{},"相":{"docs":{},"同":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"放":{"docs":{},"在":{"docs":{},"一":{"docs":{},"起":{"docs":{},"，":{"docs":{},"并":{"docs":{},"对":{"docs":{},"数":{"docs":{},"据":{"docs":{},"进":{"docs":{},"行":{"docs":{},"排":{"docs":{},"序":{"docs":{},"处":{"docs":{},"理":{"docs":{"day03_Hadoop/ha3.5.html":{"ref":"day03_Hadoop/ha3.5.html","tf":0.030303030303030304}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"o":{"docs":{},"w":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.007407407407407408},"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.004078303425774877},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.003671221700999388},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0031834460803820135},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.004228329809725159}}},"u":{"docs":{},"l":{"docs":{},"d":{"docs":{},"_":{"docs":{},"r":{"docs":{},"e":{"docs":{},"m":{"docs":{},"o":{"docs":{},"v":{"docs":{},"e":{"docs":{},"(":{"docs":{},"x":{"docs":{},")":{"docs":{},":":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}},"p":{"docs":{},"p":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"：":{"docs":{},"购":{"docs":{},"物":{"docs":{},"深":{"docs":{},"度":{"docs":{},"，":{"1":{"docs":{},":":{"docs":{},"浅":{"docs":{},"层":{"docs":{},"用":{"docs":{},"户":{"docs":{},",":{"2":{"docs":{},":":{"docs":{},"中":{"docs":{},"度":{"docs":{},"用":{"docs":{},"户":{"docs":{},",":{"3":{"docs":{},":":{"docs":{},"深":{"docs":{},"度":{"docs":{},"用":{"docs":{},"户":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}},"docs":{}}}}}}}},"docs":{}}}}}}}},"docs":{}}}}}}},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}},":":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0010197838058331635},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}},"_":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.004228329809725159}},"e":{"docs":{},"[":{"docs":{},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"p":{"docs":{},"p":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"_":{"docs":{},"r":{"docs":{},"e":{"docs":{},"l":{"docs":{},"a":{"docs":{},"[":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"[":{"docs":{},"\"":{"docs":{},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"p":{"docs":{},"p":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"\"":{"docs":{},"]":{"docs":{},")":{"docs":{},"]":{"docs":{},"]":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"：":{"docs":{},"s":{"docs":{},"h":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"底":{"docs":{},"层":{"docs":{},"使":{"docs":{},"用":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"的":{"docs":{},"基":{"docs":{},"于":{"docs":{},"内":{"docs":{},"存":{"docs":{},"的":{"docs":{},"计":{"docs":{},"算":{"docs":{},"模":{"docs":{},"型":{"docs":{},"，":{"docs":{},"从":{"docs":{},"而":{"docs":{},"让":{"docs":{},"性":{"docs":{},"能":{"docs":{},"比":{"docs":{},"h":{"docs":{},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{},"提":{"docs":{},"升":{"docs":{},"了":{"docs":{},"数":{"docs":{},"倍":{"docs":{},"到":{"docs":{},"上":{"docs":{},"百":{"docs":{},"倍":{"docs":{},"。":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"./":{"ref":"./","tf":0.0449438202247191},"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863},"day03_Hadoop/ha4.1.html":{"ref":"day03_Hadoop/ha4.1.html","tf":0.11904761904761904},"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.012605042016806723},"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102},"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.004545454545454545},"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.020134228187919462},"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.013245033112582781},"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.053763440860215055},"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.06349206349206349},"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.006993006993006993},"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.011538461538461539},"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625},"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.019083969465648856},"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.010526315789473684},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0024469820554649264}},":":{"docs":{"day03_Hadoop/ha4.1.html":{"ref":"day03_Hadoop/ha4.1.html","tf":0.023809523809523808}},"/":{"docs":{},"/":{"1":{"9":{"2":{"docs":{},".":{"1":{"6":{"8":{"docs":{},".":{"1":{"9":{"docs":{},".":{"1":{"3":{"7":{"docs":{},":":{"7":{"0":{"7":{"7":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}}},"s":{"docs":{},"t":{"docs":{},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"m":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"(":{"docs":{},"秒":{"docs":{},")":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}},"是":{"docs":{},"什":{"docs":{},"么":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}}},"的":{"docs":{},"组":{"docs":{},"件":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}}}}}}}}}}},"e":{"docs":{},"s":{"docs":{},"s":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617},"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.013422818791946308},"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391},"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965},"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.007633587786259542},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},".":{"docs":{},"b":{"docs":{},"u":{"docs":{},"i":{"docs":{},"l":{"docs":{},"d":{"docs":{},"e":{"docs":{},"r":{"docs":{},".":{"docs":{},"a":{"docs":{},"p":{"docs":{},"p":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"p":{"docs":{},"v":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"g":{"docs":{},"e":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{},"c":{"docs":{},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.006711409395973154}}}}}}}}}}}}}}}}}}}},"t":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"g":{"docs":{},"e":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{},"c":{"docs":{},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577},"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}}}}}}}}}}}}}}}}},"o":{"docs":{},"p":{"docs":{},"n":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"g":{"docs":{},"e":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{},"c":{"docs":{},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}}}}}}}}}}}}}}}}}}}},"'":{"docs":{},"t":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{},"'":{"docs":{},")":{"docs":{},".":{"docs":{},"g":{"docs":{},"e":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{},"c":{"docs":{},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}}}}}},"j":{"docs":{},"s":{"docs":{},"o":{"docs":{},"n":{"docs":{},"_":{"docs":{},"d":{"docs":{},"e":{"docs":{},"m":{"docs":{},"o":{"docs":{},"'":{"docs":{},")":{"docs":{},".":{"docs":{},"g":{"docs":{},"e":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{},"c":{"docs":{},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"m":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"l":{"docs":{},"o":{"docs":{},"c":{"docs":{},"a":{"docs":{},"l":{"docs":{},"[":{"2":{"docs":{},"]":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"g":{"docs":{},"e":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{},"c":{"docs":{},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.007633587786259542}}}}}}}}}}}}}}}}}}}},"docs":{}}}}}}}}}}}}}}},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"f":{"docs":{},"i":{"docs":{},"g":{"docs":{},"(":{"docs":{},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"f":{"docs":{},"=":{"docs":{},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"f":{"docs":{},")":{"docs":{},".":{"docs":{},"g":{"docs":{},"e":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{},"c":{"docs":{},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"d":{"docs":{},".":{"docs":{},"j":{"docs":{},"s":{"docs":{},"o":{"docs":{},"n":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}}}}}}}}}}}}}},"q":{"docs":{},"l":{"docs":{},"特":{"docs":{},"性":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}}}}}}},"几":{"docs":{},"秒":{"docs":{},"钟":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}},".":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"t":{"docs":{},"e":{"docs":{},"x":{"docs":{},"t":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102},"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.013422818791946308},"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391},"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965},"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.007633587786259542}},".":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},"c":{"docs":{},"k":{"docs":{},"p":{"docs":{},"o":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"d":{"docs":{},"i":{"docs":{},"r":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},":":{"docs":{},"/":{"docs":{},"/":{"docs":{},"l":{"docs":{},"o":{"docs":{},"c":{"docs":{},"a":{"docs":{},"l":{"docs":{},"h":{"docs":{},"o":{"docs":{},"s":{"docs":{},"t":{"docs":{},":":{"9":{"0":{"0":{"0":{"docs":{},"/":{"docs":{},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},"c":{"docs":{},"k":{"docs":{},"p":{"docs":{},"o":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"/":{"docs":{},"\"":{"docs":{},")":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"q":{"docs":{},"l":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}}}}}}}}},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"f":{"docs":{},".":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},".":{"docs":{},"s":{"docs":{},"q":{"docs":{},"l":{"docs":{},".":{"docs":{},"s":{"docs":{},"h":{"docs":{},"u":{"docs":{},"f":{"docs":{},"f":{"docs":{},"l":{"docs":{},"e":{"docs":{},".":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{},"i":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"s":{"docs":{},"\"":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"(":{"docs":{},"p":{"docs":{},"e":{"docs":{},"o":{"docs":{},"p":{"docs":{},"l":{"docs":{},"e":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}},"d":{"docs":{},"f":{"docs":{},")":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}},"[":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00684931506849315}},"[":{"1":{"docs":{},"]":{"docs":{},",":{"docs":{},"[":{"2":{"docs":{},"]":{"docs":{},",":{"docs":{},"[":{"3":{"docs":{},"]":{"docs":{},"]":{"docs":{},")":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}},"docs":{}}}}},"docs":{}}}}},"docs":{}}}}}}}}}}}}}}}}}}},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"d":{"docs":{},".":{"docs":{},"f":{"docs":{},"o":{"docs":{},"r":{"docs":{},"m":{"docs":{},"a":{"docs":{},"t":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"c":{"docs":{},"s":{"docs":{},"v":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}},"j":{"docs":{},"d":{"docs":{},"b":{"docs":{},"c":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"o":{"docs":{},"p":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"u":{"docs":{},"r":{"docs":{},"l":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"docs":{},"j":{"docs":{},"d":{"docs":{},"b":{"docs":{},"c":{"docs":{},":":{"docs":{},"m":{"docs":{},"y":{"docs":{},"s":{"docs":{},"q":{"docs":{},"l":{"docs":{},":":{"docs":{},"/":{"docs":{},"/":{"docs":{},"l":{"docs":{},"o":{"docs":{},"c":{"docs":{},"a":{"docs":{},"l":{"docs":{},"h":{"docs":{},"o":{"docs":{},"s":{"docs":{},"t":{"docs":{},":":{"3":{"3":{"0":{"6":{"docs":{},"/":{"docs":{},"d":{"docs":{},"b":{"docs":{},"_":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"o":{"docs":{},"p":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"d":{"docs":{},"b":{"docs":{},"t":{"docs":{},"a":{"docs":{},"b":{"docs":{},"l":{"docs":{},"e":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"docs":{},"t":{"docs":{},"a":{"docs":{},"b":{"docs":{},"l":{"docs":{},"e":{"docs":{},"_":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"o":{"docs":{},"p":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"docs":{},"x":{"docs":{},"x":{"docs":{},"x":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"o":{"docs":{},"p":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"p":{"docs":{},"a":{"docs":{},"s":{"docs":{},"s":{"docs":{},"w":{"docs":{},"o":{"docs":{},"r":{"docs":{},"d":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"docs":{},"x":{"docs":{},"x":{"docs":{},"x":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"l":{"docs":{},"o":{"docs":{},"a":{"docs":{},"d":{"docs":{},"(":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"'":{"docs":{},"j":{"docs":{},"s":{"docs":{},"o":{"docs":{},"n":{"docs":{},"'":{"docs":{},")":{"docs":{},".":{"docs":{},"l":{"docs":{},"o":{"docs":{},"a":{"docs":{},"d":{"docs":{},"(":{"docs":{},"'":{"docs":{},"x":{"docs":{},"x":{"docs":{},"x":{"docs":{},".":{"docs":{},"j":{"docs":{},"s":{"docs":{},"o":{"docs":{},"n":{"docs":{},"'":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965},"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.007692307692307693}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"j":{"docs":{},"s":{"docs":{},"o":{"docs":{},"n":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"x":{"docs":{},"x":{"docs":{},"x":{"docs":{},".":{"docs":{},"j":{"docs":{},"s":{"docs":{},"o":{"docs":{},"n":{"docs":{},"\"":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965},"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.007692307692307693}}}}}}}}}}}}},"j":{"docs":{},"s":{"docs":{},"o":{"docs":{},"n":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}}}}}}}}}}},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"q":{"docs":{},"u":{"docs":{},"e":{"docs":{},"t":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"x":{"docs":{},"x":{"docs":{},"x":{"docs":{},".":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"q":{"docs":{},"u":{"docs":{},"e":{"docs":{},"t":{"docs":{},"\"":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}}}}}}}},"s":{"docs":{},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},"m":{"docs":{},"a":{"docs":{},"(":{"docs":{},"j":{"docs":{},"s":{"docs":{},"o":{"docs":{},"n":{"docs":{},"s":{"docs":{},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},"m":{"docs":{},"a":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.007692307692307693}},".":{"docs":{},"j":{"docs":{},"s":{"docs":{},"o":{"docs":{},"n":{"docs":{},"(":{"docs":{},"'":{"docs":{},"x":{"docs":{},"x":{"docs":{},"x":{"docs":{},".":{"docs":{},"j":{"docs":{},"s":{"docs":{},"o":{"docs":{},"n":{"docs":{},"'":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"c":{"docs":{},"s":{"docs":{},"v":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},":":{"docs":{},"/":{"docs":{},"/":{"docs":{},"l":{"docs":{},"o":{"docs":{},"c":{"docs":{},"a":{"docs":{},"l":{"docs":{},"h":{"docs":{},"o":{"docs":{},"s":{"docs":{},"t":{"docs":{},":":{"8":{"0":{"2":{"0":{"docs":{},"/":{"docs":{},"c":{"docs":{},"s":{"docs":{},"v":{"docs":{},"/":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"p":{"docs":{},"r":{"docs":{},"o":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"e":{"docs":{},".":{"docs":{},"c":{"docs":{},"s":{"docs":{},"v":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{}},"docs":{}},"docs":{}},"9":{"0":{"0":{"0":{"docs":{},"/":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"/":{"docs":{},"b":{"docs":{},"e":{"docs":{},"h":{"docs":{},"a":{"docs":{},"v":{"docs":{},"i":{"docs":{},"o":{"docs":{},"r":{"docs":{},"_":{"docs":{},"l":{"docs":{},"o":{"docs":{},"g":{"docs":{},".":{"docs":{},"c":{"docs":{},"s":{"docs":{},"v":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}}}}}}}}}}}}}}}}}},"a":{"docs":{},"d":{"docs":{},"_":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"e":{"docs":{},".":{"docs":{},"c":{"docs":{},"s":{"docs":{},"v":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289}}}}}}}}}}}}}}}}}},"r":{"docs":{},"a":{"docs":{},"w":{"docs":{},"_":{"docs":{},"s":{"docs":{},"a":{"docs":{},"m":{"docs":{},"p":{"docs":{},"l":{"docs":{},"e":{"docs":{},".":{"docs":{},"c":{"docs":{},"s":{"docs":{},"v":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"p":{"docs":{},"r":{"docs":{},"o":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"e":{"docs":{},".":{"docs":{},"c":{"docs":{},"s":{"docs":{},"v":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}}}}}}}}}}}}}}}}}}}},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"s":{"docs":{},"/":{"docs":{},"a":{"docs":{},"d":{"docs":{},"_":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"e":{"docs":{},".":{"docs":{},"c":{"docs":{},"s":{"docs":{},"v":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"p":{"docs":{},"r":{"docs":{},"o":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"e":{"docs":{},".":{"docs":{},"c":{"docs":{},"s":{"docs":{},"v":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"p":{"docs":{},"r":{"docs":{},"e":{"docs":{},"p":{"docs":{},"r":{"docs":{},"o":{"docs":{},"c":{"docs":{},"e":{"docs":{},"s":{"docs":{},"s":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"_":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"/":{"docs":{},"b":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"_":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},".":{"docs":{},"c":{"docs":{},"s":{"docs":{},"v":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"_":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},".":{"docs":{},"c":{"docs":{},"s":{"docs":{},"v":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"中":{"docs":{},"的":{"docs":{},"j":{"docs":{},"o":{"docs":{},"b":{"docs":{},"中":{"docs":{},"间":{"docs":{},"结":{"docs":{},"果":{"docs":{},"可":{"docs":{},"以":{"docs":{},"不":{"docs":{},"落":{"docs":{},"地":{"docs":{},"，":{"docs":{},"可":{"docs":{},"以":{"docs":{},"存":{"docs":{},"放":{"docs":{},"在":{"docs":{},"内":{"docs":{},"存":{"docs":{},"中":{"docs":{},"。":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}}}}}}}}}}}}}}}}}}}}}},"使":{"docs":{},"用":{"docs":{},"热":{"docs":{},"独":{"docs":{},"编":{"docs":{},"码":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}},"启":{"docs":{},"动":{"docs":{},"（":{"docs":{},"l":{"docs":{},"o":{"docs":{},"c":{"docs":{},"a":{"docs":{},"l":{"docs":{},"模":{"docs":{},"式":{"docs":{},"）":{"docs":{},"和":{"docs":{},"w":{"docs":{},"o":{"docs":{},"r":{"docs":{},"d":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},"演":{"docs":{},"示":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}}}}}}}}}}}}}}}}}}}}}}}},"概":{"docs":{},"述":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}},"的":{"docs":{},"缺":{"docs":{},"点":{"docs":{},"是":{"docs":{},"：":{"docs":{},"吃":{"docs":{},"内":{"docs":{},"存":{"docs":{},"，":{"docs":{},"不":{"docs":{},"太":{"docs":{},"稳":{"docs":{},"定":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}}}}}}}}}}}},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"f":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}},".":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"a":{"docs":{},"p":{"docs":{},"p":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"(":{"docs":{},"a":{"docs":{},"p":{"docs":{},"p":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},")":{"docs":{},".":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"m":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"(":{"docs":{},"m":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"t":{"docs":{},"e":{"docs":{},"x":{"docs":{},"t":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617},"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}},"(":{"docs":{},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"f":{"docs":{},"=":{"docs":{},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"f":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}}}}}}},"\"":{"docs":{},"l":{"docs":{},"o":{"docs":{},"c":{"docs":{},"a":{"docs":{},"l":{"docs":{},"[":{"2":{"docs":{},"]":{"docs":{},"\"":{"docs":{},",":{"docs":{},"a":{"docs":{},"p":{"docs":{},"p":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"=":{"docs":{},"\"":{"docs":{},"n":{"docs":{},"e":{"docs":{},"t":{"docs":{},"w":{"docs":{},"o":{"docs":{},"r":{"docs":{},"k":{"docs":{},"w":{"docs":{},"o":{"docs":{},"r":{"docs":{},"d":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"\"":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{}}}}}}}}},",":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}},"代":{"docs":{},"表":{"docs":{},"了":{"docs":{},"和":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"集":{"docs":{},"群":{"docs":{},"的":{"docs":{},"链":{"docs":{},"接":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}}}}}}}}}}}},".":{"docs":{},"b":{"docs":{},"r":{"docs":{},"o":{"docs":{},"a":{"docs":{},"d":{"docs":{},"c":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"(":{"docs":{},"要":{"docs":{},"共":{"docs":{},"享":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}}}}}}}}}}}}}}}}}}}}}}},"r":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}},"将":{"docs":{},"为":{"docs":{},"群":{"docs":{},"集":{"docs":{},"的":{"docs":{},"每":{"docs":{},"个":{"docs":{},"分":{"docs":{},"区":{"docs":{},"（":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{},"i":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"）":{"docs":{},"运":{"docs":{},"行":{"docs":{},"一":{"docs":{},"个":{"docs":{},"任":{"docs":{},"务":{"docs":{},"（":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"k":{"docs":{},"）":{"docs":{},"。":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"程":{"docs":{},"序":{"docs":{},"的":{"docs":{},"入":{"docs":{},"口":{"docs":{},".":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}}},"计":{"docs":{},"算":{"docs":{},"结":{"docs":{},"束":{"docs":{},"，":{"docs":{},"一":{"docs":{},"般":{"docs":{},"会":{"docs":{},"把":{"docs":{},"数":{"docs":{},"据":{"docs":{},"做":{"docs":{},"持":{"docs":{},"久":{"docs":{},"化":{"docs":{},"到":{"docs":{},"h":{"docs":{},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{},"，":{"docs":{},"h":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"，":{"docs":{},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},"等":{"docs":{},"等":{"docs":{},"。":{"docs":{},"我":{"docs":{},"们":{"docs":{},"就":{"docs":{},"拿":{"docs":{},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},"举":{"docs":{},"例":{"docs":{},"，":{"docs":{},"将":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"持":{"docs":{},"久":{"docs":{},"化":{"docs":{},"到":{"docs":{},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},"上":{"docs":{},"，":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"的":{"docs":{},"每":{"docs":{},"个":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{},"i":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"就":{"docs":{},"会":{"docs":{},"存":{"docs":{},"成":{"docs":{},"一":{"docs":{},"个":{"docs":{},"文":{"docs":{},"件":{"docs":{},"，":{"docs":{},"如":{"docs":{},"果":{"docs":{},"文":{"docs":{},"件":{"docs":{},"小":{"docs":{},"于":{"1":{"2":{"8":{"docs":{},"m":{"docs":{},"，":{"docs":{},"就":{"docs":{},"可":{"docs":{},"以":{"docs":{},"理":{"docs":{},"解":{"docs":{},"为":{"docs":{},"一":{"docs":{},"个":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{},"i":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"对":{"docs":{},"应":{"docs":{},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},"的":{"docs":{},"一":{"docs":{},"个":{"docs":{},"b":{"docs":{},"l":{"docs":{},"o":{"docs":{},"c":{"docs":{},"k":{"docs":{},"。":{"docs":{},"反":{"docs":{},"之":{"docs":{},"，":{"docs":{},"如":{"docs":{},"果":{"docs":{},"大":{"docs":{},"于":{"1":{"2":{"8":{"docs":{},"m":{"docs":{},"，":{"docs":{},"就":{"docs":{},"会":{"docs":{},"被":{"docs":{},"且":{"docs":{},"分":{"docs":{},"为":{"docs":{},"多":{"docs":{},"个":{"docs":{},"b":{"docs":{},"l":{"docs":{},"o":{"docs":{},"c":{"docs":{},"k":{"docs":{},"，":{"docs":{},"这":{"docs":{},"样":{"docs":{},"，":{"docs":{},"一":{"docs":{},"个":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{},"i":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"就":{"docs":{},"会":{"docs":{},"对":{"docs":{},"应":{"docs":{},"多":{"docs":{},"个":{"docs":{},"b":{"docs":{},"l":{"docs":{},"o":{"docs":{},"c":{"docs":{},"k":{"docs":{},"。":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"读":{"docs":{},"取":{"docs":{},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},"的":{"docs":{},"场":{"docs":{},"景":{"docs":{},"下":{"docs":{},"，":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"把":{"docs":{},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},"的":{"docs":{},"b":{"docs":{},"l":{"docs":{},"o":{"docs":{},"c":{"docs":{},"k":{"docs":{},"读":{"docs":{},"到":{"docs":{},"内":{"docs":{},"存":{"docs":{},"就":{"docs":{},"会":{"docs":{},"抽":{"docs":{},"象":{"docs":{},"为":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"的":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{},"i":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"。":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"_":{"docs":{},"h":{"docs":{},"o":{"docs":{},"m":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.02197802197802198},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.015267175572519083},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}},"e":{"docs":{},"=":{"docs":{},"/":{"docs":{},"x":{"docs":{},"x":{"docs":{},"x":{"docs":{},"/":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"2":{"docs":{},".":{"docs":{},"x":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}}},"docs":{}}}}}}}}}}}}}}}},"m":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"h":{"docs":{},"o":{"docs":{},"s":{"docs":{},"t":{"docs":{},"=":{"docs":{},"n":{"docs":{},"o":{"docs":{},"d":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}}}}}}}},"p":{"docs":{},"o":{"docs":{},"r":{"docs":{},"t":{"docs":{},"=":{"7":{"0":{"7":{"7":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}}},"a":{"docs":{},"p":{"docs":{},"p":{"docs":{},"_":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}},"e":{"docs":{},")":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}},"u":{"docs":{},"r":{"docs":{},"l":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}},")":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}},"作":{"docs":{},"业":{"docs":{},"相":{"docs":{},"关":{"docs":{},"概":{"docs":{},"念":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}}}}}},"集":{"docs":{},"群":{"docs":{},"架":{"docs":{},"构":{"docs":{},"(":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"a":{"docs":{},"l":{"docs":{},"o":{"docs":{},"n":{"docs":{},"e":{"docs":{},"模":{"docs":{},"式":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}}}}}}}}}}}}}}}}}},"'":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}}},"框":{"docs":{},"架":{"docs":{},"本":{"docs":{},"身":{"docs":{},"不":{"docs":{},"了":{"docs":{},"解":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}},"配":{"docs":{},"置":{"docs":{},"信":{"docs":{},"息":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}},"逻":{"docs":{},"辑":{"docs":{},"回":{"docs":{},"归":{"docs":{},"(":{"docs":{},"l":{"docs":{},"r":{"docs":{},")":{"docs":{},"训":{"docs":{},"练":{"docs":{},"点":{"docs":{},"击":{"docs":{},"率":{"docs":{},"预":{"docs":{},"测":{"docs":{},"模":{"docs":{},"型":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}},"s":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},"e":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}},"v":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}},"(":{"3":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}}}}}}}}}}}}},"q":{"docs":{},"l":{"docs":{"./":{"ref":"./","tf":0.011235955056179775},"day03_Hadoop/ha4.1.html":{"ref":"day03_Hadoop/ha4.1.html","tf":0.023809523809523808},"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.012345679012345678},"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.031746031746031744},"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965},"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":3.337179487179487}},"对":{"docs":{},"j":{"docs":{},"s":{"docs":{},"o":{"docs":{},"n":{"docs":{},"数":{"docs":{},"据":{"docs":{},"的":{"docs":{},"处":{"docs":{},"理":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}}}}}}}}},"的":{"docs":{},"相":{"docs":{},"关":{"docs":{},"概":{"docs":{},"念":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}}},"开":{"docs":{},"发":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}}}}},"进":{"docs":{},"行":{"docs":{},"数":{"docs":{},"据":{"docs":{},"清":{"docs":{},"洗":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}}}}},")":{"docs":{},"查":{"docs":{},"询":{"docs":{},"功":{"docs":{},"能":{"docs":{},"，":{"docs":{},"底":{"docs":{},"层":{"docs":{},"数":{"docs":{},"据":{"docs":{},"是":{"docs":{},"存":{"docs":{},"储":{"docs":{},"在":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}}}}}}},"/":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}}}}},"优":{"docs":{},"势":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}}}},"历":{"docs":{},"史":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}}}},"概":{"docs":{},"念":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}}}},"简":{"docs":{},"介":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":5}}}},"编":{"docs":{},"写":{"docs":{},"转":{"docs":{},"换":{"docs":{},"的":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"慢":{"docs":{},"，":{"docs":{},"涉":{"docs":{},"及":{"docs":{},"到":{"docs":{},"执":{"docs":{},"行":{"docs":{},"计":{"docs":{},"划":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}}}}}}}}}}}}}}}}}}},"可":{"docs":{},"以":{"docs":{},"进":{"docs":{},"行":{"docs":{},"某":{"docs":{},"些":{"docs":{},"形":{"docs":{},"式":{"docs":{},"的":{"docs":{},"执":{"docs":{},"行":{"docs":{},"优":{"docs":{},"化":{"docs":{},"。":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}},"正":{"docs":{},"确":{"docs":{},"读":{"docs":{},"取":{"docs":{},"，":{"docs":{},"否":{"docs":{},"则":{"docs":{},"格":{"docs":{},"式":{"docs":{},"化":{"docs":{},"后":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"读":{"docs":{},"取":{"docs":{},"会":{"docs":{},"出":{"docs":{},"现":{"docs":{},"问":{"docs":{},"题":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}}}}}}}}}}}}}}}}}}},"能":{"docs":{},"够":{"docs":{},"自":{"docs":{},"动":{"docs":{},"将":{"docs":{},"j":{"docs":{},"s":{"docs":{},"o":{"docs":{},"n":{"docs":{},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{},"以":{"docs":{},"结":{"docs":{},"构":{"docs":{},"化":{"docs":{},"的":{"docs":{},"形":{"docs":{},"式":{"docs":{},"加":{"docs":{},"载":{"docs":{},"为":{"docs":{},"一":{"docs":{},"个":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"案":{"docs":{},"例":{"docs":{},"数":{"docs":{},"据":{"docs":{},"清":{"docs":{},"洗":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":5}}}}}}}},"都":{"docs":{},"是":{"docs":{},"处":{"docs":{},"理":{"docs":{},"属":{"docs":{},"于":{"docs":{},"离":{"docs":{},"线":{"docs":{},"批":{"docs":{},"处":{"docs":{},"理":{"docs":{},"任":{"docs":{},"务":{"docs":{},"，":{"docs":{},"数":{"docs":{},"据":{"docs":{},"一":{"docs":{},"般":{"docs":{},"都":{"docs":{},"是":{"docs":{},"在":{"docs":{},"固":{"docs":{},"定":{"docs":{},"位":{"docs":{},"置":{"docs":{},"上":{"docs":{},"，":{"docs":{},"通":{"docs":{},"常":{"docs":{},"我":{"docs":{},"们":{"docs":{},"写":{"docs":{},"好":{"docs":{},"一":{"docs":{},"个":{"docs":{},"脚":{"docs":{},"本":{"docs":{},"，":{"docs":{},"每":{"docs":{},"天":{"docs":{},"定":{"docs":{},"时":{"docs":{},"去":{"docs":{},"处":{"docs":{},"理":{"docs":{},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"计":{"docs":{},"算":{"docs":{},"，":{"docs":{},"保":{"docs":{},"存":{"docs":{},"数":{"docs":{},"据":{"docs":{},"结":{"docs":{},"果":{"docs":{},"。":{"docs":{},"这":{"docs":{},"类":{"docs":{},"任":{"docs":{},"务":{"docs":{},"通":{"docs":{},"常":{"docs":{},"是":{"docs":{},"t":{"docs":{},"+":{"1":{"docs":{},"(":{"docs":{},"一":{"docs":{},"天":{"docs":{},"一":{"docs":{},"个":{"docs":{},"任":{"docs":{},"务":{"docs":{},")":{"docs":{},"，":{"docs":{},"对":{"docs":{},"实":{"docs":{},"时":{"docs":{},"性":{"docs":{},"要":{"docs":{},"求":{"docs":{},"不":{"docs":{},"高":{"docs":{},"。":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}}}}}}}}}}}}}}}}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"来":{"docs":{},"进":{"docs":{},"行":{"docs":{},"离":{"docs":{},"线":{"docs":{},"批":{"docs":{},"处":{"docs":{},"理":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}},"、":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}},"：":{"docs":{},"离":{"docs":{},"线":{"docs":{},"处":{"docs":{},"理":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}},"u":{"docs":{},"a":{"docs":{},"r":{"docs":{},"e":{"docs":{},"(":{"docs":{},"x":{"docs":{},")":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}},"s":{"docs":{},"）":{"docs":{},"，":{"docs":{},"是":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"2":{"docs":{},".":{"docs":{},"*":{"docs":{},"中":{"docs":{},"加":{"docs":{},"入":{"docs":{},"的":{"docs":{},"进":{"docs":{},"行":{"docs":{},"基":{"docs":{},"于":{"docs":{},"模":{"docs":{},"型":{"docs":{},"的":{"docs":{},"协":{"docs":{},"同":{"docs":{},"过":{"docs":{},"滤":{"docs":{},"（":{"docs":{},"m":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"l":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{},"中":{"docs":{},"进":{"docs":{},"行":{"docs":{},"基":{"docs":{},"于":{"docs":{},"模":{"docs":{},"型":{"docs":{},"的":{"docs":{},"协":{"docs":{},"同":{"docs":{},"过":{"docs":{},"滤":{"docs":{},"（":{"docs":{},"m":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"l":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},":":{"docs":{},"数":{"docs":{},"据":{"docs":{},"交":{"docs":{},"换":{"docs":{},"框":{"docs":{},"架":{"docs":{},"，":{"docs":{},"例":{"docs":{},"如":{"docs":{},"：":{"docs":{},"关":{"docs":{},"系":{"docs":{},"型":{"docs":{},"数":{"docs":{},"据":{"docs":{},"库":{"docs":{},"与":{"docs":{},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},"之":{"docs":{},"间":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"交":{"docs":{},"换":{"docs":{"day03_Hadoop/ha4.1.html":{"ref":"day03_Hadoop/ha4.1.html","tf":0.023809523809523808}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"t":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"a":{"docs":{},"l":{"docs":{},"o":{"docs":{},"n":{"docs":{},"e":{"docs":{},"模":{"docs":{},"式":{"docs":{},"的":{"docs":{},"启":{"docs":{},"动":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}},"启":{"docs":{},"动":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}}}}}}}},"r":{"docs":{},"d":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0056657223796034}}}}}}},"r":{"docs":{},"t":{"docs":{"day03_Hadoop/ha2.1.html":{"ref":"day03_Hadoop/ha2.1.html","tf":0.0684931506849315},"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.006172839506172839},"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017},"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},"r":{"docs":{},"o":{"docs":{},"w":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.004405286343612335}}}}},"(":{"docs":{},")":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}}}}},"t":{"docs":{},"e":{"docs":{},"m":{"docs":{},"e":{"docs":{},"n":{"docs":{},"t":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}}},"g":{"docs":{},"e":{"docs":{},"：":{"docs":{},"一":{"docs":{},"个":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"作":{"docs":{},"业":{"docs":{},"一":{"docs":{},"般":{"docs":{},"包":{"docs":{},"含":{"docs":{},"一":{"docs":{},"到":{"docs":{},"多":{"docs":{},"个":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"g":{"docs":{},"e":{"docs":{},"。":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"m":{"docs":{"./":{"ref":"./","tf":0.011235955056179775},"day03_Hadoop/ha4.1.html":{"ref":"day03_Hadoop/ha4.1.html","tf":0.023809523809523808},"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"实":{"docs":{},"现":{"docs":{},"实":{"docs":{},"时":{"docs":{},"数":{"docs":{},"据":{"docs":{},"处":{"docs":{},"理":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}}}}},"w":{"docs":{},"o":{"docs":{},"r":{"docs":{},"d":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":5.010989010989011}}}}}}}}}}},"的":{"docs":{},"是":{"docs":{},"一":{"docs":{},"个":{"docs":{},"实":{"docs":{},"时":{"docs":{},"批":{"docs":{},"处":{"docs":{},"理":{"docs":{},"操":{"docs":{},"作":{"docs":{},"，":{"docs":{},"每":{"docs":{},"隔":{"docs":{},"一":{"docs":{},"段":{"docs":{},"时":{"docs":{},"间":{"docs":{},"将":{"docs":{},"数":{"docs":{},"据":{"docs":{},"进":{"docs":{},"行":{"docs":{},"打":{"docs":{},"包":{"docs":{},"，":{"docs":{},"封":{"docs":{},"装":{"docs":{},"成":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"，":{"docs":{},"是":{"docs":{},"无":{"docs":{},"状":{"docs":{},"态":{"docs":{},"的":{"docs":{},"。":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"的":{"docs":{},"特":{"docs":{},"点":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}},"状":{"docs":{},"态":{"docs":{},"操":{"docs":{},"作":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}},"解":{"docs":{},"决":{"docs":{},"实":{"docs":{},"际":{"docs":{},"问":{"docs":{},"题":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}}}}}}}}}},"/":{"docs":{},"s":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{},"m":{"docs":{},"/":{"docs":{},"f":{"docs":{},"l":{"docs":{},"i":{"docs":{},"n":{"docs":{},"k":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}}}}}}}}}}}},"m":{"docs":{},"l":{"docs":{},"i":{"docs":{},"b":{"docs":{},"/":{"docs":{},"g":{"docs":{},"r":{"docs":{},"a":{"docs":{},"p":{"docs":{},"h":{"docs":{},"x":{"docs":{},"）":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}}}}}}}}}}}},"优":{"docs":{},"于":{"docs":{},"s":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{},"m":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}}}}}}},"差":{"docs":{},"于":{"docs":{},"s":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{},"m":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}}}}}}},"简":{"docs":{},"介":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":5}}}},"）":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"t":{"docs":{},"e":{"docs":{},"x":{"docs":{},"t":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.007633587786259542}},"(":{"docs":{},"s":{"docs":{},"c":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.007633587786259542}}}}}}}}}}}}},"编":{"docs":{},"码":{"docs":{},"实":{"docs":{},"践":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099}}}},"步":{"docs":{},"骤":{"docs":{},"：":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099}}}}}}},"中":{"docs":{},"存":{"docs":{},"在":{"docs":{},"两":{"docs":{},"种":{"docs":{},"状":{"docs":{},"态":{"docs":{},"操":{"docs":{},"作":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}},"提":{"docs":{},"供":{"docs":{},"这":{"docs":{},"种":{"docs":{},"状":{"docs":{},"态":{"docs":{},"保":{"docs":{},"护":{"docs":{},"机":{"docs":{},"制":{"docs":{},"，":{"docs":{},"即":{"docs":{},"u":{"docs":{},"p":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"b":{"docs":{},"y":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"m":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"\\":{"docs":{},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},"、":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}}}}}}}}}}}},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196},"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.004893964110929853},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0046910055068325514},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0015917230401910067}},")":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.007407407407407408}},";":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}},")":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0045351473922902496}}}},",":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.014814814814814815},"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0045351473922902496}},"s":{"docs":{},"a":{"docs":{},"l":{"docs":{},"a":{"docs":{},"r":{"docs":{},"i":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.007407407407407408}}}}}}}},"l":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}},"t":{"docs":{},"y":{"docs":{},"p":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}},"e":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965},"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.007692307692307693}},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.011538461538461539}},"t":{"docs":{},"r":{"docs":{},"u":{"docs":{},"e":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}}}}}},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0024469820554649264},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}},"j":{"docs":{},"s":{"docs":{},"o":{"docs":{},"n":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}}}}},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0010197838058331635},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0019896538002387586}},"e":{"docs":{},"r":{"docs":{},"(":{"docs":{},"i":{"docs":{},"n":{"docs":{},"p":{"docs":{},"u":{"docs":{},"t":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"=":{"docs":{},"'":{"docs":{},"n":{"docs":{},"e":{"docs":{},"w":{"docs":{},"_":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"c":{"docs":{},"l":{"docs":{},"a":{"docs":{},"s":{"docs":{},"s":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"'":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}}}},"p":{"docs":{},"i":{"docs":{},"d":{"docs":{},"'":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"'":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}}}}}}},"对":{"docs":{},"指":{"docs":{},"定":{"docs":{},"字":{"docs":{},"符":{"docs":{},"串":{"docs":{},"列":{"docs":{},"进":{"docs":{},"行":{"docs":{},"特":{"docs":{},"征":{"docs":{},"处":{"docs":{},"理":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}},"：":{"docs":{},"对":{"docs":{},"指":{"docs":{},"定":{"docs":{},"字":{"docs":{},"符":{"docs":{},"串":{"docs":{},"列":{"docs":{},"数":{"docs":{},"据":{"docs":{},"进":{"docs":{},"行":{"docs":{},"特":{"docs":{},"征":{"docs":{},"处":{"docs":{},"理":{"docs":{},"，":{"docs":{},"如":{"docs":{},"将":{"docs":{},"性":{"docs":{},"别":{"docs":{},"数":{"docs":{},"据":{"docs":{},"“":{"docs":{},"男":{"docs":{},"”":{"docs":{},"、":{"docs":{},"“":{"docs":{},"女":{"docs":{},"”":{"docs":{},"转":{"docs":{},"化":{"docs":{},"为":{"0":{"docs":{},"和":{"1":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}},"docs":{}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"u":{"docs":{},"c":{"docs":{},"t":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}},"u":{"docs":{},"r":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}}}},"f":{"docs":{},"i":{"docs":{},"e":{"docs":{},"l":{"docs":{},"d":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"c":{"docs":{},"i":{"docs":{},"t":{"docs":{},"y":{"docs":{},"\"":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}}},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}}}},"t":{"docs":{},"e":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}}}}}}},"m":{"docs":{},"s":{"docs":{},"_":{"docs":{},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}},"s":{"docs":{},"e":{"docs":{},"g":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}},"l":{"docs":{},"o":{"docs":{},"c":{"docs":{},"\"":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}},"p":{"docs":{},"o":{"docs":{},"p":{"docs":{},"\"":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}},"v":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"\"":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}}}},"h":{"docs":{},"o":{"docs":{},"p":{"docs":{},"p":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}}}},"b":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}}}}}}}},"t":{"docs":{},"a":{"docs":{},"g":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}},"u":{"docs":{},"y":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}}}}},"f":{"docs":{},"a":{"docs":{},"v":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}}}},"i":{"docs":{},"n":{"docs":{},"a":{"docs":{},"l":{"docs":{},"_":{"docs":{},"g":{"docs":{},"e":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"c":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}}}}}}},"t":{"docs":{},"i":{"docs":{},"m":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"m":{"docs":{},"p":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0024469820554649264},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}},"a":{"docs":{},"g":{"docs":{},"e":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}},"n":{"docs":{},"e":{"docs":{},"w":{"docs":{},"_":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"c":{"docs":{},"l":{"docs":{},"a":{"docs":{},"s":{"docs":{},"s":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}}}}}}}}}},"o":{"docs":{},"c":{"docs":{},"c":{"docs":{},"u":{"docs":{},"p":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0024469820554649264},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.001193792280143255},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.005226480836236934}}}}}}}},"t":{"docs":{},"y":{"docs":{},"p":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.007692307692307693}}},"[":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0024469820554649264},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0024469820554649264},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.001193792280143255},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.005226480836236934}}}}}}}}}},"(":{"docs":{},"l":{"docs":{},"_":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},")":{"docs":{},"]":{"docs":{},")":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}}}}}}}},"e":{"docs":{},"p":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.004574042309891366},"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}},"s":{"docs":{},"(":{"docs":{},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"f":{"docs":{},")":{"docs":{},":":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}}}}},"a":{"docs":{},"m":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"的":{"docs":{},"状":{"docs":{},"态":{"docs":{},"操":{"docs":{},"作":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":5}}}}}}}}}}}}},"o":{"docs":{},"r":{"docs":{},"m":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548},"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}},":":{"docs":{"day03_Hadoop/ha4.1.html":{"ref":"day03_Hadoop/ha4.1.html","tf":0.023809523809523808}}},"(":{"docs":{},"毫":{"docs":{},"秒":{"docs":{},")":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}},"e":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.007407407407407408},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"p":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}},"u":{"docs":{},"d":{"docs":{},"e":{"docs":{},"n":{"docs":{},"t":{"2":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.007407407407407408}},";":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.007407407407407408}}}},"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}},"(":{"docs":{},"c":{"docs":{},"l":{"docs":{},"a":{"docs":{},"s":{"docs":{},"s":{"docs":{},"n":{"docs":{},"o":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}}}},";":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.011111111111111112}}}}}}},"n":{"docs":{},"o":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.007407407407407408}}}}},"d":{"docs":{},"d":{"docs":{},"e":{"docs":{},"v":{"docs":{},"(":{"docs":{},")":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}},"_":{"docs":{},"p":{"docs":{},"o":{"docs":{},"p":{"docs":{},"(":{"docs":{},")":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}},"s":{"docs":{},"a":{"docs":{},"m":{"docs":{},"p":{"docs":{},"(":{"docs":{},")":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}},"e":{"docs":{},"a":{"docs":{},"r":{"docs":{},"c":{"docs":{},"h":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}},"t":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704},"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}},"(":{"docs":{},")":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}},"d":{"docs":{},"f":{"docs":{},".":{"docs":{},"i":{"docs":{},"x":{"docs":{},"[":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"]":{"docs":{},".":{"docs":{},"r":{"docs":{},"e":{"docs":{},"p":{"docs":{},"l":{"docs":{},"a":{"docs":{},"c":{"docs":{},"e":{"docs":{},"(":{"0":{"docs":{},",":{"docs":{},"n":{"docs":{},"p":{"docs":{},".":{"docs":{},"n":{"docs":{},"a":{"docs":{},"n":{"docs":{},")":{"docs":{},".":{"docs":{},"d":{"docs":{},"r":{"docs":{},"o":{"docs":{},"p":{"docs":{},"n":{"docs":{},"a":{"docs":{},"(":{"docs":{},")":{"docs":{},".":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{},")":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147}}}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{}}}}}}}}}}}}}}}}}}}}},"r":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"s":{"docs":{},"_":{"docs":{},"m":{"docs":{},"a":{"docs":{},"t":{"docs":{},"r":{"docs":{},"i":{"docs":{},"x":{"docs":{},".":{"docs":{},"i":{"docs":{},"x":{"docs":{},"[":{"1":{"docs":{},"]":{"docs":{},".":{"docs":{},"d":{"docs":{},"r":{"docs":{},"o":{"docs":{},"p":{"docs":{},"n":{"docs":{},"a":{"docs":{},"(":{"docs":{},")":{"docs":{},".":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{},")":{"docs":{},"&":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"(":{"docs":{},"s":{"docs":{},"i":{"docs":{},"m":{"docs":{},"i":{"docs":{},"l":{"docs":{},"a":{"docs":{},"r":{"docs":{},"_":{"docs":{},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"m":{"docs":{},"s":{"docs":{},".":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{},")":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.0016233766233766235}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{}}}}},"[":{"1":{"docs":{},"]":{"docs":{},".":{"docs":{},"d":{"docs":{},"r":{"docs":{},"o":{"docs":{},"p":{"docs":{},"n":{"docs":{},"a":{"docs":{},"(":{"docs":{},")":{"docs":{},".":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{},")":{"docs":{},"&":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"(":{"docs":{},"s":{"docs":{},"i":{"docs":{},"m":{"docs":{},"i":{"docs":{},"l":{"docs":{},"a":{"docs":{},"r":{"docs":{},"_":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"s":{"docs":{},".":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{},")":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.0016233766233766235}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{},"i":{"docs":{},"i":{"docs":{},"d":{"docs":{},"]":{"docs":{},".":{"docs":{},"d":{"docs":{},"r":{"docs":{},"o":{"docs":{},"p":{"docs":{},"n":{"docs":{},"a":{"docs":{},"(":{"docs":{},")":{"docs":{},".":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{},")":{"docs":{},"&":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"(":{"docs":{},"s":{"docs":{},"i":{"docs":{},"m":{"docs":{},"i":{"docs":{},"l":{"docs":{},"a":{"docs":{},"r":{"docs":{},"_":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"s":{"docs":{},".":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{},")":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"m":{"docs":{},"o":{"docs":{},"v":{"docs":{},"i":{"docs":{},"e":{"docs":{},"s":{"docs":{},".":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{},")":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}},"t":{"docs":{},"a":{"docs":{},"g":{"docs":{},"s":{"docs":{},".":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{},")":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}},"s":{"docs":{},".":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282}}}},"u":{"docs":{},"p":{"docs":{},"s":{"docs":{},",":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}},"l":{"docs":{},"o":{"docs":{},"g":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"(":{"docs":{},"n":{"docs":{},"e":{"docs":{},"w":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},")":{"docs":{},".":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}}}}}}}}}}}}}}}}},"l":{"docs":{},"f":{"docs":{},".":{"docs":{},"a":{"docs":{},"l":{"docs":{},"p":{"docs":{},"h":{"docs":{},"a":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.003430531732418525},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0056925996204933585},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.011574074074074073}},"*":{"docs":{},"(":{"docs":{},"e":{"docs":{},"r":{"docs":{},"r":{"docs":{},"*":{"docs":{},"v":{"docs":{},"_":{"docs":{},"p":{"docs":{},"u":{"docs":{},"[":{"docs":{},"k":{"docs":{},"]":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}},"q":{"docs":{},"i":{"docs":{},"[":{"docs":{},"k":{"docs":{},"]":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}}}}}}}}}}}}},"s":{"docs":{},"(":{"docs":{},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}},"b":{"docs":{},"i":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}},"[":{"docs":{},"i":{"docs":{},"i":{"docs":{},"d":{"docs":{},"]":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}},"u":{"docs":{},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}},"[":{"docs":{},"u":{"docs":{},"i":{"docs":{},"d":{"docs":{},"]":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}}},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}},"s":{"docs":{},"[":{"2":{"docs":{},"]":{"docs":{},"]":{"docs":{},"]":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.004574042309891366},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.003795066413662239},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.004629629629629629}}}}}},"docs":{}}}}}}}}},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}},".":{"docs":{},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"t":{"docs":{},"u":{"docs":{},"p":{"docs":{},"l":{"docs":{},"e":{"docs":{},"s":{"docs":{},"(":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{},"=":{"docs":{},"f":{"docs":{},"a":{"docs":{},"l":{"docs":{},"s":{"docs":{},"e":{"docs":{},")":{"docs":{},":":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}}}}}}}}}}}}}}}}}}}}},"[":{"docs":{},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"f":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{},"s":{"docs":{},"[":{"2":{"docs":{},"]":{"docs":{},"]":{"docs":{},".":{"docs":{},"m":{"docs":{},"e":{"docs":{},"a":{"docs":{},"n":{"docs":{},"(":{"docs":{},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}}}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}},"g":{"docs":{},"l":{"docs":{},"o":{"docs":{},"b":{"docs":{},"a":{"docs":{},"l":{"docs":{},"_":{"docs":{},"m":{"docs":{},"e":{"docs":{},"a":{"docs":{},"n":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.00686106346483705}}}}}}},"m":{"docs":{},"e":{"docs":{},"a":{"docs":{},"n":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0056925996204933585},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.009259259259259259}}}}}}}}}}}},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"m":{"docs":{},"s":{"docs":{},"_":{"docs":{},"r":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"s":{"docs":{},".":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{},":":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.003795066413662239},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}},",":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"t":{"docs":{},"u":{"docs":{},"p":{"docs":{},"l":{"docs":{},"e":{"docs":{},"s":{"docs":{},"(":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{},"=":{"docs":{},"t":{"docs":{},"r":{"docs":{},"u":{"docs":{},"e":{"docs":{},")":{"docs":{},":":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"n":{"docs":{},"u":{"docs":{},"m":{"docs":{},"b":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"e":{"docs":{},"p":{"docs":{},"o":{"docs":{},"c":{"docs":{},"h":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}},"l":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"n":{"docs":{},"t":{"docs":{},"f":{"docs":{},"a":{"docs":{},"c":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}},"s":{"docs":{},")":{"docs":{},".":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"y":{"docs":{},"p":{"docs":{},"e":{"docs":{},"(":{"docs":{},"n":{"docs":{},"p":{"docs":{},".":{"docs":{},"f":{"docs":{},"l":{"docs":{},"o":{"docs":{},"a":{"docs":{},"t":{"3":{"2":{"docs":{},")":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.003795066413662239},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.004629629629629629}}}},"docs":{}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"p":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"i":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},"u":{"docs":{},"i":{"docs":{},"d":{"docs":{},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}}}}}}}},",":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}},"[":{"docs":{},"u":{"docs":{},"i":{"docs":{},"d":{"docs":{},"]":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.003795066413662239},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}},"r":{"docs":{},"e":{"docs":{},"g":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.003430531732418525}},"_":{"docs":{},"b":{"docs":{},"i":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.004629629629629629}}},"u":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.004629629629629629}}}},"p":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.003795066413662239},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.004629629629629629}},"*":{"docs":{},"v":{"docs":{},"_":{"docs":{},"p":{"docs":{},"u":{"docs":{},"[":{"docs":{},"k":{"docs":{},"]":{"docs":{},")":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}}}}}}},"q":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.003795066413662239},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.004629629629629629}},"*":{"docs":{},"v":{"docs":{},"_":{"docs":{},"q":{"docs":{},"i":{"docs":{},"[":{"docs":{},"k":{"docs":{},"]":{"docs":{},")":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}}}}}}}}}}},"s":{"docs":{},"g":{"docs":{},"d":{"docs":{},"(":{"docs":{},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"s":{"docs":{},"_":{"docs":{},"r":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"s":{"docs":{},".":{"docs":{},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"t":{"docs":{},"u":{"docs":{},"p":{"docs":{},"l":{"docs":{},"e":{"docs":{},"s":{"docs":{},"(":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{},"=":{"docs":{},"t":{"docs":{},"r":{"docs":{},"u":{"docs":{},"e":{"docs":{},")":{"docs":{},":":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}}}}}}}}}}}}}}}}}},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.003795066413662239},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}},",":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}}}}}}}}}}}}}}}},"_":{"docs":{},"i":{"docs":{},"n":{"docs":{},"i":{"docs":{},"t":{"docs":{},"_":{"docs":{},"m":{"docs":{},"a":{"docs":{},"t":{"docs":{},"r":{"docs":{},"i":{"docs":{},"x":{"docs":{},"(":{"docs":{},")":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}}}}}}}}}},"q":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}},"[":{"docs":{},"i":{"docs":{},"i":{"docs":{},"d":{"docs":{},"]":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.003795066413662239},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}},",":{"docs":{"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.007407407407407408},"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.008032128514056224},"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.024943310657596373}},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"）":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"d":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}},"a":{"docs":{},"r":{"docs":{},"i":{"docs":{"day03_Hadoop/ha2.1.html":{"ref":"day03_Hadoop/ha2.1.html","tf":0.0136986301369863},"day03_Hadoop/ha4.2.html":{"ref":"day03_Hadoop/ha4.2.html","tf":0.0196078431372549}}},"y":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"n":{"docs":{},"o":{"docs":{},"d":{"docs":{"day03_Hadoop/ha2.1.html":{"ref":"day03_Hadoop/ha2.1.html","tf":0.0410958904109589}},"e":{"docs":{},",":{"docs":{"day03_Hadoop/ha2.1.html":{"ref":"day03_Hadoop/ha2.1.html","tf":0.0136986301369863}}}}}}}}}}}}}},"s":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}},"r":{"docs":{},"v":{"docs":{},"i":{"docs":{},"c":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428},"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548},"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.006172839506172839}}}},"e":{"docs":{},"r":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}},"的":{"docs":{},"s":{"docs":{},"o":{"docs":{},"c":{"docs":{},"k":{"docs":{},"e":{"docs":{},"t":{"docs":{},"链":{"docs":{},"接":{"docs":{},".":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}}}}}}},"上":{"docs":{},"线":{"docs":{},"和":{"docs":{},"下":{"docs":{},"线":{"docs":{},"信":{"docs":{},"息":{"docs":{},"，":{"docs":{},"实":{"docs":{},"时":{"docs":{},"通":{"docs":{},"知":{"docs":{},"给":{"docs":{},"m":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"；":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}}}}}}}}}}}}}}}},"状":{"docs":{},"态":{"docs":{},"，":{"docs":{},"将":{"docs":{},"r":{"docs":{},"e":{"docs":{},"g":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}}}}}},"负":{"docs":{},"载":{"docs":{},"均":{"docs":{},"衡":{"docs":{},"；":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}},"分":{"docs":{},"配":{"docs":{},"r":{"docs":{},"e":{"docs":{},"g":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"；":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}}}}}},"并":{"docs":{},"重":{"docs":{},"新":{"docs":{},"分":{"docs":{},"配":{"docs":{},"其":{"docs":{},"上":{"docs":{},"的":{"docs":{},"r":{"docs":{},"e":{"docs":{},"g":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"；":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}}}}}}}}}}}}}},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}},"'":{"docs":{},",":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}},"e":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}},"s":{"docs":{},"s":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}}}}}},"i":{"docs":{},"m":{"docs":{},"_":{"docs":{},"u":{"docs":{},"s":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147}},"e":{"docs":{},"r":{"docs":{},"s":{"docs":{},":":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}},"_":{"docs":{},"r":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"d":{"docs":{},"_":{"docs":{},"m":{"docs":{},"o":{"docs":{},"v":{"docs":{},"i":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.00487012987012987}},"e":{"docs":{},"s":{"docs":{},"[":{"1":{"docs":{},"]":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.0016233766233766235}}}},"docs":{},"i":{"docs":{},"i":{"docs":{},"d":{"docs":{},"]":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}}}}}}}}}}}}}}}},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"_":{"docs":{},"f":{"docs":{},"o":{"docs":{},"r":{"docs":{},"_":{"docs":{},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"m":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.00974025974025974}}}}}}}}}}}}}}}}}}}}},"i":{"docs":{},"d":{"docs":{},",":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.00487012987012987}}}}}},"i":{"docs":{},"i":{"docs":{},"d":{"docs":{},",":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.0016233766233766235}}}}},"t":{"docs":{},"e":{"docs":{},"m":{"docs":{},"_":{"docs":{},"r":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"d":{"docs":{},"_":{"docs":{},"m":{"docs":{},"o":{"docs":{},"v":{"docs":{},"i":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.0016233766233766235}},"e":{"docs":{},"s":{"docs":{},"[":{"1":{"docs":{},"]":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.0016233766233766235}}}},"docs":{}}}}}}}}}}},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"_":{"docs":{},"f":{"docs":{},"r":{"docs":{},"o":{"docs":{},"m":{"docs":{},"_":{"docs":{},"u":{"docs":{},"s":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}}}}}}}}}}}}}}}}}}}}}},"i":{"docs":{},"l":{"docs":{},"a":{"docs":{},"r":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.01948051948051948}},"_":{"docs":{},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"m":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}},"s":{"docs":{},".":{"docs":{},"i":{"docs":{},"x":{"docs":{},"[":{"docs":{},"l":{"docs":{},"i":{"docs":{},"s":{"docs":{},"t":{"docs":{},"(":{"docs":{},"i":{"docs":{},"d":{"docs":{},"s":{"docs":{},")":{"docs":{},"]":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.0016233766233766235}}}}}}}}}}}}}}},"w":{"docs":{},"h":{"docs":{},"e":{"docs":{},"r":{"docs":{},"e":{"docs":{},"(":{"docs":{},"s":{"docs":{},"i":{"docs":{},"m":{"docs":{},"i":{"docs":{},"l":{"docs":{},"a":{"docs":{},"r":{"docs":{},"_":{"docs":{},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"m":{"docs":{},"s":{"docs":{},">":{"0":{"docs":{},")":{"docs":{},".":{"docs":{},"d":{"docs":{},"r":{"docs":{},"o":{"docs":{},"p":{"docs":{},"n":{"docs":{},"a":{"docs":{},"(":{"docs":{},")":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.0016233766233766235}}}}}}}}}}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}},"u":{"docs":{},"s":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.00974025974025974}},"e":{"docs":{},"r":{"docs":{},"s":{"docs":{},".":{"docs":{},"e":{"docs":{},"m":{"docs":{},"p":{"docs":{},"t":{"docs":{},"i":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}}}}}}},"i":{"docs":{},"x":{"docs":{},"[":{"docs":{},"l":{"docs":{},"i":{"docs":{},"s":{"docs":{},"t":{"docs":{},"(":{"1":{"docs":{},")":{"docs":{},"]":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.0016233766233766235}}}}},"docs":{},"i":{"docs":{},"d":{"docs":{},"s":{"docs":{},")":{"docs":{},"]":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}}}}}}}}}}}}}}},"w":{"docs":{},"h":{"docs":{},"e":{"docs":{},"r":{"docs":{},"e":{"docs":{},"(":{"docs":{},"s":{"docs":{},"i":{"docs":{},"m":{"docs":{},"i":{"docs":{},"l":{"docs":{},"a":{"docs":{},"r":{"docs":{},"_":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"s":{"docs":{},">":{"0":{"docs":{},")":{"docs":{},".":{"docs":{},"d":{"docs":{},"r":{"docs":{},"o":{"docs":{},"p":{"docs":{},"n":{"docs":{},"a":{"docs":{},"(":{"docs":{},")":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.00487012987012987}}}}}}}}}}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"p":{"docs":{},"l":{"docs":{},"i":{"docs":{},"f":{"docs":{},"i":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}}}}},"r":{"docs":{},"i":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}},"t":{"docs":{},"e":{"docs":{},".":{"docs":{},"x":{"docs":{},"m":{"docs":{},"l":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.08928571428571429},"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.017094017094017096},"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196},"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}},".":{"docs":{},"t":{"docs":{},"e":{"docs":{},"m":{"docs":{},"p":{"docs":{},"l":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428}}}}}}}}}}}}}},"n":{"docs":{},"g":{"docs":{},"l":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}},"z":{"docs":{},"e":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}},":":{"1":{"0":{"2":{"4":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}},"docs":{}},"docs":{}},"docs":{}},"5":{"0":{"3":{"2":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}},"k":{"docs":{},"l":{"docs":{},"e":{"docs":{},"a":{"docs":{},"r":{"docs":{},"n":{"docs":{},".":{"docs":{},"m":{"docs":{},"e":{"docs":{},"t":{"docs":{},"r":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}},"i":{"docs":{},"c":{"docs":{},"s":{"docs":{},".":{"docs":{},"p":{"docs":{},"a":{"docs":{},"i":{"docs":{},"r":{"docs":{},"w":{"docs":{},"i":{"docs":{},"s":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.00423728813559322}}}}}}}}}}}}}}}}}}}}}}},"e":{"docs":{},"w":{"docs":{},"n":{"docs":{},"e":{"docs":{},"s":{"docs":{},"s":{"docs":{},"(":{"docs":{},")":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}},"m":{"docs":{},"a":{"docs":{},"l":{"docs":{},"l":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.0016233766233766235}},".":{"docs":{},"z":{"docs":{},"i":{"docs":{},"p":{"docs":{},"，":{"docs":{},"数":{"docs":{},"据":{"docs":{},"量":{"docs":{},"小":{"docs":{},"，":{"docs":{},"便":{"docs":{},"于":{"docs":{},"我":{"docs":{},"们":{"docs":{},"单":{"docs":{},"机":{"docs":{},"使":{"docs":{},"用":{"docs":{},"和":{"docs":{},"运":{"docs":{},"行":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.0016233766233766235}}}}}}}}}}}}}}}}}}}}}}},"/":{"docs":{},"r":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"s":{"docs":{},".":{"docs":{},"c":{"docs":{},"s":{"docs":{},"v":{"docs":{},"\"":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002858776443682104},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.003795066413662239},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147},"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842},"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}}}}}}}}}}}}}}},"a":{"docs":{},"l":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}},"m":{"docs":{},"o":{"docs":{},"v":{"docs":{},"i":{"docs":{},"e":{"docs":{},"s":{"docs":{},".":{"docs":{},"c":{"docs":{},"s":{"docs":{},"v":{"docs":{},"\"":{"docs":{},",":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}},"中":{"docs":{},"标":{"docs":{},"签":{"docs":{},"数":{"docs":{},"据":{"docs":{},"太":{"docs":{},"多":{"docs":{},"，":{"docs":{},"因":{"docs":{},"此":{"docs":{},"借":{"docs":{},"助":{"docs":{},"其":{"docs":{},"来":{"docs":{},"扩":{"docs":{},"充":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}}},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}},"r":{"docs":{},"t":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}},"o":{"docs":{},"r":{"docs":{},"t":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}},"e":{"docs":{},"d":{"docs":{},"(":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"u":{"docs":{},"l":{"docs":{},"t":{"docs":{},"s":{"docs":{},",":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}}}}}}}}},"s":{"docs":{},"_":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"u":{"docs":{},"l":{"docs":{},"t":{"docs":{},",":{"docs":{"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}}}}}}}}}}}},"v":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{},",":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876}}}}}}}}}}}},"_":{"docs":{},"a":{"docs":{},"r":{"docs":{},"r":{"docs":{},"a":{"docs":{},"y":{"docs":{},":":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}}}}},"b":{"docs":{},"y":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}},"(":{"docs":{},"a":{"docs":{},"s":{"docs":{},"c":{"docs":{},"e":{"docs":{},"n":{"docs":{},"d":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"=":{"docs":{},"t":{"docs":{},"r":{"docs":{},"u":{"docs":{},"e":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}}}}}}}}}}}}}}}}},"u":{"docs":{},"r":{"docs":{},"c":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428},"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}},"c":{"docs":{},"k":{"docs":{},"e":{"docs":{},"t":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}}}}},"u":{"docs":{},"m":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}},"_":{"docs":{},"u":{"docs":{},"p":{"docs":{},"/":{"docs":{},"s":{"docs":{},"u":{"docs":{},"m":{"docs":{},"_":{"docs":{},"d":{"docs":{},"o":{"docs":{},"w":{"docs":{},"n":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.0016233766233766235}}}}}}}}}}}}}},"(":{"docs":{},"x":{"docs":{},"[":{"1":{"docs":{},"]":{"docs":{},")":{"docs":{},")":{"docs":{},",":{"docs":{"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}}}}}}},"docs":{}}},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"s":{"docs":{},")":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}}},")":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}},"n":{"docs":{},"e":{"docs":{},"w":{"docs":{},"_":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"s":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}}}}}},"d":{"docs":{},"i":{"docs":{},"s":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}},"p":{"docs":{},"p":{"docs":{},"o":{"docs":{},"r":{"docs":{},"t":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282}}}}}},"e":{"docs":{},"r":{"docs":{},"v":{"docs":{},"i":{"docs":{},"s":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}},"b":{"docs":{},"m":{"docs":{},"i":{"docs":{},"t":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}},",":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}},"t":{"docs":{},"r":{"docs":{},"a":{"docs":{},"c":{"docs":{},"t":{"docs":{},"，":{"docs":{},"确":{"docs":{},"保":{"docs":{},"训":{"docs":{},"练":{"docs":{},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{},"覆":{"docs":{},"盖":{"docs":{},"了":{"docs":{},"所":{"docs":{},"有":{"docs":{},"分":{"docs":{},"类":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}}}}}}},"r":{"docs":{},"f":{"docs":{},"a":{"docs":{},"c":{"docs":{},"e":{"docs":{},",":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}}}}},"g":{"docs":{},"d":{"docs":{},"(":{"docs":{},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"f":{"docs":{},")":{"docs":{},":":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}}}}},"v":{"docs":{},"d":{"docs":{},"+":{"docs":{},"+":{"docs":{},":":{"docs":{"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html":{"ref":"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html","tf":0.037037037037037035}}},"是":{"docs":{},"基":{"docs":{},"于":{"docs":{},"这":{"docs":{},"样":{"docs":{},"的":{"docs":{},"假":{"docs":{},"设":{"docs":{},"：":{"docs":{},"在":{"docs":{},"b":{"docs":{},"i":{"docs":{},"a":{"docs":{},"s":{"docs":{},"s":{"docs":{},"v":{"docs":{},"d":{"docs":{},"基":{"docs":{},"础":{"docs":{},"上":{"docs":{},"，":{"docs":{},"认":{"docs":{},"为":{"docs":{},"用":{"docs":{},"户":{"docs":{},"对":{"docs":{},"于":{"docs":{},"项":{"docs":{},"目":{"docs":{},"的":{"docs":{},"历":{"docs":{},"史":{"docs":{},"浏":{"docs":{},"览":{"docs":{},"记":{"docs":{},"录":{"docs":{},"、":{"docs":{},"购":{"docs":{},"买":{"docs":{},"记":{"docs":{},"录":{"docs":{},"、":{"docs":{},"收":{"docs":{},"听":{"docs":{},"记":{"docs":{},"录":{"docs":{},"等":{"docs":{},"可":{"docs":{},"以":{"docs":{},"从":{"docs":{},"侧":{"docs":{},"面":{"docs":{},"反":{"docs":{},"映":{"docs":{},"用":{"docs":{},"户":{"docs":{},"的":{"docs":{},"偏":{"docs":{},"好":{"docs":{},"。":{"docs":{"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html":{"ref":"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html","tf":0.037037037037037035}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},":":{"docs":{"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html":{"ref":"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html","tf":0.037037037037037035}}},"也":{"docs":{},"被":{"docs":{},"称":{"docs":{},"为":{"docs":{},"最":{"docs":{},"原":{"docs":{},"始":{"docs":{},"的":{"docs":{},"l":{"docs":{},"f":{"docs":{},"m":{"docs":{},"模":{"docs":{},"型":{"docs":{"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html":{"ref":"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html","tf":0.037037037037037035}}}}}}}}}}}}}}},"分":{"docs":{},"解":{"docs":{},"的":{"docs":{},"形":{"docs":{},"式":{"docs":{},"为":{"3":{"docs":{},"个":{"docs":{},"矩":{"docs":{},"阵":{"docs":{},"相":{"docs":{},"乘":{"docs":{},"，":{"docs":{},"中":{"docs":{},"间":{"docs":{},"矩":{"docs":{},"阵":{"docs":{},"为":{"docs":{},"奇":{"docs":{},"异":{"docs":{},"值":{"docs":{},"矩":{"docs":{},"阵":{"docs":{},"。":{"docs":{},"如":{"docs":{},"果":{"docs":{},"想":{"docs":{},"运":{"docs":{},"用":{"docs":{},"s":{"docs":{},"v":{"docs":{},"d":{"docs":{},"分":{"docs":{},"解":{"docs":{},"的":{"docs":{},"话":{"docs":{},"，":{"docs":{},"有":{"docs":{},"一":{"docs":{},"个":{"docs":{},"前":{"docs":{},"提":{"docs":{},"是":{"docs":{},"要":{"docs":{},"求":{"docs":{},"矩":{"docs":{},"阵":{"docs":{},"是":{"docs":{},"稠":{"docs":{},"密":{"docs":{},"的":{"docs":{},"，":{"docs":{},"即":{"docs":{},"矩":{"docs":{},"阵":{"docs":{},"里":{"docs":{},"的":{"docs":{},"元":{"docs":{},"素":{"docs":{},"要":{"docs":{},"非":{"docs":{},"空":{"docs":{},"，":{"docs":{},"否":{"docs":{},"则":{"docs":{},"就":{"docs":{},"不":{"docs":{},"能":{"docs":{},"运":{"docs":{},"用":{"docs":{},"s":{"docs":{},"v":{"docs":{},"d":{"docs":{},"分":{"docs":{},"解":{"docs":{},"。":{"docs":{"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html":{"ref":"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html","tf":0.037037037037037035}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{}}}}},"降":{"docs":{},"维":{"docs":{},"，":{"docs":{},"但":{"docs":{},"这":{"docs":{},"样":{"docs":{},"做":{"docs":{},"明":{"docs":{},"显":{"docs":{},"对":{"docs":{},"数":{"docs":{},"据":{"docs":{},"的":{"docs":{},"原":{"docs":{},"始":{"docs":{},"性":{"docs":{},"造":{"docs":{},"成":{"docs":{},"一":{"docs":{},"定":{"docs":{},"影":{"docs":{},"响":{"docs":{},"。":{"docs":{"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html":{"ref":"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html","tf":0.037037037037037035}}}}}}}}}}}}}}}}}}}}}}}}}}},"的":{"docs":{},"方":{"docs":{},"法":{"docs":{},"，":{"docs":{},"它":{"docs":{},"不":{"docs":{},"在":{"docs":{},"将":{"docs":{},"矩":{"docs":{},"阵":{"docs":{},"分":{"docs":{},"解":{"docs":{},"为":{"3":{"docs":{},"个":{"docs":{},"矩":{"docs":{},"阵":{"docs":{},"，":{"docs":{},"而":{"docs":{},"是":{"docs":{},"分":{"docs":{},"解":{"docs":{},"为":{"2":{"docs":{},"个":{"docs":{},"用":{"docs":{},"户":{"docs":{"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html":{"ref":"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html","tf":0.037037037037037035}}}}}},"docs":{}}}}}}}}}}},"docs":{}}}}}}}}}}}}}},"首":{"docs":{},"先":{"docs":{},"需":{"docs":{},"要":{"docs":{},"填":{"docs":{},"充":{"docs":{},"矩":{"docs":{},"阵":{"docs":{},"，":{"docs":{},"然":{"docs":{},"后":{"docs":{},"再":{"docs":{},"进":{"docs":{},"行":{"docs":{},"分":{"docs":{},"解":{"docs":{},"降":{"docs":{},"维":{"docs":{},"，":{"docs":{},"同":{"docs":{},"时":{"docs":{},"存":{"docs":{},"在":{"docs":{},"计":{"docs":{},"算":{"docs":{},"复":{"docs":{},"杂":{"docs":{},"度":{"docs":{},"高":{"docs":{},"的":{"docs":{},"问":{"docs":{},"题":{"docs":{},"，":{"docs":{},"因":{"docs":{},"为":{"docs":{},"要":{"docs":{},"分":{"docs":{},"解":{"docs":{},"成":{"3":{"docs":{},"个":{"docs":{},"矩":{"docs":{},"阵":{"docs":{},"，":{"docs":{},"所":{"docs":{},"以":{"docs":{},"后":{"docs":{},"来":{"docs":{},"提":{"docs":{},"出":{"docs":{},"了":{"docs":{},"f":{"docs":{},"u":{"docs":{},"n":{"docs":{},"k":{"docs":{"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html":{"ref":"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html","tf":0.037037037037037035}}}}}}}}}}}}}}}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"（":{"docs":{},"传":{"docs":{},"统":{"docs":{},"并":{"docs":{},"经":{"docs":{},"典":{"docs":{},"着":{"docs":{},"）":{"docs":{},"其":{"docs":{},"公":{"docs":{},"式":{"docs":{},"如":{"docs":{},"下":{"docs":{},"：":{"docs":{"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html":{"ref":"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html","tf":0.037037037037037035}}}}}}}}}}}}}}}},"，":{"docs":{},"一":{"docs":{},"般":{"docs":{},"的":{"docs":{},"做":{"docs":{},"法":{"docs":{},"是":{"docs":{},"先":{"docs":{},"用":{"docs":{},"均":{"docs":{},"值":{"docs":{},"或":{"docs":{},"者":{"docs":{},"其":{"docs":{},"他":{"docs":{},"统":{"docs":{},"计":{"docs":{},"学":{"docs":{},"方":{"docs":{},"法":{"docs":{},"来":{"docs":{},"填":{"docs":{},"充":{"docs":{},"矩":{"docs":{},"阵":{"docs":{},"，":{"docs":{},"然":{"docs":{},"后":{"docs":{},"再":{"docs":{},"运":{"docs":{},"用":{"docs":{},"t":{"docs":{},"r":{"docs":{},"a":{"docs":{},"d":{"docs":{},"i":{"docs":{},"t":{"docs":{"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html":{"ref":"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html","tf":0.037037037037037035}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"矩":{"docs":{},"阵":{"docs":{},"分":{"docs":{},"解":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}},"基":{"docs":{},"础":{"docs":{},"上":{"docs":{},"加":{"docs":{},"上":{"docs":{},"了":{"docs":{},"偏":{"docs":{},"置":{"docs":{},"项":{"docs":{},"。":{"docs":{"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}}}}}}}}}}}},"y":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269},"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112},"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}},"s":{"docs":{},"t":{"docs":{},"e":{"docs":{},"m":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.018018018018018018},"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.038461538461538464}},"c":{"docs":{},"t":{"docs":{},"l":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}}}}}},".":{"docs":{},"s":{"docs":{},"t":{"docs":{},"d":{"docs":{},"i":{"docs":{},"n":{"docs":{},":":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}}},"e":{"docs":{},"x":{"docs":{},"i":{"docs":{},"t":{"docs":{},"(":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}}}}}}},"c":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102},"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00851063829787234},"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.013422818791946308},"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391},"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965},"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464},"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.007633587786259542}},"h":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"l":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282}}}}},"m":{"docs":{},"a":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872},"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0024469820554649264},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}},"p":{"docs":{},"e":{"docs":{},"o":{"docs":{},"p":{"docs":{},"l":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}},"=":{"docs":{},"s":{"docs":{},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},"m":{"docs":{},"a":{"docs":{},")":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0024469820554649264},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}},"a":{"docs":{},"l":{"docs":{},"a":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}},"n":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.019823788546255508}},"会":{"docs":{},"加":{"docs":{},"上":{"docs":{},"一":{"docs":{},"些":{"docs":{},"条":{"docs":{},"件":{"docs":{},"限":{"docs":{},"制":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}},"查":{"docs":{},"询":{"docs":{},"中":{"docs":{},"添":{"docs":{},"加":{"docs":{},"限":{"docs":{},"制":{"docs":{},"条":{"docs":{},"件":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}},"添":{"docs":{},"加":{"docs":{},"过":{"docs":{},"滤":{"docs":{},"器":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}},"q":{"docs":{},"u":{"docs":{},"e":{"docs":{},"r":{"docs":{},"y":{"docs":{},"(":{"docs":{},")":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}},":":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0056657223796034}}}}}}}}}}}},"o":{"docs":{},"r":{"docs":{},"e":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.007407407407407408}},">":{"docs":{},"=":{"6":{"0":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}},"docs":{}},"docs":{}}}}}},"r":{"docs":{},"i":{"docs":{},"p":{"docs":{},"t":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}},".":{"docs":{},"t":{"docs":{},"e":{"docs":{},"x":{"docs":{},"t":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"e":{"docs":{},"(":{"docs":{},"'":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"e":{"docs":{},":":{"docs":{},"/":{"docs":{},"/":{"docs":{},"/":{"docs":{},"h":{"docs":{},"o":{"docs":{},"m":{"docs":{},"e":{"docs":{},"/":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},"/":{"docs":{},"t":{"docs":{},"m":{"docs":{},"p":{"docs":{},"/":{"docs":{},"w":{"docs":{},"o":{"docs":{},"r":{"docs":{},"d":{"docs":{},".":{"docs":{},"t":{"docs":{},"x":{"docs":{},"t":{"docs":{},"'":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}}}}}}}}}}}}}}}}}}}}}}}}},"r":{"docs":{},"o":{"docs":{},"o":{"docs":{},"t":{"docs":{},"/":{"docs":{},"t":{"docs":{},"m":{"docs":{},"p":{"docs":{},"/":{"docs":{},"w":{"docs":{},"o":{"docs":{},"r":{"docs":{},"d":{"docs":{},".":{"docs":{},"t":{"docs":{},"x":{"docs":{},"t":{"docs":{},"'":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"\"":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"e":{"docs":{},":":{"docs":{},"/":{"docs":{},"/":{"docs":{},"/":{"docs":{},"r":{"docs":{},"o":{"docs":{},"o":{"docs":{},"t":{"docs":{},"/":{"docs":{},"b":{"docs":{},"i":{"docs":{},"g":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"/":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"/":{"docs":{},"a":{"docs":{},"c":{"docs":{},"c":{"docs":{},"e":{"docs":{},"s":{"docs":{},"s":{"docs":{},".":{"docs":{},"l":{"docs":{},"o":{"docs":{},"g":{"docs":{},"\"":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.010067114093959731}}}}}}}}}}}}}}}}}}}}}}}}}}},"t":{"docs":{},"m":{"docs":{},"p":{"docs":{},"/":{"2":{"0":{"0":{"9":{"0":{"1":{"2":{"1":{"0":{"0":{"0":{"1":{"3":{"2":{"docs":{},".":{"3":{"9":{"4":{"2":{"5":{"1":{"docs":{},".":{"docs":{},"h":{"docs":{},"t":{"docs":{},"t":{"docs":{},"p":{"docs":{},".":{"docs":{},"f":{"docs":{},"o":{"docs":{},"r":{"docs":{},"m":{"docs":{},"a":{"docs":{},"t":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"(":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}}}}}}}}}}}}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{},"i":{"docs":{},"p":{"docs":{},".":{"docs":{},"t":{"docs":{},"x":{"docs":{},"t":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"(":{"docs":{},"l":{"docs":{},"a":{"docs":{},"m":{"docs":{},"b":{"docs":{},"d":{"docs":{},"a":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"s":{"docs":{},"y":{"docs":{},"s":{"docs":{},".":{"docs":{},"a":{"docs":{},"r":{"docs":{},"g":{"docs":{},"v":{"docs":{},"[":{"1":{"docs":{},"]":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}},"docs":{}}}}}}}}}}}}}}}}}}},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"a":{"docs":{},"l":{"docs":{},"l":{"docs":{},"e":{"docs":{},"l":{"docs":{},"i":{"docs":{},"z":{"docs":{},"e":{"docs":{},"(":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00851063829787234}}},",":{"5":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}},"docs":{}}}}}},"[":{"1":{"docs":{},",":{"2":{"docs":{},",":{"3":{"docs":{},",":{"4":{"docs":{},",":{"5":{"docs":{},",":{"6":{"docs":{},",":{"7":{"docs":{},",":{"8":{"docs":{},",":{"9":{"docs":{},"]":{"docs":{},",":{"3":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.006818181818181818}}}},"docs":{}}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}},"]":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"2":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.00909090909090909}}}},"docs":{},"\"":{"docs":{},"a":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.004545454545454545}}}},"(":{"docs":{},"\"":{"docs":{},"a":{"docs":{},"\"":{"docs":{},",":{"1":{"docs":{},")":{"docs":{},",":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"b":{"docs":{},"\"":{"docs":{},",":{"2":{"docs":{},")":{"docs":{},"]":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.006818181818181818}}}}}},"docs":{}}}}}}}}},"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}},"c":{"docs":{},"\"":{"docs":{},",":{"1":{"docs":{},")":{"docs":{},",":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"b":{"docs":{},"\"":{"docs":{},",":{"3":{"docs":{},")":{"docs":{},"]":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.006818181818181818}}}}}},"docs":{}}}}}}}}},"docs":{}}}}}}},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"g":{"docs":{},"e":{"docs":{},"(":{"1":{"0":{"0":{"docs":{},")":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}},"docs":{}},"docs":{}},"docs":{}}}}}}},"t":{"docs":{},"m":{"docs":{},"p":{"2":{"docs":{},")":{"docs":{},".":{"docs":{},"s":{"docs":{},"o":{"docs":{},"r":{"docs":{},"t":{"docs":{},"b":{"docs":{},"y":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{},"(":{"docs":{},"t":{"docs":{},"r":{"docs":{},"u":{"docs":{},"e":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}}}}}}}}}}}},"docs":{},")":{"docs":{},".":{"docs":{},"s":{"docs":{},"o":{"docs":{},"r":{"docs":{},"t":{"docs":{},"b":{"docs":{},"y":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{},"(":{"docs":{},")":{"docs":{},".":{"docs":{},"f":{"docs":{},"i":{"docs":{},"r":{"docs":{},"s":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}}},"t":{"docs":{},"r":{"docs":{},"u":{"docs":{},"e":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.004545454545454545}}}}}}}}}}}}}}}}}}}}}},"l":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}},"j":{"docs":{},"s":{"docs":{},"o":{"docs":{},"n":{"docs":{},"s":{"docs":{},"t":{"docs":{},"r":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.007692307692307693}}}}}}}}}}}}}}}}}}}}}}}}},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"l":{"docs":{},"o":{"docs":{},"g":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"(":{"docs":{},"n":{"docs":{},"e":{"docs":{},"w":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},")":{"docs":{},".":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}}}}}}}}}}}}}}}}}},"t":{"docs":{},"o":{"docs":{},"p":{"docs":{},"(":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.013422818791946308},"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}}}},"b":{"docs":{},"r":{"docs":{},"o":{"docs":{},"a":{"docs":{},"d":{"docs":{},"c":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"(":{"docs":{},"c":{"docs":{},"i":{"docs":{},"t":{"docs":{},"y":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},"_":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"b":{"docs":{},"i":{"docs":{},"n":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428}},"]":{"docs":{},"$":{"docs":{"day03_Hadoop/ha2.1.html":{"ref":"day03_Hadoop/ha2.1.html","tf":0.0273972602739726}}}},"/":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}},"o":{"docs":{},"p":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}}}}}},"l":{"docs":{},"a":{"docs":{},"v":{"docs":{},"e":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}},"结":{"docs":{},"构":{"docs":{},")":{"docs":{"day03_Hadoop/ha2.4.html":{"ref":"day03_Hadoop/ha2.4.html","tf":0.03333333333333333}}}}},".":{"docs":{},"s":{"docs":{},"h":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}}}}}}},"a":{"docs":{},"v":{"docs":{},"e":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}},"f":{"docs":{},"a":{"docs":{},"r":{"docs":{},"i":{"docs":{},"/":{"5":{"3":{"7":{"docs":{},".":{"3":{"6":{"docs":{},"\"":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.006711409395973154}}}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}}}}},"m":{"docs":{},"p":{"docs":{},"l":{"docs":{},"e":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}},"d":{"docs":{},"f":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}},"s":{"docs":{},"c":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.007633587786259542}},".":{"docs":{},"a":{"docs":{},"w":{"docs":{},"a":{"docs":{},"i":{"docs":{},"t":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"m":{"docs":{},"i":{"docs":{},"n":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"(":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.007633587786259542}}}}}}}}}}}}}}}}}}}},"s":{"docs":{},"o":{"docs":{},"c":{"docs":{},"k":{"docs":{},"e":{"docs":{},"t":{"docs":{},"t":{"docs":{},"e":{"docs":{},"x":{"docs":{},"t":{"docs":{},"s":{"docs":{},"t":{"docs":{},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"m":{"docs":{},"(":{"docs":{},"'":{"docs":{},"l":{"docs":{},"o":{"docs":{},"c":{"docs":{},"a":{"docs":{},"l":{"docs":{},"h":{"docs":{},"o":{"docs":{},"s":{"docs":{},"t":{"docs":{},"'":{"docs":{},",":{"9":{"9":{"9":{"9":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}}}}}},"\"":{"docs":{},"l":{"docs":{},"o":{"docs":{},"c":{"docs":{},"a":{"docs":{},"l":{"docs":{},"h":{"docs":{},"o":{"docs":{},"s":{"docs":{},"t":{"docs":{},"\"":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"t":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.007633587786259542}}}}}}}}},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},"c":{"docs":{},"k":{"docs":{},"p":{"docs":{},"o":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},"c":{"docs":{},"k":{"docs":{},"p":{"docs":{},"o":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"\"":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.007633587786259542}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"y":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842},"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.007633587786259542}},"a":{"docs":{},"r":{"docs":{},"n":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282},"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.017857142857142856},"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.02564102564102564}},"&":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}}}}}}}},":":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.02564102564102564}}},"特":{"docs":{},"点":{"docs":{},":":{"docs":{},"扩":{"docs":{},"展":{"docs":{},"性":{"docs":{},"&":{"docs":{},"容":{"docs":{},"错":{"docs":{},"性":{"docs":{},"&":{"docs":{},"多":{"docs":{},"框":{"docs":{},"架":{"docs":{},"资":{"docs":{},"源":{"docs":{},"统":{"docs":{},"一":{"docs":{},"调":{"docs":{},"度":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282}}}}}}}}}}}}}}}}}}}}}},".":{"docs":{},"n":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"m":{"docs":{},"a":{"docs":{},"n":{"docs":{},"a":{"docs":{},"g":{"docs":{},"e":{"docs":{},"r":{"docs":{},".":{"docs":{},"a":{"docs":{},"u":{"docs":{},"x":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428},"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}}}}}}}}}}}}},"s":{"docs":{},"h":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.017094017094017096}}}}},"产":{"docs":{},"生":{"docs":{},"背":{"docs":{},"景":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}},"环":{"docs":{},"境":{"docs":{},"搭":{"docs":{},"建":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}},"的":{"docs":{},"架":{"docs":{},"构":{"docs":{},"和":{"docs":{},"执":{"docs":{},"行":{"docs":{},"流":{"docs":{},"程":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}}}}}}}},"h":{"docs":{},"o":{"docs":{},"o":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}},"的":{"docs":{},"团":{"docs":{},"队":{"docs":{},"使":{"docs":{},"用":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},"对":{"1":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}},"docs":{}}}}}}}}}}}}}}}}},"i":{"docs":{},"e":{"docs":{},"l":{"docs":{},"d":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.03508771929824561}}}}}},",":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}},":":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938},"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.010526315789473684},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.007633587786259542}}},")":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}},"u":{"docs":{},"m":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099}}}}},"了":{"docs":{},"解":{"docs":{},"h":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"的":{"docs":{},"基":{"docs":{},"本":{"docs":{},"架":{"docs":{},"构":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}}}}}}}},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},"读":{"docs":{},"写":{"docs":{},"流":{"docs":{},"程":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}}}}}},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{},"原":{"docs":{},"理":{"docs":{},"和":{"docs":{},"架":{"docs":{},"构":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}}}}}}}},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{},"e":{"docs":{},"概":{"docs":{},"念":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}}}}}}}}}},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"概":{"docs":{},"念":{"docs":{"./":{"ref":"./","tf":0.011235955056179775},"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}},"的":{"docs":{},"安":{"docs":{},"装":{"docs":{},"部":{"docs":{},"署":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}}}}}}}}},"y":{"docs":{},"a":{"docs":{},"r":{"docs":{},"n":{"docs":{},"概":{"docs":{},"念":{"docs":{},"和":{"docs":{},"产":{"docs":{},"生":{"docs":{},"背":{"docs":{},"景":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}}}}}}}}}},"基":{"docs":{},"于":{"docs":{},"内":{"docs":{},"容":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"算":{"docs":{},"法":{"docs":{},"概":{"docs":{},"念":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}}}}}}}}},"推":{"docs":{},"荐":{"docs":{},"相":{"docs":{},"关":{"docs":{},"常":{"docs":{},"用":{"docs":{},"概":{"docs":{},"念":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}}}}},"系":{"docs":{},"统":{"docs":{},"的":{"docs":{},"冷":{"docs":{},"启":{"docs":{},"动":{"docs":{},"问":{"docs":{},"题":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}}}},"评":{"docs":{},"估":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}},"方":{"docs":{},"法":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}},"常":{"docs":{},"用":{"docs":{},"评":{"docs":{},"估":{"docs":{},"指":{"docs":{},"标":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}}}}},"与":{"docs":{},"w":{"docs":{},"e":{"docs":{},"b":{"docs":{},"项":{"docs":{},"目":{"docs":{},"区":{"docs":{},"别":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}}}}}}}},"概":{"docs":{},"念":{"docs":{},"及":{"docs":{},"产":{"docs":{},"生":{"docs":{},"背":{"docs":{},"景":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}}}}}}},"要":{"docs":{},"素":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}}}}},"模":{"docs":{},"型":{"docs":{},"构":{"docs":{},"建":{"docs":{},"流":{"docs":{},"程":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}},"物":{"docs":{},"品":{"docs":{},"冷":{"docs":{},"启":{"docs":{},"动":{"docs":{},"的":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"方":{"docs":{},"法":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}}}}}}},"画":{"docs":{},"像":{"docs":{},"，":{"docs":{},"用":{"docs":{},"户":{"docs":{},"画":{"docs":{},"像":{"docs":{},"概":{"docs":{},"念":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}}}}}}}}}},"处":{"docs":{},"理":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"系":{"docs":{},"统":{"docs":{},"冷":{"docs":{},"启":{"docs":{},"动":{"docs":{},"的":{"docs":{},"常":{"docs":{},"用":{"docs":{},"方":{"docs":{},"法":{"docs":{"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html":{"ref":"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html","tf":0.024390243902439025}}}}}}}}}}}}}}}},"公":{"docs":{},"司":{"docs":{},"目":{"docs":{},"前":{"docs":{},"发":{"docs":{},"展":{"docs":{},"的":{"docs":{},"状":{"docs":{},"况":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}}},"为":{"docs":{},"什":{"docs":{},"么":{"docs":{},"使":{"docs":{},"用":{"docs":{},"h":{"docs":{},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}},"什":{"docs":{},"么":{"docs":{},"是":{"docs":{},"h":{"docs":{},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}},"分":{"docs":{},"布":{"docs":{},"式":{"docs":{},"文":{"docs":{},"件":{"docs":{},"系":{"docs":{},"统":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}},"的":{"docs":{},"设":{"docs":{},"计":{"docs":{},"思":{"docs":{},"路":{"docs":{},"：":{"docs":{"day03_Hadoop/ha2.3.html":{"ref":"day03_Hadoop/ha2.3.html","tf":0.1111111111111111}}}}}}}}}}}},"存":{"docs":{},"储":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282}},"：":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}}}},"计":{"docs":{},"算":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282}},"：":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}},"框":{"docs":{},"架":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282}}}}}},"集":{"docs":{},"群":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"n":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"和":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"n":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"部":{"docs":{},"署":{"docs":{},"在":{"docs":{},"不":{"docs":{},"同":{"docs":{},"机":{"docs":{},"器":{"docs":{},"上":{"docs":{"day03_Hadoop/ha2.4.html":{"ref":"day03_Hadoop/ha2.4.html","tf":0.03333333333333333}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"处":{"docs":{},"理":{"docs":{},"框":{"docs":{},"架":{"docs":{"day03_Hadoop/ha3.2.html":{"ref":"day03_Hadoop/ha3.2.html","tf":0.02040816326530612}}}}}},"的":{"docs":{},"流":{"docs":{},"式":{"docs":{},"计":{"docs":{},"算":{"docs":{},"框":{"docs":{},"架":{"docs":{"day03_Hadoop/ha4.1.html":{"ref":"day03_Hadoop/ha4.1.html","tf":0.047619047619047616}}}}}}}},"计":{"docs":{},"算":{"docs":{},"框":{"docs":{},"架":{"docs":{},"基":{"docs":{},"于":{"docs":{},"内":{"docs":{},"存":{"docs":{"day03_Hadoop/ha4.1.html":{"ref":"day03_Hadoop/ha4.1.html","tf":0.023809523809523808}}}}}}}}}}},"系":{"docs":{},"统":{"docs":{},"执":{"docs":{},"行":{"docs":{},"任":{"docs":{},"务":{"docs":{},"瓶":{"docs":{},"颈":{"docs":{},":":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}},"的":{"docs":{},"最":{"docs":{},"大":{"docs":{},"难":{"docs":{},"点":{"docs":{},"，":{"docs":{},"就":{"docs":{},"是":{"docs":{},"各":{"docs":{},"个":{"docs":{},"节":{"docs":{},"点":{"docs":{},"的":{"docs":{},"状":{"docs":{},"态":{"docs":{},"如":{"docs":{},"何":{"docs":{},"同":{"docs":{},"步":{"docs":{},"。":{"docs":{},"c":{"docs":{},"a":{"docs":{},"p":{"docs":{"Hive&HBase/06_hbase数据模型.html":{"ref":"Hive&HBase/06_hbase数据模型.html","tf":0.020833333333333332}}}}}}}}}}}}}}}}}}}}}}}}}}},"、":{"docs":{},"并":{"docs":{},"发":{"docs":{},"数":{"docs":{},"据":{"docs":{},"处":{"docs":{},"理":{"docs":{},"，":{"docs":{},"效":{"docs":{},"率":{"docs":{},"极":{"docs":{},"高":{"docs":{},"；":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}}}}}}}}}}}},"类":{"docs":{},"⽬":{"docs":{},"录":{"docs":{},"（":{"1":{"9":{"9":{"0":{"docs":{},"s":{"docs":{},"）":{"docs":{},"：":{"docs":{},"覆":{"docs":{},"盖":{"docs":{},"少":{"docs":{},"量":{"docs":{},"热":{"docs":{},"门":{"docs":{},"⽹":{"docs":{},"站":{"docs":{},"。":{"docs":{},"典":{"docs":{},"型":{"docs":{},"应":{"docs":{},"用":{"docs":{},"：":{"docs":{},"h":{"docs":{},"a":{"docs":{},"o":{"1":{"2":{"3":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}}}}}}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}},"的":{"docs":{},"标":{"docs":{},"准":{"docs":{},"就":{"docs":{},"是":{"docs":{},"分":{"docs":{},"区":{"docs":{},"字":{"docs":{},"段":{"docs":{},"，":{"docs":{},"可":{"docs":{},"以":{"docs":{},"一":{"docs":{},"个":{"docs":{},"，":{"docs":{},"也":{"docs":{},"可":{"docs":{},"以":{"docs":{},"多":{"docs":{},"个":{"docs":{},"。":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}}}}}}}}}}}}}}}}},"特":{"docs":{},"征":{"docs":{},"值":{"docs":{},"个":{"docs":{},"数":{"docs":{},"情":{"docs":{},"况":{"docs":{},":":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}},"层":{"docs":{},"架":{"docs":{},"构":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}}}},"别":{"docs":{},"都":{"docs":{},"是":{"docs":{},"n":{"docs":{},"个":{"docs":{},"坐":{"docs":{},"标":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}},"子":{"docs":{},"是":{"docs":{},"两":{"docs":{},"个":{"docs":{},"布":{"docs":{},"尔":{"docs":{},"向":{"docs":{},"量":{"docs":{},"做":{"docs":{},"点":{"docs":{},"积":{"docs":{},"计":{"docs":{},"算":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}},"母":{"docs":{},"是":{"docs":{},"两":{"docs":{},"个":{"docs":{},"布":{"docs":{},"尔":{"docs":{},"向":{"docs":{},"量":{"docs":{},"做":{"docs":{},"或":{"docs":{},"运":{"docs":{},"算":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}},"组":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}},"查":{"docs":{},"询":{"docs":{},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}},"每":{"docs":{},"个":{"docs":{},"用":{"docs":{},"户":{"docs":{},"的":{"docs":{},"浏":{"docs":{},"览":{"docs":{},"记":{"docs":{},"录":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}}}}}}}}},"统":{"docs":{},"计":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.006993006993006993}}}}},"析":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009},"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}},"函":{"docs":{},"数":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{},"字":{"docs":{},"段":{"docs":{},"的":{"docs":{},"类":{"docs":{},"型":{"docs":{},"和":{"docs":{},"格":{"docs":{},"式":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}},"并":{"docs":{},"预":{"docs":{},"处":{"docs":{},"理":{"docs":{},"a":{"docs":{},"d":{"docs":{},"_":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"e":{"docs":{},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}},"r":{"docs":{},"a":{"docs":{},"w":{"docs":{},"_":{"docs":{},"s":{"docs":{},"a":{"docs":{},"m":{"docs":{},"p":{"docs":{},"l":{"docs":{},"e":{"docs":{},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"p":{"docs":{},"r":{"docs":{},"o":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"e":{"docs":{},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}},"发":{"docs":{},"到":{"docs":{},"这":{"docs":{},"个":{"docs":{},"容":{"docs":{},"器":{"docs":{},"上":{"docs":{},"面":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}}}}}},"治":{"docs":{},"策":{"docs":{},"略":{"docs":{"day03_Hadoop/ha3.2.html":{"ref":"day03_Hadoop/ha3.2.html","tf":0.02040816326530612}}}}},"：":{"docs":{},"把":{"docs":{},"复":{"docs":{},"杂":{"docs":{},"的":{"docs":{},"问":{"docs":{},"题":{"docs":{},"分":{"docs":{},"解":{"docs":{},"为":{"docs":{},"若":{"docs":{},"干":{"docs":{},"\"":{"docs":{},"简":{"docs":{},"单":{"docs":{},"的":{"docs":{},"任":{"docs":{},"务":{"docs":{},"\"":{"docs":{"day03_Hadoop/ha3.2.html":{"ref":"day03_Hadoop/ha3.2.html","tf":0.02040816326530612}}}}}}}}}}}}}}}}}}}}},"区":{"docs":{},"及":{"docs":{},"其":{"docs":{},"属":{"docs":{},"性":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}},"仅":{"docs":{},"仅":{"docs":{},"是":{"docs":{},"一":{"docs":{},"个":{"docs":{},"目":{"docs":{},"录":{"docs":{},"名":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}}}},"可":{"docs":{},"以":{"docs":{},"理":{"docs":{},"解":{"docs":{},"为":{"docs":{},"分":{"docs":{},"类":{"docs":{},"，":{"docs":{},"通":{"docs":{},"过":{"docs":{},"分":{"docs":{},"类":{"docs":{},"把":{"docs":{},"不":{"docs":{},"同":{"docs":{},"类":{"docs":{},"型":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"放":{"docs":{},"到":{"docs":{},"不":{"docs":{},"同":{"docs":{},"的":{"docs":{},"目":{"docs":{},"录":{"docs":{},"下":{"docs":{},"。":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"字":{"docs":{},"段":{"docs":{},"不":{"docs":{},"是":{"docs":{},"表":{"docs":{},"中":{"docs":{},"的":{"docs":{},"列":{"docs":{},",":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}}}}},"表":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}},"的":{"docs":{},"意":{"docs":{},"义":{"docs":{},"在":{"docs":{},"于":{"docs":{},"优":{"docs":{},"化":{"docs":{},"查":{"docs":{},"询":{"docs":{},"。":{"docs":{},"查":{"docs":{},"询":{"docs":{},"时":{"docs":{},"尽":{"docs":{},"量":{"docs":{},"利":{"docs":{},"用":{"docs":{},"分":{"docs":{},"区":{"docs":{},"字":{"docs":{},"段":{"docs":{},"。":{"docs":{},"如":{"docs":{},"果":{"docs":{},"不":{"docs":{},"使":{"docs":{},"用":{"docs":{},"分":{"docs":{},"区":{"docs":{},"字":{"docs":{},"段":{"docs":{},"，":{"docs":{},"就":{"docs":{},"会":{"docs":{},"全":{"docs":{},"部":{"docs":{},"扫":{"docs":{},"描":{"docs":{},"。":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"容":{"docs":{},"错":{"docs":{},"性":{"docs":{},"(":{"docs":{},"系":{"docs":{},"统":{"docs":{},"中":{"docs":{},"任":{"docs":{},"意":{"docs":{},"信":{"docs":{},"息":{"docs":{},"的":{"docs":{},"丢":{"docs":{},"失":{"docs":{},"或":{"docs":{},"失":{"docs":{},"败":{"docs":{},"不":{"docs":{},"会":{"docs":{},"影":{"docs":{},"响":{"docs":{},"系":{"docs":{},"统":{"docs":{},"的":{"docs":{},"运":{"docs":{},"行":{"docs":{},",":{"docs":{},"系":{"docs":{},"统":{"docs":{},"如":{"docs":{},"果":{"docs":{},"不":{"docs":{},"能":{"docs":{},"在":{"docs":{},"某":{"docs":{},"个":{"docs":{},"时":{"docs":{},"限":{"docs":{},"内":{"docs":{},"达":{"docs":{},"成":{"docs":{},"数":{"docs":{},"据":{"docs":{},"一":{"docs":{},"致":{"docs":{},"性":{"docs":{},",":{"docs":{},"就":{"docs":{},"必":{"docs":{},"须":{"docs":{},"在":{"docs":{},"上":{"docs":{},"面":{"docs":{},"两":{"docs":{},"个":{"docs":{},"操":{"docs":{},"作":{"docs":{},"之":{"docs":{},"间":{"docs":{},"做":{"docs":{},"出":{"docs":{},"选":{"docs":{},"择":{"docs":{},")":{"docs":{"Hive&HBase/06_hbase数据模型.html":{"ref":"Hive&HBase/06_hbase数据模型.html","tf":0.020833333333333332}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"配":{"docs":{},"r":{"docs":{},"e":{"docs":{},"g":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"和":{"docs":{},"m":{"docs":{},"e":{"docs":{},"t":{"docs":{},"a":{"docs":{},"信":{"docs":{},"息":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}}}}}}}}}},"位":{"docs":{},"数":{"docs":{},"去":{"docs":{},"极":{"docs":{},"值":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}},"和":{"docs":{"./":{"ref":"./","tf":0.011235955056179775},"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863},"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938},"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.017857142857142856},"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907},"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196},"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0022675736961451248},"Hive&HBase/06_hbase数据模型.html":{"ref":"Hive&HBase/06_hbase数据模型.html","tf":0.020833333333333332},"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676},"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"m":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}},"完":{"docs":{},"成":{"docs":{},"提":{"docs":{},"交":{"docs":{},"作":{"docs":{},"业":{"docs":{},"到":{"docs":{},"y":{"docs":{},"a":{"docs":{},"r":{"docs":{},"n":{"docs":{},"上":{"docs":{},"执":{"docs":{},"行":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}}}}}}}}}}}},"善":{"docs":{},"画":{"docs":{},"像":{"docs":{},"关":{"docs":{},"键":{"docs":{},"词":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}},"全":{"docs":{},"支":{"docs":{},"持":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}},"整":{"docs":{},"代":{"docs":{},"码":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}},"掌":{"docs":{},"握":{"docs":{},"h":{"docs":{},"a":{"docs":{},"p":{"docs":{},"p":{"docs":{},"y":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"的":{"docs":{},"常":{"docs":{},"用":{"docs":{},"a":{"docs":{},"p":{"docs":{},"i":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}}}}}}}}}}}}},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"的":{"docs":{},"s":{"docs":{},"h":{"docs":{},"e":{"docs":{},"l":{"docs":{},"l":{"docs":{},"操":{"docs":{},"作":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}}}}}}}}}}}},"t":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"s":{"docs":{},"f":{"docs":{},"o":{"docs":{},"r":{"docs":{},"m":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"和":{"docs":{},"a":{"docs":{},"c":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"算":{"docs":{},"子":{"docs":{},"的":{"docs":{},"基":{"docs":{},"本":{"docs":{},"使":{"docs":{},"用":{"docs":{"./":{"ref":"./","tf":0.011235955056179775},"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"推":{"docs":{},"荐":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}},"系":{"docs":{},"统":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}},"基":{"docs":{},"础":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}},"案":{"docs":{},"例":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}},"简":{"docs":{},"介":{"docs":{"./":{"ref":"./","tf":0.011235955056179775},"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}},"算":{"docs":{},"法":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}},":":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.02247191011235955}}},"和":{"docs":{},"w":{"docs":{},"e":{"docs":{},"b":{"docs":{},"项":{"docs":{},"目":{"docs":{},"的":{"docs":{},"区":{"docs":{},"别":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}}}}}}}}},"概":{"docs":{},"念":{"docs":{},"及":{"docs":{},"产":{"docs":{},"生":{"docs":{},"背":{"docs":{},"景":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}}}}}}},"的":{"docs":{},"作":{"docs":{},"用":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}},"工":{"docs":{},"作":{"docs":{},"原":{"docs":{},"理":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}},"及":{"docs":{},"作":{"docs":{},"用":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}}}}}}},"应":{"docs":{},"用":{"docs":{},"场":{"docs":{},"景":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}}}},"整":{"docs":{},"体":{"docs":{},"架":{"docs":{},"构":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}}}}},"评":{"docs":{},"估":{"docs":{},"指":{"docs":{},"标":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}},"冷":{"docs":{},"启":{"docs":{},"动":{"docs":{},"问":{"docs":{},"题":{"docs":{"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html":{"ref":"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html","tf":0.024390243902439025}}}}}}}},"（":{"2":{"0":{"1":{"0":{"docs":{},"s":{"docs":{},"）":{"docs":{},"：":{"docs":{},"不":{"docs":{},"需":{"docs":{},"要":{"docs":{},"⽤":{"docs":{},"户":{"docs":{},"提":{"docs":{},"供":{"docs":{},"明":{"docs":{},"确":{"docs":{},"的":{"docs":{},"需":{"docs":{},"求":{"docs":{},"，":{"docs":{},"通":{"docs":{},"过":{"docs":{},"分":{"docs":{},"析":{"docs":{},"⽤":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}}}}}}}}}}}}}}}}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"整":{"docs":{},"体":{"docs":{},"架":{"docs":{},"构":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}}}}},"架":{"docs":{},"构":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}}},"要":{"docs":{},"素":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}}},"设":{"docs":{},"计":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}}},"评":{"docs":{},"估":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}},"方":{"docs":{},"法":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}},"冷":{"docs":{},"启":{"docs":{},"动":{"docs":{},"概":{"docs":{},"念":{"docs":{"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html":{"ref":"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html","tf":0.024390243902439025}}}}}}}}},"算":{"docs":{},"法":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734},"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}},"架":{"docs":{},"构":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}}}}},"模":{"docs":{},"型":{"docs":{},"构":{"docs":{},"建":{"docs":{},"流":{"docs":{},"程":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}},"业":{"docs":{},"务":{"docs":{},"处":{"docs":{},"理":{"docs":{},"主":{"docs":{},"要":{"docs":{},"流":{"docs":{},"程":{"docs":{},"：":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}}}}},"任":{"docs":{},"务":{"docs":{},"部":{"docs":{},"分":{"docs":{},"：":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}},"处":{"docs":{},"理":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}},"结":{"docs":{},"果":{"docs":{},"存":{"docs":{},"放":{"docs":{},"在":{"docs":{},"r":{"docs":{},"e":{"docs":{},"c":{"docs":{},"o":{"docs":{},"m":{"docs":{},"m":{"docs":{},"e":{"docs":{},"n":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"s":{"docs":{},"列":{"docs":{},"中":{"docs":{},"，":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}}},"独":{"docs":{},"立":{"docs":{},"完":{"docs":{},"成":{"docs":{},"m":{"docs":{},"r":{"docs":{},"j":{"docs":{},"o":{"docs":{},"b":{"docs":{},"实":{"docs":{},"现":{"docs":{},"w":{"docs":{},"o":{"docs":{},"r":{"docs":{},"d":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}}}}}}}}}}}}}}}}},"实":{"docs":{},"现":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"./":{"ref":"./","tf":0.033707865168539325},"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102},"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.006711409395973154}}}}}}},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"的":{"docs":{},"创":{"docs":{},"建":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}}},"i":{"docs":{},"p":{"docs":{},"地":{"docs":{},"址":{"docs":{},"查":{"docs":{},"询":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}}}}}}}},"知":{"docs":{},"道":{"docs":{},"c":{"docs":{},"t":{"docs":{},"r":{"docs":{},"预":{"docs":{},"估":{"docs":{},"概":{"docs":{},"念":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}}}}}},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},"生":{"docs":{},"态":{"docs":{},"组":{"docs":{},"成":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}}},"的":{"docs":{},"优":{"docs":{},"势":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}},"概":{"docs":{},"念":{"docs":{},"及":{"docs":{},"发":{"docs":{},"展":{"docs":{},"历":{"docs":{},"史":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}}}}}}}}}}}},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"和":{"docs":{},"关":{"docs":{},"系":{"docs":{},"型":{"docs":{},"数":{"docs":{},"据":{"docs":{},"库":{"docs":{},"的":{"docs":{},"区":{"docs":{},"别":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}}}}}}}}}}}}},"d":{"docs":{},"f":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{},"的":{"docs":{},"u":{"docs":{},"d":{"docs":{},"f":{"docs":{},"（":{"docs":{},"自":{"docs":{},"定":{"docs":{},"义":{"docs":{},"函":{"docs":{},"数":{"docs":{},"）":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}}}}}}}}},"内":{"docs":{},"部":{"docs":{},"表":{"docs":{},"、":{"docs":{},"外":{"docs":{},"部":{"docs":{},"表":{"docs":{},"、":{"docs":{},"分":{"docs":{},"区":{"docs":{},"表":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}}}}}}}}}}}}}},"q":{"docs":{},"l":{"docs":{},"和":{"docs":{},"s":{"docs":{},"q":{"docs":{},"l":{"docs":{},"的":{"docs":{},"区":{"docs":{},"别":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}}}}}}}}},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"的":{"docs":{},"概":{"docs":{},"念":{"docs":{"./":{"ref":"./","tf":0.011235955056179775},"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}}},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"作":{"docs":{},"业":{"docs":{},"提":{"docs":{},"交":{"docs":{},"集":{"docs":{},"群":{"docs":{},"的":{"docs":{},"过":{"docs":{},"程":{"docs":{"./":{"ref":"./","tf":0.011235955056179775},"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}}}}}}}}},"的":{"docs":{},"特":{"docs":{},"点":{"docs":{},"（":{"docs":{},"与":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},"对":{"docs":{},"比":{"docs":{},"）":{"docs":{"./":{"ref":"./","tf":0.011235955056179775},"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}}}}}}}}}}}},"安":{"docs":{},"装":{"docs":{},"过":{"docs":{},"程":{"docs":{},"，":{"docs":{},"知":{"docs":{},"道":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"a":{"docs":{},"l":{"docs":{},"o":{"docs":{},"n":{"docs":{},"e":{"docs":{},"启":{"docs":{},"动":{"docs":{},"模":{"docs":{},"式":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"什":{"docs":{},"么":{"docs":{},"是":{"docs":{},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}}}}},"列":{"docs":{},"式":{"docs":{},"数":{"docs":{},"据":{"docs":{},"库":{"docs":{},"与":{"docs":{},"行":{"docs":{},"数":{"docs":{},"据":{"docs":{},"库":{"docs":{},"的":{"docs":{},"区":{"docs":{},"别":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}}}}}}}}}}}},"协":{"docs":{},"同":{"docs":{},"过":{"docs":{},"滤":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"的":{"docs":{},"相":{"docs":{},"关":{"docs":{},"原":{"docs":{},"理":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}}}}}}}}}},"基":{"docs":{},"于":{"docs":{},"回":{"docs":{},"归":{"docs":{},"模":{"docs":{},"型":{"docs":{},"的":{"docs":{},"协":{"docs":{},"同":{"docs":{},"过":{"docs":{},"滤":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"原":{"docs":{},"理":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}}}}}}}}}}}},"矩":{"docs":{},"阵":{"docs":{},"分":{"docs":{},"解":{"docs":{},"的":{"docs":{},"协":{"docs":{},"同":{"docs":{},"过":{"docs":{},"滤":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"原":{"docs":{},"理":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}}}}}}}}}}}}}},"常":{"docs":{},"用":{"docs":{},"的":{"docs":{},"基":{"docs":{},"于":{"docs":{},"模":{"docs":{},"型":{"docs":{},"的":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"算":{"docs":{},"法":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}}}}}}}}}}},"推":{"docs":{},"荐":{"docs":{},"系":{"docs":{},"统":{"docs":{},"的":{"docs":{},"工":{"docs":{},"程":{"docs":{},"架":{"docs":{},"构":{"docs":{},"和":{"docs":{},"算":{"docs":{},"法":{"docs":{},"架":{"docs":{},"构":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}}}}}}}},"常":{"docs":{},"用":{"docs":{},"算":{"docs":{},"法":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}}}}}}}}}},"简":{"docs":{},"介":{"docs":{"./":{"ref":"./","tf":10},"day02_推荐算法/07_基于内容的推荐算法.html":{"ref":"day02_推荐算法/07_基于内容的推荐算法.html","tf":0.03125},"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}},"明":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}},"单":{"docs":{},"函":{"docs":{},"数":{"docs":{},":":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}},"能":{"docs":{},"够":{"docs":{},"应":{"docs":{},"用":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"./":{"ref":"./","tf":0.02247191011235955}},"m":{"docs":{},"l":{"docs":{},"l":{"docs":{},"i":{"docs":{},"b":{"docs":{},"训":{"docs":{},"练":{"docs":{},"l":{"docs":{},"r":{"docs":{},"模":{"docs":{},"型":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}}}}}}}},"训":{"docs":{},"练":{"docs":{},"a":{"docs":{},"l":{"docs":{},"s":{"docs":{},"模":{"docs":{},"型":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}}}}}}}}}}}}}}},"掌":{"docs":{},"握":{"docs":{},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}},"s":{"docs":{},"的":{"docs":{},"环":{"docs":{},"境":{"docs":{},"搭":{"docs":{},"建":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}}}}}}}}}}},"处":{"docs":{},"理":{"docs":{},"稀":{"docs":{},"疏":{"docs":{},"评":{"docs":{},"分":{"docs":{},"矩":{"docs":{},"阵":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}}}}}},"说":{"docs":{},"出":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"与":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"的":{"docs":{},"联":{"docs":{},"系":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}}}}}}}}}}}}}},"s":{"docs":{},"t":{"docs":{},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"m":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"的":{"docs":{},"常":{"docs":{},"见":{"docs":{},"操":{"docs":{},"作":{"docs":{},"a":{"docs":{},"p":{"docs":{},"i":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}}}}}}}}}}}}}}}}},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},"发":{"docs":{},"行":{"docs":{},"版":{"docs":{},"本":{"docs":{},"的":{"docs":{},"选":{"docs":{},"择":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}}}}}},"的":{"docs":{},"核":{"docs":{},"心":{"docs":{},"组":{"docs":{},"件":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}}}}}}}}},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},"的":{"docs":{},"架":{"docs":{},"构":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}}}}}},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{},"e":{"docs":{},"原":{"docs":{},"理":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}}}}}}}}}},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"./":{"ref":"./","tf":0.02247191011235955}},"m":{"docs":{},"l":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}}}}}},"y":{"docs":{},"a":{"docs":{},"r":{"docs":{},"n":{"docs":{},"执":{"docs":{},"行":{"docs":{},"流":{"docs":{},"程":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}}}}}}},"处":{"docs":{},"理":{"docs":{},"缺":{"docs":{},"失":{"docs":{},"值":{"docs":{},"的":{"docs":{},"常":{"docs":{},"件":{"docs":{},"办":{"docs":{},"法":{"docs":{"./":{"ref":"./","tf":0.011235955056179775}}}}}}}}}}}},"广":{"docs":{},"播":{"docs":{},"变":{"docs":{},"量":{"docs":{},"的":{"docs":{},"概":{"docs":{},"念":{"docs":{"./":{"ref":"./","tf":0.011235955056179775},"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}}}}},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"的":{"docs":{},"三":{"docs":{},"类":{"docs":{},"算":{"docs":{},"子":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}}},"明":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}},"启":{"docs":{},"动":{"docs":{},"成":{"docs":{},"功":{"docs":{"day03_Hadoop/ha2.1.html":{"ref":"day03_Hadoop/ha2.1.html","tf":0.0136986301369863}}}}}}}},"&":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775},"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863},"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.016},"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}},":":{"docs":{},"=":{"docs":{},"b":{"docs":{},"_":{"docs":{},"i":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}},"u":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}},"=":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}},"r":{"docs":{},"_":{"docs":{},"{":{"docs":{},"u":{"docs":{},"i":{"docs":{},"}":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}},"\\":{"docs":{},"v":{"docs":{},"e":{"docs":{},"c":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}},"m":{"docs":{},"u":{"docs":{"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}},"j":{"docs":{},"(":{"docs":{},"\\":{"docs":{},"t":{"docs":{},"h":{"docs":{},"e":{"docs":{},"t":{"docs":{},"a":{"docs":{},")":{"docs":{},"=":{"docs":{},"\\":{"docs":{},"s":{"docs":{},"u":{"docs":{},"m":{"docs":{},"_":{"docs":{},"{":{"docs":{},"u":{"docs":{},",":{"docs":{},"i":{"docs":{},"\\":{"docs":{},"i":{"docs":{},"n":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}},"c":{"docs":{},"o":{"docs":{},"s":{"docs":{},"t":{"docs":{},"=":{"docs":{},"f":{"docs":{},"(":{"docs":{},"b":{"docs":{},"_":{"docs":{},"u":{"docs":{},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}}}}}}}}}}},"p":{"docs":{},"_":{"docs":{},"{":{"docs":{},"u":{"docs":{},"k":{"docs":{},"}":{"docs":{},":":{"docs":{},"=":{"docs":{},"p":{"docs":{},"_":{"docs":{},"{":{"docs":{},"u":{"docs":{},"k":{"docs":{},"}":{"docs":{},"+":{"docs":{},"\\":{"docs":{},"a":{"docs":{},"l":{"docs":{},"p":{"docs":{},"h":{"docs":{},"a":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}}}}}}}}}}}}}}}}}},"(":{"1":{"5":{"5":{"8":{"3":{"2":{"3":{"1":{"3":{"9":{"7":{"3":{"2":{"docs":{},",":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"9":{"0":{"4":{"1":{"3":{"0":{"docs":{},",":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{},",":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.0016233766233766235},"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00684931506849315}}}},"2":{"docs":{},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416},"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.0091324200913242}}}},"3":{"docs":{},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416},"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.01141552511415525}}}},"4":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.0091324200913242}},"[":{"0":{"docs":{},"]":{"docs":{},",":{"docs":{},"[":{"1":{"docs":{},".":{"0":{"docs":{},"]":{"docs":{},")":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.004283091984499286},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.003581376840429765}}}}}},"docs":{}}},"docs":{}}}}},"1":{"docs":{},",":{"3":{"docs":{},"]":{"docs":{},",":{"docs":{},"[":{"3":{"docs":{},".":{"0":{"docs":{},",":{"4":{"docs":{},".":{"0":{"docs":{},"]":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}}}},"docs":{}},"]":{"docs":{},",":{"docs":{},"[":{"1":{"docs":{},".":{"0":{"docs":{},"]":{"docs":{},")":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.005506832551499082},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.006764822920811779}}}}}},"docs":{}}},"docs":{}}}}},"2":{"docs":{},"]":{"docs":{},",":{"docs":{},"[":{"1":{"docs":{},".":{"0":{"docs":{},"]":{"docs":{},")":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0024474811339995923},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0015917230401910067}}}}}},"docs":{}}},"docs":{}}}}},"docs":{}}}},"5":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.01141552511415525}},"[":{"0":{"docs":{},"]":{"docs":{},",":{"docs":{},"[":{"1":{"docs":{},".":{"0":{"docs":{},"]":{"docs":{},")":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0012237405669997961},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.00238758456028651}},"(":{"1":{"0":{"docs":{},",":{"docs":{},"[":{"0":{"docs":{},",":{"1":{"docs":{},",":{"5":{"docs":{},"]":{"docs":{},",":{"docs":{},"[":{"4":{"docs":{},".":{"0":{"docs":{},",":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981}}}}}}}},"docs":{}}},"docs":{}}}}},"docs":{}}},"2":{"docs":{},",":{"5":{"docs":{},"]":{"docs":{},",":{"docs":{},"[":{"2":{"docs":{},".":{"0":{"docs":{},",":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}},"docs":{}}},"4":{"docs":{},".":{"0":{"docs":{},",":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}}}}}}},"docs":{}}},"docs":{}}}}},"docs":{}}},"docs":{}}},"docs":{}}}},"docs":{}},"docs":{}}}}}},"docs":{}}},"docs":{}}}}},"1":{"docs":{},"]":{"docs":{},",":{"docs":{},"[":{"1":{"docs":{},".":{"0":{"docs":{},"]":{"docs":{},")":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0014276973281664286},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.002785515320334262}},"(":{"1":{"0":{"docs":{},",":{"docs":{},"[":{"0":{"docs":{},",":{"1":{"docs":{},",":{"6":{"docs":{},"]":{"docs":{},",":{"docs":{},"[":{"3":{"docs":{},".":{"0":{"docs":{},",":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}},"docs":{}}},"docs":{}}}}},"docs":{}}},"2":{"docs":{},",":{"6":{"docs":{},"]":{"docs":{},",":{"docs":{},"[":{"2":{"docs":{},".":{"0":{"docs":{},",":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}}}}}}},"docs":{}}},"4":{"docs":{},".":{"0":{"docs":{},",":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}},"docs":{}}},"5":{"docs":{},".":{"0":{"docs":{},",":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}},"docs":{}}},"6":{"docs":{},".":{"0":{"docs":{},",":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}},"docs":{}}},"docs":{}}}}},"docs":{}}},"3":{"docs":{},",":{"6":{"docs":{},"]":{"docs":{},",":{"docs":{},"[":{"2":{"docs":{},".":{"0":{"docs":{},",":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}},"docs":{}}},"docs":{}}}}},"docs":{}}},"docs":{}}},"docs":{}}}},"8":{"docs":{},",":{"docs":{},"[":{"1":{"docs":{},",":{"2":{"docs":{},",":{"3":{"docs":{},",":{"4":{"docs":{},",":{"5":{"docs":{},",":{"6":{"docs":{},",":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.00238758456028651}}}}}}}},"docs":{}}},"docs":{}}},"docs":{}}},"4":{"docs":{},",":{"5":{"docs":{},",":{"6":{"docs":{},",":{"7":{"docs":{},",":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}}}}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}}},"docs":{}},"docs":{}}}}}},"docs":{}}},"docs":{}}}}},"2":{"docs":{},"]":{"docs":{},",":{"docs":{},"[":{"1":{"docs":{},".":{"0":{"docs":{},"]":{"docs":{},")":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}},"(":{"1":{"0":{"docs":{},",":{"docs":{},"[":{"0":{"docs":{},",":{"1":{"docs":{},",":{"7":{"docs":{},"]":{"docs":{},",":{"docs":{},"[":{"5":{"docs":{},".":{"0":{"docs":{},",":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}},"docs":{}}},"docs":{}}}}},"docs":{}}},"3":{"docs":{},",":{"7":{"docs":{},"]":{"docs":{},",":{"docs":{},"[":{"2":{"docs":{},".":{"0":{"docs":{},",":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}},"docs":{}}},"docs":{}}}}},"docs":{}}},"docs":{}}},"docs":{}}}},"docs":{}},"docs":{}}}}}},"docs":{}}},"docs":{}}}}},"3":{"docs":{},"]":{"docs":{},",":{"docs":{},"[":{"1":{"docs":{},".":{"0":{"docs":{},"]":{"docs":{},")":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0008158270446665307},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0015917230401910067}},"(":{"1":{"0":{"docs":{},",":{"docs":{},"[":{"0":{"docs":{},",":{"1":{"docs":{},",":{"8":{"docs":{},"]":{"docs":{},",":{"docs":{},"[":{"5":{"docs":{},".":{"0":{"docs":{},",":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}}}}}}},"docs":{}}},"docs":{}}}}},"docs":{}}},"3":{"docs":{},",":{"8":{"docs":{},"]":{"docs":{},",":{"docs":{},"[":{"1":{"docs":{},".":{"0":{"docs":{},",":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}},"docs":{}}},"4":{"docs":{},".":{"0":{"docs":{},",":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}},"docs":{}}},"docs":{}}}}},"docs":{}}},"docs":{}}},"docs":{}}}},"docs":{}},"docs":{}}}}}},"docs":{}}},"docs":{}}}}},"4":{"docs":{},"]":{"docs":{},",":{"docs":{},"[":{"1":{"docs":{},".":{"0":{"docs":{},"]":{"docs":{},")":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}},"(":{"1":{"0":{"docs":{},",":{"docs":{},"[":{"0":{"docs":{},",":{"2":{"docs":{},",":{"9":{"docs":{},"]":{"docs":{},",":{"docs":{},"[":{"5":{"docs":{},".":{"0":{"docs":{},",":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}},"docs":{}}},"docs":{}}}}},"docs":{}}},"docs":{}}},"docs":{}}}},"8":{"docs":{},",":{"docs":{},"[":{"1":{"docs":{},",":{"2":{"docs":{},",":{"3":{"docs":{},",":{"4":{"docs":{},",":{"5":{"docs":{},",":{"6":{"docs":{},",":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}}}}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}}},"docs":{}},"docs":{}}}}}},"docs":{}}},"docs":{}}}}},"docs":{}}}},"6":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00684931506849315}}}},"7":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00684931506849315}}}},"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"s":{"docs":{},"s":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}}}},"l":{"docs":{},"o":{"docs":{},"b":{"docs":{},"a":{"docs":{},"l":{"docs":{},"_":{"docs":{},"m":{"docs":{},"e":{"docs":{},"a":{"docs":{},"n":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}},"v":{"docs":{},"i":{"docs":{},"d":{"docs":{},"e":{"docs":{},"o":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}}}}},"l":{"docs":{},"a":{"docs":{},"m":{"docs":{},"b":{"docs":{},"d":{"docs":{},"a":{"docs":{},"架":{"docs":{},"构":{"docs":{},")":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}}}}}}}},"s":{"docs":{},"t":{"docs":{},"_":{"docs":{},"s":{"docs":{},"u":{"docs":{},"m":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}},"海":{"docs":{},"选":{"docs":{},")":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}}}},"点":{"docs":{},"击":{"docs":{},"率":{"docs":{},"预":{"docs":{},"估":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}}}}}},"u":{"docs":{},"i":{"docs":{},"d":{"docs":{},",":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.00487012987012987}}}}},"d":{"docs":{},"a":{"docs":{},"f":{"docs":{},"s":{"docs":{},")":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}},"f":{"docs":{},"s":{"docs":{},")":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}},"业":{"docs":{},"务":{"docs":{},"角":{"docs":{},"度":{"docs":{},")":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}}},"理":{"docs":{},"论":{"docs":{},"角":{"docs":{},"度":{"docs":{},")":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}}},"\"":{"docs":{},"m":{"docs":{},"o":{"docs":{},"v":{"docs":{},"i":{"docs":{},"e":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0017152658662092624},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.003795066413662239},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}}}}},"r":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"\"":{"docs":{},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0017152658662092624},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.003795066413662239},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}}}},"a":{"docs":{},"\"":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}},"b":{"docs":{},"\"":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},".":{"docs":{},"a":{"docs":{},"p":{"docs":{},"p":{"docs":{},".":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}},"d":{"docs":{},"y":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"i":{"docs":{},"c":{"docs":{},"a":{"docs":{},"l":{"docs":{},"l":{"docs":{},"o":{"docs":{},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},".":{"docs":{},"e":{"docs":{},"n":{"docs":{},"a":{"docs":{},"b":{"docs":{},"l":{"docs":{},"e":{"docs":{},"d":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}},"i":{"docs":{},"n":{"docs":{},"i":{"docs":{},"t":{"docs":{},"i":{"docs":{},"a":{"docs":{},"l":{"docs":{},"e":{"docs":{},"x":{"docs":{},"e":{"docs":{},"c":{"docs":{},"u":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{},"s":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"e":{"docs":{},"x":{"docs":{},"e":{"docs":{},"c":{"docs":{},"u":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}},"m":{"docs":{},"e":{"docs":{},"m":{"docs":{},"o":{"docs":{},"r":{"docs":{},"y":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}},"m":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}},"s":{"docs":{},"h":{"docs":{},"u":{"docs":{},"f":{"docs":{},"f":{"docs":{},"l":{"docs":{},"e":{"docs":{},".":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"v":{"docs":{},"i":{"docs":{},"c":{"docs":{},"e":{"docs":{},".":{"docs":{},"e":{"docs":{},"n":{"docs":{},"a":{"docs":{},"b":{"docs":{},"l":{"docs":{},"e":{"docs":{},"d":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"\\":{"docs":{},"m":{"docs":{},"u":{"docs":{},"+":{"docs":{},"b":{"docs":{},"_":{"docs":{},"u":{"docs":{},"+":{"docs":{},"b":{"docs":{},"_":{"docs":{},"i":{"docs":{},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}},"e":{"docs":{},"r":{"docs":{},"r":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.003795066413662239},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.009259259259259259}},"o":{"docs":{},"r":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.003430531732418525}}}}}}},"p":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"_":{"docs":{},"r":{"docs":{},"a":{"docs":{},"t":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683}}}}}}}}},"o":{"docs":{},"s":{"docs":{},"i":{"docs":{},"t":{"docs":{},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{},")":{"docs":{},".":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}},"r":{"docs":{},"e":{"docs":{},"g":{"docs":{},"_":{"docs":{},"b":{"docs":{},"i":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}},"u":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}},"d":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}},"_":{"docs":{},"{":{"docs":{},"u":{"docs":{},"i":{"docs":{},"}":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.009487666034155597},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.016203703703703703}}}}}}}},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"f":{"docs":{},".":{"docs":{},"g":{"docs":{},"l":{"docs":{},"o":{"docs":{},"b":{"docs":{},"a":{"docs":{},"l":{"docs":{},"_":{"docs":{},"m":{"docs":{},"e":{"docs":{},"a":{"docs":{},"n":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}}}}}}},"r":{"docs":{},"e":{"docs":{},"g":{"docs":{},"_":{"docs":{},"b":{"docs":{},"i":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}},"u":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}}}},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.006802721088435374}}}}}}}},"d":{"docs":{},"c":{"docs":{},"t":{"docs":{},"[":{"docs":{},"x":{"docs":{},"[":{"0":{"docs":{},"]":{"docs":{},"]":{"docs":{},",":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}},"docs":{}}}}}},"a":{"docs":{},"t":{"docs":{},"e":{"1":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.007407407407407408}}},"docs":{}}}},"e":{"docs":{},"f":{"docs":{},"a":{"docs":{},"u":{"docs":{},"l":{"docs":{},"t":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00851063829787234}}}}}}}}},"f":{"docs":{},"_":{"docs":{},"o":{"docs":{},"u":{"docs":{},"t":{"docs":{},"l":{"docs":{},"i":{"docs":{},"e":{"docs":{},"r":{"docs":{},"s":{"docs":{},"[":{"docs":{},"c":{"docs":{},"]":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}}}}}}},"x":{"docs":{},"[":{"0":{"docs":{},"]":{"docs":{},",":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876},"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}}}}},"2":{"docs":{},"]":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}},"docs":{}},",":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102},"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},")":{"docs":{},"成":{"docs":{},"为":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},"项":{"docs":{},"目":{"docs":{},"的":{"docs":{},"独":{"docs":{},"立":{"docs":{},"子":{"docs":{},"项":{"docs":{},"目":{"docs":{},"。":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}}}}}}}}}}}}}}}}},"™":{"docs":{},")":{"docs":{},":":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282}}}}}}}}},"m":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{"day03_Hadoop/ha2.4.html":{"ref":"day03_Hadoop/ha2.4.html","tf":0.03333333333333333}}}}}},"n":{"docs":{},"a":{"docs":{},"g":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}},"这":{"docs":{},"个":{"docs":{},"命":{"docs":{},"令":{"docs":{},"只":{"docs":{},"运":{"docs":{},"行":{"docs":{},"一":{"docs":{},"次":{"docs":{},")":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428}}}}}}}}}}}},"y":{"docs":{},"e":{"docs":{},"t":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}},"内":{"docs":{},"部":{"docs":{},"表":{"docs":{},")":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}},"容":{"docs":{},"较":{"docs":{},"多":{"docs":{},"，":{"docs":{},"见":{"docs":{},"《":{"docs":{},"h":{"docs":{},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}}}}}}}},"c":{"docs":{},"l":{"docs":{},"a":{"docs":{},"s":{"docs":{},"s":{"docs":{},"n":{"docs":{},"o":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"1":{"docs":{},")":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0045351473922902496}}}},"docs":{}}}}},"m":{"docs":{},"p":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"b":{"docs":{},"l":{"docs":{},"e":{"docs":{},";":{"docs":{},")":{"docs":{},"\"":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}}}}}}}}}}}},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.007407407407407408}}}}},"u":{"docs":{},"l":{"docs":{},"l":{"docs":{},"a":{"docs":{},"b":{"docs":{},"l":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.01794453507340946},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.012033448908831328},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.015519299641862315}}}}}}}},"e":{"docs":{},"g":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}},"f":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},",":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},"c":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}}}},"a":{"docs":{},".":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{},"i":{"docs":{},"c":{"docs":{},"l":{"docs":{},"e":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.006802721088435374}}}}}}}}}}}}}},"'":{"1":{"docs":{},"'":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.004545454545454545}}}}},"2":{"docs":{},"'":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.006818181818181818}}}}},"docs":{},"a":{"docs":{},"b":{"docs":{},"'":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}},"c":{"docs":{},"'":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}}},"c":{"docs":{},"'":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}},"'":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.00909090909090909}}}}},"b":{"docs":{},"a":{"docs":{},"r":{"docs":{},"'":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}}},"c":{"docs":{},"'":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}},"e":{"docs":{},"c":{"docs":{},"'":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}}},"y":{"docs":{},"'":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}},"'":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.01818181818181818}}}}},"f":{"docs":{},"o":{"docs":{},"o":{"docs":{},"'":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}}},"l":{"docs":{},"e":{"docs":{},"e":{"docs":{},"c":{"docs":{},"e":{"docs":{},"'":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.004545454545454545}}}}}}}}}},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},"'":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}}}},"'":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.004545454545454545}}}}}}},"l":{"docs":{},"a":{"docs":{},"b":{"docs":{},"s":{"docs":{},"'":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}}},"m":{"docs":{},"b":{"docs":{},"'":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.004545454545454545}}}}}}},"i":{"docs":{},"t":{"docs":{},"t":{"docs":{},"l":{"docs":{},"e":{"docs":{},"'":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}},"m":{"docs":{},"e":{"docs":{},"'":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}}},"q":{"docs":{},"u":{"docs":{},"u":{"docs":{},"x":{"docs":{},"'":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}}}}},"s":{"docs":{},"e":{"docs":{},"e":{"docs":{},"'":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}}},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},".":{"docs":{},"s":{"docs":{},"q":{"docs":{},"l":{"docs":{},".":{"docs":{},"p":{"docs":{},"i":{"docs":{},"v":{"docs":{},"o":{"docs":{},"t":{"docs":{},"m":{"docs":{},"a":{"docs":{},"x":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"s":{"docs":{},"'":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}}}},"t":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{},"'":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}}}}},"w":{"docs":{},"e":{"docs":{},"l":{"docs":{},"c":{"docs":{},"o":{"docs":{},"m":{"docs":{},"e":{"docs":{},"'":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}}}}}}},"a":{"docs":{},"s":{"docs":{},"'":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}},"h":{"docs":{},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"'":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}},"o":{"docs":{},"s":{"docs":{},"e":{"docs":{},"'":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}},"y":{"docs":{},"o":{"docs":{},"u":{"docs":{},"'":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}}}},"c":{"docs":{},"'":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.004545454545454545}}}}},"d":{"docs":{},"'":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.006818181818181818}}}}}},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}},"h":{"docs":{},"t":{"docs":{},"m":{"docs":{},"l":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.006711409395973154}}}}}}}},"w":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"o":{"docs":{},"w":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.006711409395973154}}}}}}},"o":{"docs":{},"r":{"docs":{},"d":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.006711409395973154},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}},"(":{"docs":{},"c":{"docs":{},"i":{"docs":{},"t":{"docs":{},"y":{"docs":{},"_":{"docs":{},"b":{"docs":{},"r":{"docs":{},"o":{"docs":{},"a":{"docs":{},"d":{"docs":{},"c":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"_":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"[":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{},"]":{"docs":{},"[":{"2":{"docs":{},"]":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"i":{"docs":{},"n":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}},"离":{"docs":{},"散":{"docs":{},"流":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}}}},"o":{"docs":{},"u":{"docs":{},"t":{"docs":{},"p":{"docs":{},"u":{"docs":{},"t":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}},"b":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208},"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.006802721088435374},"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.006818181818181818}},"a":{"docs":{},"i":{"docs":{},"d":{"docs":{},"u":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}}},"s":{"docs":{},"e":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.014124293785310734},"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247},"day02_推荐算法/01_基于模型的协同过滤推荐.html":{"ref":"day02_推荐算法/01_基于模型的协同过滤推荐.html","tf":0.15384615384615385},"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282},"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}},"l":{"docs":{},"i":{"docs":{},"n":{"docs":{},"e":{"docs":{},"c":{"docs":{},"f":{"docs":{},"b":{"docs":{},"y":{"docs":{},"a":{"docs":{},"l":{"docs":{},"s":{"docs":{},"(":{"2":{"0":{"docs":{},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}},"docs":{}},"docs":{},"o":{"docs":{},"b":{"docs":{},"j":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},")":{"docs":{},":":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}}}}}}}},"s":{"docs":{},"g":{"docs":{},"d":{"docs":{},"(":{"2":{"0":{"docs":{},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}},"docs":{}},"docs":{},"o":{"docs":{},"b":{"docs":{},"j":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},")":{"docs":{},":":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}}}}}}}}}}}},"目":{"docs":{},"标":{"docs":{},"：":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}},"设":{"docs":{},"计":{"docs":{},"思":{"docs":{},"想":{"docs":{},"基":{"docs":{},"于":{"docs":{},"以":{"docs":{},"下":{"docs":{},"的":{"docs":{},"假":{"docs":{},"设":{"docs":{},"：":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}},"：":{"docs":{},"基":{"docs":{},"准":{"docs":{},"预":{"docs":{},"测":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}},"d":{"docs":{},"）":{"docs":{"day02_推荐算法/07_基于内容的推荐算法.html":{"ref":"day02_推荐算法/07_基于内容的推荐算法.html","tf":0.03125}}}},"_":{"docs":{},"i":{"docs":{},"n":{"docs":{},"f":{"docs":{},"o":{"docs":{},":":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.013215859030837005}}}}}}}}}}}}}}}},"i":{"docs":{},"c":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}},"g":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}},"r":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00851063829787234}}},"t":{"docs":{},"c":{"docs":{},"h":{"docs":{},"方":{"docs":{},"式":{"docs":{},"（":{"docs":{},"t":{"docs":{},"r":{"docs":{},"i":{"docs":{},"d":{"docs":{},"e":{"docs":{},"n":{"docs":{},"t":{"docs":{},"）":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}}}}}}}}}}},"流":{"docs":{},"式":{"docs":{},"处":{"docs":{},"理":{"docs":{},"数":{"docs":{},"据":{"docs":{},"（":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}}}}}}}}}}}},"_":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"v":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.011450381679389313}},"a":{"docs":{},"l":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}}}}}}}},"\"":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.007062146892655367}}},"]":{"docs":{},")":{"docs":{},")":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}},"各":{"docs":{},"自":{"docs":{},"减":{"docs":{},"去":{"docs":{},"向":{"docs":{},"量":{"docs":{},"的":{"docs":{},"均":{"docs":{},"值":{"docs":{},"后":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}},"的":{"docs":{},"相":{"docs":{},"似":{"docs":{},"度":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}},"_":{"docs":{},"i":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.027777777777777776}},"&":{"docs":{},":":{"docs":{},"=":{"docs":{},"b":{"docs":{},"_":{"docs":{},"i":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.004002287021154946}},"(":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}},"\\":{"docs":{},"\\":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}},"^":{"2":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683}},"是":{"docs":{},"用":{"docs":{},"来":{"docs":{},"寻":{"docs":{},"找":{"docs":{},"与":{"docs":{},"已":{"docs":{},"知":{"docs":{},"评":{"docs":{},"分":{"docs":{},"数":{"docs":{},"据":{"docs":{},"拟":{"docs":{},"合":{"docs":{},"最":{"docs":{},"好":{"docs":{},"的":{"docs":{},"b":{"docs":{},"_":{"docs":{},"u":{"docs":{},"和":{"docs":{},"b":{"docs":{},"_":{"docs":{},"i":{"docs":{},"​":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{}},"}":{"docs":{},"{":{"docs":{},"\\":{"docs":{},"l":{"docs":{},"a":{"docs":{},"m":{"docs":{},"b":{"docs":{},"d":{"docs":{},"a":{"docs":{},"_":{"1":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}},"docs":{}}}}}}}}}}}},":":{"docs":{},"=":{"docs":{},"b":{"docs":{},"_":{"docs":{},"i":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.004629629629629629}}}}}}},"]":{"docs":{"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.004629629629629629}}}},"u":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.008576329331046312},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.027777777777777776}},"&":{"docs":{},":":{"docs":{},"=":{"docs":{},"b":{"docs":{},"_":{"docs":{},"u":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}},"\\":{"docs":{},"\\":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}},"}":{"docs":{},"{":{"docs":{},"\\":{"docs":{},"l":{"docs":{},"a":{"docs":{},"m":{"docs":{},"b":{"docs":{},"d":{"docs":{},"a":{"docs":{},"_":{"2":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}},"docs":{}}}}}}}}}}}},"}":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}},"​":{"docs":{},"更":{"docs":{},"新":{"docs":{},"(":{"docs":{},"因":{"docs":{},"为":{"docs":{},"a":{"docs":{},"l":{"docs":{},"p":{"docs":{},"h":{"docs":{},"a":{"docs":{},"可":{"docs":{},"以":{"docs":{},"人":{"docs":{},"为":{"docs":{},"控":{"docs":{},"制":{"docs":{},"，":{"docs":{},"所":{"docs":{},"以":{"2":{"docs":{},"可":{"docs":{},"以":{"docs":{},"省":{"docs":{},"略":{"docs":{},"掉":{"docs":{},")":{"docs":{},"：":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}},"docs":{}}}}}}}}}}}}}}}}}}}}},"和":{"docs":{},"b":{"docs":{},"_":{"docs":{},"i":{"docs":{},"​":{"docs":{},"分":{"docs":{},"别":{"docs":{},"属":{"docs":{},"于":{"docs":{},"用":{"docs":{},"户":{"docs":{},"和":{"docs":{},"物":{"docs":{},"品":{"docs":{},"的":{"docs":{},"偏":{"docs":{},"置":{"docs":{},"，":{"docs":{},"因":{"docs":{},"此":{"docs":{},"他":{"docs":{},"们":{"docs":{},"的":{"docs":{},"正":{"docs":{},"则":{"docs":{},"参":{"docs":{},"数":{"docs":{},"可":{"docs":{},"以":{"docs":{},"分":{"docs":{},"别":{"docs":{},"设":{"docs":{},"置":{"docs":{},"两":{"docs":{},"个":{"docs":{},"独":{"docs":{},"立":{"docs":{},"的":{"docs":{},"参":{"docs":{},"数":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},":":{"docs":{},"=":{"docs":{},"b":{"docs":{},"_":{"docs":{},"u":{"docs":{"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.004629629629629629}}}}}}},"]":{"docs":{"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.004629629629629629}}}},"{":{"docs":{},"u":{"docs":{},"i":{"docs":{},"}":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}},"c":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}},"f":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683}},".":{"docs":{},"f":{"docs":{},"i":{"docs":{},"t":{"docs":{},"(":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}}}},"t":{"docs":{},"r":{"docs":{},"a":{"docs":{},"i":{"docs":{},"n":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}}}}}}}}},"t":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{},"(":{"docs":{},"t":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}}}}}}}}}}}},"i":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.009148084619782733},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.004629629629629629}},"[":{"docs":{},"i":{"docs":{},"i":{"docs":{},"d":{"docs":{},"]":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.005717552887364208},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.004629629629629629}},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.003430531732418525},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}},"的":{"docs":{},"正":{"docs":{},"则":{"docs":{},"参":{"docs":{},"数":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}},"a":{"docs":{},"s":{"docs":{},"s":{"docs":{},"v":{"docs":{},"d":{"docs":{"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.004629629629629629}},":":{"docs":{"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html":{"ref":"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html","tf":0.037037037037037035}}},"(":{"0":{"docs":{},".":{"0":{"2":{"docs":{},",":{"docs":{"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}},"docs":{}},"docs":{}}},"docs":{},"o":{"docs":{},"b":{"docs":{},"j":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},")":{"docs":{},":":{"docs":{"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}}}}},"其":{"docs":{},"实":{"docs":{},"就":{"docs":{},"是":{"docs":{},"前":{"docs":{},"面":{"docs":{},"提":{"docs":{},"到":{"docs":{},"的":{"docs":{},"f":{"docs":{},"u":{"docs":{},"n":{"docs":{},"k":{"docs":{"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}}}}}}}}}}}}}},"g":{"docs":{},"t":{"docs":{},"a":{"docs":{},"b":{"docs":{},"l":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}},"e":{"docs":{},"：":{"docs":{},"一":{"docs":{},"个":{"docs":{},"大":{"docs":{},"型":{"docs":{},"的":{"docs":{},"分":{"docs":{},"布":{"docs":{},"式":{"docs":{},"数":{"docs":{},"据":{"docs":{},"库":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}}}}}}}}}}},"是":{"docs":{},"g":{"docs":{},"o":{"docs":{},"o":{"docs":{},"g":{"docs":{},"l":{"docs":{},"e":{"docs":{},"设":{"docs":{},"计":{"docs":{},"的":{"docs":{},"分":{"docs":{},"布":{"docs":{},"式":{"docs":{},"数":{"docs":{},"据":{"docs":{},"存":{"docs":{},"储":{"docs":{},"系":{"docs":{},"统":{"docs":{},"，":{"docs":{},"用":{"docs":{},"来":{"docs":{},"处":{"docs":{},"理":{"docs":{},"海":{"docs":{},"量":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"的":{"docs":{},"一":{"docs":{},"种":{"docs":{},"非":{"docs":{},"关":{"docs":{},"系":{"docs":{},"型":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"库":{"docs":{},"。":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"的":{"docs":{},"开":{"docs":{},"源":{"docs":{},"实":{"docs":{},"现":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}}}}}}},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}},")":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.007407407407407408}}},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0032626427406199023}}}}}}},"n":{"docs":{},"/":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625}}}}}}},"i":{"docs":{},"v":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}},"a":{"docs":{},"r":{"docs":{},"i":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}},"y":{"docs":{},"_":{"docs":{},"s":{"docs":{},"e":{"docs":{},"a":{"docs":{},"r":{"docs":{},"c":{"docs":{},"h":{"docs":{},"(":{"docs":{},"i":{"docs":{},"p":{"docs":{},"_":{"docs":{},"n":{"docs":{},"u":{"docs":{},"m":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}}}}}}}}}}}}}}}}}},"u":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002858776443682104},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.004574042309891366},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}},"[":{"docs":{},"u":{"docs":{},"i":{"docs":{},"d":{"docs":{},"]":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.007432818753573471},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.004629629629629629}},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0017152658662092624},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}},"的":{"docs":{},"正":{"docs":{},"则":{"docs":{},"参":{"docs":{},"数":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}},"f":{"docs":{},"f":{"docs":{},"e":{"docs":{},"r":{"docs":{"day03_Hadoop/ha3.5.html":{"ref":"day03_Hadoop/ha3.5.html","tf":0.030303030303030304}}}}}},"c":{"docs":{},"k":{"docs":{},"e":{"docs":{},"t":{"docs":{},"：":{"docs":{},"在":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}},"i":{"docs":{},"l":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}},"y":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}},":":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}},"=":{"docs":{},"n":{"docs":{},"o":{"docs":{},"n":{"docs":{},"e":{"docs":{},")":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}},"s":{"docs":{},"v":{"docs":{},"d":{"docs":{"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}},".":{"docs":{},"f":{"docs":{},"i":{"docs":{},"t":{"docs":{},"(":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},")":{"docs":{"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}}}}}}}}}}}},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"k":{"docs":{"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},":":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}},"：":{"docs":{},"脱":{"docs":{},"敏":{"docs":{},"过":{"docs":{},"的":{"docs":{},"品":{"docs":{},"牌":{"docs":{},"i":{"docs":{},"d":{"docs":{},"；":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}}}}}}}},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"_":{"docs":{},"d":{"docs":{},"f":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}},".":{"docs":{},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}}}}}},"w":{"docs":{},"r":{"docs":{},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},".":{"docs":{},"c":{"docs":{},"s":{"docs":{},"v":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},":":{"docs":{},"/":{"docs":{},"/":{"docs":{},"l":{"docs":{},"o":{"docs":{},"c":{"docs":{},"a":{"docs":{},"l":{"docs":{},"h":{"docs":{},"o":{"docs":{},"s":{"docs":{},"t":{"docs":{},":":{"9":{"0":{"0":{"0":{"docs":{},"/":{"docs":{},"p":{"docs":{},"r":{"docs":{},"e":{"docs":{},"p":{"docs":{},"r":{"docs":{},"o":{"docs":{},"c":{"docs":{},"e":{"docs":{},"s":{"docs":{},"s":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"_":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"/":{"docs":{},"b":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"_":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},".":{"docs":{},"c":{"docs":{},"s":{"docs":{},"v":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},":":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}},"i":{"docs":{},"d":{"docs":{},":":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}},"数":{"docs":{},"值":{"docs":{},"个":{"docs":{},"数":{"docs":{},"：":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}},"：":{"docs":{},"脱":{"docs":{},"敏":{"docs":{},"过":{"docs":{},"的":{"docs":{},"品":{"docs":{},"牌":{"docs":{},"i":{"docs":{},"d":{"docs":{},"；":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}},"p":{"docs":{},"r":{"docs":{},"i":{"docs":{},"c":{"docs":{},"e":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}},"l":{"docs":{},"o":{"docs":{},"c":{"docs":{},"k":{"1":{"docs":{},"的":{"docs":{},"两":{"docs":{},"个":{"docs":{},"备":{"docs":{},"份":{"docs":{},"存":{"docs":{},"储":{"docs":{},"在":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"n":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"0":{"docs":{},"和":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"n":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"2":{"docs":{},"两":{"docs":{},"个":{"docs":{},"服":{"docs":{},"务":{"docs":{},"器":{"docs":{},"上":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282}}}}}}}}},"docs":{}}}}}}}}}}},"docs":{}}}}}}}}}}}}}}}}}},"3":{"docs":{},"的":{"docs":{},"两":{"docs":{},"个":{"docs":{},"备":{"docs":{},"份":{"docs":{},"存":{"docs":{},"储":{"docs":{},"在":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"n":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"4":{"docs":{},"和":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"n":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"6":{"docs":{},"两":{"docs":{},"个":{"docs":{},"服":{"docs":{},"务":{"docs":{},"器":{"docs":{},"上":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282}}}}}}}}},"docs":{}}}}}}}}}}},"docs":{}}}}}}}}}}}}}}}}}},"docs":{},"i":{"docs":{},"d":{"docs":{"day03_Hadoop/ha4.2.html":{"ref":"day03_Hadoop/ha4.2.html","tf":0.0196078431372549}}}},"c":{"docs":{},"a":{"docs":{},"c":{"docs":{},"h":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}},"s":{"docs":{},"i":{"docs":{},"z":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}},"o":{"docs":{},"o":{"docs":{},"l":{"docs":{},"e":{"docs":{},"a":{"docs":{},"n":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}},"u":{"docs":{},"n":{"docs":{},"d":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.0045662100456621}},"s":{"docs":{},"[":{"docs":{},"c":{"docs":{},"]":{"docs":{},"[":{"1":{"docs":{},"]":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}},"docs":{}}},"o":{"docs":{},"l":{"docs":{},"]":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}},"t":{"docs":{},"h":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}},"y":{"docs":{},"和":{"docs":{},"统":{"docs":{},"计":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}},"c":{"docs":{},"l":{"docs":{},"u":{"docs":{},"s":{"docs":{},"t":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}}},"中":{"docs":{},"的":{"docs":{},"就":{"docs":{},"是":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{},"e":{"docs":{},"r":{"docs":{},"。":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}}}}}}},"某":{"docs":{},"列":{"docs":{},"转":{"docs":{},"为":{"docs":{},"一":{"docs":{},"个":{"docs":{},"数":{"docs":{},"组":{"docs":{},"返":{"docs":{},"回":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}}}}}}}}}},"t":{"docs":{},"e":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}},".":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{},"i":{"docs":{},"c":{"docs":{},"l":{"docs":{},"e":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},")":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.006802721088435374}}}}}}}}}}}}},"k":{"docs":{},"w":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}},",":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"1":{"docs":{},")":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}},"docs":{}}}}}}}}}}},"e":{"docs":{},"f":{"docs":{},"o":{"docs":{},"r":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}},"l":{"docs":{},"o":{"docs":{},"w":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}},"c":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}},"h":{"docs":{},"a":{"docs":{},"v":{"docs":{},"i":{"docs":{},"o":{"docs":{},"r":{"docs":{},"_":{"docs":{},"l":{"docs":{},"o":{"docs":{},"g":{"docs":{},".":{"docs":{},"c":{"docs":{},"s":{"docs":{},"v":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}},"_":{"docs":{},"d":{"docs":{},"f":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}},".":{"docs":{},"b":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"i":{"docs":{},"d":{"docs":{},")":{"docs":{},".":{"docs":{},"p":{"docs":{},"i":{"docs":{},"v":{"docs":{},"o":{"docs":{},"t":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"b":{"docs":{},"t":{"docs":{},"a":{"docs":{},"g":{"docs":{},"\"":{"docs":{},",":{"docs":{},"[":{"docs":{},"\"":{"docs":{},"p":{"docs":{},"v":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"docs":{},"f":{"docs":{},"a":{"docs":{},"v":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"docs":{},"c":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"docs":{},"b":{"docs":{},"u":{"docs":{},"y":{"docs":{},"\"":{"docs":{},"]":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"i":{"docs":{},"d":{"docs":{},")":{"docs":{},".":{"docs":{},"p":{"docs":{},"i":{"docs":{},"v":{"docs":{},"o":{"docs":{},"t":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"b":{"docs":{},"t":{"docs":{},"a":{"docs":{},"g":{"docs":{},"\"":{"docs":{},",":{"docs":{},"[":{"docs":{},"\"":{"docs":{},"p":{"docs":{},"v":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"docs":{},"f":{"docs":{},"a":{"docs":{},"v":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"docs":{},"c":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"docs":{},"b":{"docs":{},"u":{"docs":{},"y":{"docs":{},"\"":{"docs":{},"]":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}},"d":{"docs":{},"r":{"docs":{},"o":{"docs":{},"p":{"docs":{},"n":{"docs":{},"a":{"docs":{},"(":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"b":{"docs":{},"y":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"b":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}}},"t":{"docs":{},"a":{"docs":{},"g":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}}},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}}}},"b":{"docs":{},"e":{"docs":{},"h":{"docs":{},"a":{"docs":{},"v":{"docs":{},"i":{"docs":{},"o":{"docs":{},"r":{"docs":{},"_":{"docs":{},"l":{"docs":{},"o":{"docs":{},"g":{"docs":{},"_":{"docs":{},"d":{"docs":{},"f":{"docs":{},".":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"i":{"docs":{},"d":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}},")":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617},"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577},"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}}}}}}}}}},":":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102},"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617},"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577},"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}},"t":{"docs":{},"a":{"docs":{},"g":{"docs":{},"：":{"docs":{},"行":{"docs":{},"为":{"docs":{},"类":{"docs":{},"型":{"docs":{},",":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}},":":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}}}}},"v":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}},".":{"docs":{},"s":{"docs":{},".":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.033707865168539325},"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.02564102564102564}}}}},"i":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625},"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.017857142857142856},"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.009259259259259259}},"e":{"docs":{},"w":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734},"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907},"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.013605442176870748}},")":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}}}},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"e":{"docs":{},"电":{"docs":{},"商":{"docs":{},"网":{"docs":{},"站":{"docs":{},"成":{"docs":{},"交":{"docs":{},"金":{"docs":{},"额":{"docs":{},")":{"docs":{},"/":{"docs":{},"视":{"docs":{},"频":{"docs":{},"网":{"docs":{},"站":{"docs":{},"v":{"docs":{},"v":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}}}}}}}}}}}}}}}},")":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}},"_":{"docs":{},"p":{"docs":{},"u":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.007590132827324478},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.009259259259259259}},")":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}},"[":{"docs":{},"k":{"docs":{},"]":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}},"k":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0056925996204933585}}}}},"q":{"docs":{},"i":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.007590132827324478},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.009259259259259259}},")":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}},")":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}},"[":{"docs":{},"k":{"docs":{},"]":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}},"k":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0056925996204933585}}}}}},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.001193792280143255},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}},".":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}},"a":{"docs":{},"s":{"docs":{},"s":{"docs":{},"e":{"docs":{},"m":{"docs":{},"b":{"docs":{},"l":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}},"e":{"docs":{},"r":{"docs":{},"(":{"docs":{},")":{"docs":{},".":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"p":{"docs":{},"u":{"docs":{},"t":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"s":{"docs":{},"(":{"docs":{},"[":{"docs":{},"\"":{"docs":{},"a":{"docs":{},"g":{"docs":{},"e":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"f":{"docs":{},"u":{"docs":{},"l":{"docs":{},"_":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"s":{"docs":{},"[":{"2":{"docs":{},":":{"docs":{},"]":{"docs":{},")":{"docs":{},".":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"o":{"docs":{},"u":{"docs":{},"t":{"docs":{},"p":{"docs":{},"u":{"docs":{},"t":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"t":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"s":{"docs":{},"f":{"docs":{},"o":{"docs":{},"r":{"docs":{},"m":{"docs":{},"(":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"s":{"docs":{},"_":{"1":{"docs":{},")":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},")":{"docs":{},")":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}},"r":{"docs":{},"s":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676},"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00851063829787234}},"s":{"docs":{},"=":{"docs":{},">":{"docs":{},"'":{"1":{"docs":{},"'":{"docs":{},"说":{"docs":{},"明":{"docs":{},"最":{"docs":{},"多":{"docs":{},"可":{"docs":{},"以":{"docs":{},"显":{"docs":{},"示":{"docs":{},"一":{"docs":{},"个":{"docs":{},"版":{"docs":{},"本":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}}}}}}},"docs":{}}}}}}}}}}},"s":{"docs":{"day03_Hadoop/ha4.1.html":{"ref":"day03_Hadoop/ha4.1.html","tf":0.023809523809523808},"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.006993006993006993},"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.010526315789473684}}},"a":{"docs":{},"r":{"docs":{},"c":{"docs":{},"h":{"docs":{},"a":{"docs":{},"r":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}},"_":{"docs":{},"p":{"docs":{},"o":{"docs":{},"p":{"docs":{},"(":{"docs":{},")":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}},"s":{"docs":{},"a":{"docs":{},"m":{"docs":{},"p":{"docs":{},"(":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}},"i":{"docs":{},"a":{"docs":{},"n":{"docs":{},"c":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"s":{"docs":{},"(":{"docs":{},"'":{"docs":{},"b":{"docs":{},"i":{"docs":{},"l":{"docs":{},"l":{"docs":{},"'":{"docs":{},",":{"docs":{},"'":{"docs":{},"c":{"docs":{},"l":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"o":{"docs":{},"n":{"docs":{},"'":{"docs":{},")":{"docs":{},";":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}}}}}}},"g":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"s":{"docs":{},"'":{"docs":{},")":{"docs":{},";":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}}}}}}}}}}}},"g":{"docs":{},"e":{"docs":{},"o":{"docs":{},"r":{"docs":{},"g":{"docs":{},"e":{"docs":{},"'":{"docs":{},",":{"docs":{},"'":{"docs":{},"b":{"docs":{},"u":{"docs":{},"s":{"docs":{},"h":{"docs":{},"'":{"docs":{},")":{"docs":{},";":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}}}},"w":{"docs":{},"a":{"docs":{},"s":{"docs":{},"h":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"t":{"docs":{},"o":{"docs":{},"n":{"docs":{},"'":{"docs":{},")":{"docs":{},";":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}}}}}}}}}}}}}}}}}}}}}},")":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}},",":{"docs":{"Hive&HBase/06_hbase数据模型.html":{"ref":"Hive&HBase/06_hbase数据模型.html","tf":0.020833333333333332}}}},"=":{"1":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.011013215859030838}}},"2":{"0":{"1":{"4":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.011013215859030838}}},"docs":{}},"docs":{}},"docs":{}},"docs":{},"b":{"docs":{},"e":{"docs":{},"i":{"docs":{},"j":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.004405286343612335}}}}}},"j":{"docs":{},"e":{"docs":{},"r":{"docs":{},"r":{"docs":{},"i":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.004405286343612335}}}}}}},"n":{"docs":{},"e":{"docs":{},"w":{"docs":{},"y":{"docs":{},"o":{"docs":{},"r":{"docs":{},"k":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.004405286343612335}}}}}}}}},"t":{"docs":{},"o":{"docs":{},"k":{"docs":{},"y":{"docs":{},"o":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}},"m":{"2":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}},"3":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.004405286343612335}}},"4":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.006607929515418502}}},"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}},"是":{"docs":{},"一":{"docs":{},"个":{"docs":{},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}}}}}},"w":{"docs":{},"e":{"docs":{},"b":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}},"项":{"docs":{},"目":{"docs":{},":":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.02247191011235955}}}}},"g":{"docs":{},"u":{"docs":{},"i":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}},"i":{"docs":{},"g":{"docs":{},"h":{"docs":{},"t":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876},"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}},")":{"docs":{},")":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}},"s":{"docs":{},".":{"docs":{},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"m":{"docs":{},"s":{"docs":{},"(":{"docs":{},")":{"docs":{},":":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}},"l":{"docs":{},"c":{"docs":{},"o":{"docs":{},"m":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00851063829787234}}}}}}},"o":{"docs":{},"r":{"docs":{},"d":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009},"day03_Hadoop/ha3.2.html":{"ref":"day03_Hadoop/ha3.2.html","tf":0.02040816326530612},"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.011695906432748537},"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102},"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099}},"s":{"docs":{},")":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}},"）":{"docs":{},"，":{"docs":{},"如":{"docs":{},"“":{"docs":{},"是":{"docs":{},"”":{"docs":{},"、":{"docs":{},"“":{"docs":{},"的":{"docs":{},"”":{"docs":{},"之":{"docs":{},"类":{"docs":{},"的":{"docs":{},"，":{"docs":{},"对":{"docs":{},"于":{"docs":{},"文":{"docs":{},"档":{"docs":{},"的":{"docs":{},"中":{"docs":{},"心":{"docs":{},"思":{"docs":{},"想":{"docs":{},"表":{"docs":{},"达":{"docs":{},"没":{"docs":{},"有":{"docs":{},"意":{"docs":{},"义":{"docs":{},"的":{"docs":{},"词":{"docs":{},"，":{"docs":{},"在":{"docs":{},"分":{"docs":{},"词":{"docs":{},"时":{"docs":{},"需":{"docs":{},"要":{"docs":{},"先":{"docs":{},"过":{"docs":{},"滤":{"docs":{},"掉":{"docs":{},"再":{"docs":{},"计":{"docs":{},"算":{"docs":{},"其":{"docs":{},"他":{"docs":{},"词":{"docs":{},"语":{"docs":{},"的":{"docs":{},"t":{"docs":{},"f":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},".":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"(":{"docs":{},"l":{"docs":{},"a":{"docs":{},"m":{"docs":{},"b":{"docs":{},"d":{"docs":{},"a":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099}}}}}}}}}}}}}},",":{"1":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.011695906432748537}}},"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.023391812865497075}},"c":{"docs":{},"n":{"docs":{},"t":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}},"s":{"docs":{},"u":{"docs":{},"m":{"docs":{},"(":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"s":{"docs":{},")":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}}}}}}}},"_":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},".":{"docs":{},"p":{"docs":{},"i":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.023391812865497075}}}}}}}}}}},"相":{"docs":{},"同":{"docs":{},"的":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099}},"程":{"docs":{},"序":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}},"s":{"docs":{},".":{"docs":{},"p":{"docs":{},"p":{"docs":{},"r":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099}}}}}}}}}}}}}}}}},":":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}},"(":{"docs":{},"w":{"docs":{},"o":{"docs":{},"r":{"docs":{},"d":{"docs":{},",":{"1":{"docs":{},")":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099}}}}},"docs":{}}}}}}}}},"k":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}},"e":{"docs":{},"r":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.021505376344086023}},"：":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"a":{"docs":{},"l":{"docs":{},"o":{"docs":{},"n":{"docs":{},"e":{"docs":{},"模":{"docs":{},"式":{"docs":{},"中":{"docs":{},"s":{"docs":{},"l":{"docs":{},"a":{"docs":{},"v":{"docs":{},"e":{"docs":{},"节":{"docs":{},"点":{"docs":{},"上":{"docs":{},"的":{"docs":{},"守":{"docs":{},"护":{"docs":{},"进":{"docs":{},"程":{"docs":{},"，":{"docs":{},"负":{"docs":{},"责":{"docs":{},"管":{"docs":{},"理":{"docs":{},"本":{"docs":{},"节":{"docs":{},"点":{"docs":{},"的":{"docs":{},"资":{"docs":{},"源":{"docs":{},"，":{"docs":{},"定":{"docs":{},"期":{"docs":{},"向":{"docs":{},"m":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"汇":{"docs":{},"报":{"docs":{},"心":{"docs":{},"跳":{"docs":{},"，":{"docs":{},"接":{"docs":{},"收":{"docs":{},"m":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"的":{"docs":{},"命":{"docs":{},"令":{"docs":{},"，":{"docs":{},"启":{"docs":{},"动":{"docs":{},"d":{"docs":{},"r":{"docs":{},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{},"r":{"docs":{},"和":{"docs":{},"e":{"docs":{},"x":{"docs":{},"e":{"docs":{},"c":{"docs":{},"u":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{},"。":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},",":{"docs":{},"c":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}},"a":{"docs":{},"t":{"docs":{},"c":{"docs":{},"h":{"docs":{},"_":{"docs":{},"r":{"docs":{},"e":{"docs":{},"c":{"docs":{},"o":{"docs":{},"r":{"docs":{},"d":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.010526315789473684},"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.02247191011235955}},".":{"docs":{},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"b":{"docs":{},"y":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"a":{"docs":{},"g":{"docs":{},"g":{"docs":{},"(":{"docs":{},"l":{"docs":{},"i":{"docs":{},"s":{"docs":{},"t":{"docs":{},")":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842},"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"t":{"docs":{},"u":{"docs":{},"p":{"docs":{},"l":{"docs":{},"e":{"docs":{},"s":{"docs":{},"(":{"docs":{},")":{"docs":{},":":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}}}}}}}}}}}}}}}}}}}}}}}},"n":{"docs":{},"t":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}},"y":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}},"r":{"docs":{},"n":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}},"m":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0022675736961451248}},"[":{"docs":{},"'":{"docs":{},"k":{"docs":{},"w":{"1":{"docs":{},"'":{"docs":{},"]":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}},"docs":{}}}}}},"c":{"docs":{},".":{"docs":{},"p":{"docs":{},"i":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}}},"r":{"docs":{},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}}}}}},"i":{"docs":{},"t":{"docs":{},"h":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}},"(":{"docs":{},"\"":{"docs":{},"a":{"docs":{},"d":{"docs":{},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289}}}}}}}}}}}}}},"b":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289}}}}}}}}},"c":{"docs":{},"a":{"docs":{},"m":{"docs":{},"p":{"docs":{},"a":{"docs":{},"i":{"docs":{},"g":{"docs":{},"n":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289}}}}}}}}}}}}},"t":{"docs":{},"e":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289}}}}}}}}}},"l":{"docs":{},"k":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}},"u":{"docs":{},"s":{"docs":{},"t":{"docs":{},"o":{"docs":{},"m":{"docs":{},"e":{"docs":{},"r":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289}}}}}}}}}}}},"n":{"docs":{},"o":{"docs":{},"n":{"docs":{},"c":{"docs":{},"l":{"docs":{},"k":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}},"p":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}},"r":{"docs":{},"i":{"docs":{},"c":{"docs":{},"e":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289}}}}}}}}},"t":{"docs":{},"i":{"docs":{},"m":{"docs":{},"e":{"docs":{},"_":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"m":{"docs":{},"p":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}},"n":{"docs":{},"d":{"docs":{},"o":{"docs":{},"w":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}},"_":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}},"s":{"docs":{},".":{"docs":{},"p":{"docs":{},"p":{"docs":{},"r":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}}}}}}}}},"l":{"docs":{},"e":{"docs":{},"n":{"docs":{},"g":{"docs":{},"t":{"docs":{},"h":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}},",":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}},"s":{"docs":{},"操":{"docs":{},"作":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}},"操":{"docs":{},"作":{"docs":{},"是":{"docs":{},"基":{"docs":{},"于":{"docs":{},"窗":{"docs":{},"口":{"docs":{},"长":{"docs":{},"度":{"docs":{},"和":{"docs":{},"滑":{"docs":{},"动":{"docs":{},"间":{"docs":{},"隔":{"docs":{},"来":{"docs":{},"工":{"docs":{},"作":{"docs":{},"的":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}}}}}}}}}}}}}}}}}},"不":{"docs":{},"确":{"docs":{},"定":{"docs":{},"思":{"docs":{},"维":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}}}},"过":{"docs":{},"先":{"docs":{},"对":{"docs":{},"向":{"docs":{},"量":{"docs":{},"做":{"docs":{},"了":{"docs":{},"中":{"docs":{},"心":{"docs":{},"化":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}},"适":{"docs":{},"合":{"docs":{},"计":{"docs":{},"算":{"docs":{},"布":{"docs":{},"尔":{"docs":{},"值":{"docs":{},"向":{"docs":{},"量":{"docs":{},"之":{"docs":{},"间":{"docs":{},"的":{"docs":{},"相":{"docs":{},"关":{"docs":{},"度":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}},"用":{"docs":{},"于":{"docs":{},"传":{"docs":{},"统":{"docs":{},"关":{"docs":{},"系":{"docs":{},"型":{"docs":{},"数":{"docs":{},"据":{"docs":{},"的":{"docs":{},"存":{"docs":{},"储":{"docs":{},"；":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}}}}}}}}}}},"损":{"docs":{},"害":{"docs":{},"用":{"docs":{},"户":{"docs":{},"体":{"docs":{},"验":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}}}},"依":{"docs":{},"靠":{"docs":{},"硬":{"docs":{},"件":{"docs":{},"来":{"docs":{},"提":{"docs":{},"供":{"docs":{},"高":{"docs":{},"可":{"docs":{},"用":{"docs":{},"性":{"docs":{},"(":{"docs":{},"h":{"docs":{},"i":{"docs":{},"g":{"docs":{},"h":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}}}}}}}}}}}}}}},"同":{"docs":{},"计":{"docs":{},"算":{"docs":{},"框":{"docs":{},"架":{"docs":{},"可":{"docs":{},"以":{"docs":{},"共":{"docs":{},"享":{"docs":{},"同":{"docs":{},"一":{"docs":{},"个":{"docs":{},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},"集":{"docs":{},"群":{"docs":{},"上":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"享":{"docs":{},"受":{"docs":{},"整":{"docs":{},"体":{"docs":{},"的":{"docs":{},"资":{"docs":{},"源":{"docs":{},"调":{"docs":{},"度":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"数":{"docs":{},"据":{"docs":{},"源":{"docs":{},"产":{"docs":{},"生":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"质":{"docs":{},"量":{"docs":{},"可":{"docs":{},"能":{"docs":{},"差":{"docs":{},"别":{"docs":{},"很":{"docs":{},"大":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}}}}}}}}}}},"算":{"docs":{},"是":{"docs":{},"一":{"docs":{},"个":{"docs":{},"标":{"docs":{},"准":{"docs":{},"的":{"docs":{},"流":{"docs":{},"式":{"docs":{},"计":{"docs":{},"算":{"docs":{"day03_Hadoop/ha4.1.html":{"ref":"day03_Hadoop/ha4.1.html","tf":0.023809523809523808}}}}}}}}}}}}},"完":{"docs":{},"全":{"docs":{},"支":{"docs":{},"持":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}},"支":{"docs":{},"持":{"docs":{},"(":{"docs":{},"默":{"docs":{},"认":{"docs":{},")":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}},"插":{"docs":{},"入":{"docs":{},"数":{"docs":{},"据":{"docs":{},"直":{"docs":{},"接":{"docs":{},"查":{"docs":{},"询":{"docs":{},"查":{"docs":{},"看":{"docs":{},"结":{"docs":{},"果":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}}}}}}}},"方":{"docs":{},"便":{"docs":{},"用":{"docs":{},"数":{"docs":{},"据":{"docs":{},"库":{"docs":{},"二":{"docs":{},"维":{"docs":{},"逻":{"docs":{},"辑":{"docs":{},"表":{"docs":{},"来":{"docs":{},"表":{"docs":{},"现":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}}}}}}}}}}},"可":{"docs":{},"变":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}},"会":{"docs":{},"立":{"docs":{},"即":{"docs":{},"计":{"docs":{},"算":{"docs":{},"结":{"docs":{},"果":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}},"且":{"docs":{},"服":{"docs":{},"务":{"docs":{},"的":{"docs":{},"物":{"docs":{},"品":{"docs":{},"对":{"docs":{},"用":{"docs":{},"户":{"docs":{},"构":{"docs":{},"成":{"docs":{},"了":{"docs":{},"信":{"docs":{},"息":{"docs":{},"过":{"docs":{},"载":{"docs":{},",":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}}}}}}}}}}}}}}}},"跟":{"docs":{},"线":{"docs":{},"上":{"docs":{},"真":{"docs":{},"实":{"docs":{},"效":{"docs":{},"果":{"docs":{},"存":{"docs":{},"在":{"docs":{},"偏":{"docs":{},"差":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}}}}}}}}},"数":{"docs":{},"量":{"docs":{},"会":{"docs":{},"持":{"docs":{},"续":{"docs":{},"增":{"docs":{},"长":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}}}},"当":{"docs":{},"前":{"docs":{},"我":{"docs":{},"们":{"docs":{},"缺":{"docs":{},"少":{"docs":{},"对":{"docs":{},"这":{"docs":{},"些":{"docs":{},"特":{"docs":{},"征":{"docs":{},"更":{"docs":{},"加":{"docs":{},"具":{"docs":{},"体":{"docs":{},"的":{"docs":{},"信":{"docs":{},"息":{"docs":{},"，":{"docs":{},"（":{"docs":{},"如":{"docs":{},"商":{"docs":{},"品":{"docs":{},"类":{"docs":{},"目":{"docs":{},"具":{"docs":{},"体":{"docs":{},"信":{"docs":{},"息":{"docs":{},"、":{"docs":{},"品":{"docs":{},"牌":{"docs":{},"具":{"docs":{},"体":{"docs":{},"信":{"docs":{},"息":{"docs":{},"等":{"docs":{},"）":{"docs":{},"，":{"docs":{},"从":{"docs":{},"而":{"docs":{},"无":{"docs":{},"法":{"docs":{},"对":{"docs":{},"这":{"docs":{},"些":{"docs":{},"特":{"docs":{},"征":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"做":{"docs":{},"聚":{"docs":{},"类":{"docs":{},"、":{"docs":{},"降":{"docs":{},"维":{"docs":{},"处":{"docs":{},"理":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"个":{"docs":{},"性":{"docs":{},"化":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}},"推":{"docs":{},"荐":{"docs":{},"(":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"系":{"docs":{},"统":{"docs":{},")":{"docs":{},"经":{"docs":{},"历":{"docs":{},"了":{"docs":{},"多":{"docs":{},"年":{"docs":{},"的":{"docs":{},"发":{"docs":{},"展":{"docs":{},"，":{"docs":{},"已":{"docs":{},"经":{"docs":{},"成":{"docs":{},"为":{"docs":{},"互":{"docs":{},"联":{"docs":{},"网":{"docs":{},"产":{"docs":{},"品":{"docs":{},"的":{"docs":{},"标":{"docs":{},"配":{"docs":{},"，":{"docs":{},"也":{"docs":{},"是":{"docs":{},"a":{"docs":{},"i":{"docs":{},"成":{"docs":{},"功":{"docs":{},"落":{"docs":{},"地":{"docs":{},"的":{"docs":{},"分":{"docs":{},"支":{"docs":{},"之":{"docs":{},"一":{"docs":{},"，":{"docs":{},"在":{"docs":{},"电":{"docs":{},"商":{"docs":{},"(":{"docs":{},"淘":{"docs":{},"宝":{"docs":{},"/":{"docs":{},"京":{"docs":{},"东":{"docs":{},")":{"docs":{},"、":{"docs":{},"资":{"docs":{},"讯":{"docs":{},"(":{"docs":{},"今":{"docs":{},"日":{"docs":{},"头":{"docs":{},"条":{"docs":{},"/":{"docs":{},"微":{"docs":{},"博":{"docs":{},")":{"docs":{},"、":{"docs":{},"音":{"docs":{},"乐":{"docs":{},"(":{"docs":{},"网":{"docs":{},"易":{"docs":{},"云":{"docs":{},"音":{"docs":{},"乐":{"docs":{},"/":{"docs":{},"q":{"docs":{},"q":{"docs":{},"音":{"docs":{},"乐":{"docs":{},")":{"docs":{},"、":{"docs":{},"短":{"docs":{},"视":{"docs":{},"频":{"docs":{},"(":{"docs":{},"抖":{"docs":{},"音":{"docs":{},"/":{"docs":{},"快":{"docs":{},"手":{"docs":{},")":{"docs":{},"等":{"docs":{},"热":{"docs":{},"门":{"docs":{},"应":{"docs":{},"用":{"docs":{},"中":{"docs":{},",":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"系":{"docs":{},"统":{"docs":{},"都":{"docs":{},"是":{"docs":{},"核":{"docs":{},"心":{"docs":{},"组":{"docs":{},"件":{"docs":{},"之":{"docs":{},"一":{"docs":{},"。":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"电":{"docs":{},"商":{"docs":{},"广":{"docs":{},"告":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"系":{"docs":{},"统":{"docs":{},"介":{"docs":{},"绍":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}}}}}}}},"元":{"docs":{},"素":{"docs":{},"进":{"docs":{},"行":{"docs":{},"操":{"docs":{},"作":{"docs":{},"，":{"docs":{},"得":{"docs":{},"到":{"docs":{},"的":{"docs":{},"结":{"docs":{},"果":{"docs":{},"再":{"docs":{},"与":{"docs":{},"第":{"docs":{},"三":{"docs":{},"个":{"docs":{},"数":{"docs":{},"据":{"docs":{},"用":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}}}}}}}}}}}}}}}}}}}}},"主":{"docs":{},"动":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}},"从":{"docs":{},"热":{"docs":{},"备":{"docs":{"day03_Hadoop/ha4.2.html":{"ref":"day03_Hadoop/ha4.2.html","tf":0.0196078431372549}}}}},"要":{"docs":{},"用":{"docs":{},"途":{"docs":{},"：":{"docs":{},"用":{"docs":{},"来":{"docs":{},"做":{"docs":{},"离":{"docs":{},"线":{"docs":{},"数":{"docs":{},"据":{"docs":{},"分":{"docs":{},"析":{"docs":{},"，":{"docs":{},"比":{"docs":{},"直":{"docs":{},"接":{"docs":{},"用":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}}}}}}}}}},"包":{"docs":{},"括":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}},"什":{"docs":{},"么":{"docs":{},"是":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}},"推":{"docs":{},"荐":{"docs":{},"系":{"docs":{},"统":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}}}},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}}},"p":{"docs":{},"p":{"docs":{},"y":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}}}}},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}},"y":{"docs":{},"a":{"docs":{},"r":{"docs":{},"n":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{"day03_Hadoop/ha3.2.html":{"ref":"day03_Hadoop/ha3.2.html","tf":0.02040816326530612}}}}}}}}}},"分":{"docs":{},"区":{"docs":{},"表":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}},"非":{"docs":{},"结":{"docs":{},"构":{"docs":{},"化":{"docs":{},"数":{"docs":{},"据":{"docs":{},"存":{"docs":{},"储":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}}}}},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}}},"信":{"docs":{},"息":{"docs":{},"过":{"docs":{},"载":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}},"熵":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}},"任":{"docs":{},"度":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}},"向":{"docs":{},"朋":{"docs":{},"友":{"docs":{},"咨":{"docs":{},"询":{"docs":{},",":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}}}}},"量":{"docs":{},"a":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}},"乘":{"docs":{},"法":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}},"（":{"docs":{},"v":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{},"）":{"docs":{},"：":{"docs":{},"由":{"docs":{},"一":{"docs":{},"组":{"docs":{},"文":{"docs":{},"本":{"docs":{},"特":{"docs":{},"征":{"docs":{},"构":{"docs":{},"成":{"docs":{},"的":{"docs":{},"列":{"docs":{},"表":{"docs":{},"。":{"docs":{},"是":{"docs":{},"一":{"docs":{},"段":{"docs":{},"文":{"docs":{},"本":{"docs":{},"在":{"docs":{},"g":{"docs":{},"e":{"docs":{},"n":{"docs":{},"s":{"docs":{},"i":{"docs":{},"m":{"docs":{},"中":{"docs":{},"的":{"docs":{},"内":{"docs":{},"部":{"docs":{},"表":{"docs":{},"达":{"docs":{},"。":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"表":{"docs":{},"中":{"docs":{},"插":{"docs":{},"入":{"docs":{},"数":{"docs":{},"据":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}}}},"基":{"docs":{},"于":{"docs":{},"内":{"docs":{},"容":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863},"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}},"的":{"docs":{},"推":{"docs":{},"荐":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775},"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html":{"ref":"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html","tf":0.024390243902439025}},"和":{"docs":{},"协":{"docs":{},"同":{"docs":{},"过":{"docs":{},"滤":{"docs":{},"的":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"结":{"docs":{},"果":{"docs":{},"都":{"docs":{},"计":{"docs":{},"算":{"docs":{},"出":{"docs":{},"来":{"docs":{"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html":{"ref":"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html","tf":0.024390243902439025}}}}}}}}}}}}}}}}},"逐":{"docs":{},"渐":{"docs":{},"过":{"docs":{},"渡":{"docs":{},"到":{"docs":{},"协":{"docs":{},"同":{"docs":{},"过":{"docs":{},"滤":{"docs":{"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html":{"ref":"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html","tf":0.024390243902439025}}}}}}}}}}},"实":{"docs":{},"现":{"docs":{},"步":{"docs":{},"骤":{"docs":{"day02_推荐算法/07_基于内容的推荐算法.html":{"ref":"day02_推荐算法/07_基于内容的推荐算法.html","tf":0.03125}}}}}},"方":{"docs":{},"法":{"docs":{},"是":{"docs":{},"非":{"docs":{},"常":{"docs":{},"直":{"docs":{},"接":{"docs":{},"的":{"docs":{},"，":{"docs":{},"它":{"docs":{},"以":{"docs":{},"物":{"docs":{},"品":{"docs":{},"的":{"docs":{},"内":{"docs":{},"容":{"docs":{},"描":{"docs":{},"述":{"docs":{},"信":{"docs":{},"息":{"docs":{},"为":{"docs":{},"依":{"docs":{},"据":{"docs":{},"来":{"docs":{},"做":{"docs":{},"出":{"docs":{},"的":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"，":{"docs":{},"本":{"docs":{},"质":{"docs":{},"上":{"docs":{},"是":{"docs":{},"基":{"docs":{},"于":{"docs":{},"对":{"docs":{},"物":{"docs":{},"品":{"docs":{},"和":{"docs":{},"用":{"docs":{},"户":{"docs":{},"自":{"docs":{},"身":{"docs":{},"的":{"docs":{},"特":{"docs":{},"征":{"docs":{},"或":{"docs":{},"属":{"docs":{},"性":{"docs":{},"的":{"docs":{},"直":{"docs":{},"接":{"docs":{},"分":{"docs":{},"析":{"docs":{},"和":{"docs":{},"计":{"docs":{},"算":{"docs":{},"。":{"docs":{"day02_推荐算法/07_基于内容的推荐算法.html":{"ref":"day02_推荐算法/07_基于内容的推荐算法.html","tf":0.03125}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"算":{"docs":{},"法":{"docs":{},"（":{"docs":{},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"t":{"docs":{},"e":{"docs":{},"n":{"docs":{},"t":{"docs":{"day02_推荐算法/07_基于内容的推荐算法.html":{"ref":"day02_推荐算法/07_基于内容的推荐算法.html","tf":0.03125}}}}}}}}}}}}}},"电":{"docs":{},"影":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"：":{"docs":{},"物":{"docs":{},"品":{"docs":{},"画":{"docs":{},"像":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}},"用":{"docs":{},"户":{"docs":{},"画":{"docs":{},"像":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}}}},"为":{"docs":{},"用":{"docs":{},"户":{"docs":{},"产":{"docs":{},"生":{"docs":{},"t":{"docs":{},"o":{"docs":{},"p":{"docs":{"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}}}}}}}}}}}}}}}},"推":{"docs":{},"荐":{"docs":{},"的":{"docs":{},"算":{"docs":{},"法":{"docs":{},"流":{"docs":{},"程":{"docs":{},"：":{"docs":{"day02_推荐算法/07_基于内容的推荐算法.html":{"ref":"day02_推荐算法/07_基于内容的推荐算法.html","tf":0.03125}}}}}}}}}}},"存":{"docs":{},"的":{"docs":{},"计":{"docs":{},"算":{"docs":{},"引":{"docs":{},"擎":{"docs":{},"，":{"docs":{},"它":{"docs":{},"的":{"docs":{},"计":{"docs":{},"算":{"docs":{},"速":{"docs":{},"度":{"docs":{},"非":{"docs":{},"常":{"docs":{},"快":{"docs":{},"。":{"docs":{},"但":{"docs":{},"是":{"docs":{},"仅":{"docs":{},"仅":{"docs":{},"只":{"docs":{},"涉":{"docs":{},"及":{"docs":{},"到":{"docs":{},"数":{"docs":{},"据":{"docs":{},"的":{"docs":{},"计":{"docs":{},"算":{"docs":{},"，":{"docs":{},"并":{"docs":{},"没":{"docs":{},"有":{"docs":{},"涉":{"docs":{},"及":{"docs":{},"到":{"docs":{},"数":{"docs":{},"据":{"docs":{},"的":{"docs":{},"存":{"docs":{},"储":{"docs":{},"。":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"协":{"docs":{},"同":{"docs":{},"过":{"docs":{},"滤":{"docs":{},"的":{"docs":{},"推":{"docs":{},"荐":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}},"电":{"docs":{},"影":{"docs":{},"推":{"docs":{},"荐":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":5.001623376623376}}}}}}}}}}},"流":{"docs":{},"行":{"docs":{},"度":{"docs":{},"的":{"docs":{},"推":{"docs":{},"荐":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}}}}}},"分":{"docs":{},"类":{"docs":{},"算":{"docs":{},"法":{"docs":{},"、":{"docs":{},"回":{"docs":{},"归":{"docs":{},"算":{"docs":{},"法":{"docs":{},"、":{"docs":{},"聚":{"docs":{},"类":{"docs":{},"算":{"docs":{},"法":{"docs":{"day02_推荐算法/01_基于模型的协同过滤推荐.html":{"ref":"day02_推荐算法/01_基于模型的协同过滤推荐.html","tf":0.07692307692307693}}}}}}}}}}}}}}}},"回":{"docs":{},"归":{"docs":{},"模":{"docs":{},"型":{"docs":{},"的":{"docs":{},"协":{"docs":{},"同":{"docs":{},"过":{"docs":{},"滤":{"docs":{},"推":{"docs":{},"荐":{"docs":{"day02_推荐算法/01_基于模型的协同过滤推荐.html":{"ref":"day02_推荐算法/01_基于模型的协同过滤推荐.html","tf":0.07692307692307693},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}},"图":{"docs":{},"模":{"docs":{},"型":{"docs":{},"算":{"docs":{},"法":{"docs":{"day02_推荐算法/01_基于模型的协同过滤推荐.html":{"ref":"day02_推荐算法/01_基于模型的协同过滤推荐.html","tf":0.07692307692307693}}}}}}},"矩":{"docs":{},"阵":{"docs":{},"分":{"docs":{},"解":{"docs":{},"的":{"docs":{},"协":{"docs":{},"同":{"docs":{},"过":{"docs":{},"滤":{"docs":{},"推":{"docs":{},"荐":{"docs":{"day02_推荐算法/01_基于模型的协同过滤推荐.html":{"ref":"day02_推荐算法/01_基于模型的协同过滤推荐.html","tf":0.07692307692307693}}}}}}}},"推":{"docs":{},"荐":{"docs":{"day02_推荐算法/01_基于模型的协同过滤推荐.html":{"ref":"day02_推荐算法/01_基于模型的协同过滤推荐.html","tf":0.07692307692307693}}}},"c":{"docs":{},"f":{"docs":{},"算":{"docs":{},"法":{"docs":{"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html":{"ref":"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html","tf":0.037037037037037035}},"实":{"docs":{},"现":{"docs":{},"（":{"docs":{},"一":{"docs":{},"）":{"docs":{},"：":{"docs":{},"l":{"docs":{},"f":{"docs":{},"m":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}}},"二":{"docs":{},"）":{"docs":{},"：":{"docs":{},"b":{"docs":{},"i":{"docs":{},"a":{"docs":{},"s":{"docs":{},"s":{"docs":{},"v":{"docs":{},"d":{"docs":{"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}}}}}}}}}}}}}}}}}},"神":{"docs":{},"经":{"docs":{},"网":{"docs":{},"络":{"docs":{},"算":{"docs":{},"法":{"docs":{"day02_推荐算法/01_基于模型的协同过滤推荐.html":{"ref":"day02_推荐算法/01_基于模型的协同过滤推荐.html","tf":0.07692307692307693}}}}}}}},"t":{"docs":{},"f":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}},"·":{"docs":{},"i":{"docs":{},"d":{"docs":{},"f":{"docs":{},"提":{"docs":{},"取":{"docs":{},"t":{"docs":{},"o":{"docs":{},"p":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"创":{"docs":{},"建":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"的":{"docs":{},"a":{"docs":{},"l":{"docs":{},"s":{"docs":{},"隐":{"docs":{},"因":{"docs":{},"子":{"docs":{},"模":{"docs":{},"型":{"docs":{},"进":{"docs":{},"行":{"docs":{},"c":{"docs":{},"f":{"docs":{},"评":{"docs":{},"分":{"docs":{},"预":{"docs":{},"测":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}}}}}}}}}}}}}}}}}}}}}}},"本":{"docs":{},"的":{"docs":{},"协":{"docs":{},"同":{"docs":{},"过":{"docs":{},"滤":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"算":{"docs":{},"法":{"docs":{},"基":{"docs":{},"于":{"docs":{},"以":{"docs":{},"下":{"docs":{},"假":{"docs":{},"设":{"docs":{},"：":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}}}},"判":{"docs":{},"断":{"docs":{},",":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}},"操":{"docs":{},"作":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}},"统":{"docs":{},"计":{"docs":{},"功":{"docs":{},"能":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}},"源":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}},"础":{"docs":{},"架":{"docs":{},"构":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}},"处":{"docs":{},"理":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}},"复":{"docs":{},"杂":{"docs":{},"业":{"docs":{},"务":{"docs":{},"逻":{"docs":{},"辑":{"docs":{},"，":{"docs":{},"处":{"docs":{},"理":{"docs":{},"高":{"docs":{},"并":{"docs":{},"发":{"docs":{},"，":{"docs":{},"为":{"docs":{},"用":{"docs":{},"户":{"docs":{},"构":{"docs":{},"建":{"docs":{},"一":{"docs":{},"个":{"docs":{},"稳":{"docs":{},"定":{"docs":{},"的":{"docs":{},"信":{"docs":{},"息":{"docs":{},"流":{"docs":{},"通":{"docs":{},"服":{"docs":{},"务":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"推":{"docs":{},"荐":{"docs":{},"系":{"docs":{},"统":{"docs":{},"冷":{"docs":{},"启":{"docs":{},"动":{"docs":{},"问":{"docs":{},"题":{"docs":{},"的":{"docs":{},"常":{"docs":{},"用":{"docs":{},"方":{"docs":{},"法":{"docs":{"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html":{"ref":"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html","tf":0.024390243902439025}}}}}}}}}}}}}}}},"流":{"docs":{},"式":{"docs":{},"数":{"docs":{},"据":{"docs":{"day03_Hadoop/ha2.4.html":{"ref":"day03_Hadoop/ha2.4.html","tf":0.03333333333333333}}}}}},"客":{"docs":{},"户":{"docs":{},"端":{"docs":{},"的":{"docs":{},"请":{"docs":{},"求":{"docs":{},"：":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}}}}},"来":{"docs":{},"自":{"docs":{},"a":{"docs":{},"m":{"docs":{},"的":{"docs":{},"命":{"docs":{},"令":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}}}}},"和":{"docs":{},"计":{"docs":{},"算":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}},"数":{"docs":{},"据":{"docs":{},"所":{"docs":{},"面":{"docs":{},"临":{"docs":{},"的":{"docs":{},"问":{"docs":{},"题":{"docs":{},"：":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}},"规":{"docs":{},"模":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}},"j":{"docs":{},"s":{"docs":{},"o":{"docs":{},"n":{"docs":{},"数":{"docs":{},"据":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":3.333333333333333}}}}}}}},"每":{"docs":{},"一":{"docs":{},"行":{"docs":{},"数":{"docs":{},"据":{"docs":{},"：":{"docs":{},"r":{"docs":{},"表":{"docs":{},"示":{"docs":{},"r":{"docs":{},"o":{"docs":{},"w":{"docs":{},"对":{"docs":{},"象":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0024469820554649264}}}}}}}}}}}}}}}}},"于":{"docs":{},"b":{"docs":{},"a":{"docs":{},"c":{"docs":{},"k":{"docs":{},"u":{"docs":{},"p":{"docs":{},"状":{"docs":{},"态":{"docs":{},"的":{"docs":{},"其":{"docs":{},"他":{"docs":{},"h":{"docs":{},"m":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"节":{"docs":{},"点":{"docs":{},"推":{"docs":{},"选":{"docs":{},"出":{"docs":{},"一":{"docs":{},"个":{"docs":{},"转":{"docs":{},"为":{"docs":{},"a":{"docs":{},"c":{"docs":{},"t":{"docs":{},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{},"状":{"docs":{},"态":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"复":{"docs":{},"杂":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}},"数":{"docs":{},"据":{"docs":{},"类":{"docs":{},"型":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}},"运":{"docs":{},"算":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}},"够":{"docs":{},"满":{"docs":{},"⾜":{"docs":{},"他":{"docs":{},"们":{"docs":{},"兴":{"docs":{},"趣":{"docs":{},"和":{"docs":{},"需":{"docs":{},"求":{"docs":{},"的":{"docs":{},"信":{"docs":{},"息":{"docs":{},"。":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}}}}}}}}}}}}}},"学":{"docs":{},"习":{"docs":{},"目":{"docs":{},"标":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775},"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863},"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734},"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.0016233766233766235},"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008},"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html":{"ref":"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html","tf":0.024390243902439025},"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196},"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}},"率":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}},"对":{"docs":{},"结":{"docs":{},"果":{"docs":{},"有":{"docs":{},"确":{"docs":{},"定":{"docs":{},"预":{"docs":{},"期":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}}}}}}},"推":{"docs":{},"荐":{"docs":{},"结":{"docs":{},"果":{"docs":{},"进":{"docs":{},"行":{"docs":{},"评":{"docs":{},"估":{"docs":{},"（":{"docs":{},"评":{"docs":{},"估":{"docs":{},"方":{"docs":{},"法":{"docs":{},"后":{"docs":{},"面":{"docs":{},"章":{"docs":{},"节":{"docs":{},"介":{"docs":{},"绍":{"docs":{},"）":{"docs":{},"，":{"docs":{},"评":{"docs":{},"估":{"docs":{},"通":{"docs":{},"过":{"docs":{},"后":{"docs":{},"上":{"docs":{},"线":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"比":{"docs":{},"可":{"docs":{},"见":{"docs":{},"，":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}},"：":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}},"于":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"越":{"docs":{},"大":{"docs":{},"越":{"docs":{},"好":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}}}},"所":{"docs":{},"有":{"docs":{},"电":{"docs":{},"影":{"docs":{},"的":{"docs":{},"平":{"docs":{},"均":{"docs":{},"评":{"docs":{},"分":{"docs":{},"是":{"docs":{},"直":{"docs":{},"接":{"docs":{},"能":{"docs":{},"计":{"docs":{},"算":{"docs":{},"出":{"docs":{},"的":{"docs":{},"，":{"docs":{},"因":{"docs":{},"此":{"docs":{},"问":{"docs":{},"题":{"docs":{},"在":{"docs":{},"于":{"docs":{},"要":{"docs":{},"测":{"docs":{},"出":{"docs":{},"每":{"docs":{},"个":{"docs":{},"用":{"docs":{},"户":{"docs":{},"的":{"docs":{},"评":{"docs":{},"分":{"docs":{},"偏":{"docs":{},"置":{"docs":{},"和":{"docs":{},"每":{"docs":{},"部":{"docs":{},"电":{"docs":{},"影":{"docs":{},"的":{"docs":{},"得":{"docs":{},"分":{"docs":{},"偏":{"docs":{},"置":{"docs":{},"。":{"docs":{},"对":{"docs":{},"于":{"docs":{},"线":{"docs":{},"性":{"docs":{},"回":{"docs":{},"归":{"docs":{},"问":{"docs":{},"题":{"docs":{},"，":{"docs":{},"我":{"docs":{},"们":{"docs":{},"可":{"docs":{},"以":{"docs":{},"利":{"docs":{},"用":{"docs":{},"平":{"docs":{},"方":{"docs":{},"差":{"docs":{},"构":{"docs":{},"建":{"docs":{},"损":{"docs":{},"失":{"docs":{},"函":{"docs":{},"数":{"docs":{},"如":{"docs":{},"下":{"docs":{},"：":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"最":{"docs":{},"小":{"docs":{},"过":{"docs":{},"程":{"docs":{},"的":{"docs":{},"求":{"docs":{},"解":{"docs":{},"，":{"docs":{},"我":{"docs":{},"们":{"docs":{},"一":{"docs":{},"般":{"docs":{},"采":{"docs":{},"用":{"docs":{},"随":{"docs":{},"机":{"docs":{},"梯":{"docs":{},"度":{"docs":{},"下":{"docs":{},"降":{"docs":{},"法":{"docs":{},"或":{"docs":{},"者":{"docs":{},"交":{"docs":{},"替":{"docs":{},"最":{"docs":{},"小":{"docs":{},"二":{"docs":{},"乘":{"docs":{},"法":{"docs":{},"来":{"docs":{},"优":{"docs":{},"化":{"docs":{},"实":{"docs":{},"现":{"docs":{},"。":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"计":{"docs":{},"算":{"docs":{},"影":{"docs":{},"评":{"docs":{},"的":{"docs":{},"t":{"docs":{},"f":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}},"存":{"docs":{},"储":{"docs":{},"在":{"docs":{"day03_Hadoop/ha4.2.html":{"ref":"day03_Hadoop/ha4.2.html","tf":0.0196078431372549}}}}},"b":{"docs":{},"o":{"docs":{},"o":{"docs":{},"l":{"docs":{},"类":{"docs":{},"型":{"docs":{},"、":{"docs":{},"或":{"docs":{},"者":{"docs":{},"分":{"docs":{},"类":{"docs":{},"类":{"docs":{},"型":{"docs":{},"，":{"docs":{},"可":{"docs":{},"以":{"docs":{},"为":{"docs":{},"缺":{"docs":{},"失":{"docs":{},"值":{"docs":{},"单":{"docs":{},"独":{"docs":{},"设":{"docs":{},"置":{"docs":{},"一":{"docs":{},"个":{"docs":{},"类":{"docs":{},"型":{"docs":{},"，":{"docs":{},"m":{"docs":{},"i":{"docs":{},"s":{"docs":{},"s":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"数":{"docs":{},"值":{"docs":{},"类":{"docs":{},"型":{"docs":{},"，":{"docs":{},"可":{"docs":{},"以":{"docs":{},"用":{"docs":{},"均":{"docs":{},"值":{"docs":{},"或":{"docs":{},"者":{"docs":{},"中":{"docs":{},"位":{"docs":{},"数":{"docs":{},"等":{"docs":{},"填":{"docs":{},"充":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}}}}}}}}}}},"每":{"docs":{},"个":{"docs":{},"b":{"docs":{},"a":{"docs":{},"t":{"docs":{},"c":{"docs":{},"h":{"docs":{},"，":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"都":{"docs":{},"会":{"docs":{},"为":{"docs":{},"每":{"docs":{},"个":{"docs":{},"之":{"docs":{},"前":{"docs":{},"已":{"docs":{},"经":{"docs":{},"存":{"docs":{},"在":{"docs":{},"的":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{},"去":{"docs":{},"应":{"docs":{},"用":{"docs":{},"一":{"docs":{},"次":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"更":{"docs":{},"新":{"docs":{},"函":{"docs":{},"数":{"docs":{},"，":{"docs":{},"无":{"docs":{},"论":{"docs":{},"这":{"docs":{},"个":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{},"在":{"docs":{},"b":{"docs":{},"a":{"docs":{},"t":{"docs":{},"c":{"docs":{},"h":{"docs":{},"中":{"docs":{},"是":{"docs":{},"否":{"docs":{},"有":{"docs":{},"新":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"。":{"docs":{},"如":{"docs":{},"果":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"更":{"docs":{},"新":{"docs":{},"函":{"docs":{},"数":{"docs":{},"返":{"docs":{},"回":{"docs":{},"n":{"docs":{},"o":{"docs":{},"n":{"docs":{},"e":{"docs":{},"，":{"docs":{},"那":{"docs":{},"么":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{},"对":{"docs":{},"应":{"docs":{},"的":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"就":{"docs":{},"会":{"docs":{},"被":{"docs":{},"删":{"docs":{},"除":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"新":{"docs":{},"出":{"docs":{},"现":{"docs":{},"的":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{},"，":{"docs":{},"也":{"docs":{},"会":{"docs":{},"执":{"docs":{},"行":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"更":{"docs":{},"新":{"docs":{},"函":{"docs":{},"数":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}}}}}}}}}}}}}}}}}},"咨":{"docs":{},"询":{"docs":{},"信":{"docs":{},"息":{"docs":{},"分":{"docs":{},"类":{"docs":{},"统":{"docs":{},"计":{"docs":{},"后":{"docs":{},"发":{"docs":{},"现":{"docs":{},"，":{"docs":{},"新":{"docs":{},"用":{"docs":{},"户":{"docs":{},"的":{"docs":{},"咨":{"docs":{},"询":{"docs":{},"量":{"docs":{},"几":{"docs":{},"乎":{"docs":{},"为":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}}}}}}}}}}}}}}}},"数":{"docs":{},"据":{"docs":{},"清":{"docs":{},"洗":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}},"以":{"docs":{},"空":{"docs":{},"格":{"docs":{},"进":{"docs":{},"行":{"docs":{},"拆":{"docs":{},"分":{"docs":{},"，":{"docs":{},"分":{"docs":{},"为":{"docs":{},"多":{"docs":{},"个":{"docs":{},"单":{"docs":{},"词":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}}}}}}}}},"组":{"docs":{},"排":{"docs":{},"序":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}},"r":{"docs":{},"o":{"docs":{},"w":{"docs":{},"和":{"docs":{},"表":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}},"应":{"docs":{},"关":{"docs":{},"系":{"docs":{},"型":{"docs":{},"数":{"docs":{},"据":{"docs":{},"库":{"docs":{},"的":{"docs":{},"列":{"docs":{"Hive&HBase/06_hbase数据模型.html":{"ref":"Hive&HBase/06_hbase数据模型.html","tf":0.020833333333333332}}}}}}}}}},"的":{"docs":{},"召":{"docs":{},"回":{"docs":{},"集":{"docs":{},"(":{"docs":{},"缓":{"docs":{},"存":{"docs":{},")":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}}}}},"各":{"docs":{},"个":{"docs":{},"r":{"docs":{},"e":{"docs":{},"g":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"v":{"docs":{},"e":{"docs":{},"r":{"docs":{},"(":{"docs":{},"包":{"docs":{},"括":{"docs":{},"失":{"docs":{},"效":{"docs":{},"的":{"docs":{},")":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"进":{"docs":{},"行":{"docs":{},"整":{"docs":{},"理":{"docs":{},",":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"两":{"docs":{},"个":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"求":{"docs":{},"交":{"docs":{},"集":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}},"并":{"docs":{},"集":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}},"缺":{"docs":{},"失":{"docs":{},"值":{"docs":{},"对":{"docs":{},"应":{"docs":{},"的":{"docs":{},"行":{"docs":{},"或":{"docs":{},"列":{"docs":{},"进":{"docs":{},"行":{"docs":{},"标":{"docs":{},"记":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}}},"进":{"docs":{},"行":{"docs":{},"删":{"docs":{},"除":{"docs":{},"操":{"docs":{},"作":{"docs":{},"(":{"docs":{},"行":{"docs":{},"，":{"docs":{},"列":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}},"填":{"docs":{},"充":{"docs":{},"操":{"docs":{},"作":{"docs":{},"(":{"docs":{},"列":{"docs":{},"的":{"docs":{},"均":{"docs":{},"值":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}}}}}}}},"每":{"docs":{},"个":{"docs":{},"分":{"docs":{},"片":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"进":{"docs":{},"行":{"docs":{},"处":{"docs":{},"理":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}},"一":{"docs":{},"组":{"docs":{},"特":{"docs":{},"征":{"docs":{},"，":{"docs":{},"使":{"docs":{},"用":{"docs":{},"枚":{"docs":{},"举":{"docs":{},"类":{"docs":{},"型":{"docs":{},"，":{"docs":{},"从":{"0":{"docs":{},"开":{"docs":{},"始":{"docs":{},"；":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}},"docs":{}}}}}}}}}}}}}}},"p":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"进":{"docs":{},"行":{"docs":{},"热":{"docs":{},"独":{"docs":{},"编":{"docs":{},"码":{"docs":{},"，":{"docs":{},"求":{"docs":{},"值":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}},"编":{"docs":{},"码":{"docs":{},"，":{"docs":{},"求":{"docs":{},"值":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}},"处":{"docs":{},"理":{"docs":{},"出":{"docs":{},"来":{"docs":{},"的":{"docs":{},"特":{"docs":{},"征":{"docs":{},"处":{"docs":{},"理":{"docs":{},"列":{"docs":{},"进":{"docs":{},"行":{"docs":{},"，":{"docs":{},"热":{"docs":{},"独":{"docs":{},"编":{"docs":{},"码":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}},"模":{"docs":{},"型":{"docs":{},"进":{"docs":{},"行":{"docs":{},"存":{"docs":{},"储":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}},"弱":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}},"强":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}},"快":{"docs":{},"速":{"docs":{},"满":{"docs":{},"足":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}}}},"意":{"docs":{},"图":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}},"户":{"docs":{},"的":{"docs":{},"历":{"docs":{},"史":{"docs":{},"⾏":{"docs":{},"为":{"docs":{},"给":{"docs":{},"⽤":{"docs":{},"户":{"docs":{},"的":{"docs":{},"兴":{"docs":{},"趣":{"docs":{},"进":{"docs":{},"⾏":{"docs":{},"建":{"docs":{},"模":{"docs":{},"，":{"docs":{},"从":{"docs":{},"⽽":{"docs":{},"主":{"docs":{},"动":{"docs":{},"给":{"docs":{},"⽤":{"docs":{},"户":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"能":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"打":{"docs":{},"开":{"docs":{},"搜":{"docs":{},"索":{"docs":{},"引":{"docs":{},"擎":{"docs":{},",":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}}}}},"命":{"docs":{},"令":{"docs":{},"行":{"docs":{},",":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}},"a":{"docs":{},"p":{"docs":{},"p":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}},"产":{"docs":{},"品":{"docs":{},"就":{"docs":{},"算":{"docs":{},"活":{"docs":{},"跃":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}},"以":{"docs":{},"后":{"docs":{},"是":{"docs":{},"否":{"docs":{},"频":{"docs":{},"繁":{"docs":{},"操":{"docs":{},"作":{"docs":{},"就":{"docs":{},"用":{"docs":{},"p":{"docs":{},"v":{"docs":{},"衡":{"docs":{},"量":{"docs":{},",":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}}}}}}}}},"使":{"docs":{},"用":{"docs":{},"产":{"docs":{},"品":{"docs":{},"的":{"docs":{},"用":{"docs":{},"户":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}},"用":{"docs":{},"户":{"docs":{},"数":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.008403361344537815}}}}}},"分":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}},"规":{"docs":{},"则":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}},"乱":{"docs":{},"列":{"docs":{},"表":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}},"点":{"docs":{},":":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}},"印":{"docs":{},"当":{"docs":{},"前":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"的":{"docs":{},"结":{"docs":{},"构":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}},"d":{"docs":{},"f":{"docs":{},"结":{"docs":{},"构":{"docs":{},"信":{"docs":{},"息":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}}}}}}}}},"找":{"docs":{},"到":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}},"和":{"docs":{},"自":{"docs":{},"己":{"docs":{},"历":{"docs":{},"史":{"docs":{},"兴":{"docs":{},"趣":{"docs":{},"相":{"docs":{},"似":{"docs":{},"的":{"docs":{},"用":{"docs":{},"户":{"docs":{},",":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}}}}}}}}}}}}},"一":{"docs":{},"篇":{"docs":{},"文":{"docs":{},"本":{"docs":{},"文":{"docs":{},"档":{"docs":{},",":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}}},"数":{"docs":{},"据":{"docs":{},"入":{"docs":{},"口":{"docs":{},"地":{"docs":{},"址":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}},"前":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"出":{"docs":{},"最":{"docs":{},"相":{"docs":{},"似":{"docs":{},"的":{"docs":{},"人":{"docs":{},"或":{"docs":{},"物":{"docs":{},"品":{"docs":{},"：":{"docs":{},"t":{"docs":{},"o":{"docs":{},"p":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}},"i":{"docs":{},"i":{"docs":{},"d":{"docs":{},"物":{"docs":{},"品":{"docs":{},"的":{"docs":{},"相":{"docs":{},"似":{"docs":{},"物":{"docs":{},"品":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.0016233766233766235}}}}}}}}}}}},"u":{"docs":{},"i":{"docs":{},"d":{"docs":{},"用":{"docs":{},"户":{"docs":{},"的":{"docs":{},"相":{"docs":{},"似":{"docs":{},"用":{"docs":{},"户":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.00487012987012987}}}}}}}}}}}},"每":{"docs":{},"个":{"docs":{},"用":{"docs":{},"户":{"docs":{},"普":{"docs":{},"遍":{"docs":{},"高":{"docs":{},"于":{"docs":{},"或":{"docs":{},"低":{"docs":{},"于":{"docs":{},"他":{"docs":{},"人":{"docs":{},"的":{"docs":{},"偏":{"docs":{},"置":{"docs":{},"值":{"docs":{},"b":{"docs":{},"_":{"docs":{},"u":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}}}}}}}}},"件":{"docs":{},"物":{"docs":{},"品":{"docs":{},"普":{"docs":{},"遍":{"docs":{},"高":{"docs":{},"于":{"docs":{},"或":{"docs":{},"低":{"docs":{},"于":{"docs":{},"其":{"docs":{},"他":{"docs":{},"物":{"docs":{},"品":{"docs":{},"的":{"docs":{},"偏":{"docs":{},"置":{"docs":{},"值":{"docs":{},"b":{"docs":{},"_":{"docs":{},"i":{"docs":{},"​":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}}}}}}}}}}}}}}},"持":{"docs":{},"续":{"docs":{},"服":{"docs":{},"务":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}},"计":{"docs":{},"算":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}}}}},"提":{"docs":{},"高":{"docs":{},"用":{"docs":{},"户":{"docs":{},"停":{"docs":{},"留":{"docs":{},"时":{"docs":{},"间":{"docs":{},"和":{"docs":{},"用":{"docs":{},"户":{"docs":{},"活":{"docs":{},"跃":{"docs":{},"程":{"docs":{},"度":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}}}}}}}}}}}}}},"供":{"docs":{},"了":{"docs":{},"两":{"docs":{},"个":{"docs":{},"列":{"docs":{},"表":{"docs":{},"，":{"docs":{},"对":{"docs":{},"相":{"docs":{},"同":{"docs":{},"位":{"docs":{},"置":{"docs":{},"的":{"docs":{},"列":{"docs":{},"表":{"docs":{},"数":{"docs":{},"据":{"docs":{},"进":{"docs":{},"行":{"docs":{},"相":{"docs":{},"加":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}}}}}}},"的":{"docs":{},"内":{"docs":{},"置":{"docs":{},"函":{"docs":{},"数":{"docs":{},"无":{"docs":{},"法":{"docs":{},"满":{"docs":{},"足":{"docs":{},"你":{"docs":{},"的":{"docs":{},"业":{"docs":{},"务":{"docs":{},"处":{"docs":{},"理":{"docs":{},"需":{"docs":{},"要":{"docs":{},"时":{"docs":{},"，":{"docs":{},"此":{"docs":{},"时":{"docs":{},"就":{"docs":{},"可":{"docs":{},"以":{"docs":{},"考":{"docs":{},"虑":{"docs":{},"使":{"docs":{},"用":{"docs":{},"用":{"docs":{},"户":{"docs":{},"自":{"docs":{},"定":{"docs":{},"义":{"docs":{},"函":{"docs":{},"数":{"docs":{},"（":{"docs":{},"u":{"docs":{},"d":{"docs":{},"f":{"docs":{},"：":{"docs":{},"u":{"docs":{},"s":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"取":{"docs":{},"用":{"docs":{},"户":{"docs":{},"观":{"docs":{},"看":{"docs":{},"列":{"docs":{},"表":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}}}}}},"部":{"docs":{},"分":{"docs":{},"列":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}},"交":{"docs":{},"作":{"docs":{},"业":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}},"升":{"docs":{},"活":{"docs":{},"跃":{"docs":{},"是":{"docs":{},"网":{"docs":{},"站":{"docs":{},"运":{"docs":{},"营":{"docs":{},"的":{"docs":{},"重":{"docs":{},"要":{"docs":{},"目":{"docs":{},"标":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}}}}}}}},"搜":{"docs":{},"索":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}},"引":{"docs":{},"擎":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775},"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}},"（":{"2":{"0":{"0":{"0":{"docs":{},"s":{"docs":{},"）":{"docs":{},"：":{"docs":{},"通":{"docs":{},"过":{"docs":{},"搜":{"docs":{},"索":{"docs":{},"词":{"docs":{},"明":{"docs":{},"确":{"docs":{},"需":{"docs":{},"求":{"docs":{},"。":{"docs":{},"典":{"docs":{},"型":{"docs":{},"应":{"docs":{},"用":{"docs":{},"：":{"docs":{},"g":{"docs":{},"o":{"docs":{},"o":{"docs":{},"g":{"docs":{},"l":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"时":{"docs":{},"代":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}}},"记":{"docs":{},"录":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}},")":{"docs":{},"：":{"docs":{},"选":{"docs":{},"择":{"docs":{},"现":{"docs":{},"在":{"docs":{},"不":{"docs":{},"确":{"docs":{},"定":{"docs":{},"的":{"docs":{},"⼀":{"docs":{},"些":{"docs":{},"⽅":{"docs":{},"案":{"docs":{},"，":{"docs":{},"但":{"docs":{},"未":{"docs":{},"来":{"docs":{},"可":{"docs":{},"能":{"docs":{},"会":{"docs":{},"有":{"docs":{},"⾼":{"docs":{},"收":{"docs":{},"益":{"docs":{},"的":{"docs":{},"⽅":{"docs":{},"案":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"关":{"docs":{},"键":{"docs":{},"词":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}},"打":{"docs":{},"开":{"docs":{},"转":{"docs":{},"化":{"docs":{},"率":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}},"用":{"docs":{},"户":{"docs":{},"数":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}},"中":{"docs":{},"有":{"docs":{},"很":{"docs":{},"强":{"docs":{},"的":{"docs":{},"搜":{"docs":{},"索":{"docs":{},"信":{"docs":{},"号":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}}}}},"和":{"docs":{},"非":{"docs":{},"搜":{"docs":{},"索":{"docs":{},"广":{"docs":{},"告":{"docs":{},"点":{"docs":{},"击":{"docs":{},"率":{"docs":{},"预":{"docs":{},"测":{"docs":{},"的":{"docs":{},"区":{"docs":{},"别":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}}}}}}}}}}}},"明":{"docs":{},"确":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}},"有":{"docs":{},"效":{"docs":{},"的":{"docs":{},"帮":{"docs":{},"助":{"docs":{},"产":{"docs":{},"品":{"docs":{},"实":{"docs":{},"现":{"docs":{},"其":{"docs":{},"商":{"docs":{},"业":{"docs":{},"价":{"docs":{},"值":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}}}}}}}}}}}}},"了":{"docs":{},"两":{"docs":{},"两":{"docs":{},"的":{"docs":{},"相":{"docs":{},"似":{"docs":{},"度":{"docs":{},"，":{"docs":{},"接":{"docs":{},"下":{"docs":{},"来":{"docs":{},"就":{"docs":{},"可":{"docs":{},"以":{"docs":{},"筛":{"docs":{},"选":{"docs":{},"t":{"docs":{},"o":{"docs":{},"p":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}}}}}},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{},"，":{"docs":{},"接":{"docs":{},"下":{"docs":{},"来":{"docs":{},"我":{"docs":{},"们":{"docs":{},"就":{"docs":{},"可":{"docs":{},"以":{"docs":{},"进":{"docs":{},"行":{"docs":{},"相":{"docs":{},"似":{"docs":{},"度":{"docs":{},"的":{"docs":{},"计":{"docs":{},"算":{"docs":{},"，":{"docs":{},"不":{"docs":{},"过":{"docs":{},"对":{"docs":{},"于":{"docs":{},"相":{"docs":{},"似":{"docs":{},"度":{"docs":{},"的":{"docs":{},"计":{"docs":{},"算":{"docs":{},"其":{"docs":{},"实":{"docs":{},"是":{"docs":{},"有":{"docs":{},"很":{"docs":{},"多":{"docs":{},"专":{"docs":{},"门":{"docs":{},"的":{"docs":{},"相":{"docs":{},"似":{"docs":{},"度":{"docs":{},"计":{"docs":{},"算":{"docs":{},"方":{"docs":{},"法":{"docs":{},"的":{"docs":{},"，":{"docs":{},"比":{"docs":{},"如":{"docs":{},"余":{"docs":{},"弦":{"docs":{},"相":{"docs":{},"似":{"docs":{},"度":{"docs":{},"、":{"docs":{},"皮":{"docs":{},"尔":{"docs":{},"逊":{"docs":{},"相":{"docs":{},"关":{"docs":{},"系":{"docs":{},"数":{"docs":{},"、":{"docs":{},"杰":{"docs":{},"卡":{"docs":{},"德":{"docs":{},"相":{"docs":{},"似":{"docs":{},"度":{"docs":{},"等":{"docs":{},"等":{"docs":{},"。":{"docs":{},"这":{"docs":{},"里":{"docs":{},"我":{"docs":{},"们":{"docs":{},"选":{"docs":{},"择":{"docs":{},"使":{"docs":{},"用":{"docs":{},"杰":{"docs":{},"卡":{"docs":{},"德":{"docs":{},"相":{"docs":{},"似":{"docs":{},"系":{"docs":{},"数":{"docs":{},"[":{"0":{"docs":{},",":{"1":{"docs":{},"]":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}},"docs":{}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"些":{"docs":{},"用":{"docs":{},"户":{"docs":{},"的":{"docs":{},"评":{"docs":{},"分":{"docs":{},"普":{"docs":{},"遍":{"docs":{},"高":{"docs":{},"于":{"docs":{},"其":{"docs":{},"他":{"docs":{},"用":{"docs":{},"户":{"docs":{},"，":{"docs":{},"有":{"docs":{},"些":{"docs":{},"用":{"docs":{},"户":{"docs":{},"的":{"docs":{},"评":{"docs":{},"分":{"docs":{},"普":{"docs":{},"遍":{"docs":{},"低":{"docs":{},"于":{"docs":{},"其":{"docs":{},"他":{"docs":{},"用":{"docs":{},"户":{"docs":{},"。":{"docs":{},"比":{"docs":{},"如":{"docs":{},"有":{"docs":{},"些":{"docs":{},"用":{"docs":{},"户":{"docs":{},"天":{"docs":{},"生":{"docs":{},"愿":{"docs":{},"意":{"docs":{},"给":{"docs":{},"别":{"docs":{},"人":{"docs":{},"好":{"docs":{},"评":{"docs":{},"，":{"docs":{},"心":{"docs":{},"慈":{"docs":{},"手":{"docs":{},"软":{"docs":{},"，":{"docs":{},"比":{"docs":{},"较":{"docs":{},"好":{"docs":{},"说":{"docs":{},"话":{"docs":{},"，":{"docs":{},"而":{"docs":{},"有":{"docs":{},"的":{"docs":{},"人":{"docs":{},"就":{"docs":{},"比":{"docs":{},"较":{"docs":{},"苛":{"docs":{},"刻":{"docs":{},"，":{"docs":{},"总":{"docs":{},"是":{"docs":{},"评":{"docs":{},"分":{"docs":{},"不":{"docs":{},"超":{"docs":{},"过":{"3":{"docs":{},"分":{"docs":{},"（":{"5":{"docs":{},"分":{"docs":{},"满":{"docs":{},"分":{"docs":{},"）":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}},"docs":{}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"保":{"docs":{},"存":{"docs":{},"大":{"docs":{},"量":{"docs":{},"网":{"docs":{},"页":{"docs":{},"的":{"docs":{},"需":{"docs":{},"求":{"docs":{},"(":{"docs":{},"单":{"docs":{},"机":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}}}}}}}}}}},"明":{"docs":{},"显":{"docs":{},"降":{"docs":{},"幅":{"docs":{},"的":{"docs":{},"是":{"docs":{},"咨":{"docs":{},"询":{"docs":{},"详":{"docs":{},"情":{"docs":{},"转":{"docs":{},"化":{"docs":{},"率":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}}}}}}},"购":{"docs":{},"买":{"docs":{},"意":{"docs":{},"向":{"docs":{},"开":{"docs":{},"始":{"docs":{},"咨":{"docs":{},"询":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}},"行":{"docs":{},"为":{"docs":{},"的":{"docs":{},"用":{"docs":{},"户":{"docs":{},"数":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}},"复":{"docs":{},"杂":{"docs":{},"的":{"docs":{},"索":{"docs":{},"引":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}},"四":{"docs":{},"种":{"docs":{},"类":{"docs":{},"型":{"docs":{},"的":{"docs":{},"运":{"docs":{},"算":{"docs":{},"符":{"docs":{},"：":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}}}}}},"两":{"docs":{},"个":{"docs":{},"分":{"docs":{},"类":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}},"关":{"docs":{},"a":{"docs":{},"p":{"docs":{},"i":{"docs":{},"的":{"docs":{},"更":{"docs":{},"多":{"docs":{},"详":{"docs":{},"细":{"docs":{},"信":{"docs":{},"息":{"docs":{},"，":{"docs":{},"请":{"docs":{},"参":{"docs":{},"阅":{"docs":{},"l":{"docs":{},"a":{"docs":{},"b":{"docs":{},"e":{"docs":{},"l":{"docs":{},"e":{"docs":{},"d":{"docs":{},"p":{"docs":{},"o":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"p":{"docs":{},"y":{"docs":{},"t":{"docs":{},"h":{"docs":{},"o":{"docs":{},"n":{"docs":{},"文":{"docs":{},"档":{"docs":{},"。":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"查":{"docs":{},"看":{"docs":{},"票":{"docs":{},"房":{"docs":{},"排":{"docs":{},"行":{"docs":{},"榜":{"docs":{},",":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}}}}}},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},"中":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625}}}}}}},"日":{"docs":{},"活":{"docs":{},"数":{"docs":{},"据":{"docs":{},",":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}},"数":{"docs":{},"据":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}},"时":{"docs":{},",":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}},"表":{"docs":{},"的":{"docs":{},"分":{"docs":{},"区":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}},"中":{"docs":{},"的":{"docs":{},"记":{"docs":{},"录":{"docs":{},"总":{"docs":{},"数":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}},"每":{"docs":{},"一":{"docs":{},"篇":{"docs":{},"文":{"docs":{},"章":{"docs":{},"的":{"docs":{},"关":{"docs":{},"键":{"docs":{},"字":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}}}}}},"列":{"docs":{},"数":{"docs":{},"据":{"docs":{},"的":{"docs":{},"类":{"docs":{},"别":{"docs":{},"情":{"docs":{},"况":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}},"型":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}},"所":{"docs":{},"有":{"docs":{},"记":{"docs":{},"录":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}},"指":{"docs":{},"定":{"docs":{},"表":{"docs":{},"指":{"docs":{},"定":{"docs":{},"列":{"docs":{},"所":{"docs":{},"有":{"docs":{},"数":{"docs":{},"据":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}}},"记":{"docs":{},"录":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}},"两":{"docs":{},"个":{"docs":{},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{},"在":{"docs":{},"类":{"docs":{},"别":{"docs":{},"上":{"docs":{},"的":{"docs":{},"差":{"docs":{},"异":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}},"某":{"docs":{},"一":{"docs":{},"列":{"docs":{},"是":{"docs":{},"否":{"docs":{},"有":{"docs":{},"重":{"docs":{},"复":{"docs":{},"值":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}},"重":{"docs":{},"复":{"docs":{},"记":{"docs":{},"录":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}},"b":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"i":{"docs":{},"d":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"情":{"docs":{},"况":{"docs":{},"：":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}},"t":{"docs":{},"a":{"docs":{},"g":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"情":{"docs":{},"况":{"docs":{},"：":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"i":{"docs":{},"d":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"情":{"docs":{},"况":{"docs":{},"：":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"，":{"docs":{},"默":{"docs":{},"认":{"docs":{},"显":{"docs":{},"示":{"docs":{},"前":{"2":{"0":{"docs":{},"条":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}},"docs":{}},"docs":{}}}}}}}}}}}},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"s":{"docs":{},"条":{"docs":{},"目":{"docs":{},"数":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}},"的":{"docs":{},"结":{"docs":{},"构":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"情":{"docs":{},"况":{"docs":{},"：":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}},"是":{"docs":{},"否":{"docs":{},"有":{"docs":{},"空":{"docs":{},"值":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}},"更":{"docs":{},"详":{"docs":{},"细":{"docs":{},"配":{"docs":{},"置":{"docs":{},"及":{"docs":{},"说":{"docs":{},"明":{"docs":{},"：":{"docs":{},"h":{"docs":{},"t":{"docs":{},"t":{"docs":{},"p":{"docs":{},"s":{"docs":{},":":{"docs":{},"/":{"docs":{},"/":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},".":{"docs":{},"a":{"docs":{},"p":{"docs":{},"a":{"docs":{},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},".":{"docs":{},"o":{"docs":{},"r":{"docs":{},"g":{"docs":{},"/":{"docs":{},"d":{"docs":{},"o":{"docs":{},"c":{"docs":{},"s":{"docs":{},"/":{"docs":{},"l":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{},"/":{"docs":{},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"f":{"docs":{},"i":{"docs":{},"g":{"docs":{},"u":{"docs":{},"r":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},".":{"docs":{},"h":{"docs":{},"t":{"docs":{},"m":{"docs":{},"l":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"前":{"2":{"0":{"docs":{},"条":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"各":{"docs":{},"项":{"docs":{},"数":{"docs":{},"据":{"docs":{},"的":{"docs":{},"特":{"docs":{},"征":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}},"最":{"docs":{},"大":{"docs":{},"时":{"docs":{},"间":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}},"样":{"docs":{},"本":{"docs":{},"中":{"docs":{},"点":{"docs":{},"击":{"docs":{},"的":{"docs":{},"被":{"docs":{},"实":{"docs":{},"际":{"docs":{},"点":{"docs":{},"击":{"docs":{},"的":{"docs":{},"条":{"docs":{},"目":{"docs":{},"的":{"docs":{},"预":{"docs":{},"测":{"docs":{},"情":{"docs":{},"况":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}},"询":{"docs":{},"作":{"docs":{},"业":{"docs":{},"的":{"docs":{},"运":{"docs":{},"行":{"docs":{},"进":{"docs":{},"度":{"docs":{},",":{"docs":{},"杀":{"docs":{},"死":{"docs":{},"作":{"docs":{},"业":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}}}}}}}}}},"分":{"docs":{},"析":{"docs":{},"数":{"docs":{},"据":{"docs":{},"。":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}},"语":{"docs":{},"句":{"docs":{},"从":{"docs":{},"词":{"docs":{},"法":{"docs":{},"分":{"docs":{},"析":{"docs":{},"、":{"docs":{},"语":{"docs":{},"法":{"docs":{},"分":{"docs":{},"析":{"docs":{},"、":{"docs":{},"编":{"docs":{},"译":{"docs":{},"、":{"docs":{},"优":{"docs":{},"化":{"docs":{},"以":{"docs":{},"及":{"docs":{},"查":{"docs":{},"询":{"docs":{},"计":{"docs":{},"划":{"docs":{},"的":{"docs":{},"生":{"docs":{},"成":{"docs":{},"。":{"docs":{},"生":{"docs":{},"成":{"docs":{},"的":{"docs":{},"查":{"docs":{},"询":{"docs":{},"计":{"docs":{},"划":{"docs":{},"存":{"docs":{},"储":{"docs":{},"在":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"言":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}},"表":{"docs":{},"中":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}},"所":{"docs":{},"有":{"docs":{},"数":{"docs":{},"据":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}},"某":{"docs":{},"个":{"docs":{},"r":{"docs":{},"o":{"docs":{},"w":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}},"列":{"docs":{},"簇":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}},"一":{"docs":{},"行":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}},"多":{"docs":{},"行":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}},"操":{"docs":{},"作":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}},"模":{"docs":{},"糊":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}},"型":{"docs":{},"（":{"docs":{},"m":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"l":{"docs":{},"）":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}},"训":{"docs":{},"练":{"docs":{},"好":{"docs":{},"后":{"docs":{},"，":{"docs":{},"调":{"docs":{},"用":{"docs":{},"方":{"docs":{},"法":{"docs":{},"进":{"docs":{},"行":{"docs":{},"使":{"docs":{},"用":{"docs":{},"，":{"docs":{},"具":{"docs":{},"体":{"docs":{},"a":{"docs":{},"p":{"docs":{},"i":{"docs":{},"查":{"docs":{},"看":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}},"式":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}},"没":{"docs":{},"有":{"docs":{},"明":{"docs":{},"确":{"docs":{},"需":{"docs":{},"求":{"docs":{},"的":{"docs":{},"用":{"docs":{},"户":{"docs":{},"访":{"docs":{},"问":{"docs":{},"了":{"docs":{},"我":{"docs":{},"们":{"docs":{},"的":{"docs":{},"服":{"docs":{},"务":{"docs":{},",":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}}}}}}}}}}}}}}}},"全":{"docs":{},"部":{"docs":{},"开":{"docs":{},"源":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}},"找":{"docs":{},"到":{"docs":{},"原":{"docs":{},"因":{"docs":{},",":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}},"预":{"docs":{},"定":{"docs":{},"义":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"模":{"docs":{},"型":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}}}}},"嵌":{"docs":{},"套":{"docs":{},"结":{"docs":{},"构":{"docs":{},"的":{"docs":{},"j":{"docs":{},"s":{"docs":{},"o":{"docs":{},"n":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}}}}}}}}},"流":{"docs":{},"量":{"docs":{},"分":{"docs":{},"布":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}}},"式":{"docs":{},"处":{"docs":{},"理":{"docs":{},",":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}}}},"计":{"docs":{},"算":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}},"：":{"docs":{},"s":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{},"m":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}}}}},"框":{"docs":{},"架":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}}}}}},"然":{"docs":{},"后":{"docs":{},"看":{"docs":{},"看":{"docs":{},"返":{"docs":{},"回":{"docs":{},"结":{"docs":{},"果":{"docs":{},"中":{"docs":{},"还":{"docs":{},"有":{"docs":{},"什":{"docs":{},"么":{"docs":{},"电":{"docs":{},"影":{"docs":{},"是":{"docs":{},"自":{"docs":{},"己":{"docs":{},"没":{"docs":{},"看":{"docs":{},"过":{"docs":{},"的":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}}}}}}}}}}}}}}}}}}}},"解":{"docs":{},"压":{"docs":{},"到":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428}}}}},"再":{"docs":{},"需":{"docs":{},"要":{"docs":{},"连":{"docs":{},"接":{"docs":{},"是":{"docs":{},"调":{"docs":{},"用":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}}}}}},"添":{"docs":{},"加":{"docs":{},"其":{"docs":{},"它":{"docs":{},"的":{"docs":{},"列":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}},"用":{"docs":{},"户":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734},"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.006493506493506494},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}},"需":{"docs":{},"求":{"docs":{},"不":{"docs":{},"明":{"docs":{},"确":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}}}}},"之":{"docs":{},"间":{"docs":{},"的":{"docs":{},"两":{"docs":{},"两":{"docs":{},"相":{"docs":{},"似":{"docs":{},"度":{"docs":{},"：":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}},"相":{"docs":{},"似":{"docs":{},"度":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}},"购":{"docs":{},"买":{"docs":{},"记":{"docs":{},"录":{"docs":{},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.005649717514124294}}}}}}}}},"i":{"docs":{},"d":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.006493506493506494},"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.0125}}}},"两":{"docs":{},"两":{"docs":{},"相":{"docs":{},"似":{"docs":{},"度":{"docs":{},"矩":{"docs":{},"阵":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}}}}}}},"间":{"docs":{},"的":{"docs":{},"相":{"docs":{},"似":{"docs":{},"度":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.0016233766233766235}}}}}}}}},"聚":{"docs":{},"类":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}},"冷":{"docs":{},"启":{"docs":{},"动":{"docs":{"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html":{"ref":"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html","tf":0.024390243902439025}}}}},"a":{"docs":{},"比":{"docs":{},"较":{"docs":{},"苛":{"docs":{},"刻":{"docs":{},"，":{"docs":{},"普":{"docs":{},"遍":{"docs":{},"比":{"docs":{},"平":{"docs":{},"均":{"docs":{},"评":{"docs":{},"分":{"docs":{},"低":{"0":{"docs":{},".":{"5":{"docs":{},"分":{"docs":{},"，":{"docs":{},"即":{"docs":{},"用":{"docs":{},"户":{"docs":{},"a":{"docs":{},"的":{"docs":{},"偏":{"docs":{},"置":{"docs":{},"值":{"docs":{},"b":{"docs":{},"_":{"docs":{},"i":{"docs":{},"​":{"docs":{},"是":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}}}}}},"docs":{}}},"docs":{}}}}}}}}}}}}}}},"评":{"docs":{},"分":{"docs":{},"数":{"docs":{},"据":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002858776443682104},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}},"在":{"docs":{},"享":{"docs":{},"受":{"docs":{},"服":{"docs":{},"务":{"docs":{},"过":{"docs":{},"程":{"docs":{},"中":{"docs":{},"提":{"docs":{},"供":{"docs":{},"的":{"docs":{},"物":{"docs":{},"品":{"docs":{},"的":{"docs":{},"属":{"docs":{},"性":{"docs":{},"：":{"docs":{},"如":{"docs":{},"用":{"docs":{},"户":{"docs":{},"评":{"docs":{},"论":{"docs":{},"内":{"docs":{},"容":{"docs":{},"，":{"docs":{},"微":{"docs":{},"博":{"docs":{},"话":{"docs":{},"题":{"docs":{},"（":{"docs":{},"用":{"docs":{},"户":{"docs":{},"拟":{"docs":{},"定":{"docs":{},"）":{"docs":{"day02_推荐算法/07_基于内容的推荐算法.html":{"ref":"day02_推荐算法/07_基于内容的推荐算法.html","tf":0.03125}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"访":{"docs":{},"问":{"docs":{},"网":{"docs":{},"站":{"docs":{},"的":{"docs":{},"过":{"docs":{},"程":{"docs":{},"中":{"docs":{},",":{"docs":{},"转":{"docs":{},"化":{"docs":{},"出":{"docs":{},"了":{"docs":{},"问":{"docs":{},"题":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}}}}}}}}}},"画":{"docs":{},"像":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}},"：":{"docs":{},"例":{"docs":{},"如":{"docs":{},"已":{"docs":{},"知":{"docs":{},"用":{"docs":{},"户":{"docs":{},"的":{"docs":{},"观":{"docs":{},"影":{"docs":{},"历":{"docs":{},"史":{"docs":{},"是":{"docs":{},"：":{"docs":{},"\"":{"docs":{},"《":{"docs":{},"战":{"docs":{},"狼":{"1":{"docs":{},"》":{"docs":{},"\"":{"docs":{},"、":{"docs":{},"\"":{"docs":{},"《":{"docs":{},"战":{"docs":{},"狼":{"2":{"docs":{},"》":{"docs":{},"\"":{"docs":{},"、":{"docs":{},"\"":{"docs":{},"《":{"docs":{},"建":{"docs":{},"党":{"docs":{},"伟":{"docs":{},"业":{"docs":{},"》":{"docs":{},"\"":{"docs":{},"、":{"docs":{},"\"":{"docs":{},"《":{"docs":{},"建":{"docs":{},"军":{"docs":{},"大":{"docs":{},"业":{"docs":{},"》":{"docs":{},"\"":{"docs":{},"、":{"docs":{},"\"":{"docs":{},"《":{"docs":{},"建":{"docs":{},"国":{"docs":{},"大":{"docs":{},"业":{"docs":{},"》":{"docs":{},"\"":{"docs":{},"、":{"docs":{},"\"":{"docs":{},"《":{"docs":{},"红":{"docs":{},"海":{"docs":{},"行":{"docs":{},"动":{"docs":{},"》":{"docs":{},"\"":{"docs":{},"、":{"docs":{},"\"":{"docs":{},"《":{"docs":{},"速":{"docs":{},"度":{"docs":{},"与":{"docs":{},"激":{"docs":{},"情":{"1":{"docs":{"day02_推荐算法/07_基于内容的推荐算法.html":{"ref":"day02_推荐算法/07_基于内容的推荐算法.html","tf":0.03125}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{}}}}}}}}},"docs":{}}}}}}}}}}}}}}}}}}},"建":{"docs":{},"立":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}},"构":{"docs":{},"建":{"docs":{},"步":{"docs":{},"骤":{"docs":{},"：":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}}}}},"/":{"docs":{},"物":{"docs":{},"品":{"docs":{},"画":{"docs":{},"像":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}}}}}},"只":{"docs":{},"需":{"docs":{},"要":{"docs":{},"实":{"docs":{},"现":{"docs":{},"两":{"docs":{},"个":{"docs":{},"函":{"docs":{},"数":{"docs":{},"接":{"docs":{},"口":{"docs":{},"：":{"docs":{"day03_Hadoop/ha3.2.html":{"ref":"day03_Hadoop/ha3.2.html","tf":0.02040816326530612}}}}}}}}}}}}}},"增":{"docs":{},"长":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}},"留":{"docs":{},"存":{"docs":{},"率":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.008403361344537815}}}}},"名":{"docs":{},"：":{"docs":{},"r":{"docs":{},"o":{"docs":{},"o":{"docs":{},"t":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}},"接":{"docs":{},"口":{"docs":{},"：":{"docs":{},"包":{"docs":{},"括":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}},"行":{"docs":{},"为":{"docs":{},"表":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}},"数":{"docs":{},"据":{"docs":{},"拆":{"docs":{},"分":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}},"写":{"docs":{},"入":{"docs":{},"数":{"docs":{},"据":{"docs":{},"的":{"docs":{},"流":{"docs":{},"程":{"docs":{},"为":{"docs":{},"：":{"docs":{},"c":{"docs":{},"l":{"docs":{},"i":{"docs":{},"e":{"docs":{},"n":{"docs":{},"t":{"docs":{},"访":{"docs":{},"问":{"docs":{},"z":{"docs":{},"k":{"docs":{},",":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}}}}}}}}}}}}}}}},"自":{"docs":{},"己":{"docs":{},"写":{"docs":{},"的":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"应":{"docs":{},"用":{"docs":{},"程":{"docs":{},"序":{"docs":{},"，":{"docs":{},"批":{"docs":{},"处":{"docs":{},"理":{"docs":{},"作":{"docs":{},"业":{"docs":{},"的":{"docs":{},"集":{"docs":{},"合":{"docs":{},"。":{"docs":{},"a":{"docs":{},"p":{"docs":{},"p":{"docs":{},"l":{"docs":{},"i":{"docs":{},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"的":{"docs":{},"m":{"docs":{},"a":{"docs":{},"i":{"docs":{},"n":{"docs":{},"方":{"docs":{},"法":{"docs":{},"为":{"docs":{},"应":{"docs":{},"用":{"docs":{},"程":{"docs":{},"序":{"docs":{},"的":{"docs":{},"入":{"docs":{},"口":{"docs":{},"，":{"docs":{},"用":{"docs":{},"户":{"docs":{},"通":{"docs":{},"过":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"的":{"docs":{},"a":{"docs":{},"p":{"docs":{},"i":{"docs":{},"，":{"docs":{},"定":{"docs":{},"义":{"docs":{},"了":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"和":{"docs":{},"对":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"的":{"docs":{},"操":{"docs":{},"作":{"docs":{},"。":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"基":{"docs":{},"本":{"docs":{},"信":{"docs":{},"息":{"docs":{},"表":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"p":{"docs":{},"r":{"docs":{},"o":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}}}}}}}}}}}},"特":{"docs":{},"征":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}},"数":{"docs":{},"据":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}},"合":{"docs":{},"并":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}},"的":{"docs":{},"行":{"docs":{},"为":{"docs":{},"日":{"docs":{},"志":{"docs":{},"b":{"docs":{},"e":{"docs":{},"h":{"docs":{},"a":{"docs":{},"v":{"docs":{},"i":{"docs":{},"o":{"docs":{},"r":{"docs":{},"_":{"docs":{},"l":{"docs":{},"o":{"docs":{},"g":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}}}}}}}}}}}}},"对":{"docs":{},"商":{"docs":{},"品":{"docs":{},"类":{"docs":{},"别":{"docs":{},"的":{"docs":{},"打":{"docs":{},"分":{"docs":{},"数":{"docs":{},"据":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}}}}}}}}},"应":{"docs":{},"的":{"docs":{},"行":{"docs":{},"为":{"docs":{},"次":{"docs":{},"数":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}}}}}},"类":{"docs":{},"别":{"docs":{},"的":{"docs":{},"偏":{"docs":{},"好":{"docs":{},"打":{"docs":{},"分":{"docs":{},"数":{"docs":{},"据":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"总":{"docs":{},"数":{"docs":{},"：":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}},"微":{"docs":{},"群":{"docs":{},"i":{"docs":{},"d":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}},"性":{"docs":{},"别":{"docs":{},"特":{"docs":{},"征":{"docs":{},"，":{"docs":{},"[":{"1":{"docs":{},",":{"2":{"docs":{},"]":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}}},"docs":{}}}}}}},"组":{"docs":{},"i":{"docs":{},"d":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}},"夹":{"docs":{},"角":{"docs":{},"的":{"docs":{},"余":{"docs":{},"弦":{"docs":{},"值":{"docs":{},"来":{"docs":{},"度":{"docs":{},"量":{"docs":{},"相":{"docs":{},"似":{"docs":{},"的":{"docs":{},"情":{"docs":{},"况":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}},"数":{"docs":{},"据":{"docs":{},"表":{"docs":{},"示":{"docs":{},"特":{"docs":{},"征":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}},"t":{"docs":{},"a":{"docs":{},"g":{"docs":{},"作":{"docs":{},"为":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{},"去":{"docs":{},"取":{"docs":{},"值":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}},"途":{"docs":{},"：":{"docs":{},"在":{"docs":{},"目":{"docs":{},"标":{"docs":{},"文":{"docs":{},"档":{"docs":{},"中":{"docs":{},"，":{"docs":{},"提":{"docs":{},"取":{"docs":{},"关":{"docs":{},"键":{"docs":{},"词":{"docs":{},"(":{"docs":{},"特":{"docs":{},"征":{"docs":{},"标":{"docs":{},"签":{"docs":{},")":{"docs":{},"的":{"docs":{},"方":{"docs":{},"法":{"docs":{},"就":{"docs":{},"是":{"docs":{},"将":{"docs":{},"该":{"docs":{},"文":{"docs":{},"档":{"docs":{},"所":{"docs":{},"有":{"docs":{},"词":{"docs":{},"语":{"docs":{},"的":{"docs":{},"t":{"docs":{},"f":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"于":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},"环":{"docs":{},"境":{"docs":{},"，":{"docs":{},"支":{"docs":{},"持":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},"运":{"docs":{},"行":{"docs":{},"调":{"docs":{},"度":{"docs":{},"控":{"docs":{},"制":{"docs":{},"参":{"docs":{},"数":{"docs":{},"，":{"docs":{},"如":{"docs":{},"：":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"本":{"docs":{},"地":{"docs":{},"模":{"docs":{},"拟":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},"调":{"docs":{},"试":{"docs":{},"，":{"docs":{},"与":{"docs":{},"内":{"docs":{},"嵌":{"docs":{},"(":{"docs":{},"i":{"docs":{},"n":{"docs":{},"l":{"docs":{},"i":{"docs":{},"n":{"docs":{},"e":{"docs":{},")":{"docs":{},"方":{"docs":{},"式":{"docs":{},"的":{"docs":{},"区":{"docs":{},"别":{"docs":{},"是":{"docs":{},"启":{"docs":{},"动":{"docs":{},"了":{"docs":{},"多":{"docs":{},"进":{"docs":{},"程":{"docs":{},"执":{"docs":{},"行":{"docs":{},"每":{"docs":{},"一":{"docs":{},"个":{"docs":{},"任":{"docs":{},"务":{"docs":{},"。":{"docs":{},"如":{"docs":{},"：":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"海":{"docs":{},"量":{"docs":{},"数":{"docs":{},"据":{"docs":{},"的":{"docs":{},"离":{"docs":{},"线":{"docs":{},"数":{"docs":{},"据":{"docs":{},"分":{"docs":{},"析":{"docs":{},"。":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}}}}}},"l":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}}},"来":{"docs":{},"传":{"docs":{},"递":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"应":{"docs":{},"用":{"docs":{},"的":{"docs":{},"基":{"docs":{},"本":{"docs":{},"信":{"docs":{},"息":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}}}}}}}}}}}},"s":{"docs":{},"c":{"docs":{},"a":{"docs":{},"l":{"docs":{},"a":{"docs":{},"/":{"docs":{},"p":{"docs":{},"y":{"docs":{},"t":{"docs":{},"h":{"docs":{},"o":{"docs":{},"n":{"docs":{},"编":{"docs":{},"写":{"docs":{},"的":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"比":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}}}}}}}}}}}}}}}}}}}}}}}}}},"任":{"docs":{},"何":{"docs":{},"语":{"docs":{},"言":{"docs":{},"编":{"docs":{},"写":{"docs":{},"生":{"docs":{},"成":{"docs":{},"的":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"都":{"docs":{},"一":{"docs":{},"样":{"docs":{},"，":{"docs":{},"而":{"docs":{},"使":{"docs":{},"用":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}}}}}}}}}}}}}}}}}}}}}}}}}},"边":{"docs":{},"界":{"docs":{},"值":{"docs":{},"替":{"docs":{},"换":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}},"前":{"docs":{},"面":{"7":{"docs":{},"天":{"docs":{},"的":{"docs":{},"做":{"docs":{},"训":{"docs":{},"练":{"docs":{},"样":{"docs":{},"本":{"docs":{},"（":{"2":{"0":{"1":{"7":{"0":{"5":{"0":{"6":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}},"docs":{}}}},"留":{"docs":{},"存":{"docs":{},"率":{"docs":{},"/":{"docs":{},"阅":{"docs":{},"读":{"docs":{},"时":{"docs":{},"间":{"docs":{},"/":{"docs":{},"g":{"docs":{},"m":{"docs":{},"v":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}}}}}}}}}},"用":{"docs":{},"户":{"docs":{},"数":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}},"目":{"docs":{},"标":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}},"值":{"docs":{},"的":{"docs":{},"分":{"docs":{},"类":{"docs":{},"个":{"docs":{},"数":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}},"的":{"docs":{},"：":{"docs":{},"预":{"docs":{},"测":{"docs":{},"用":{"docs":{},"户":{"1":{"docs":{},"对":{"docs":{},"物":{"docs":{},"品":{"docs":{},"e":{"docs":{},"的":{"docs":{},"评":{"docs":{},"分":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}},"docs":{}}}}}}},"录":{"docs":{},"名":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625}}},"下":{"docs":{},"一":{"docs":{},"个":{"docs":{},"文":{"docs":{},"件":{"docs":{},"夹":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.006172839506172839}}}}}}},"的":{"docs":{},"子":{"docs":{},"目":{"docs":{},"录":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}},"看":{"docs":{},"看":{"docs":{},"他":{"docs":{},"们":{"docs":{},"最":{"docs":{},"近":{"docs":{},"在":{"docs":{},"看":{"docs":{},"什":{"docs":{},"么":{"docs":{},"电":{"docs":{},"影":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}}}}}}}}}}}},"确":{"docs":{},"定":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}},"社":{"docs":{},"会":{"docs":{},"化":{"docs":{},"推":{"docs":{},"荐":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}},",":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}}}}},"交":{"docs":{},"信":{"docs":{},"息":{"docs":{},"、":{"docs":{},"推":{"docs":{},"⼴":{"docs":{},"素":{"docs":{},"材":{"docs":{},"、":{"docs":{},"安":{"docs":{},"装":{"docs":{},"来":{"docs":{},"源":{"docs":{"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html":{"ref":"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html","tf":0.024390243902439025}}}}}}}}}}}}}}}},"稳":{"docs":{},"定":{"docs":{},"的":{"docs":{},"信":{"docs":{},"息":{"docs":{},"流":{"docs":{},"通":{"docs":{},"系":{"docs":{},"统":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}}}}}}}}},"系":{"docs":{},"统":{"docs":{},"通":{"docs":{},"过":{"docs":{},"一":{"docs":{},"定":{"docs":{},"的":{"docs":{},"规":{"docs":{},"则":{"docs":{},"对":{"docs":{},"物":{"docs":{},"品":{"docs":{},"进":{"docs":{},"行":{"docs":{},"排":{"docs":{},"序":{"docs":{},",":{"docs":{},"并":{"docs":{},"将":{"docs":{},"排":{"docs":{},"在":{"docs":{},"前":{"docs":{},"面":{"docs":{},"的":{"docs":{},"物":{"docs":{},"品":{"docs":{},"展":{"docs":{},"示":{"docs":{},"给":{"docs":{},"用":{"docs":{},"户":{"docs":{},",":{"docs":{},"这":{"docs":{},"样":{"docs":{},"的":{"docs":{},"系":{"docs":{},"统":{"docs":{},"就":{"docs":{},"是":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"系":{"docs":{},"统":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"过":{"docs":{},"度":{"docs":{},"强":{"docs":{},"调":{"docs":{},"实":{"docs":{},"时":{"docs":{},"性":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}}}}},"冷":{"docs":{},"启":{"docs":{},"动":{"docs":{"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html":{"ref":"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html","tf":0.024390243902439025}},"：":{"docs":{},"⽤":{"docs":{},"户":{"docs":{},"冷":{"docs":{},"启":{"docs":{},"动":{"docs":{},"+":{"docs":{},"物":{"docs":{},"品":{"docs":{},"冷":{"docs":{},"启":{"docs":{},"动":{"docs":{"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html":{"ref":"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html","tf":0.024390243902439025}}}}}}}}}}}}}}}}},"早":{"docs":{},"期":{"docs":{"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html":{"ref":"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html","tf":0.024390243902439025}}}}}},"结":{"docs":{},"果":{"docs":{},"是":{"docs":{},"概":{"docs":{},"率":{"docs":{},"问":{"docs":{},"题":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}}}}},"输":{"docs":{},"出":{"docs":{"day03_Hadoop/ha3.2.html":{"ref":"day03_Hadoop/ha3.2.html","tf":0.02040816326530612}}}},"再":{"docs":{},"保":{"docs":{},"存":{"docs":{},"到":{"docs":{},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}},"合":{"docs":{},"i":{"docs":{},"i":{"docs":{},"d":{"docs":{},"物":{"docs":{},"品":{"docs":{},"与":{"docs":{},"其":{"docs":{},"相":{"docs":{},"似":{"docs":{},"物":{"docs":{},"品":{"docs":{},"的":{"docs":{},"相":{"docs":{},"似":{"docs":{},"度":{"docs":{},"和":{"docs":{},"u":{"docs":{},"i":{"docs":{},"d":{"docs":{},"用":{"docs":{},"户":{"docs":{},"对":{"docs":{},"其":{"docs":{},"相":{"docs":{},"似":{"docs":{},"物":{"docs":{},"品":{"docs":{},"的":{"docs":{},"评":{"docs":{},"分":{"docs":{},"，":{"docs":{},"预":{"docs":{},"测":{"docs":{},"u":{"docs":{},"i":{"docs":{},"d":{"docs":{},"对":{"docs":{},"i":{"docs":{},"i":{"docs":{},"d":{"docs":{},"的":{"docs":{},"评":{"docs":{},"分":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.0016233766233766235}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"u":{"docs":{},"i":{"docs":{},"d":{"docs":{},"用":{"docs":{},"户":{"docs":{},"与":{"docs":{},"其":{"docs":{},"近":{"docs":{},"邻":{"docs":{},"用":{"docs":{},"户":{"docs":{},"的":{"docs":{},"相":{"docs":{},"似":{"docs":{},"度":{"docs":{},"预":{"docs":{},"测":{"docs":{},"u":{"docs":{},"i":{"docs":{},"d":{"docs":{},"用":{"docs":{},"户":{"docs":{},"对":{"docs":{},"i":{"docs":{},"i":{"docs":{},"d":{"docs":{},"物":{"docs":{},"品":{"docs":{},"的":{"docs":{},"评":{"docs":{},"分":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.00487012987012987}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"论":{"docs":{},"：":{"docs":{},"t":{"docs":{},"f":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}},"构":{"docs":{},"化":{"docs":{},"数":{"docs":{},"据":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}},"，":{"docs":{},"可":{"docs":{},"以":{"docs":{},"直":{"docs":{},"接":{"docs":{},"看":{"docs":{},"出":{"docs":{},"数":{"docs":{},"据":{"docs":{},"的":{"docs":{},"详":{"docs":{},"情":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}}}}}}}}}}}}}}}}}}},"行":{"docs":{},"为":{"docs":{},"方":{"docs":{},"式":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}}},"数":{"docs":{},"据":{"docs":{},"库":{"docs":{},"&":{"docs":{},"列":{"docs":{},"数":{"docs":{},"据":{"docs":{},"库":{"docs":{},"存":{"docs":{},"储":{"docs":{},"方":{"docs":{},"式":{"docs":{},"比":{"docs":{},"较":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}}}}}}}}}}},"(":{"docs":{},"r":{"docs":{},"o":{"docs":{},"w":{"docs":{},")":{"docs":{},"：":{"docs":{},"在":{"docs":{},"表":{"docs":{},"里":{"docs":{},"面":{"docs":{},",":{"docs":{},"每":{"docs":{},"一":{"docs":{},"行":{"docs":{},"代":{"docs":{},"表":{"docs":{},"着":{"docs":{},"一":{"docs":{},"个":{"docs":{},"数":{"docs":{},"据":{"docs":{},"对":{"docs":{},"象":{"docs":{},",":{"docs":{},"每":{"docs":{},"一":{"docs":{},"行":{"docs":{},"都":{"docs":{},"是":{"docs":{},"以":{"docs":{},"一":{"docs":{},"个":{"docs":{},"行":{"docs":{},"键":{"docs":{},"(":{"docs":{},"r":{"docs":{},"o":{"docs":{},"w":{"docs":{"Hive&HBase/06_hbase数据模型.html":{"ref":"Hive&HBase/06_hbase数据模型.html","tf":0.020833333333333332}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"键":{"docs":{},"(":{"docs":{},"r":{"docs":{},"o":{"docs":{},"w":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{},")":{"docs":{},"：":{"docs":{},"类":{"docs":{},"似":{"docs":{},"于":{"docs":{},"m":{"docs":{},"y":{"docs":{},"s":{"docs":{},"q":{"docs":{},"l":{"docs":{},"中":{"docs":{},"的":{"docs":{},"主":{"docs":{},"键":{"docs":{},"，":{"docs":{},"h":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"根":{"docs":{},"据":{"docs":{},"行":{"docs":{},"键":{"docs":{},"来":{"docs":{},"快":{"docs":{},"速":{"docs":{},"检":{"docs":{},"索":{"docs":{},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"一":{"docs":{},"个":{"docs":{},"行":{"docs":{},"键":{"docs":{},"对":{"docs":{},"应":{"docs":{},"一":{"docs":{},"条":{"docs":{},"记":{"docs":{},"录":{"docs":{},"。":{"docs":{},"与":{"docs":{},"m":{"docs":{},"y":{"docs":{},"s":{"docs":{},"q":{"docs":{},"l":{"docs":{},"主":{"docs":{},"键":{"docs":{},"不":{"docs":{},"同":{"docs":{},"的":{"docs":{},"是":{"docs":{},"，":{"docs":{},"h":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"的":{"docs":{},"行":{"docs":{},"键":{"docs":{},"是":{"docs":{},"天":{"docs":{},"然":{"docs":{},"固":{"docs":{},"有":{"docs":{},"的":{"docs":{},"，":{"docs":{},"每":{"docs":{},"一":{"docs":{},"行":{"docs":{},"数":{"docs":{},"据":{"docs":{},"都":{"docs":{},"存":{"docs":{},"在":{"docs":{},"行":{"docs":{},"键":{"docs":{},"。":{"docs":{"Hive&HBase/06_hbase数据模型.html":{"ref":"Hive&HBase/06_hbase数据模型.html","tf":0.020833333333333332}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"并":{"docs":{},"没":{"docs":{},"有":{"docs":{},"什":{"docs":{},"么":{"docs":{},"特":{"docs":{},"定":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"类":{"docs":{},"型":{"docs":{},",":{"docs":{"Hive&HBase/06_hbase数据模型.html":{"ref":"Hive&HBase/06_hbase数据模型.html","tf":0.020833333333333332}}}}}}}}}}}}}}}}},"被":{"docs":{},"动":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}},"让":{"docs":{},"好":{"docs":{},"友":{"docs":{},"给":{"docs":{},"自":{"docs":{},"己":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"物":{"docs":{},"品":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}}}}}}}}},"用":{"docs":{},"户":{"docs":{},"实":{"docs":{},"时":{"docs":{},"展":{"docs":{},"示":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}},"记":{"docs":{},"忆":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"系":{"docs":{},"统":{"docs":{},"工":{"docs":{},"作":{"docs":{},"原":{"docs":{},"理":{"docs":{},"及":{"docs":{},"作":{"docs":{},"用":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}}}}}}},"架":{"docs":{},"构":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}}},"冷":{"docs":{},"启":{"docs":{},"动":{"docs":{},"概":{"docs":{},"念":{"docs":{"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html":{"ref":"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html","tf":0.024390243902439025}}}}}}}}}}},"相":{"docs":{},"似":{"docs":{},"度":{"docs":{},"计":{"docs":{},"算":{"docs":{},"方":{"docs":{},"法":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}},"评":{"docs":{},"估":{"docs":{},"指":{"docs":{},"标":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}},"数":{"docs":{},"据":{"docs":{},"来":{"docs":{},"源":{"docs":{},"显":{"docs":{},"示":{"docs":{},"反":{"docs":{},"馈":{"docs":{},"和":{"docs":{},"隐":{"docs":{},"式":{"docs":{},"反":{"docs":{},"馈":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}}}}}}}}}}},"方":{"docs":{},"法":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}},"分":{"docs":{},"预":{"docs":{},"测":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.003795066413662239}},"：":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}},"使":{"docs":{},"用":{"docs":{},"物":{"docs":{},"品":{"docs":{},"间":{"docs":{},"的":{"docs":{},"相":{"docs":{},"似":{"docs":{},"度":{"docs":{},"进":{"docs":{},"行":{"docs":{},"预":{"docs":{},"测":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}},"用":{"docs":{},"户":{"docs":{},"间":{"docs":{},"的":{"docs":{},"相":{"docs":{},"似":{"docs":{},"度":{"docs":{},"进":{"docs":{},"行":{"docs":{},"预":{"docs":{},"测":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}},"公":{"docs":{},"式":{"docs":{},"的":{"docs":{},"分":{"docs":{},"子":{"docs":{},"部":{"docs":{},"分":{"docs":{},"的":{"docs":{},"值":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.006493506493506494}}}}}}},"母":{"docs":{},"部":{"docs":{},"分":{"docs":{},"的":{"docs":{},"值":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.006493506493506494}}}}}}}}}}}}},"公":{"docs":{},"式":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}}}},"数":{"docs":{},"据":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}},"论":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}},"/":{"docs":{},"评":{"docs":{},"价":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}},"输":{"docs":{},"入":{"docs":{},"自":{"docs":{},"己":{"docs":{},"喜":{"docs":{},"欢":{"docs":{},"的":{"docs":{},"演":{"docs":{},"员":{"docs":{},"的":{"docs":{},"名":{"docs":{},"字":{"docs":{},",":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}}}}}}}}}}},"数":{"docs":{},"据":{"docs":{"day03_Hadoop/ha3.5.html":{"ref":"day03_Hadoop/ha3.5.html","tf":0.030303030303030304}}}},"h":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}},"出":{"docs":{},"结":{"docs":{},"果":{"docs":{},"：":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}}}},"追":{"docs":{},"求":{"docs":{},"指":{"docs":{},"标":{"docs":{},"增":{"docs":{},"长":{"docs":{},",":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}}}}}},"加":{"docs":{},"型":{"docs":{},"数":{"docs":{},"据":{"docs":{},"库":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}},"通":{"docs":{},"过":{"docs":{},"信":{"docs":{},"息":{"docs":{},"过":{"docs":{},"滤":{"docs":{},"实":{"docs":{},"现":{"docs":{},"目":{"docs":{},"标":{"docs":{},"提":{"docs":{},"升":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}}}}}}}}}},"前":{"docs":{},"面":{"docs":{},"两":{"docs":{},"个":{"docs":{},"d":{"docs":{},"e":{"docs":{},"m":{"docs":{},"o":{"docs":{},"，":{"docs":{},"相":{"docs":{},"信":{"docs":{},"大":{"docs":{},"家":{"docs":{},"应":{"docs":{},"该":{"docs":{},"已":{"docs":{},"经":{"docs":{},"对":{"docs":{},"协":{"docs":{},"同":{"docs":{},"过":{"docs":{},"滤":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"算":{"docs":{},"法":{"docs":{},"的":{"docs":{},"设":{"docs":{},"计":{"docs":{},"与":{"docs":{},"实":{"docs":{},"现":{"docs":{},"有":{"docs":{},"了":{"docs":{},"比":{"docs":{},"较":{"docs":{},"清":{"docs":{},"晰":{"docs":{},"的":{"docs":{},"认":{"docs":{},"识":{"docs":{},"。":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"计":{"docs":{},"算":{"docs":{},"两":{"docs":{},"两":{"docs":{},"的":{"docs":{},"相":{"docs":{},"似":{"docs":{},"度":{"docs":{},"来":{"docs":{},"进":{"docs":{},"行":{"docs":{},"排":{"docs":{},"序":{"docs":{},"，":{"docs":{},"即":{"docs":{},"可":{"docs":{},"找":{"docs":{},"出":{"docs":{},"t":{"docs":{},"o":{"docs":{},"p":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}}}}}}}}},"最":{"docs":{},"小":{"docs":{},"二":{"docs":{},"乘":{"docs":{},"推":{"docs":{},"导":{"docs":{},"，":{"docs":{},"我":{"docs":{},"们":{"docs":{},"最":{"docs":{},"终":{"docs":{},"分":{"docs":{},"别":{"docs":{},"得":{"docs":{},"到":{"docs":{},"了":{"docs":{},"b":{"docs":{},"_":{"docs":{},"u":{"docs":{},"和":{"docs":{},"b":{"docs":{},"_":{"docs":{},"i":{"docs":{},"​":{"docs":{},"的":{"docs":{},"表":{"docs":{},"达":{"docs":{},"式":{"docs":{},"，":{"docs":{},"但":{"docs":{},"他":{"docs":{},"们":{"docs":{},"的":{"docs":{},"表":{"docs":{},"达":{"docs":{},"式":{"docs":{},"中":{"docs":{},"却":{"docs":{},"又":{"docs":{},"各":{"docs":{},"自":{"docs":{},"包":{"docs":{},"含":{"docs":{},"对":{"docs":{},"方":{"docs":{},"，":{"docs":{},"因":{"docs":{},"此":{"docs":{},"这":{"docs":{},"里":{"docs":{},"我":{"docs":{},"们":{"docs":{},"将":{"docs":{},"利":{"docs":{},"用":{"docs":{},"一":{"docs":{},"种":{"docs":{},"叫":{"docs":{},"交":{"docs":{},"替":{"docs":{},"最":{"docs":{},"小":{"docs":{},"二":{"docs":{},"乘":{"docs":{},"的":{"docs":{},"方":{"docs":{},"法":{"docs":{},"来":{"docs":{},"计":{"docs":{},"算":{"docs":{},"他":{"docs":{},"们":{"docs":{},"的":{"docs":{},"值":{"docs":{},"：":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"物":{"docs":{},"品":{"docs":{},"的":{"docs":{},"u":{"docs":{},"i":{"docs":{},"d":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}}},"用":{"docs":{},"户":{"docs":{},"的":{"docs":{},"i":{"docs":{},"d":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}},"对":{"docs":{},"比":{"docs":{},"可":{"docs":{},"得":{"docs":{},"，":{"docs":{},"该":{"docs":{},"篇":{"docs":{},"影":{"docs":{},"评":{"docs":{},"的":{"docs":{},"关":{"docs":{},"键":{"docs":{},"词":{"docs":{},"排":{"docs":{},"序":{"docs":{},"应":{"docs":{},"为":{"docs":{},"：":{"docs":{},"“":{"docs":{},"自":{"docs":{},"由":{"docs":{},"”":{"docs":{},"、":{"docs":{},"“":{"docs":{},"船":{"docs":{},"长":{"docs":{},"”":{"docs":{},"、":{"docs":{},"“":{"docs":{},"海":{"docs":{},"盗":{"docs":{},"”":{"docs":{},"。":{"docs":{},"把":{"docs":{},"这":{"docs":{},"些":{"docs":{},"词":{"docs":{},"语":{"docs":{},"的":{"docs":{},"t":{"docs":{},"f":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"用":{"docs":{},"户":{"docs":{},"观":{"docs":{},"影":{"docs":{},"标":{"docs":{},"签":{"docs":{},"的":{"docs":{},"次":{"docs":{},"数":{"docs":{},"进":{"docs":{},"行":{"docs":{},"统":{"docs":{},"计":{"docs":{},"，":{"docs":{},"计":{"docs":{},"算":{"docs":{},"用":{"docs":{},"户":{"docs":{},"的":{"docs":{},"每":{"docs":{},"个":{"docs":{},"初":{"docs":{},"始":{"docs":{},"标":{"docs":{},"签":{"docs":{},"的":{"docs":{},"权":{"docs":{},"重":{"docs":{},"值":{"docs":{},"，":{"docs":{},"排":{"docs":{},"序":{"docs":{},"后":{"docs":{},"选":{"docs":{},"取":{"docs":{},"t":{"docs":{},"o":{"docs":{},"p":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"j":{"docs":{},"p":{"docs":{},"s":{"docs":{},"命":{"docs":{},"令":{"docs":{},"查":{"docs":{},"看":{"docs":{},"当":{"docs":{},"前":{"docs":{},"运":{"docs":{},"行":{"docs":{},"的":{"docs":{},"进":{"docs":{},"程":{"docs":{"day03_Hadoop/ha2.1.html":{"ref":"day03_Hadoop/ha2.1.html","tf":0.0136986301369863}}}}}}}}}}}}}}}},"可":{"docs":{},"视":{"docs":{},"化":{"docs":{},"界":{"docs":{},"面":{"docs":{},"查":{"docs":{},"看":{"docs":{},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},"的":{"docs":{},"运":{"docs":{},"行":{"docs":{},"情":{"docs":{},"况":{"docs":{"day03_Hadoop/ha2.1.html":{"ref":"day03_Hadoop/ha2.1.html","tf":0.0136986301369863}}}}}}}}}}}}}}}}}},"浏":{"docs":{},"览":{"docs":{},"器":{"docs":{},"查":{"docs":{},"看":{"docs":{"day03_Hadoop/ha2.1.html":{"ref":"day03_Hadoop/ha2.1.html","tf":0.0136986301369863}}}}}}},"心":{"docs":{},"跳":{"docs":{},"和":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"n":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"保":{"docs":{},"持":{"docs":{},"通":{"docs":{},"讯":{"docs":{"day03_Hadoop/ha4.2.html":{"ref":"day03_Hadoop/ha4.2.html","tf":0.0196078431372549}}}}}}}}}}}}}}}}},"数":{"docs":{},"据":{"docs":{},"分":{"docs":{},"析":{"docs":{},"指":{"docs":{},"标":{"docs":{},"监":{"docs":{},"控":{"docs":{},"企":{"docs":{},"业":{"docs":{},"运":{"docs":{},"营":{"docs":{},"状":{"docs":{},"态":{"docs":{},",":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}}}}}}}}},"d":{"docs":{},"o":{"docs":{},"c":{"docs":{},"k":{"docs":{},"e":{"docs":{},"r":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},"向":{"docs":{},"h":{"docs":{},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{},"中":{"docs":{},"a":{"docs":{},"d":{"docs":{},"d":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}}}}}}}}}},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}},"n":{"docs":{},"n":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"找":{"docs":{},"到":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"表":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0169971671388102}}}}}}}}}}}}}}}}}}},"t":{"docs":{},"i":{"docs":{},"m":{"docs":{},"e":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"g":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}},"指":{"docs":{},"定":{"docs":{},"时":{"docs":{},"间":{"docs":{},"戳":{"docs":{},"获":{"docs":{},"取":{"docs":{},"不":{"docs":{},"同":{"docs":{},"版":{"docs":{},"本":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}}}}}}},"时":{"docs":{},"间":{"docs":{},"戳":{"docs":{},"查":{"docs":{},"询":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}},"过":{"docs":{},"滤":{"docs":{},"器":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}},"s":{"docs":{},"c":{"docs":{},"直":{"docs":{},"接":{"docs":{},"使":{"docs":{},"用":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}},"实":{"docs":{},"现":{"docs":{},"点":{"docs":{},"击":{"docs":{},"流":{"docs":{},"日":{"docs":{},"志":{"docs":{},"分":{"docs":{},"析":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}}}}}}}}}}}}},"外":{"docs":{},"部":{"docs":{},"数":{"docs":{},"据":{"docs":{},"创":{"docs":{},"建":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}}}}}}},"常":{"docs":{},"计":{"docs":{},"算":{"docs":{},"相":{"docs":{},"似":{"docs":{},"度":{"docs":{},"的":{"docs":{},"结":{"docs":{},"果":{"docs":{},"希":{"docs":{},"望":{"docs":{},"是":{"docs":{},"[":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}},"s":{"docs":{},"v":{"docs":{},"d":{"docs":{},"矩":{"docs":{},"阵":{"docs":{},"分":{"docs":{},"解":{"docs":{},"指":{"docs":{},"的":{"docs":{},"是":{"docs":{},"s":{"docs":{},"v":{"docs":{},"d":{"docs":{},"（":{"docs":{},"奇":{"docs":{},"异":{"docs":{},"值":{"docs":{},"）":{"docs":{},"分":{"docs":{},"解":{"docs":{},"技":{"docs":{},"术":{"docs":{},"，":{"docs":{},"在":{"docs":{},"这":{"docs":{},"我":{"docs":{},"们":{"docs":{},"姑":{"docs":{},"且":{"docs":{},"将":{"docs":{},"其":{"docs":{},"命":{"docs":{},"名":{"docs":{},"为":{"docs":{},"t":{"docs":{},"r":{"docs":{},"a":{"docs":{},"d":{"docs":{},"i":{"docs":{},"t":{"docs":{"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html":{"ref":"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html","tf":0.037037037037037035}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"数":{"docs":{},"据":{"docs":{},"存":{"docs":{},"储":{"docs":{},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"都":{"docs":{},"是":{"docs":{},"以":{"docs":{},"物":{"docs":{},"品":{"docs":{},"的":{"docs":{},"i":{"docs":{},"d":{"docs":{},"作":{"docs":{},"为":{"docs":{},"索":{"docs":{},"引":{"docs":{},"，":{"docs":{},"去":{"docs":{},"提":{"docs":{},"取":{"docs":{},"物":{"docs":{},"品":{"docs":{},"的":{"docs":{},"其":{"docs":{},"他":{"docs":{},"信":{"docs":{},"息":{"docs":{},"数":{"docs":{},"据":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"，":{"docs":{},"可":{"docs":{},"以":{"docs":{},"根":{"docs":{},"据":{"docs":{},"c":{"docs":{},"p":{"docs":{},"u":{"docs":{},"核":{"docs":{},"心":{"docs":{},"数":{"docs":{},"量":{"docs":{},"指":{"docs":{},"定":{"docs":{},"分":{"docs":{},"区":{"docs":{},"数":{"docs":{},"量":{"docs":{},"（":{"docs":{},"每":{"docs":{},"个":{"docs":{},"c":{"docs":{},"p":{"docs":{},"u":{"docs":{},"有":{"2":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}},"如":{"docs":{},"果":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}},"用":{"docs":{},"资":{"docs":{},"源":{"docs":{},"管":{"docs":{},"理":{"docs":{},"系":{"docs":{},"统":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.017094017094017096}}}}}}}}},"知":{"docs":{},"相":{"docs":{},"应":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"n":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},",":{"docs":{"day03_Hadoop/ha4.2.html":{"ref":"day03_Hadoop/ha4.2.html","tf":0.0196078431372549}}}}}}}}}}}}},"当":{"docs":{},"前":{"docs":{},"w":{"docs":{},"o":{"docs":{},"r":{"docs":{},"k":{"docs":{},"e":{"docs":{},"r":{"docs":{},"上":{"docs":{},"所":{"docs":{},"有":{"docs":{},"的":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"k":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}}}}}}}}}}}}}}}},"信":{"docs":{},"很":{"docs":{},"耗":{"docs":{},"费":{"docs":{},"性":{"docs":{},"能":{"docs":{},"。":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}}}}}}}}}},"长":{"docs":{},"尾":{"docs":{},"效":{"docs":{},"应":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}}},"期":{"docs":{},"的":{"docs":{},"⽬":{"docs":{},"标":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}}},"马":{"docs":{},"太":{"docs":{},"效":{"docs":{},"应":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}}}},"高":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.016},"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.006172839506172839}},"效":{"docs":{},"连":{"docs":{},"接":{"docs":{},"用":{"docs":{},"户":{"docs":{},"和":{"docs":{},"物":{"docs":{},"品":{"docs":{"day01_推荐系统介绍/01_推荐系统简介.html":{"ref":"day01_推荐系统介绍/01_推荐系统简介.html","tf":0.011235955056179775}}}}}}}}}},"延":{"docs":{},"迟":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}}},"可":{"docs":{},"靠":{"docs":{"day03_Hadoop/ha1.3.html":{"ref":"day03_Hadoop/ha1.3.html","tf":0.07692307692307693}}},"用":{"docs":{"day03_Hadoop/ha4.2.html":{"ref":"day03_Hadoop/ha4.2.html","tf":0.0196078431372549}}}},"扩":{"docs":{},"展":{"docs":{},"性":{"docs":{"day03_Hadoop/ha1.3.html":{"ref":"day03_Hadoop/ha1.3.html","tf":0.07692307692307693}}}}},"度":{"docs":{},"容":{"docs":{},"错":{"docs":{},"性":{"docs":{},"的":{"docs":{},"系":{"docs":{},"统":{"docs":{},"，":{"docs":{},"适":{"docs":{},"合":{"docs":{},"部":{"docs":{},"署":{"docs":{},"在":{"docs":{},"廉":{"docs":{},"价":{"docs":{},"的":{"docs":{},"机":{"docs":{},"器":{"docs":{},"上":{"docs":{"day03_Hadoop/ha2.3.html":{"ref":"day03_Hadoop/ha2.3.html","tf":0.1111111111111111}}}}}}}}}}}}}}}}}}}}},"级":{"docs":{},"源":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}},"于":{"1":{"0":{"docs":{},"%":{"docs":{},"：":{"docs":{},"往":{"docs":{},"往":{"docs":{},"会":{"docs":{},"考":{"docs":{},"虑":{"docs":{},"舍":{"docs":{},"弃":{"docs":{},"该":{"docs":{},"特":{"docs":{},"征":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}},"docs":{}},"docs":{}}},"f":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"e":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0273972602739726},"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907},"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}},":":{"docs":{},"日":{"docs":{},"志":{"docs":{},"收":{"docs":{},"集":{"docs":{},"框":{"docs":{},"架":{"docs":{"day03_Hadoop/ha4.1.html":{"ref":"day03_Hadoop/ha4.1.html","tf":0.023809523809523808}}}}}}}}},"*":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}},"：":{"docs":{},"日":{"docs":{},"志":{"docs":{},"数":{"docs":{},"据":{"docs":{},"收":{"docs":{},"集":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}}}}},"s":{"docs":{},"h":{"docs":{},"成":{"docs":{},"s":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{},"e":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}}}}}}}},"i":{"docs":{},"n":{"docs":{},"k":{"docs":{},":":{"docs":{"day03_Hadoop/ha4.1.html":{"ref":"day03_Hadoop/ha4.1.html","tf":0.023809523809523808}}}}}},"o":{"docs":{},"a":{"docs":{},"t":{"6":{"4":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}},"docs":{}},"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}},"t":{"docs":{},"y":{"docs":{},"p":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.005226480836236934}},"e":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"(":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"[":{"docs":{},"\"":{"docs":{},"p":{"docs":{},"r":{"docs":{},"i":{"docs":{},"c":{"docs":{},"e":{"docs":{},"\"":{"docs":{},"]":{"docs":{},")":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}}}}}}}}}}}}}}}}}},"a":{"docs":{},"t":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}},"会":{"docs":{},"先":{"docs":{},"执":{"docs":{},"行":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"的":{"docs":{},"操":{"docs":{},"作":{"docs":{},"，":{"docs":{},"再":{"docs":{},"将":{"docs":{},"所":{"docs":{},"有":{"docs":{},"对":{"docs":{},"象":{"docs":{},"合":{"docs":{},"并":{"docs":{},"为":{"docs":{},"一":{"docs":{},"个":{"docs":{},"对":{"docs":{},"象":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}}}}}}}}}}}}}}}}}},"和":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"的":{"docs":{},"区":{"docs":{},"别":{"docs":{},"：":{"docs":{},"f":{"docs":{},"l":{"docs":{},"a":{"docs":{},"t":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"在":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"的":{"docs":{},"基":{"docs":{},"础":{"docs":{},"上":{"docs":{},"将":{"docs":{},"结":{"docs":{},"果":{"docs":{},"合":{"docs":{},"并":{"docs":{},"到":{"docs":{},"一":{"docs":{},"个":{"docs":{},"l":{"docs":{},"i":{"docs":{},"s":{"docs":{},"t":{"docs":{},"中":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"i":{"docs":{},"l":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0056657223796034},"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"）":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}},"(":{"docs":{},"f":{"docs":{},"u":{"docs":{},"n":{"docs":{},"c":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}}},"e":{"1":{"docs":{},".":{"docs":{},"t":{"docs":{},"x":{"docs":{},"t":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}},"2":{"docs":{},".":{"docs":{},"t":{"docs":{},"x":{"docs":{},"t":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}},"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.018018018018018018},"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.02564102564102564},"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269},"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.012048192771084338},"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.043478260869565216},"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}},":":{"docs":{},"/":{"docs":{},"r":{"docs":{},"o":{"docs":{},"o":{"docs":{},"t":{"docs":{},"/":{"docs":{},"b":{"docs":{},"i":{"docs":{},"g":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"/":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},"/":{"docs":{},"t":{"docs":{},"m":{"docs":{},"p":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428}}}}}}}}}}}}}}}}}}}}}}}}},"/":{"docs":{},"/":{"docs":{},"r":{"docs":{},"o":{"docs":{},"o":{"docs":{},"t":{"docs":{},"/":{"docs":{},"b":{"docs":{},"i":{"docs":{},"g":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"/":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"/":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"_":{"docs":{},"t":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{},".":{"docs":{},"l":{"docs":{},"o":{"docs":{},"g":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"，":{"docs":{},"比":{"docs":{},"如":{"docs":{},"下":{"docs":{},"面":{"docs":{},"两":{"docs":{},"种":{"docs":{},"运":{"docs":{},"行":{"docs":{},"方":{"docs":{},"式":{"docs":{},"是":{"docs":{},"等":{"docs":{},"价":{"docs":{},"的":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}}}}}}}}}}},"=":{"docs":{},"s":{"docs":{},"y":{"docs":{},"s":{"docs":{},".":{"docs":{},"s":{"docs":{},"t":{"docs":{},"d":{"docs":{},"e":{"docs":{},"r":{"docs":{},"r":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}}}}}}}}}}},"s":{"docs":{},"y":{"docs":{},"s":{"docs":{},"t":{"docs":{},"e":{"docs":{},"m":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}}}}}}},"l":{"docs":{},"n":{"docs":{},"a":{"docs":{},"可":{"docs":{},"以":{"docs":{},"接":{"docs":{},"收":{"docs":{},"两":{"docs":{},"种":{"docs":{},"类":{"docs":{},"型":{"docs":{},"的":{"docs":{},"参":{"docs":{},"数":{"docs":{},"：":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}}}}}}}}},"n":{"docs":{},"a":{"docs":{},"l":{"docs":{},"l":{"docs":{},"y":{"docs":{},"_":{"docs":{},"s":{"docs":{},"i":{"docs":{},"m":{"docs":{},"i":{"docs":{},"l":{"docs":{},"a":{"docs":{},"r":{"docs":{},"_":{"docs":{},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"m":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.0016233766233766235}},"s":{"docs":{},".":{"docs":{},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"m":{"docs":{},"s":{"docs":{},"(":{"docs":{},")":{"docs":{},":":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.0016233766233766235}}}}}}}}}}}}}}}}}}}},"u":{"docs":{},"s":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.00487012987012987}},"e":{"docs":{},"r":{"docs":{},"s":{"docs":{},".":{"docs":{},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"m":{"docs":{},"s":{"docs":{},"(":{"docs":{},")":{"docs":{},":":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.00487012987012987}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"_":{"docs":{},"g":{"docs":{},"e":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"c":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"：":{"docs":{},"性":{"docs":{},"别":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}},":":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0010197838058331635},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}},"_":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}},"e":{"docs":{},"[":{"docs":{},"f":{"docs":{},"i":{"docs":{},"n":{"docs":{},"a":{"docs":{},"l":{"docs":{},"_":{"docs":{},"g":{"docs":{},"e":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"c":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"_":{"docs":{},"r":{"docs":{},"e":{"docs":{},"l":{"docs":{},"a":{"docs":{},"[":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"[":{"docs":{},"\"":{"docs":{},"f":{"docs":{},"i":{"docs":{},"n":{"docs":{},"a":{"docs":{},"l":{"docs":{},"_":{"docs":{},"g":{"docs":{},"e":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"c":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"\"":{"docs":{},"]":{"docs":{},")":{"docs":{},"]":{"docs":{},"]":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"\\":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}}}}}}}}}}}}}}}}}},"t":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}},"(":{"docs":{},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"f":{"docs":{},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}}},"e":{"docs":{},"l":{"docs":{},"d":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.018518518518518517},"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0022675736961451248}}}}},"r":{"docs":{},"s":{"docs":{},"t":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017},"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}},"(":{"docs":{},")":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}},"e":{"docs":{},"w":{"docs":{},"a":{"docs":{},"l":{"docs":{},"l":{"docs":{},"d":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}}}}}}}},"(":{"docs":{},"b":{"docs":{},"_":{"docs":{},"u":{"docs":{},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}},"u":{"docs":{},"n":{"docs":{},"k":{"docs":{},"s":{"docs":{},"v":{"docs":{},"d":{"docs":{},"（":{"docs":{},"l":{"docs":{},"f":{"docs":{},"m":{"docs":{},"）":{"docs":{"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html":{"ref":"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html","tf":0.037037037037037035}}}}}}}}}}},"c":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.007782101167315175},"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.010526315789473684},"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.024096385542168676},"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}},"（":{"docs":{},"有":{"docs":{},"两":{"docs":{},"个":{"docs":{},"参":{"docs":{},"数":{"docs":{},"）":{"docs":{},"先":{"docs":{},"对":{"docs":{},"集":{"docs":{},"合":{"docs":{},"中":{"docs":{},"的":{"docs":{},"第":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}}}}}}}}}}}}}},"s":{"docs":{},";":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}},"）":{"docs":{},"。":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}},"o":{"docs":{},"o":{"docs":{},"l":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}}}},":":{"docs":{},"正":{"docs":{},"向":{"docs":{},"操":{"docs":{},"作":{"docs":{},"，":{"docs":{},"类":{"docs":{},"似":{"docs":{},"于":{"docs":{},"u":{"docs":{},"p":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"b":{"docs":{},"y":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"a":{"docs":{},"c":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}},"e":{"docs":{},"b":{"docs":{},"o":{"docs":{},"o":{"docs":{},"k":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}},"推":{"docs":{},"出":{"docs":{},"h":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}}}}}}}},"l":{"docs":{},"s":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}},"e":{"docs":{},")":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},":":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}},"|":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.01598173515981735}},"f":{"docs":{},"a":{"docs":{},"l":{"docs":{},"s":{"docs":{},"e":{"docs":{},"|":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.0136986301369863}}}}}}}}}}}},"m":{"docs":{},"i":{"docs":{},"l":{"docs":{},"i":{"docs":{"Hive&HBase/06_hbase数据模型.html":{"ref":"Hive&HBase/06_hbase数据模型.html","tf":0.020833333333333332},"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}},"y":{"docs":{},":":{"docs":{"Hive&HBase/06_hbase数据模型.html":{"ref":"Hive&HBase/06_hbase数据模型.html","tf":0.020833333333333332}}},"。":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}},"会":{"docs":{},"更":{"docs":{},"高":{"docs":{},"效":{"docs":{},"。":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}},"就":{"docs":{},"是":{"docs":{},"一":{"docs":{},"个":{"docs":{},"集":{"docs":{},"中":{"docs":{},"的":{"docs":{},"存":{"docs":{},"储":{"docs":{},"单":{"docs":{},"元":{"docs":{},"，":{"docs":{},"故":{"docs":{},"将":{"docs":{},"具":{"docs":{},"有":{"docs":{},"相":{"docs":{},"同":{"docs":{},"i":{"docs":{},"o":{"docs":{},"特":{"docs":{},"性":{"docs":{},"的":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{},"放":{"docs":{},"在":{"docs":{},"一":{"docs":{},"个":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"的":{"docs":{},"存":{"docs":{},"储":{"docs":{},"，":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}}}}}},"，":{"docs":{},"就":{"docs":{},"只":{"docs":{},"有":{"docs":{},"一":{"docs":{},"个":{"docs":{},"s":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{},"e":{"docs":{},"。":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}}}}}}}}}}}},"v":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}},":":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}},"=":{"docs":{},"n":{"docs":{},"o":{"docs":{},"n":{"docs":{},"e":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}},"r":{"docs":{},"e":{"docs":{},"q":{"docs":{},"u":{"docs":{},"e":{"docs":{},"n":{"docs":{},"c":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}},"y":{"docs":{},"，":{"docs":{},"i":{"docs":{},"d":{"docs":{},"f":{"docs":{},"）":{"docs":{},"两":{"docs":{},"部":{"docs":{},"分":{"docs":{},"，":{"docs":{},"由":{"docs":{},"t":{"docs":{},"f":{"docs":{},"和":{"docs":{},"i":{"docs":{},"d":{"docs":{},"f":{"docs":{},"的":{"docs":{},"乘":{"docs":{},"积":{"docs":{},"来":{"docs":{},"设":{"docs":{},"置":{"docs":{},"文":{"docs":{},"档":{"docs":{},"词":{"docs":{},"语":{"docs":{},"的":{"docs":{},"权":{"docs":{},"重":{"docs":{},"。":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}}}}}}}}}}}},"的":{"docs":{},"乘":{"docs":{},"积":{"docs":{},"。":{"docs":{},"t":{"docs":{},"f":{"docs":{},"指":{"docs":{},"的":{"docs":{},"是":{"docs":{},"某":{"docs":{},"一":{"docs":{},"个":{"docs":{},"给":{"docs":{},"定":{"docs":{},"的":{"docs":{},"词":{"docs":{},"语":{"docs":{},"在":{"docs":{},"该":{"docs":{},"文":{"docs":{},"件":{"docs":{},"中":{"docs":{},"出":{"docs":{},"现":{"docs":{},"的":{"docs":{},"次":{"docs":{},"数":{"docs":{},"。":{"docs":{},"这":{"docs":{},"个":{"docs":{},"数":{"docs":{},"字":{"docs":{},"通":{"docs":{},"常":{"docs":{},"会":{"docs":{},"被":{"docs":{},"正":{"docs":{},"规":{"docs":{},"化":{"docs":{},"，":{"docs":{},"以":{"docs":{},"防":{"docs":{},"止":{"docs":{},"它":{"docs":{},"偏":{"docs":{},"向":{"docs":{},"长":{"docs":{},"的":{"docs":{},"文":{"docs":{},"件":{"docs":{},"（":{"docs":{},"同":{"docs":{},"一":{"docs":{},"个":{"docs":{},"词":{"docs":{},"语":{"docs":{},"在":{"docs":{},"长":{"docs":{},"文":{"docs":{},"件":{"docs":{},"里":{"docs":{},"可":{"docs":{},"能":{"docs":{},"会":{"docs":{},"比":{"docs":{},"短":{"docs":{},"文":{"docs":{},"件":{"docs":{},"有":{"docs":{},"更":{"docs":{},"高":{"docs":{},"的":{"docs":{},"词":{"docs":{},"频":{"docs":{},"，":{"docs":{},"而":{"docs":{},"不":{"docs":{},"管":{"docs":{},"该":{"docs":{},"词":{"docs":{},"语":{"docs":{},"重":{"docs":{},"要":{"docs":{},"与":{"docs":{},"否":{"docs":{},"）":{"docs":{},"。":{"docs":{},"i":{"docs":{},"d":{"docs":{},"f":{"docs":{},"是":{"docs":{},"一":{"docs":{},"个":{"docs":{},"词":{"docs":{},"语":{"docs":{},"普":{"docs":{},"遍":{"docs":{},"重":{"docs":{},"要":{"docs":{},"性":{"docs":{},"的":{"docs":{},"度":{"docs":{},"量":{"docs":{},"，":{"docs":{},"某":{"docs":{},"一":{"docs":{},"特":{"docs":{},"定":{"docs":{},"词":{"docs":{},"语":{"docs":{},"的":{"docs":{},"i":{"docs":{},"d":{"docs":{},"f":{"docs":{},"，":{"docs":{},"可":{"docs":{},"以":{"docs":{},"由":{"docs":{},"总":{"docs":{},"文":{"docs":{},"件":{"docs":{},"数":{"docs":{},"目":{"docs":{},"除":{"docs":{},"以":{"docs":{},"包":{"docs":{},"含":{"docs":{},"该":{"docs":{},"词":{"docs":{},"语":{"docs":{},"之":{"docs":{},"文":{"docs":{},"件":{"docs":{},"的":{"docs":{},"数":{"docs":{},"目":{"docs":{},"，":{"docs":{},"再":{"docs":{},"将":{"docs":{},"得":{"docs":{},"到":{"docs":{},"的":{"docs":{},"商":{"docs":{},"取":{"docs":{},"对":{"docs":{},"数":{"docs":{},"得":{"docs":{},"到":{"docs":{},"。":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"t":{"docs":{},"f":{"docs":{},"）":{"docs":{},"和":{"docs":{},"逆":{"docs":{},"转":{"docs":{},"文":{"docs":{},"档":{"docs":{},"频":{"docs":{},"率":{"docs":{},"（":{"docs":{},"i":{"docs":{},"n":{"docs":{},"v":{"docs":{},"e":{"docs":{},"r":{"docs":{},"s":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876}}}}}}}}}}}}}}}}}}}},")":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"w":{"docs":{},"o":{"docs":{},"r":{"docs":{},"k":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282}}}}}}}}},"o":{"docs":{},"m":{"docs":{},"(":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.003401360544217687}}}}},"i":{"docs":{},"e":{"docs":{},"n":{"docs":{},"d":{"docs":{},"l":{"docs":{},"i":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}}}}},"s":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.11875},"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196},"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.007407407407407408},"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.012048192771084338},"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0022675736961451248}},".":{"docs":{},"d":{"docs":{},"e":{"docs":{},"f":{"docs":{},"a":{"docs":{},"u":{"docs":{},"l":{"docs":{},"t":{"docs":{},"f":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428}}}}}}}}}}}},"o":{"docs":{},"r":{"docs":{},"m":{"docs":{},"a":{"docs":{},"t":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428},"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.022222222222222223},"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0022675736961451248}}}},"u":{"docs":{},"l":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}},"e":{"docs":{},"a":{"docs":{},"c":{"docs":{},"h":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{},"i":{"docs":{},"t":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}},"i":{"docs":{},"o":{"docs":{},"n":{"2":{"docs":{},"(":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{},"i":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},")":{"docs":{},":":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}},"docs":{},"(":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{},"i":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},")":{"docs":{},":":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}}}}}}}}},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}},"o":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.01276595744680851}}}},"n":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965},"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},"'":{"docs":{},"*":{"docs":{},"'":{"docs":{},")":{"docs":{},")":{"docs":{},")":{"docs":{},".":{"docs":{},"a":{"docs":{},"l":{"docs":{},"i":{"docs":{},"a":{"docs":{},"s":{"docs":{},"(":{"docs":{},"c":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}}}}}}}}}}}}}}},"\"":{"docs":{},",":{"docs":{},"\"":{"docs":{},"h":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.004545454545454545}}}}}},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}},"e":{"docs":{},"_":{"docs":{},"d":{"docs":{},"f":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},".":{"docs":{},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"s":{"docs":{},"_":{"docs":{},"f":{"docs":{},"r":{"docs":{},"o":{"docs":{},"m":{"docs":{},"_":{"docs":{},"a":{"docs":{},"d":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"u":{"docs":{},"s":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}},"s":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.004228329809725159}}},":":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}},".":{"docs":{},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"m":{"docs":{},"s":{"docs":{},"(":{"docs":{},")":{"docs":{},":":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}}}},"u":{"docs":{},"p":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"(":{"docs":{},"a":{"docs":{},"d":{"docs":{},"_":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"e":{"docs":{},")":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"e":{"docs":{},")":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}}}}}}}}}}}}}}}}},"[":{"docs":{},"k":{"docs":{},"]":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}},"_":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}}}}}}}},"k":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}},"a":{"docs":{},"f":{"docs":{},"k":{"docs":{},"a":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863},"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907},"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}},":":{"docs":{"day03_Hadoop/ha4.1.html":{"ref":"day03_Hadoop/ha4.1.html","tf":0.023809523809523808}}},"：":{"docs":{},"实":{"docs":{},"时":{"docs":{},"日":{"docs":{},"志":{"docs":{},"数":{"docs":{},"据":{"docs":{},"处":{"docs":{},"理":{"docs":{},"队":{"docs":{},"列":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}}}}}}}}}}},"e":{"docs":{},"y":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}},"=":{"docs":{},"l":{"docs":{},"a":{"docs":{},"m":{"docs":{},"b":{"docs":{},"d":{"docs":{},"a":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247},"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876},"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}}}}}}}}},":":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"形":{"docs":{},"式":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}},"并":{"docs":{},"按":{"docs":{},"用":{"docs":{},"户":{"docs":{},"聚":{"docs":{},"合":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}}}}}}}}}}}},"_":{"docs":{},"w":{"docs":{},"o":{"docs":{},"r":{"docs":{},"d":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0022675736961451248}}}}}}},"w":{"docs":{},"o":{"docs":{},"r":{"docs":{},"d":{"docs":{},",":{"docs":{},"w":{"docs":{},"e":{"docs":{},"i":{"docs":{},"g":{"docs":{},"h":{"docs":{},"t":{"docs":{},";":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}}}}}}}}}},")":{"docs":{},"来":{"docs":{},"进":{"docs":{},"行":{"docs":{},"唯":{"docs":{},"一":{"docs":{},"标":{"docs":{},"识":{"docs":{},"的":{"docs":{},",":{"docs":{"Hive&HBase/06_hbase数据模型.html":{"ref":"Hive&HBase/06_hbase数据模型.html","tf":0.020833333333333332}}}}}}}}}}}},",":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0169971671388102}}}}}}},"f":{"docs":{},"u":{"docs":{},"n":{"docs":{},"c":{"docs":{},"=":{"docs":{},">":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}},"l":{"docs":{},"a":{"docs":{},"m":{"docs":{},"b":{"docs":{},"d":{"docs":{},"a":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}}}}}}},"p":{"docs":{},"i":{"docs":{},"压":{"docs":{},"力":{"docs":{},"大":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}}},"值":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}},"个":{"docs":{},"词":{"docs":{},"，":{"docs":{},"这":{"docs":{},"里":{"docs":{},"k":{"docs":{},"设":{"docs":{},"为":{"1":{"0":{"0":{"docs":{},"，":{"docs":{},"作":{"docs":{},"为":{"docs":{},"用":{"docs":{},"户":{"docs":{},"的":{"docs":{},"标":{"docs":{},"签":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}}}}}}}}},"docs":{}},"docs":{}},"docs":{}}}}}}}}},"i":{"docs":{},"l":{"docs":{},"l":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}},"w":{"1":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.021541950113378686}},":":{"1":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.003401360544217687}},",":{"docs":{},"k":{"docs":{},"w":{"3":{"docs":{},":":{"1":{"docs":{},",":{"docs":{},"k":{"docs":{},"w":{"4":{"docs":{},":":{"1":{"docs":{},",":{"docs":{},"k":{"docs":{},"w":{"5":{"docs":{},":":{"1":{"docs":{},",":{"docs":{},"k":{"docs":{},"w":{"6":{"docs":{},":":{"1":{"docs":{},",":{"docs":{},"k":{"docs":{},"w":{"7":{"docs":{},":":{"2":{"docs":{},",":{"docs":{},"k":{"docs":{},"w":{"9":{"docs":{},":":{"1":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}},"docs":{}}},"docs":{}}}}},"docs":{}}},"docs":{}}}}},"docs":{}}},"docs":{}}}}},"docs":{}}},"docs":{}}}}},"docs":{}}},"6":{"docs":{},":":{"1":{"docs":{},",":{"docs":{},"k":{"docs":{},"w":{"7":{"docs":{},":":{"1":{"docs":{},",":{"docs":{},"k":{"docs":{},"w":{"8":{"docs":{},":":{"1":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}},"docs":{}}},"docs":{}}}}},"docs":{}}},"docs":{}}}}},"docs":{}}},"docs":{}}}}},"docs":{}}},"4":{"docs":{},":":{"1":{"docs":{},",":{"docs":{},"k":{"docs":{},"w":{"5":{"docs":{},":":{"1":{"docs":{},",":{"docs":{},"k":{"docs":{},"w":{"7":{"docs":{},":":{"1":{"docs":{},",":{"docs":{},"k":{"docs":{},"w":{"9":{"docs":{},":":{"1":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}},"docs":{}}},"docs":{}}}}},"docs":{}}},"docs":{}}}}},"docs":{}}},"docs":{}}}}},"docs":{}}},"docs":{}}}}},"4":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}},",":{"docs":{},"k":{"docs":{},"w":{"4":{"docs":{},":":{"1":{"docs":{},",":{"docs":{},"k":{"docs":{},"w":{"5":{"docs":{},":":{"1":{"docs":{},",":{"docs":{},"k":{"docs":{},"w":{"8":{"docs":{},":":{"3":{"docs":{},",":{"docs":{},"k":{"docs":{},"w":{"9":{"docs":{},":":{"1":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}},"docs":{}}},"docs":{}}}}},"docs":{}}},"docs":{}}}}},"docs":{}}},"docs":{}}}}},"docs":{}}},"docs":{}}}}},"docs":{}}},"3":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.015873015873015872}},":":{"1":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0045351473922902496}}},"docs":{}}},"4":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.012471655328798186}},":":{"1":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.003401360544217687}}},"docs":{}}},"5":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.012471655328798186}},":":{"1":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.003401360544217687}}},"docs":{}}},"6":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.015873015873015872}},":":{"1":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0045351473922902496}}},"docs":{}}},"7":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.013605442176870748}},":":{"1":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0022675736961451248}}},"2":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}},"docs":{}}},"8":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.011337868480725623}},":":{"1":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}},"3":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}},"docs":{}}},"9":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.012471655328798186}},":":{"1":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.003401360544217687}}},"docs":{}}},"docs":{},")":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.006802721088435374}}},";":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0022675736961451248}}},"_":{"docs":{},"w":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0045351473922902496}}}}},".":{"docs":{},"l":{"docs":{},"o":{"docs":{},"w":{"docs":{},"e":{"docs":{},"r":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}}}}}}}}}}}}},":":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}},"u":{"docs":{},"r":{"docs":{},"t":{"docs":{},"o":{"docs":{},"s":{"docs":{},"i":{"docs":{},"s":{"docs":{},"(":{"docs":{},")":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}},",":{"docs":{},"v":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}},"l":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}},"a":{"docs":{},"m":{"docs":{},"b":{"docs":{},"d":{"docs":{},"a":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876},"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842},"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.007633587786259542},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}},"架":{"docs":{},"构":{"docs":{},"图":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}},"是":{"docs":{},"由":{"docs":{},"实":{"docs":{},"时":{"docs":{},"大":{"docs":{},"数":{"docs":{},"据":{"docs":{},"处":{"docs":{},"理":{"docs":{},"框":{"docs":{},"架":{"docs":{},"s":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{},"m":{"docs":{},"的":{"docs":{},"作":{"docs":{},"者":{"docs":{},"n":{"docs":{},"a":{"docs":{},"t":{"docs":{},"h":{"docs":{},"a":{"docs":{},"n":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}}}}}}}}}}}}}}}}}}}}}}}}}},"的":{"docs":{},"将":{"docs":{},"离":{"docs":{},"线":{"docs":{},"计":{"docs":{},"算":{"docs":{},"和":{"docs":{},"实":{"docs":{},"时":{"docs":{},"计":{"docs":{},"算":{"docs":{},"整":{"docs":{},"合":{"docs":{},"，":{"docs":{},"设":{"docs":{},"计":{"docs":{},"出":{"docs":{},"一":{"docs":{},"个":{"docs":{},"能":{"docs":{},"满":{"docs":{},"足":{"docs":{},"实":{"docs":{},"时":{"docs":{},"大":{"docs":{},"数":{"docs":{},"据":{"docs":{},"系":{"docs":{},"统":{"docs":{},"关":{"docs":{},"键":{"docs":{},"特":{"docs":{},"性":{"docs":{},"的":{"docs":{},"架":{"docs":{},"构":{"docs":{},"，":{"docs":{},"包":{"docs":{},"括":{"docs":{},"有":{"docs":{},"：":{"docs":{},"高":{"docs":{},"容":{"docs":{},"错":{"docs":{},"、":{"docs":{},"低":{"docs":{},"延":{"docs":{},"时":{"docs":{},"和":{"docs":{},"可":{"docs":{},"扩":{"docs":{},"展":{"docs":{},"等":{"docs":{},"。":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"t":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.006493506493506494},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002858776443682104},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.003795066413662239},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147},"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.005836575875486381},"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842},"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{},"中":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}},"r":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.012471655328798186}}}}},"r":{"docs":{},"g":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009},"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282}}}},"b":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00851063829787234}},"e":{"docs":{},"l":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0022435243728329596}},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}},"/":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"p":{"docs":{},"o":{"docs":{},"n":{"docs":{},"s":{"docs":{},"e":{"docs":{},".":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}},"e":{"docs":{},"d":{"docs":{},"p":{"docs":{},"o":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0008158270446665307}},"(":{"0":{"docs":{},".":{"0":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}}},"1":{"docs":{},".":{"0":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}}},"docs":{}},".":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}},"。":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}},"目":{"docs":{},"标":{"docs":{},"值":{"docs":{},"字":{"docs":{},"段":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}},"n":{"docs":{},"g":{"docs":{},"u":{"docs":{},"a":{"docs":{},"g":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}}}}}}},"z":{"docs":{},"i":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.006993006993006993}}}},"s":{"docs":{},"t":{"docs":{},"_":{"docs":{},"s":{"docs":{},"u":{"docs":{},"m":{"docs":{},")":{"docs":{},":":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}}},"i":{"docs":{},"s":{"docs":{},"t":{"docs":{"day03_Hadoop/ha3.2.html":{"ref":"day03_Hadoop/ha3.2.html","tf":0.061224489795918366}},"(":{"docs":{},"_":{"docs":{},"d":{"docs":{},"f":{"docs":{},"_":{"docs":{},"s":{"docs":{},"o":{"docs":{},"r":{"docs":{},"t":{"docs":{},"e":{"docs":{},"d":{"docs":{},".":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{},"[":{"docs":{},":":{"2":{"docs":{},"]":{"docs":{},")":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147}}}}},"docs":{}}}}}}}}}}}}}}}}}}},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}},"a":{"1":{"docs":{},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}},"2":{"docs":{},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}},"docs":{}},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{},"[":{"docs":{},"_":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{},":":{"docs":{},"]":{"docs":{},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}}}}}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"r":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"_":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},".":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}},".":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"s":{"docs":{},"[":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{},":":{"docs":{},"]":{"docs":{},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"z":{"docs":{},"i":{"docs":{},"p":{"docs":{},"(":{"docs":{},"a":{"docs":{},",":{"docs":{},"c":{"docs":{},")":{"docs":{},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}},"p":{"docs":{},"e":{"docs":{},"d":{"docs":{},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}},"x":{"docs":{},")":{"docs":{},"+":{"docs":{},"l":{"docs":{},"i":{"docs":{},"s":{"docs":{},"t":{"docs":{},"(":{"docs":{},"y":{"docs":{},")":{"docs":{},",":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}}}}}}}}}}},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"u":{"docs":{},"l":{"docs":{},"t":{"docs":{},"[":{"2":{"docs":{},"]":{"docs":{},"[":{"1":{"docs":{},"]":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}},"docs":{}}}},"docs":{}}}}}}}}},"_":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"c":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}},"e":{"docs":{},"_":{"docs":{},"t":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}}}}}}},"n":{"docs":{},"e":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876},"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196},"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.007407407407407408},"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.008032128514056224},"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.007633587786259542}},")":{"docs":{},":":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.011695906432748537}}}},".":{"docs":{},"s":{"docs":{},"p":{"docs":{},"l":{"docs":{},"i":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},":":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}},"'":{"docs":{},"\\":{"docs":{},"t":{"docs":{},"'":{"docs":{},")":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}},"\"":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102},"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577},"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}},"t":{"docs":{},"r":{"docs":{},"i":{"docs":{},"p":{"docs":{},"(":{"docs":{},")":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269},"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}},".":{"docs":{},"s":{"docs":{},"p":{"docs":{},"l":{"docs":{},"i":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},":":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}}}}}}}}}}}}},":":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102},"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577},"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}},"s":{"docs":{},".":{"docs":{},"f":{"docs":{},"l":{"docs":{},"a":{"docs":{},"t":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"(":{"docs":{},"l":{"docs":{},"a":{"docs":{},"m":{"docs":{},"b":{"docs":{},"d":{"docs":{},"a":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}}}}}}}},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"(":{"docs":{},"g":{"docs":{},"e":{"docs":{},"t":{"docs":{},"_":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"r":{"docs":{},"y":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}}}}}}}}}}}}}}}}},"u":{"docs":{},"x":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}},"b":{"docs":{},"/":{"docs":{},"h":{"docs":{},"i":{"docs":{},"v":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}},"r":{"docs":{},"a":{"docs":{},"r":{"docs":{},"i":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0056657223796034},"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}},"m":{"docs":{},"i":{"docs":{},"t":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.004405286343612335}}}}}},"o":{"docs":{},"g":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734},"day03_Hadoop/ha2.1.html":{"ref":"day03_Hadoop/ha2.1.html","tf":0.0410958904109589},"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00851063829787234}},"，":{"docs":{},"先":{"docs":{},"写":{"docs":{},"l":{"docs":{},"o":{"docs":{},"g":{"docs":{},"，":{"docs":{},"再":{"docs":{},"写":{"docs":{},"内":{"docs":{},"存":{"docs":{},"，":{"docs":{},"因":{"docs":{},"为":{"docs":{},"e":{"docs":{},"d":{"docs":{},"i":{"docs":{},"t":{"docs":{},"l":{"docs":{},"o":{"docs":{},"g":{"docs":{},"记":{"docs":{},"录":{"docs":{},"的":{"docs":{},"是":{"docs":{},"最":{"docs":{},"新":{"docs":{},"的":{"docs":{},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},"客":{"docs":{},"户":{"docs":{},"端":{"docs":{},"执":{"docs":{},"行":{"docs":{},"所":{"docs":{},"有":{"docs":{},"的":{"docs":{},"写":{"docs":{},"操":{"docs":{},"作":{"docs":{},"。":{"docs":{},"如":{"docs":{},"果":{"docs":{},"后":{"docs":{},"续":{"docs":{},"真":{"docs":{},"实":{"docs":{},"写":{"docs":{},"操":{"docs":{},"作":{"docs":{},"失":{"docs":{},"败":{"docs":{},"了":{"docs":{},"，":{"docs":{},"由":{"docs":{},"于":{"docs":{},"在":{"docs":{},"真":{"docs":{},"实":{"docs":{},"写":{"docs":{},"操":{"docs":{},"作":{"docs":{},"之":{"docs":{},"前":{"docs":{},"，":{"docs":{},"操":{"docs":{},"作":{"docs":{},"就":{"docs":{},"被":{"docs":{},"写":{"docs":{},"入":{"docs":{},"e":{"docs":{},"d":{"docs":{},"i":{"docs":{},"t":{"docs":{},"l":{"docs":{},"o":{"docs":{},"g":{"docs":{},"中":{"docs":{},"了":{"docs":{},"，":{"docs":{},"故":{"docs":{},"e":{"docs":{},"d":{"docs":{},"i":{"docs":{},"t":{"docs":{},"l":{"docs":{},"o":{"docs":{},"g":{"docs":{},"中":{"docs":{},"仍":{"docs":{},"会":{"docs":{},"有":{"docs":{},"记":{"docs":{},"录":{"docs":{},"，":{"docs":{},"我":{"docs":{},"们":{"docs":{},"不":{"docs":{},"用":{"docs":{},"担":{"docs":{},"心":{"docs":{},"后":{"docs":{},"续":{"docs":{},"c":{"docs":{},"l":{"docs":{},"i":{"docs":{},"e":{"docs":{},"n":{"docs":{},"t":{"docs":{},"读":{"docs":{},"不":{"docs":{},"到":{"docs":{},"相":{"docs":{},"应":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"块":{"docs":{},"，":{"docs":{},"因":{"docs":{},"为":{"docs":{},"在":{"docs":{},"第":{"5":{"docs":{},"步":{"docs":{},"中":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"n":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"收":{"docs":{},"到":{"docs":{},"块":{"docs":{},"后":{"docs":{},"会":{"docs":{},"有":{"docs":{},"一":{"docs":{},"返":{"docs":{},"回":{"docs":{},"确":{"docs":{},"认":{"docs":{},"信":{"docs":{},"息":{"docs":{},"，":{"docs":{},"若":{"docs":{},"没":{"docs":{},"写":{"docs":{},"成":{"docs":{},"功":{"docs":{},"，":{"docs":{},"发":{"docs":{},"送":{"docs":{},"端":{"docs":{},"没":{"docs":{},"收":{"docs":{},"到":{"docs":{},"确":{"docs":{},"认":{"docs":{},"信":{"docs":{},"息":{"docs":{},"，":{"docs":{},"会":{"docs":{},"一":{"docs":{},"直":{"docs":{},"重":{"docs":{},"试":{"docs":{},"，":{"docs":{},"直":{"docs":{},"到":{"docs":{},"成":{"docs":{},"功":{"docs":{},"）":{"docs":{"day03_Hadoop/ha4.2.html":{"ref":"day03_Hadoop/ha4.2.html","tf":0.0196078431372549}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"i":{"docs":{},"s":{"docs":{},"t":{"docs":{},"i":{"docs":{},"c":{"docs":{},"r":{"docs":{},"e":{"docs":{},"g":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"s":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"m":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"l":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}},".":{"docs":{},"l":{"docs":{},"o":{"docs":{},"a":{"docs":{},"d":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},":":{"docs":{},"/":{"docs":{},"/":{"docs":{},"l":{"docs":{},"o":{"docs":{},"c":{"docs":{},"a":{"docs":{},"l":{"docs":{},"h":{"docs":{},"o":{"docs":{},"s":{"docs":{},"t":{"docs":{},":":{"9":{"0":{"0":{"0":{"docs":{},"/":{"docs":{},"m":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"l":{"docs":{},"s":{"docs":{},"/":{"docs":{},"c":{"docs":{},"t":{"docs":{},"r":{"docs":{},"m":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"l":{"docs":{},"_":{"docs":{},"n":{"docs":{},"o":{"docs":{},"r":{"docs":{},"m":{"docs":{},"a":{"docs":{},"l":{"docs":{},".":{"docs":{},"o":{"docs":{},"b":{"docs":{},"j":{"docs":{},"\"":{"docs":{},")":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}},"a":{"docs":{},"l":{"docs":{},"l":{"docs":{},"o":{"docs":{},"n":{"docs":{},"e":{"docs":{},"h":{"docs":{},"o":{"docs":{},"t":{"docs":{},".":{"docs":{},"o":{"docs":{},"b":{"docs":{},"j":{"docs":{},"\"":{"docs":{},")":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"c":{"docs":{},"a":{"docs":{},"l":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269},"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196},"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.011111111111111112},"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},"h":{"docs":{},"o":{"docs":{},"s":{"docs":{},"t":{"docs":{},":":{"docs":{"day03_Hadoop/ha2.1.html":{"ref":"day03_Hadoop/ha2.1.html","tf":0.0136986301369863}}}}}}},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.0125}},"e":{"1":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625}}},"2":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625}}},"docs":{}}}}},")":{"docs":{},"方":{"docs":{},"式":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}},"模":{"docs":{},"式":{"docs":{},"的":{"docs":{},"启":{"docs":{},"动":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}}}}},"t":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704},"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0022675736961451248}}}}},"a":{"docs":{},"d":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.011111111111111112},"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617},"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}},"n":{"docs":{},"g":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.004078303425774877},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}},"t":{"docs":{},"y":{"docs":{},"p":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}},"e":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}},"t":{"docs":{},"r":{"docs":{},"u":{"docs":{},"e":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}}}}}},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}},"e":{"docs":{},"n":{"docs":{},"(":{"docs":{},"i":{"docs":{},"i":{"docs":{},"d":{"docs":{},"s":{"docs":{},")":{"docs":{},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0017152658662092624}}}}}}}},"u":{"docs":{},"i":{"docs":{},"d":{"docs":{},"s":{"docs":{},")":{"docs":{},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0017152658662092624}}}}}}}},"s":{"docs":{},"y":{"docs":{},"s":{"docs":{},".":{"docs":{},"a":{"docs":{},"r":{"docs":{},"g":{"docs":{},"v":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}}}}}}}},"r":{"docs":{},"e":{"docs":{},"t":{"docs":{},")":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289}}}}}}},"g":{"docs":{},"t":{"docs":{},"h":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.00686106346483705}},")":{"docs":{},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683}}}},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683}}},":":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}},"f":{"docs":{},"t":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.006802721088435374}}}},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00851063829787234}}}}},"s":{"docs":{},"s":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}}}},"a":{"docs":{},"r":{"docs":{},"n":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}},"f":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.011385199240986717},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.004629629629629629}},"m":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.003795066413662239}},"(":{"0":{"docs":{},".":{"0":{"2":{"docs":{},",":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}},"docs":{}},"docs":{}}},"docs":{},"l":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"n":{"docs":{},"t":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}}},"o":{"docs":{},"b":{"docs":{},"j":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},")":{"docs":{},":":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}}}}}},".":{"docs":{},"f":{"docs":{},"i":{"docs":{},"t":{"docs":{},"(":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},")":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}}}}}}}}}},"也":{"docs":{},"就":{"docs":{},"是":{"docs":{},"前":{"docs":{},"面":{"docs":{},"提":{"docs":{},"到":{"docs":{},"的":{"docs":{},"f":{"docs":{},"u":{"docs":{},"n":{"docs":{},"k":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}}}}}}}}},"原":{"docs":{},"理":{"docs":{},"解":{"docs":{},"析":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}},"矩":{"docs":{},"阵":{"docs":{},"，":{"docs":{},"即":{"docs":{},"用":{"docs":{},"户":{"docs":{},"和":{"docs":{},"隐":{"docs":{},"含":{"docs":{},"特":{"docs":{},"征":{"docs":{},"矩":{"docs":{},"阵":{"docs":{},"。":{"docs":{},"l":{"docs":{},"f":{"docs":{},"有":{"docs":{},"三":{"docs":{},"个":{"docs":{},"，":{"docs":{},"表":{"docs":{},"示":{"docs":{},"共":{"docs":{},"总":{"docs":{},"有":{"docs":{},"三":{"docs":{},"个":{"docs":{},"隐":{"docs":{},"含":{"docs":{},"特":{"docs":{},"征":{"docs":{},"。":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"s":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.01875}}},"_":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}},")":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}}},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}},")":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}},".":{"docs":{},"u":{"docs":{},"p":{"docs":{},"p":{"docs":{},"e":{"docs":{},"r":{"docs":{},"(":{"docs":{},")":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}}}}}}}}},"k":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099}}},"r":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}},".":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"l":{"docs":{},"a":{"docs":{},"b":{"docs":{},"e":{"docs":{},"l":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"c":{"docs":{},"l":{"docs":{},"k":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"f":{"docs":{},"i":{"docs":{},"t":{"docs":{},"(":{"docs":{},"t":{"docs":{},"r":{"docs":{},"a":{"docs":{},"i":{"docs":{},"n":{"docs":{},"_":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"s":{"docs":{},"_":{"1":{"docs":{},")":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"实":{"docs":{},"现":{"docs":{},"c":{"docs":{},"t":{"docs":{},"r":{"docs":{},"预":{"docs":{},"估":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}},"n":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.015789473684210527}},"o":{"docs":{},"s":{"docs":{},"q":{"docs":{},"l":{"docs":{},"(":{"docs":{},"h":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"/":{"docs":{},"c":{"docs":{},"a":{"docs":{},"s":{"docs":{},"s":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"r":{"docs":{},"a":{"docs":{},")":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}}}}}}}}}}}}}}}}}}}}},"d":{"docs":{},"e":{"docs":{},"m":{"docs":{},"a":{"docs":{},"n":{"docs":{},"a":{"docs":{},"g":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.017094017094017096}},"e":{"docs":{},"r":{"docs":{},":":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}},"：":{"docs":{},"由":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"o":{"docs":{},"u":{"docs":{},"r":{"docs":{},"c":{"docs":{},"e":{"docs":{},"m":{"docs":{},"a":{"docs":{},"n":{"docs":{},"a":{"docs":{},"g":{"docs":{},"e":{"docs":{},"r":{"docs":{},"指":{"docs":{},"派":{"docs":{},"任":{"docs":{},"务":{"docs":{},"，":{"docs":{},"定":{"docs":{},"期":{"docs":{},"向":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"o":{"docs":{},"u":{"docs":{},"r":{"docs":{},"c":{"docs":{},"e":{"docs":{},"m":{"docs":{},"a":{"docs":{},"n":{"docs":{},"a":{"docs":{},"g":{"docs":{},"e":{"docs":{},"r":{"docs":{},"汇":{"docs":{},"报":{"docs":{},"状":{"docs":{},"态":{"docs":{"day03_Hadoop/ha3.5.html":{"ref":"day03_Hadoop/ha3.5.html","tf":0.030303030303030304}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"n":{"docs":{},"e":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00684931506849315}},"(":{"docs":{},"s":{"docs":{},"u":{"docs":{},"m":{"docs":{},"(":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"s":{"docs":{},")":{"docs":{},",":{"docs":{},"w":{"docs":{},"o":{"docs":{},"r":{"docs":{},"d":{"docs":{},")":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}}}}}}}}}}}}}}},")":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.01141552511415525}}}},":":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}},"c":{"docs":{},"l":{"docs":{},"k":{"docs":{},":":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}},"和":{"docs":{},"c":{"docs":{},"l":{"docs":{},"k":{"docs":{},"在":{"docs":{},"这":{"docs":{},"里":{"docs":{},"是":{"docs":{},"作":{"docs":{},"为":{"docs":{},"目":{"docs":{},"标":{"docs":{},"值":{"docs":{},"，":{"docs":{},"不":{"docs":{},"做":{"docs":{},"为":{"docs":{},"特":{"docs":{},"征":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}},"w":{"docs":{},".":{"docs":{},"'":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}},"v":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00851063829787234}}},"t":{"docs":{},"_":{"docs":{},"e":{"docs":{},"x":{"docs":{},"i":{"docs":{},"s":{"docs":{},"t":{"docs":{},"_":{"docs":{},"c":{"docs":{},"l":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}},"s":{"docs":{},":":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}},"c":{"docs":{},"l":{"docs":{},"k":{"docs":{},"：":{"docs":{},"为":{"1":{"docs":{},"代":{"docs":{},"表":{"docs":{},"没":{"docs":{},"有":{"docs":{},"点":{"docs":{},"击":{"docs":{},"；":{"docs":{},"为":{"0":{"docs":{},"代":{"docs":{},"表":{"docs":{},"点":{"docs":{},"击":{"docs":{},"；":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}},"docs":{}}}}}}}}}},"docs":{}}}}}}},"p":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147},"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002858776443682104},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.003795066413662239},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147},"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.005836575875486381},"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289}},".":{"docs":{},"f":{"docs":{},"l":{"docs":{},"o":{"docs":{},"a":{"docs":{},"t":{"3":{"2":{"docs":{},"}":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}},")":{"docs":{},"]":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0017152658662092624},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.003795066413662239},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}},"(":{"docs":{},"r":{"docs":{},"_":{"docs":{},"u":{"docs":{},"i":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}},"docs":{}},"docs":{}}}}}},"i":{"docs":{},"n":{"docs":{},"t":{"3":{"2":{"docs":{},",":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.006493506493506494},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683},"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}}},")":{"docs":{},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.003430531732418525},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.007590132827324478},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.004629629629629629}}}},"}":{"docs":{},")":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842},"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}}}}},"docs":{}},"docs":{}}}},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"o":{"docs":{},"m":{"docs":{},".":{"docs":{},"s":{"docs":{},"h":{"docs":{},"u":{"docs":{},"f":{"docs":{},"f":{"docs":{},"l":{"docs":{},"e":{"docs":{},"(":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}}}}}}}}}},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"(":{"docs":{},"l":{"docs":{},"e":{"docs":{},"n":{"docs":{},"(":{"docs":{},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"f":{"docs":{},".":{"docs":{},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"m":{"docs":{},"s":{"docs":{},"_":{"docs":{},"r":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"s":{"docs":{},")":{"docs":{},",":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}}}}}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"s":{"docs":{},"_":{"docs":{},"r":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"s":{"docs":{},")":{"docs":{},",":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"c":{"docs":{},"h":{"docs":{},"o":{"docs":{},"i":{"docs":{},"c":{"docs":{},"e":{"docs":{},"(":{"docs":{},"p":{"docs":{},"d":{"docs":{},"f":{"docs":{},".":{"docs":{},"w":{"docs":{},"h":{"docs":{},"e":{"docs":{},"r":{"docs":{},"e":{"docs":{},"(":{"docs":{},"p":{"docs":{},"d":{"docs":{},"f":{"docs":{},".":{"docs":{},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"i":{"docs":{},"d":{"docs":{},"=":{"docs":{},"=":{"1":{"1":{"1":{"5":{"6":{"docs":{},")":{"docs":{},".":{"docs":{},"d":{"docs":{},"r":{"docs":{},"o":{"docs":{},"p":{"docs":{},"n":{"docs":{},"a":{"docs":{},"(":{"docs":{},")":{"docs":{},".":{"docs":{},"a":{"docs":{},"d":{"docs":{},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"i":{"docs":{},"d":{"docs":{},".":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"y":{"docs":{},"p":{"docs":{},"e":{"docs":{},"(":{"docs":{},"n":{"docs":{},"p":{"docs":{},".":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"6":{"4":{"docs":{},")":{"docs":{},",":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}},"docs":{}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"z":{"docs":{},"e":{"docs":{},"r":{"docs":{},"o":{"docs":{},"s":{"docs":{},"(":{"docs":{},"l":{"docs":{},"e":{"docs":{},"n":{"docs":{},"(":{"docs":{},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"m":{"docs":{},"s":{"docs":{},"_":{"docs":{},"r":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"s":{"docs":{},")":{"docs":{},")":{"docs":{},")":{"docs":{},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}}}}}}},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"f":{"docs":{},".":{"docs":{},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"m":{"docs":{},"s":{"docs":{},"_":{"docs":{},"r":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"s":{"docs":{},")":{"docs":{},")":{"docs":{},")":{"docs":{},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}}}}}}}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"s":{"docs":{},"_":{"docs":{},"r":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"s":{"docs":{},")":{"docs":{},")":{"docs":{},")":{"docs":{},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}}}}}}}}}}}}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"s":{"docs":{},"_":{"docs":{},"r":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"s":{"docs":{},")":{"docs":{},")":{"docs":{},")":{"docs":{},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"d":{"docs":{},"o":{"docs":{},"t":{"docs":{},"(":{"docs":{},"p":{"docs":{},"_":{"docs":{},"u":{"docs":{},",":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.003795066413662239},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}},"v":{"docs":{},"_":{"docs":{},"p":{"docs":{},"u":{"docs":{},",":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}},"k":{"docs":{},",":{"docs":{},"v":{"docs":{},"_":{"docs":{},"q":{"docs":{},"i":{"docs":{},"k":{"docs":{},")":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}}}}}}}}}}}}},"n":{"docs":{},"a":{"docs":{},"n":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}},"a":{"docs":{},"r":{"docs":{},"r":{"docs":{},"a":{"docs":{},"y":{"docs":{},"(":{"docs":{},"t":{"docs":{},"e":{"docs":{},"m":{"docs":{},"p":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}},"[":{"8":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}},"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"i":{"docs":{},"d":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}}},"u":{"docs":{},"m":{"docs":{},"p":{"docs":{},"i":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147},"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002858776443682104},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.003795066413662239},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147},"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.005836575875486381},"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289}}},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{},"i":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"s":{"docs":{},"=":{"docs":{},"n":{"docs":{},"o":{"docs":{},"n":{"docs":{},"e":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}}}}}}}}}},"e":{"docs":{},"r":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.012987012987012988}},"a":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{},"/":{"docs":{},"d":{"docs":{},"e":{"docs":{},"n":{"docs":{},"o":{"docs":{},"m":{"docs":{},"i":{"docs":{},"n":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.00487012987012987}}}}}}}}}}}}}}}},"b":{"docs":{},"e":{"docs":{},"r":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}},"_":{"docs":{},"e":{"docs":{},"p":{"docs":{},"o":{"docs":{},"c":{"docs":{},"h":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}},"s":{"docs":{},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683}}},"=":{"1":{"0":{"docs":{},",":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}},"docs":{}},"docs":{}}}}}}}},"l":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"n":{"docs":{},"t":{"docs":{},"f":{"docs":{},"a":{"docs":{},"c":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}},"s":{"docs":{},"=":{"1":{"0":{"docs":{},",":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}},"docs":{}},"docs":{}}}}}}}}}}}}}}}}}}}},"l":{"docs":{},"l":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.01020408163265306}},"。":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0022675736961451248}}},"，":{"docs":{},"则":{"docs":{},"结":{"docs":{},"果":{"docs":{},"为":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}}},"|":{"2":{"4":{"9":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981}}}},"docs":{}}},"docs":{}},"docs":{}},"3":{"4":{"4":{"9":{"2":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"6":{"8":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}}},"docs":{}}},"docs":{}},"docs":{}},"4":{"2":{"8":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}}},"docs":{}},"docs":{}},"5":{"7":{"5":{"9":{"1":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"6":{"3":{"9":{"docs":{},".":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.006526616357332245}}}},"_":{"docs":{},"n":{"docs":{},"a":{"docs":{},"_":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}},"d":{"docs":{},"f":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},".":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},".":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"(":{"docs":{},"r":{"docs":{},"o":{"docs":{},"w":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"1":{"0":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}}}}}}}}}}}}}},"c":{"docs":{},"l":{"docs":{},"_":{"docs":{},"o":{"docs":{},"n":{"docs":{},"e":{"docs":{},"h":{"docs":{},"o":{"docs":{},"t":{"docs":{},"_":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"e":{"docs":{},":":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},":":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}},"物":{"docs":{},"品":{"docs":{},"，":{"docs":{},"构":{"docs":{},"建":{"docs":{},"初":{"docs":{},"始":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"结":{"docs":{},"果":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}},"进":{"docs":{},"行":{"docs":{},"推":{"docs":{},"荐":{"docs":{"day02_推荐算法/07_基于内容的推荐算法.html":{"ref":"day02_推荐算法/07_基于内容的推荐算法.html","tf":0.03125}}}}}}}},"相":{"docs":{},"似":{"docs":{},"的":{"docs":{},"人":{"docs":{},"或":{"docs":{},"物":{"docs":{},"品":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147}}}}}}},"结":{"docs":{},"果":{"docs":{},"，":{"docs":{},"并":{"docs":{},"进":{"docs":{},"行":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"了":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}},"结":{"docs":{},"果":{"docs":{},"生":{"docs":{},"成":{"docs":{},"初":{"docs":{},"始":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"结":{"docs":{},"果":{"docs":{},"，":{"docs":{},"然":{"docs":{},"后":{"docs":{},"过":{"docs":{},"滤":{"docs":{},"掉":{"docs":{},"用":{"docs":{},"户":{"docs":{},"已":{"docs":{},"经":{"docs":{},"有":{"docs":{},"过":{"docs":{},"记":{"docs":{},"录":{"docs":{},"的":{"docs":{},"物":{"docs":{},"品":{"docs":{},"或":{"docs":{},"明":{"docs":{},"确":{"docs":{},"表":{"docs":{},"示":{"docs":{},"不":{"docs":{},"感":{"docs":{},"兴":{"docs":{},"趣":{"docs":{},"的":{"docs":{},"物":{"docs":{},"品":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"e":{"docs":{},"t":{"docs":{},"f":{"docs":{},"l":{"docs":{},"i":{"docs":{},"x":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}}},"w":{"docs":{},"_":{"docs":{},"t":{"docs":{},"a":{"docs":{},"g":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}},"v":{"docs":{},"e":{"docs":{},"r":{"docs":{},"s":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"_":{"docs":{},"b":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"c":{"docs":{},"l":{"docs":{},"a":{"docs":{},"s":{"docs":{},"s":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},"：":{"docs":{},"城":{"docs":{},"市":{"docs":{},"层":{"docs":{},"级":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}},":":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}},"的":{"docs":{},"空":{"docs":{},"值":{"docs":{},"情":{"docs":{},"况":{"docs":{},"：":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}},"_":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}},"e":{"docs":{},")":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}},"[":{"docs":{},"n":{"docs":{},"e":{"docs":{},"w":{"docs":{},"_":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"c":{"docs":{},"l":{"docs":{},"a":{"docs":{},"s":{"docs":{},"s":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"_":{"docs":{},"r":{"docs":{},"e":{"docs":{},"l":{"docs":{},"a":{"docs":{},"[":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"[":{"docs":{},"\"":{"docs":{},"n":{"docs":{},"e":{"docs":{},"w":{"docs":{},"_":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"c":{"docs":{},"l":{"docs":{},"a":{"docs":{},"s":{"docs":{},"s":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"\"":{"docs":{},"]":{"docs":{},")":{"docs":{},"]":{"docs":{},"]":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"p":{"docs":{},"r":{"docs":{},"o":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"e":{"docs":{},"_":{"docs":{},"d":{"docs":{},"f":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},".":{"docs":{},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}},"d":{"docs":{},"f":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}},".":{"docs":{},"n":{"docs":{},"e":{"docs":{},"w":{"docs":{},"_":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"c":{"docs":{},"l":{"docs":{},"a":{"docs":{},"s":{"docs":{},"s":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},".":{"docs":{},"c":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"(":{"docs":{},"s":{"docs":{},"t":{"docs":{},"r":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"t":{"docs":{},"y":{"docs":{},"p":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"p":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},".":{"docs":{},"c":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"(":{"docs":{},"s":{"docs":{},"t":{"docs":{},"r":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"t":{"docs":{},"y":{"docs":{},"p":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{},")":{"docs":{},"\\":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}},"o":{"docs":{},"r":{"docs":{},"t":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"t":{"docs":{},"i":{"docs":{},"m":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"m":{"docs":{},"p":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}},"w":{"docs":{},"i":{"docs":{},"t":{"docs":{},"h":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"p":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"g":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}},"o":{"docs":{},"t":{"docs":{},"i":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282}},"a":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{},")":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}},",":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}}}}}}},"e":{"docs":{},"d":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}},")":{"docs":{},")":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}},"最":{"docs":{},"相":{"docs":{},"似":{"docs":{},"的":{"docs":{},"物":{"docs":{},"品":{"docs":{},"进":{"docs":{},"行":{"docs":{},"相":{"docs":{},"关":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"：":{"docs":{},"如":{"docs":{},"与":{"docs":{},"该":{"docs":{},"商":{"docs":{},"品":{"docs":{},"相":{"docs":{},"似":{"docs":{},"的":{"docs":{},"商":{"docs":{},"品":{"docs":{},"有":{"docs":{},"哪":{"docs":{},"些":{"docs":{},"？":{"docs":{},"与":{"docs":{},"该":{"docs":{},"文":{"docs":{},"章":{"docs":{},"相":{"docs":{},"似":{"docs":{},"文":{"docs":{},"章":{"docs":{},"有":{"docs":{},"哪":{"docs":{},"些":{"docs":{},"？":{"docs":{"day02_推荐算法/07_基于内容的推荐算法.html":{"ref":"day02_推荐算法/07_基于内容的推荐算法.html","tf":0.03125}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"个":{"docs":{},"关":{"docs":{},"键":{"docs":{},"词":{"docs":{},"作":{"docs":{},"为":{"docs":{},"电":{"docs":{},"影":{"docs":{},"画":{"docs":{},"像":{"docs":{},"标":{"docs":{},"签":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876}}}}}}}}}}}}},"物":{"docs":{},"品":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.004078303425774877}}}}},"关":{"docs":{},"键":{"docs":{},"词":{"docs":{},"，":{"docs":{},"构":{"docs":{},"建":{"docs":{},"电":{"docs":{},"影":{"docs":{},"画":{"docs":{},"像":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}},"的":{"docs":{},"关":{"docs":{},"键":{"docs":{},"词":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}},"推":{"docs":{},"荐":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842},"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044},"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676},"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}},"s":{"docs":{},"_":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}}}}}},"p":{"docs":{},"a":{"docs":{},"c":{"docs":{},"e":{"docs":{},":":{"docs":{"Hive&HBase/06_hbase数据模型.html":{"ref":"Hive&HBase/06_hbase数据模型.html","tf":0.020833333333333332}}}}}}}},"n":{"docs":{},"o":{"docs":{},"d":{"docs":{"day03_Hadoop/ha2.1.html":{"ref":"day03_Hadoop/ha2.1.html","tf":0.0684931506849315},"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428},"day03_Hadoop/ha4.2.html":{"ref":"day03_Hadoop/ha4.2.html","tf":0.058823529411764705}},"e":{"docs":{},",":{"docs":{"day03_Hadoop/ha2.1.html":{"ref":"day03_Hadoop/ha2.1.html","tf":0.0136986301369863}}},"和":{"docs":{"day03_Hadoop/ha2.1.html":{"ref":"day03_Hadoop/ha2.1.html","tf":0.0136986301369863}}},"(":{"docs":{},"n":{"docs":{},"n":{"docs":{},")":{"docs":{"day03_Hadoop/ha2.4.html":{"ref":"day03_Hadoop/ha2.4.html","tf":0.03333333333333333}}}}}},"会":{"docs":{},"认":{"docs":{},"为":{"docs":{},"这":{"docs":{},"个":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"n":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"已":{"docs":{},"经":{"docs":{},"宕":{"docs":{},"机":{"docs":{"day03_Hadoop/ha4.2.html":{"ref":"day03_Hadoop/ha4.2.html","tf":0.0196078431372549}}}}}}}}}}}}}}}}}}},"故":{"docs":{},"障":{"docs":{},"容":{"docs":{},"错":{"docs":{"day03_Hadoop/ha4.2.html":{"ref":"day03_Hadoop/ha4.2.html","tf":0.0196078431372549}}}}}},"查":{"docs":{},"找":{"docs":{},"这":{"docs":{},"个":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"n":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"上":{"docs":{},"有":{"docs":{},"哪":{"docs":{},"些":{"docs":{},"数":{"docs":{},"据":{"docs":{},"块":{"docs":{},",":{"docs":{"day03_Hadoop/ha4.2.html":{"ref":"day03_Hadoop/ha4.2.html","tf":0.0196078431372549}}}}}}}}}}}}}}}}}}}}}}}}}},",":{"docs":{},"s":{"docs":{},"a":{"docs":{},"l":{"docs":{},"a":{"docs":{},"r":{"docs":{},"y":{"docs":{},",":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"1":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}},"docs":{}}}}}}}}}}}}},":":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"e":{"1":{"docs":{},".":{"docs":{},"t":{"docs":{},"x":{"docs":{},"t":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}},"2":{"docs":{},".":{"docs":{},"j":{"docs":{},"p":{"docs":{},"g":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}},"docs":{}}}}}}}},"t":{"docs":{},"i":{"docs":{},"v":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}},"作":{"docs":{},"为":{"docs":{},"用":{"docs":{},"户":{"docs":{},"最":{"docs":{},"终":{"docs":{},"的":{"docs":{},"画":{"docs":{},"像":{"docs":{},"标":{"docs":{},"签":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}}}}}}}}}}},"推":{"docs":{},"荐":{"docs":{},"结":{"docs":{},"果":{"docs":{"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}}}}}},"用":{"docs":{},"户":{"docs":{},"推":{"docs":{},"荐":{"docs":{"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":5}}}}}},"m":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}},"t":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.006711409395973154}}},"c":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099}}},"列":{"docs":{},"表":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}},"r":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625},"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.04093567251461988},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.005226480836236934}},"e":{"docs":{},"d":{"docs":{},"i":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.005226480836236934},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}},"s":{"docs":{},"/":{"docs":{},"m":{"docs":{},"e":{"docs":{},"m":{"docs":{},"c":{"docs":{},"a":{"docs":{},"c":{"docs":{},"h":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}}}}}}}}},"：":{"docs":{},"缓":{"docs":{},"存":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"n":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"p":{"docs":{},"o":{"docs":{},"o":{"docs":{},"l":{"docs":{},"(":{"docs":{},"h":{"docs":{},"o":{"docs":{},"s":{"docs":{},"t":{"docs":{},"=":{"docs":{},"h":{"docs":{},"o":{"docs":{},"s":{"docs":{},"t":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}}},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"i":{"docs":{},"s":{"docs":{},"(":{"docs":{},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"n":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"_":{"docs":{},"p":{"docs":{},"o":{"docs":{},"o":{"docs":{},"l":{"docs":{},"=":{"docs":{},"p":{"docs":{},"o":{"docs":{},"o":{"docs":{},"l":{"docs":{},")":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"s":{"docs":{},"t":{"docs":{},"r":{"docs":{},"i":{"docs":{},"c":{"docs":{},"t":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"i":{"docs":{},"s":{"docs":{},"(":{"docs":{},"h":{"docs":{},"o":{"docs":{},"s":{"docs":{},"t":{"docs":{},"=":{"docs":{},"\"":{"1":{"9":{"2":{"docs":{},".":{"1":{"6":{"8":{"docs":{},".":{"1":{"9":{"9":{"docs":{},".":{"1":{"8":{"8":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}},"docs":{}},"docs":{}},"docs":{}}},"docs":{},".":{"1":{"3":{"7":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.004228329809725159}}}}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}}}}}}}}}}}},"u":{"docs":{},"c":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.010526315789473684},"day03_Hadoop/ha3.2.html":{"ref":"day03_Hadoop/ha3.2.html","tf":0.02040816326530612},"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.012048192771084338},"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.006818181818181818}},"e":{"docs":{},"(":{"docs":{},")":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.010526315789473684}}},"a":{"docs":{},"d":{"docs":{},"d":{"docs":{},",":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}}}},"f":{"docs":{},"u":{"docs":{},"n":{"docs":{},"c":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},",":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}}}}}}}}},"l":{"docs":{},"a":{"docs":{},"m":{"docs":{},"b":{"docs":{},"d":{"docs":{},"a":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}}}}}},"o":{"docs":{},"u":{"docs":{},"t":{"docs":{},"_":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{},",":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"m":{"docs":{},"e":{"docs":{},"d":{"docs":{},"i":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"_":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},")":{"docs":{"day03_Hadoop/ha3.2.html":{"ref":"day03_Hadoop/ha3.2.html","tf":0.02040816326530612}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"函":{"docs":{},"数":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}},"处":{"docs":{},"理":{"docs":{"day03_Hadoop/ha3.2.html":{"ref":"day03_Hadoop/ha3.2.html","tf":0.02040816326530612}}}},"阶":{"docs":{},"段":{"docs":{},":":{"docs":{"day03_Hadoop/ha3.2.html":{"ref":"day03_Hadoop/ha3.2.html","tf":0.02040816326530612}}}}},"r":{"docs":{},"(":{"docs":{},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"f":{"docs":{},",":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}},"=":{"docs":{},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"f":{"docs":{},".":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"s":{"docs":{},"u":{"docs":{},"m":{"docs":{},")":{"docs":{},",":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}}}}}}}}}}}}}}},"_":{"docs":{},"s":{"docs":{},"u":{"docs":{},"m":{"docs":{},"(":{"docs":{},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"f":{"docs":{},",":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}}}}}}},"：":{"docs":{},"不":{"docs":{},"同":{"docs":{},"的":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{},"e":{"docs":{},"任":{"docs":{},"务":{"docs":{},"，":{"docs":{},"会":{"docs":{},"从":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"中":{"docs":{},"对":{"docs":{},"应":{"docs":{},"的":{"docs":{},"任":{"docs":{},"务":{"docs":{},"中":{"docs":{},"c":{"docs":{},"o":{"docs":{},"p":{"docs":{},"y":{"docs":{},"数":{"docs":{},"据":{"docs":{"day03_Hadoop/ha3.5.html":{"ref":"day03_Hadoop/ha3.5.html","tf":0.030303030303030304}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"将":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"输":{"docs":{},"出":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"进":{"docs":{},"行":{"docs":{},"h":{"docs":{},"a":{"docs":{},"s":{"docs":{},"h":{"docs":{},"计":{"docs":{},"算":{"docs":{},"，":{"docs":{},"对":{"docs":{},"每":{"docs":{},"个":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"数":{"docs":{},"据":{"docs":{},"进":{"docs":{},"行":{"docs":{},"统":{"docs":{},"计":{"docs":{},"计":{"docs":{},"算":{"docs":{"day03_Hadoop/ha3.5.html":{"ref":"day03_Hadoop/ha3.5.html","tf":0.030303030303030304}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"b":{"docs":{},"y":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"w":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"docs":{},"f":{"docs":{},"u":{"docs":{},"n":{"docs":{},"c":{"docs":{},",":{"docs":{},"i":{"docs":{},"n":{"docs":{},"v":{"docs":{},"f":{"docs":{},"u":{"docs":{},"n":{"docs":{},"c":{"docs":{},",":{"docs":{},"w":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"o":{"docs":{},"w":{"docs":{},"l":{"docs":{},"e":{"docs":{},"n":{"docs":{},"g":{"docs":{},"t":{"docs":{},"h":{"docs":{},",":{"docs":{},"s":{"docs":{},"l":{"docs":{},"i":{"docs":{},"d":{"docs":{},"e":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},",":{"docs":{},"[":{"docs":{},"n":{"docs":{},"u":{"docs":{},"m":{"docs":{},",":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"k":{"docs":{},"s":{"docs":{},"]":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"将":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"中":{"docs":{},"元":{"docs":{},"素":{"docs":{},"两":{"docs":{},"两":{"docs":{},"传":{"docs":{},"递":{"docs":{},"给":{"docs":{},"输":{"docs":{},"入":{"docs":{},"函":{"docs":{},"数":{"docs":{},"，":{"docs":{},"同":{"docs":{},"时":{"docs":{},"产":{"docs":{},"生":{"docs":{},"一":{"docs":{},"个":{"docs":{},"新":{"docs":{},"的":{"docs":{},"值":{"docs":{},"，":{"docs":{},"新":{"docs":{},"产":{"docs":{},"生":{"docs":{},"的":{"docs":{},"值":{"docs":{},"与":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"中":{"docs":{},"下":{"docs":{},"一":{"docs":{},"个":{"docs":{},"元":{"docs":{},"素":{"docs":{},"再":{"docs":{},"被":{"docs":{},"传":{"docs":{},"递":{"docs":{},"给":{"docs":{},"输":{"docs":{},"入":{"docs":{},"函":{"docs":{},"数":{"docs":{},"直":{"docs":{},"到":{"docs":{},"最":{"docs":{},"后":{"docs":{},"只":{"docs":{},"有":{"docs":{},"一":{"docs":{},"个":{"docs":{},"值":{"docs":{},"为":{"docs":{},"止":{"docs":{},"。":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"s":{"docs":{},"u":{"docs":{},"l":{"docs":{},"t":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.006493506493506494},"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0113314447592068},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.004228329809725159}},"_":{"1":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}},".":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"(":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"u":{"docs":{},"l":{"docs":{},"t":{"docs":{},"_":{"1":{"docs":{},".":{"docs":{},"c":{"docs":{},"l":{"docs":{},"k":{"docs":{},"=":{"docs":{},"=":{"1":{"docs":{},")":{"docs":{},".":{"docs":{},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"c":{"docs":{},"l":{"docs":{},"k":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}},"docs":{}}}}}}}},"docs":{}}}}}}}}}}}}}}},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"c":{"docs":{},"l":{"docs":{},"k":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}},"docs":{},"t":{"docs":{"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}},"a":{"docs":{},"b":{"docs":{},"l":{"docs":{},"e":{"docs":{},".":{"docs":{},"g":{"docs":{},"e":{"docs":{},"t":{"docs":{},"(":{"docs":{},"m":{"docs":{},"i":{"docs":{},"d":{"docs":{},",":{"docs":{"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}}}}}}}}}},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"m":{"docs":{},"s":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}}}}}}}}}},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"d":{"docs":{},"e":{"docs":{},"f":{"docs":{},"a":{"docs":{},"u":{"docs":{},"l":{"docs":{},"t":{"docs":{},"(":{"docs":{},"m":{"docs":{},"i":{"docs":{},"d":{"docs":{},",":{"docs":{"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}}}}}}}}}}}}}}}}}}}}}}},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}},"[":{"2":{"docs":{},"]":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}},"[":{"1":{"docs":{},"]":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}},"docs":{}}}},"docs":{}},"d":{"docs":{},"f":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965},"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}},".":{"docs":{},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"1":{"0":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}},"docs":{}},"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}},"f":{"docs":{},"o":{"docs":{},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"c":{"docs":{},"h":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{},"i":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"(":{"docs":{},"r":{"docs":{},"e":{"docs":{},"c":{"docs":{},"a":{"docs":{},"l":{"docs":{},"l":{"docs":{},"_":{"docs":{},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"_":{"docs":{},"b":{"docs":{},"y":{"docs":{},"_":{"docs":{},"c":{"docs":{},"f":{"docs":{},")":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}},"a":{"docs":{},"p":{"docs":{},"p":{"docs":{},"e":{"docs":{},"n":{"docs":{},"d":{"docs":{},"(":{"docs":{},"(":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"i":{"docs":{},"d":{"docs":{},",":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}}}}}}}}}}}}}}},"o":{"docs":{},"u":{"docs":{},"r":{"docs":{},"c":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.02564102564102564},"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.017094017094017096}},"e":{"docs":{},"m":{"docs":{},"a":{"docs":{},"n":{"docs":{},"a":{"docs":{},"g":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}},"e":{"docs":{},"r":{"docs":{},":":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}},"：":{"docs":{},"负":{"docs":{},"责":{"docs":{},"资":{"docs":{},"源":{"docs":{},"的":{"docs":{},"管":{"docs":{},"理":{"docs":{},"，":{"docs":{},"负":{"docs":{},"责":{"docs":{},"提":{"docs":{},"交":{"docs":{},"任":{"docs":{},"务":{"docs":{},"到":{"docs":{},"n":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"m":{"docs":{},"a":{"docs":{},"n":{"docs":{},"a":{"docs":{},"g":{"docs":{},"e":{"docs":{},"r":{"docs":{},"所":{"docs":{},"在":{"docs":{},"的":{"docs":{},"节":{"docs":{},"点":{"docs":{},"运":{"docs":{},"行":{"docs":{},"，":{"docs":{},"检":{"docs":{},"查":{"docs":{},"节":{"docs":{},"点":{"docs":{},"的":{"docs":{},"状":{"docs":{},"态":{"docs":{"day03_Hadoop/ha3.5.html":{"ref":"day03_Hadoop/ha3.5.html","tf":0.030303030303030304}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"i":{"docs":{},"l":{"docs":{},"i":{"docs":{},"e":{"docs":{},"n":{"docs":{},"t":{"docs":{},"：":{"docs":{},"弹":{"docs":{},"性":{"docs":{},"的":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}}}}}}}},"t":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289}},"u":{"docs":{},"r":{"docs":{},"n":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.006493506493506494},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.010863350485991996},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.011385199240986717},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.009259259259259259},"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.009727626459143969},"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.010526315789473684},"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269},"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726},"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.013245033112582781},"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.01048951048951049},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.007633587786259542},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}},".":{"docs":{},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"t":{"docs":{},"u":{"docs":{},"p":{"docs":{},"l":{"docs":{},"e":{"docs":{},"s":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"r":{"docs":{},"e":{"docs":{},"c":{"docs":{},"o":{"docs":{},"m":{"docs":{},"m":{"docs":{},"e":{"docs":{},"n":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"s":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}}}}}},"u":{"docs":{},"n":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"(":{"docs":{},"n":{"docs":{},"p":{"docs":{},".":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"o":{"docs":{},"m":{"docs":{},".":{"docs":{},"c":{"docs":{},"h":{"docs":{},"o":{"docs":{},"i":{"docs":{},"c":{"docs":{},"e":{"docs":{},"(":{"docs":{},"p":{"docs":{},"d":{"docs":{},"f":{"docs":{},".":{"docs":{},"w":{"docs":{},"h":{"docs":{},"e":{"docs":{},"r":{"docs":{},"e":{"docs":{},"(":{"docs":{},"p":{"docs":{},"d":{"docs":{},"f":{"docs":{},".":{"docs":{},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"i":{"docs":{},"d":{"docs":{},"=":{"docs":{},"=":{"docs":{},"i":{"docs":{},".":{"docs":{},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"i":{"docs":{},"d":{"docs":{},")":{"docs":{},".":{"docs":{},"a":{"docs":{},"d":{"docs":{},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"i":{"docs":{},"d":{"docs":{},".":{"docs":{},"d":{"docs":{},"r":{"docs":{},"o":{"docs":{},"p":{"docs":{},"n":{"docs":{},"a":{"docs":{},"(":{"docs":{},")":{"docs":{},".":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"y":{"docs":{},"p":{"docs":{},"e":{"docs":{},"(":{"docs":{},"n":{"docs":{},"p":{"docs":{},".":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"6":{"4":{"docs":{},")":{"docs":{},",":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}},"docs":{}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"v":{"docs":{},"e":{"docs":{},"r":{"docs":{},"s":{"docs":{},"e":{"docs":{},"=":{"docs":{},"t":{"docs":{},"r":{"docs":{},"u":{"docs":{},"e":{"docs":{},")":{"docs":{},"[":{"docs":{},":":{"1":{"0":{"0":{"docs":{},"]":{"docs":{"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}}}},"docs":{}},"docs":{}},"3":{"0":{"docs":{},"]":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876}}}},"docs":{}},"docs":{},"k":{"docs":{},"]":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}}}}}}}}}}}}}}}}},"a":{"docs":{},"l":{"docs":{},"_":{"docs":{},"r":{"docs":{},"a":{"docs":{},"t":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.004574042309891366},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.003795066413662239}},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.004574042309891366}}},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.004574042309891366},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}}}}}},"d":{"docs":{},"e":{"docs":{},"r":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.007692307692307693},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}},".":{"docs":{},"j":{"docs":{},"s":{"docs":{},"o":{"docs":{},"n":{"docs":{},"(":{"docs":{},"'":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"/":{"docs":{},"n":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{},".":{"docs":{},"j":{"docs":{},"s":{"docs":{},"o":{"docs":{},"n":{"docs":{},"'":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}}}}}}}}}}}}}}},"j":{"docs":{},"s":{"docs":{},"o":{"docs":{},"n":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}}}}}}}}}}}},":":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}},"g":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002858776443682104}},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}},"_":{"docs":{},"b":{"docs":{},"i":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}},"u":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}},"p":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}},",":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}},"q":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}},",":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"v":{"docs":{},"e":{"docs":{},"r":{"docs":{},"失":{"docs":{},"效":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}},"注":{"docs":{},"册":{"docs":{},"到":{"docs":{},"z":{"docs":{},"o":{"docs":{},"o":{"docs":{},"k":{"docs":{},"e":{"docs":{},"e":{"docs":{},"p":{"docs":{},"e":{"docs":{},"r":{"docs":{},",":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}}}}}}}}}}}}}}},"会":{"docs":{},"随":{"docs":{},"着":{"docs":{},"插":{"docs":{},"入":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"越":{"docs":{},"来":{"docs":{},"越":{"docs":{},"多":{"docs":{},"，":{"docs":{},"会":{"docs":{},"进":{"docs":{},"行":{"docs":{},"拆":{"docs":{},"分":{"docs":{},"。":{"docs":{},"默":{"docs":{},"认":{"docs":{},"大":{"docs":{},"小":{"docs":{},"是":{"1":{"0":{"docs":{},"g":{"docs":{},"一":{"docs":{},"个":{"docs":{},"。":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}},"docs":{}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"s":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},")":{"docs":{},"这":{"docs":{},"样":{"docs":{},"的":{"docs":{},"机":{"docs":{},"器":{"docs":{},"学":{"docs":{},"习":{"docs":{},"算":{"docs":{},"法":{"docs":{},"，":{"docs":{},"而":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"算":{"docs":{},"法":{"docs":{},"则":{"docs":{},"是":{"docs":{},"一":{"docs":{},"些":{"docs":{},"基":{"docs":{},"于":{"docs":{},"协":{"docs":{},"同":{"docs":{},"过":{"docs":{},"滤":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"、":{"docs":{},"基":{"docs":{},"于":{"docs":{},"内":{"docs":{},"容":{"docs":{},"的":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"等":{"docs":{},"思":{"docs":{},"想":{"docs":{},"实":{"docs":{},"现":{"docs":{},"的":{"docs":{},"算":{"docs":{},"法":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"c":{"docs":{},"o":{"docs":{},"r":{"docs":{},"d":{"docs":{},"_":{"docs":{},"m":{"docs":{},"o":{"docs":{},"v":{"docs":{},"i":{"docs":{},"e":{"docs":{},"_":{"docs":{},"p":{"docs":{},"r":{"docs":{},"i":{"docs":{},"f":{"docs":{},"o":{"docs":{},"l":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}},"e":{"docs":{},"[":{"docs":{},"\"":{"docs":{},"p":{"docs":{},"r":{"docs":{},"o":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"e":{"docs":{},"\"":{"docs":{},"]":{"docs":{},".":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"s":{"docs":{},")":{"docs":{},")":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"m":{"docs":{},"m":{"docs":{},"e":{"docs":{},"n":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"s":{"docs":{},"=":{"docs":{},"[":{"docs":{},"r":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"docs":{},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"i":{"docs":{},"d":{"docs":{},"=":{"1":{"6":{"1":{"0":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}},"docs":{}},"docs":{}},"docs":{}},"5":{"5":{"7":{"9":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}},"docs":{}},"docs":{}},"6":{"0":{"7":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}}}},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0032626427406199023}}}}}}}}}}}}}}},"a":{"docs":{},"l":{"docs":{},"l":{"docs":{},"_":{"docs":{},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"_":{"docs":{},"b":{"docs":{},"y":{"docs":{},"_":{"docs":{},"c":{"docs":{},"f":{"docs":{},"(":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{},"i":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},")":{"docs":{},":":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}},"s":{"docs":{},":":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}}}}}},"l":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"d":{"docs":{},"_":{"docs":{},"m":{"docs":{},"o":{"docs":{},"v":{"docs":{},"i":{"docs":{"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}},"e":{"docs":{},"s":{"docs":{},":":{"docs":{"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}}}}}}}}},"w":{"docs":{},"e":{"docs":{},"i":{"docs":{},"g":{"docs":{},"h":{"docs":{},"t":{"docs":{"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}}}}}}}}}}}}},"e":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"s":{"docs":{},".":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}}}}},"p":{"docs":{},"a":{"docs":{},"i":{"docs":{},"r":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}},"f":{"docs":{},"e":{"docs":{},"r":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734},"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.00974025974025974},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0137221269296741},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}},")":{"docs":{},"概":{"docs":{},"念":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}},"预":{"docs":{},"测":{"docs":{},"来":{"docs":{},"实":{"docs":{},"现":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"s":{"docs":{},".":{"docs":{},"p":{"docs":{},"i":{"docs":{},"v":{"docs":{},"o":{"docs":{},"t":{"docs":{},"_":{"docs":{},"t":{"docs":{},"a":{"docs":{},"b":{"docs":{},"l":{"docs":{},"e":{"docs":{},"(":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{},"=":{"docs":{},"[":{"docs":{},"\"":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},"]":{"docs":{},",":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"d":{"docs":{},"r":{"docs":{},"o":{"docs":{},"p":{"docs":{},"(":{"docs":{},"t":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"_":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}}}}}}}}}}}}}}},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"b":{"docs":{},"y":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"a":{"docs":{},"n":{"docs":{},"y":{"docs":{},"(":{"docs":{},")":{"docs":{},".":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{},":":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"l":{"docs":{},"o":{"docs":{},"c":{"docs":{},"[":{"docs":{},"t":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"_":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{},"]":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}}}}}}}}}}}}}},"w":{"docs":{},"h":{"docs":{},"e":{"docs":{},"r":{"docs":{},"e":{"docs":{},"(":{"docs":{},"r":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"s":{"docs":{},"[":{"docs":{},"\"":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},"]":{"docs":{},"=":{"docs":{},"=":{"docs":{},"u":{"docs":{},"i":{"docs":{},"d":{"docs":{},")":{"docs":{},".":{"docs":{},"d":{"docs":{},"r":{"docs":{},"o":{"docs":{},"p":{"docs":{},"n":{"docs":{},"a":{"docs":{},"(":{"docs":{},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"_":{"docs":{},"m":{"docs":{},"a":{"docs":{},"t":{"docs":{},"r":{"docs":{},"i":{"docs":{},"x":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}},",":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.016233766233766232}}},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}}}}}},"r":{"docs":{},"r":{"docs":{},"(":{"docs":{},")":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.0016233766233766235}}}}}}}},"i":{"docs":{},"x":{"docs":{},"[":{"docs":{},"s":{"docs":{},"i":{"docs":{},"m":{"docs":{},"_":{"docs":{},"u":{"docs":{},"i":{"docs":{},"d":{"docs":{},"]":{"docs":{},".":{"docs":{},"d":{"docs":{},"r":{"docs":{},"o":{"docs":{},"p":{"docs":{},"n":{"docs":{},"a":{"docs":{},"(":{"docs":{},")":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.00487012987012987}}}}}}}}}}}}}}}}}}}}}},"t":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"r":{"docs":{},"r":{"docs":{},"(":{"docs":{},")":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.0016233766233766235}}}}}}}}}}},":":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.006493506493506494}}},"[":{"docs":{},"s":{"docs":{},"i":{"docs":{},"m":{"docs":{},"_":{"docs":{},"i":{"docs":{},"i":{"docs":{},"d":{"docs":{},"]":{"docs":{},".":{"docs":{},"d":{"docs":{},"r":{"docs":{},"o":{"docs":{},"p":{"docs":{},"n":{"docs":{},"a":{"docs":{},"(":{"docs":{},")":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.0016233766233766235}}}}}}}}}}}}}}}}}}}}}}}}}}},")":{"docs":{},":":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.003430531732418525}}}}},"字":{"docs":{},"段":{"docs":{},"的":{"docs":{},"名":{"docs":{},"称":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683}}}}}}},":":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}},"=":{"1":{"0":{"docs":{},".":{"3":{"5":{"6":{"9":{"0":{"6":{"8":{"9":{"0":{"8":{"6":{"9":{"1":{"4":{"docs":{},")":{"docs":{},"]":{"docs":{},")":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"1":{"docs":{},".":{"7":{"7":{"0":{"1":{"7":{"1":{"1":{"6":{"5":{"4":{"6":{"6":{"3":{"0":{"9":{"docs":{},")":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"3":{"docs":{},".":{"6":{"6":{"5":{"9":{"4":{"2":{"1":{"9":{"2":{"0":{"7":{"7":{"6":{"3":{"7":{"docs":{},")":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"2":{"0":{"docs":{},".":{"7":{"3":{"6":{"7":{"8":{"5":{"8":{"8":{"8":{"6":{"7":{"1":{"8":{"7":{"5":{"docs":{},")":{"docs":{},"]":{"docs":{},")":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"4":{"docs":{},".":{"9":{"0":{"1":{"5":{"4":{"8":{"3":{"8":{"5":{"6":{"2":{"0":{"1":{"1":{"7":{"docs":{},")":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"5":{"docs":{},".":{"4":{"9":{"8":{"8":{"9":{"9":{"4":{"5":{"9":{"8":{"3":{"8":{"8":{"6":{"7":{"docs":{},")":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"5":{"docs":{},".":{"2":{"5":{"5":{"5":{"7":{"4":{"2":{"2":{"6":{"3":{"7":{"9":{"3":{"9":{"4":{"5":{"docs":{},")":{"docs":{},"]":{"docs":{},")":{"docs":{},"]":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"6":{"2":{"4":{"5":{"7":{"5":{"1":{"3":{"8":{"0":{"9":{"2":{"0":{"4":{"1":{"docs":{},")":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"9":{"0":{"5":{"1":{"8":{"8":{"5":{"6":{"0":{"4":{"8":{"5":{"8":{"4":{"docs":{},")":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"=":{"docs":{},"'":{"docs":{},"r":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"'":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}}}}}}}}}}}}}}}}},"i":{"docs":{},"s":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}},"n":{"docs":{},"d":{"docs":{},"o":{"docs":{},"m":{"docs":{"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}},":":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683}}},"=":{"docs":{},"f":{"docs":{},"a":{"docs":{},"l":{"docs":{},"s":{"docs":{},"e":{"docs":{},")":{"docs":{},":":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}}},"t":{"docs":{},"r":{"docs":{},"u":{"docs":{},"e":{"docs":{},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}},"s":{"docs":{},"p":{"docs":{},"l":{"docs":{},"i":{"docs":{},"t":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}},"f":{"docs":{},"o":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}},".":{"docs":{},"t":{"docs":{},"r":{"docs":{},"a":{"docs":{},"i":{"docs":{},"n":{"docs":{},"c":{"docs":{},"l":{"docs":{},"a":{"docs":{},"s":{"docs":{},"s":{"docs":{},"i":{"docs":{},"f":{"docs":{},"i":{"docs":{},"e":{"docs":{},"r":{"docs":{},"(":{"docs":{},"t":{"docs":{},"r":{"docs":{},"a":{"docs":{},"i":{"docs":{},"n":{"docs":{},"_":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"2":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"g":{"docs":{},"e":{"docs":{},"(":{"1":{"3":{"docs":{},")":{"docs":{},"]":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}},"5":{"docs":{},")":{"docs":{},":":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}},"docs":{}},"2":{"docs":{},")":{"docs":{},"]":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.004228329809725159}},"#":{"docs":{},"[":{"0":{"docs":{},",":{"0":{"docs":{},"]":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}},"docs":{}}},"docs":{}}}}}},"3":{"docs":{},")":{"docs":{},"]":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}},"4":{"docs":{},")":{"docs":{},"]":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}},"5":{"docs":{},")":{"docs":{},"]":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}},"6":{"7":{"6":{"9":{"docs":{},")":{"docs":{},"]":{"docs":{},")":{"docs":{},")":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289}}}}}}},"docs":{}},"docs":{}},"docs":{}},"7":{"docs":{},")":{"docs":{},"]":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}},"docs":{},"n":{"docs":{},"u":{"docs":{},"m":{"docs":{},"b":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"e":{"docs":{},"p":{"docs":{},"o":{"docs":{},"c":{"docs":{},"h":{"docs":{},"s":{"docs":{},")":{"docs":{},":":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}}}}}}}}}}},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"f":{"docs":{},".":{"docs":{},"n":{"docs":{},"u":{"docs":{},"m":{"docs":{},"b":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"e":{"docs":{},"p":{"docs":{},"o":{"docs":{},"c":{"docs":{},"h":{"docs":{},"s":{"docs":{},")":{"docs":{},":":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}}}},"o":{"docs":{},"f":{"docs":{},"_":{"docs":{},"l":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"n":{"docs":{},"t":{"docs":{},"f":{"docs":{},"a":{"docs":{},"c":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{},"s":{"docs":{},")":{"docs":{},":":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"w":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}},"_":{"docs":{},"s":{"docs":{},"a":{"docs":{},"m":{"docs":{},"p":{"docs":{},"l":{"docs":{},"e":{"docs":{},".":{"docs":{},"c":{"docs":{},"s":{"docs":{},"v":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.010526315789473684}}}}}},"_":{"docs":{},"d":{"docs":{},"f":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}},".":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"(":{"docs":{},"r":{"docs":{},"a":{"docs":{},"w":{"docs":{},"_":{"docs":{},"s":{"docs":{},"a":{"docs":{},"m":{"docs":{},"p":{"docs":{},"l":{"docs":{},"e":{"docs":{},"_":{"docs":{},"d":{"docs":{},"f":{"docs":{},".":{"docs":{},"t":{"docs":{},"i":{"docs":{},"m":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"m":{"docs":{},"p":{"docs":{},"(":{"1":{"4":{"9":{"4":{"6":{"9":{"1":{"1":{"8":{"6":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"p":{"docs":{},"r":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"s":{"docs":{},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},"m":{"docs":{},"a":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}},"j":{"docs":{},"o":{"docs":{},"i":{"docs":{},"n":{"docs":{},"(":{"docs":{},"a":{"docs":{},"d":{"docs":{},"_":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"e":{"docs":{},"_":{"docs":{},"d":{"docs":{},"f":{"docs":{},",":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}},"和":{"docs":{},"a":{"docs":{},"d":{"docs":{},"_":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"e":{"docs":{},"_":{"docs":{},"d":{"docs":{},"f":{"docs":{},"合":{"docs":{},"并":{"docs":{},"条":{"docs":{},"件":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"p":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"i":{"docs":{},"c":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"|":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}}}}}}}}},"s":{"docs":{},"_":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"u":{"docs":{},"l":{"docs":{},"t":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.014124293785310734},"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.02247191011235955}},".":{"docs":{},"u":{"docs":{},"n":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"(":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"(":{"docs":{},"d":{"docs":{},"f":{"docs":{},".":{"docs":{},"i":{"docs":{},"x":{"docs":{},"[":{"docs":{},"s":{"docs":{},"i":{"docs":{},"m":{"docs":{},"_":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"]":{"docs":{},".":{"docs":{},"r":{"docs":{},"e":{"docs":{},"p":{"docs":{},"l":{"docs":{},"a":{"docs":{},"c":{"docs":{},"e":{"docs":{},"(":{"0":{"docs":{},",":{"docs":{},"n":{"docs":{},"p":{"docs":{},".":{"docs":{},"n":{"docs":{},"a":{"docs":{},"n":{"docs":{},")":{"docs":{},".":{"docs":{},"d":{"docs":{},"r":{"docs":{},"o":{"docs":{},"p":{"docs":{},"n":{"docs":{},"a":{"docs":{},"(":{"docs":{},")":{"docs":{},".":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{},")":{"docs":{},")":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"t":{"docs":{},"o":{"docs":{},"p":{"docs":{},"n":{"docs":{},"_":{"docs":{},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"m":{"docs":{},"s":{"docs":{},"[":{"docs":{},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"m":{"docs":{},"]":{"docs":{},")":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}}}}}}}}}}}},"s":{"docs":{},"[":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"]":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147}}}}}}}}}}}}}}}}},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"d":{"docs":{},"(":{"docs":{},"p":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"i":{"docs":{},"c":{"docs":{},"t":{"docs":{},"_":{"docs":{},"r":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},",":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}}}}}}}}}}}}}}}}},"_":{"docs":{},"m":{"docs":{},"a":{"docs":{},"e":{"docs":{},"_":{"docs":{},"s":{"docs":{},"u":{"docs":{},"m":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683}}}}}}}}}},"l":{"docs":{},"e":{"docs":{},"n":{"docs":{},"(":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"r":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"_":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683}}}}}}}}}}}}}}}}}}}}}}},"n":{"docs":{},"p":{"docs":{},".":{"docs":{},"s":{"docs":{},"q":{"docs":{},"r":{"docs":{},"t":{"docs":{},"(":{"docs":{},"_":{"docs":{},"r":{"docs":{},"m":{"docs":{},"s":{"docs":{},"e":{"docs":{},"_":{"docs":{},"s":{"docs":{},"u":{"docs":{},"m":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683}}}}}}}}}}}}}}}}}}}}}}},"o":{"docs":{},"t":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196},"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0032626427406199023},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0016316540893330613},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.001193792280143255}}}},"b":{"docs":{},",":{"1":{"0":{"0":{"0":{"0":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"w":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.018518518518518517},"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.01606425702811245},"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0022675736961451248},"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.006607929515418502},"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.004893964110929853},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.003671221700999388},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0031834460803820135},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.005226480836236934},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.004228329809725159}},".":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}},"r":{"docs":{},"e":{"docs":{},"c":{"docs":{},"o":{"docs":{},"m":{"docs":{},"m":{"docs":{},"e":{"docs":{},"n":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"s":{"docs":{},"]":{"docs":{},")":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"i":{"docs":{},"d":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}},"_":{"docs":{},"s":{"docs":{},"e":{"docs":{},"q":{"docs":{},"u":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.008032128514056224}},"e":{"docs":{},"n":{"docs":{},"c":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{},",":{"docs":{},"*":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}}}}}}}}}},"s":{"docs":{},",":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044},"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}},"_":{"1":{"0":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.00881057268722467}}},"6":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.013215859030837005}}},"docs":{}},"2":{"2":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.01762114537444934}}},"docs":{}},"docs":{}}}}},"(":{"docs":{},"s":{"docs":{},")":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"=":{"docs":{},"x":{"docs":{},"[":{"0":{"docs":{},"]":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}},"docs":{}}}}}}}},"b":{"docs":{},"t":{"docs":{},"a":{"docs":{},"g":{"docs":{},"=":{"docs":{},"'":{"docs":{},"c":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{},"'":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}},"f":{"docs":{},"a":{"docs":{},"v":{"docs":{},"'":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}},"p":{"docs":{},"v":{"docs":{},"'":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"i":{"docs":{},"d":{"docs":{},"=":{"1":{"6":{"1":{"0":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}},"docs":{}},"docs":{}},"docs":{}},"2":{"4":{"4":{"7":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}},"docs":{}},"docs":{}},"docs":{}},"3":{"3":{"4":{"7":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}},"docs":{}},"docs":{}},"docs":{}},"5":{"6":{"9":{"0":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}},"docs":{}},"docs":{}},"7":{"3":{"7":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}}}},"l":{"docs":{},"k":{"docs":{},"=":{"docs":{},"'":{"1":{"docs":{},"'":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}},"docs":{}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"i":{"docs":{},"d":{"docs":{},"=":{"1":{"0":{"6":{"1":{"6":{"5":{"0":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"2":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}},"3":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}},"docs":{}}}}}}}},"p":{"docs":{},"i":{"docs":{},"d":{"docs":{},"=":{"docs":{},"'":{"4":{"3":{"0":{"5":{"3":{"9":{"docs":{},"_":{"1":{"0":{"0":{"7":{"docs":{},"'":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"_":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"=":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"s":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{},"(":{"2":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}},"r":{"docs":{},")":{"docs":{},":":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}}}},"a":{"docs":{},"d":{"docs":{},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"i":{"docs":{},"d":{"docs":{},"=":{"1":{"3":{"3":{"4":{"5":{"7":{"docs":{},")":{"docs":{},",":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"6":{"4":{"8":{"0":{"7":{"docs":{},")":{"docs":{},",":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"7":{"3":{"3":{"2":{"7":{"docs":{},")":{"docs":{},",":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"2":{"0":{"1":{"8":{"6":{"7":{"docs":{},")":{"docs":{},",":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"2":{"7":{"7":{"3":{"1":{"docs":{},")":{"docs":{},",":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}},"docs":{}},"docs":{}},"docs":{}},"9":{"8":{"2":{"7":{"docs":{},")":{"docs":{},",":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"4":{"1":{"4":{"0":{"2":{"docs":{},")":{"docs":{},",":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"5":{"5":{"4":{"2":{"docs":{},")":{"docs":{},",":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}},"docs":{}},"docs":{}},"docs":{}},"6":{"5":{"4":{"0":{"3":{"docs":{},")":{"docs":{},",":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"7":{"5":{"8":{"1":{"9":{"docs":{},")":{"docs":{},",":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}},"docs":{}},"docs":{}},"docs":{}},"7":{"3":{"3":{"5":{"docs":{},")":{"docs":{},",":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"9":{"4":{"6":{"6":{"docs":{},")":{"docs":{},",":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"3":{"3":{"9":{"3":{"8":{"2":{"docs":{},")":{"docs":{},"]":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"5":{"1":{"3":{"6":{"6":{"docs":{},")":{"docs":{},",":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"4":{"0":{"1":{"4":{"3":{"3":{"docs":{},")":{"docs":{},",":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"9":{"4":{"2":{"2":{"4":{"docs":{},")":{"docs":{},",":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"5":{"6":{"9":{"9":{"3":{"9":{"docs":{},")":{"docs":{},",":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"7":{"5":{"6":{"3":{"3":{"docs":{},")":{"docs":{},",":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"8":{"3":{"2":{"1":{"5":{"docs":{},")":{"docs":{},",":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}},"p":{"docs":{},"r":{"docs":{},"e":{"docs":{},"f":{"docs":{},"i":{"docs":{},"x":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"t":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}}},":":{"docs":{},"(":{"docs":{},"r":{"docs":{},"o":{"docs":{},"w":{"docs":{},"[":{"docs":{},"'":{"docs":{},"i":{"docs":{},"d":{"docs":{},"'":{"docs":{},"]":{"docs":{},",":{"docs":{},"s":{"docs":{},"u":{"docs":{},"m":{"docs":{},"(":{"docs":{},"[":{"docs":{},"c":{"docs":{},"=":{"docs":{},"=":{"docs":{},"n":{"docs":{},"o":{"docs":{},"n":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}}}}}}}}}}}}}}}},"]":{"docs":{},")":{"docs":{},")":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}}}}}}}}},"m":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.025},"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}},"s":{"docs":{},"e":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}},"(":{"docs":{},"p":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"i":{"docs":{},"c":{"docs":{},"t":{"docs":{},"_":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"u":{"docs":{},"l":{"docs":{},"t":{"docs":{},"s":{"docs":{},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}},":":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}}}}}}}}}}}}}},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.003430531732418525}}},"_":{"docs":{},"m":{"docs":{},"a":{"docs":{},"e":{"docs":{},"(":{"docs":{},"p":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"i":{"docs":{},"c":{"docs":{},"t":{"docs":{},"_":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"u":{"docs":{},"l":{"docs":{},"t":{"docs":{},"s":{"docs":{},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}},":":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}}}}}}}}}}}}}}}}}},"和":{"docs":{},"m":{"docs":{},"a":{"docs":{},"e":{"docs":{},"评":{"docs":{},"估":{"docs":{},"指":{"docs":{},"标":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}}}},"评":{"docs":{},"估":{"docs":{},"指":{"docs":{},"标":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}}},"_":{"docs":{},"{":{"docs":{},"u":{"docs":{},"i":{"docs":{},"}":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}},"u":{"docs":{},"i":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}},"}":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.009487666034155597},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.016203703703703703}},"(":{"docs":{},"r":{"docs":{},"_":{"docs":{},"{":{"docs":{},"u":{"docs":{},"i":{"docs":{},"}":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.007432818753573471}}}}}}}}}},"矩":{"docs":{},"阵":{"docs":{},"是":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}}}},":":{"docs":{},"数":{"docs":{},"据":{"docs":{},"分":{"docs":{},"析":{"docs":{"day03_Hadoop/ha4.1.html":{"ref":"day03_Hadoop/ha4.1.html","tf":0.023809523809523808}}}}}},"l":{"docs":{},"a":{"docs":{},"b":{"docs":{},"e":{"docs":{},"l":{"docs":{},"e":{"docs":{},"d":{"docs":{},"p":{"docs":{},"o":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},"r":{"docs":{},".":{"docs":{},"n":{"docs":{},"e":{"docs":{},"w":{"docs":{},"_":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"c":{"docs":{},"l":{"docs":{},"a":{"docs":{},"s":{"docs":{},"s":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}},"p":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"u":{"docs":{},"n":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}},"p":{"docs":{},"c":{"docs":{},"机":{"docs":{},"制":{"docs":{},"与":{"docs":{},"h":{"docs":{},"m":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"和":{"docs":{},"h":{"docs":{},"r":{"docs":{},"e":{"docs":{},"g":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"v":{"docs":{},"e":{"docs":{},"r":{"docs":{},"进":{"docs":{},"行":{"docs":{},"通":{"docs":{},"信":{"docs":{},"；":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"d":{"docs":{},"d":{"1":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00851063829787234},"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.020454545454545454},"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.010067114093959731}},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}}}}}},"f":{"docs":{},"l":{"docs":{},"a":{"docs":{},"t":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"(":{"docs":{},"l":{"docs":{},"a":{"docs":{},"m":{"docs":{},"b":{"docs":{},"d":{"docs":{},"a":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}}}}}}}},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"(":{"docs":{},"a":{"docs":{},"d":{"docs":{},"d":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}},"l":{"docs":{},"a":{"docs":{},"m":{"docs":{},"b":{"docs":{},"d":{"docs":{},"a":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.006818181818181818},"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.010067114093959731}}}}}}}}}}}},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{},"e":{"docs":{},"(":{"docs":{},"l":{"docs":{},"a":{"docs":{},"m":{"docs":{},"b":{"docs":{},"d":{"docs":{},"a":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}}}}}}},"u":{"docs":{},"n":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"(":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"2":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.006818181818181818}}}},"docs":{}}}}}}}}}}}},"2":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.01818181818181818},"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.010067114093959731},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.00909090909090909},"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}}}}}}}},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"(":{"docs":{},"l":{"docs":{},"a":{"docs":{},"m":{"docs":{},"b":{"docs":{},"d":{"docs":{},"a":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}}}}}}},"d":{"docs":{},"i":{"docs":{},"s":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},".":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"(":{"docs":{},"l":{"docs":{},"a":{"docs":{},"m":{"docs":{},"b":{"docs":{},"d":{"docs":{},"a":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}}}}}}}}}}}}}}}}}}}},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{},"e":{"docs":{},"b":{"docs":{},"y":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{},"(":{"docs":{},"l":{"docs":{},"a":{"docs":{},"m":{"docs":{},"b":{"docs":{},"d":{"docs":{},"a":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}}}}}}}}}}}}}}}}}}},"3":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.00909090909090909},"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.006711409395973154}},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.004545454545454545}}}}}}}}}}},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"b":{"docs":{},"y":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{},"(":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}}}}}},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"s":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"(":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"2":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}},"docs":{}}}}}}}}}}}}}}}}},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{},"e":{"docs":{},"b":{"docs":{},"y":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{},"(":{"docs":{},"l":{"docs":{},"a":{"docs":{},"m":{"docs":{},"b":{"docs":{},"d":{"docs":{},"a":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}}}}}}}}}}}}}}}}},"t":{"docs":{},"a":{"docs":{},"k":{"docs":{},"e":{"docs":{},"(":{"5":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}},"docs":{}}}}}}}},"4":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.004545454545454545},"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.006711409395973154}},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.004545454545454545},"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}}}}}}}},"s":{"docs":{},"a":{"docs":{},"v":{"docs":{},"e":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"e":{"docs":{},"x":{"docs":{},"t":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"e":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},":":{"docs":{},"/":{"docs":{},"/":{"docs":{},"/":{"docs":{},"u":{"docs":{},"v":{"docs":{},"/":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"u":{"docs":{},"l":{"docs":{},"t":{"docs":{},"\"":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.015909090909090907},"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.006993006993006993},"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},"会":{"docs":{},"在":{"docs":{},"多":{"docs":{},"个":{"docs":{},"节":{"docs":{},"点":{"docs":{},"上":{"docs":{},"存":{"docs":{},"储":{"docs":{},"，":{"docs":{},"就":{"docs":{},"和":{"docs":{},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},"的":{"docs":{},"分":{"docs":{},"布":{"docs":{},"式":{"docs":{},"道":{"docs":{},"理":{"docs":{},"是":{"docs":{},"一":{"docs":{},"样":{"docs":{},"的":{"docs":{},"。":{"docs":{},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},"文":{"docs":{},"件":{"docs":{},"被":{"docs":{},"切":{"docs":{},"分":{"docs":{},"为":{"docs":{},"多":{"docs":{},"个":{"docs":{},"b":{"docs":{},"l":{"docs":{},"o":{"docs":{},"c":{"docs":{},"k":{"docs":{},"存":{"docs":{},"储":{"docs":{},"在":{"docs":{},"各":{"docs":{},"个":{"docs":{},"节":{"docs":{},"点":{"docs":{},"上":{"docs":{},"，":{"docs":{},"而":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"是":{"docs":{},"被":{"docs":{},"切":{"docs":{},"分":{"docs":{},"为":{"docs":{},"多":{"docs":{},"个":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{},"i":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"。":{"docs":{},"不":{"docs":{},"同":{"docs":{},"的":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{},"i":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"可":{"docs":{},"能":{"docs":{},"在":{"docs":{},"不":{"docs":{},"同":{"docs":{},"的":{"docs":{},"节":{"docs":{},"点":{"docs":{},"上":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"概":{"docs":{},"述":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}},"的":{"docs":{},"创":{"docs":{},"建":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}},"p":{"docs":{},"v":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}},"w":{"docs":{},"o":{"docs":{},"r":{"docs":{},"d":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}}}},"（":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"i":{"docs":{},"l":{"docs":{},"i":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}}}},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}},".":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{},"e":{"docs":{},"b":{"docs":{},"y":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{},"(":{"docs":{},"l":{"docs":{},"a":{"docs":{},"m":{"docs":{},"b":{"docs":{},"d":{"docs":{},"a":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}}}}}}}}}}}},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"(":{"docs":{},"l":{"docs":{},"a":{"docs":{},"m":{"docs":{},"b":{"docs":{},"d":{"docs":{},"a":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}},"两":{"docs":{},"类":{"docs":{},"算":{"docs":{},"子":{"docs":{},"执":{"docs":{},"行":{"docs":{},"示":{"docs":{},"意":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}},"中":{"docs":{},"的":{"docs":{},"所":{"docs":{},"有":{"docs":{},"元":{"docs":{},"素":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}},"常":{"docs":{},"用":{"docs":{},"算":{"docs":{},"子":{"docs":{},"练":{"docs":{},"习":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}},"具":{"docs":{},"有":{"docs":{},"面":{"docs":{},"向":{"docs":{},"对":{"docs":{},"象":{"docs":{},"编":{"docs":{},"程":{"docs":{},"的":{"docs":{},"特":{"docs":{},"性":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}}}}}}}}}}}}},"编":{"docs":{},"译":{"docs":{},"时":{"docs":{},"进":{"docs":{},"行":{"docs":{},"类":{"docs":{},"型":{"docs":{},"检":{"docs":{},"查":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}}}}}}}}}}},"是":{"docs":{},"分":{"docs":{},"布":{"docs":{},"式":{"docs":{},"的":{"docs":{},"j":{"docs":{},"a":{"docs":{},"v":{"docs":{},"a":{"docs":{},"对":{"docs":{},"象":{"docs":{},"的":{"docs":{},"集":{"docs":{},"合":{"docs":{},"。":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"是":{"docs":{},"分":{"docs":{},"布":{"docs":{},"式":{"docs":{},"的":{"docs":{},"r":{"docs":{},"o":{"docs":{},"w":{"docs":{},"对":{"docs":{},"象":{"docs":{},"的":{"docs":{},"集":{"docs":{},"合":{"docs":{},"。":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"除":{"docs":{},"了":{"docs":{},"提":{"docs":{},"供":{"docs":{},"了":{"docs":{},"比":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"更":{"docs":{},"丰":{"docs":{},"富":{"docs":{},"的":{"docs":{},"算":{"docs":{},"子":{"docs":{},"以":{"docs":{},"外":{"docs":{},"，":{"docs":{},"更":{"docs":{},"重":{"docs":{},"要":{"docs":{},"的":{"docs":{},"特":{"docs":{},"点":{"docs":{},"是":{"docs":{},"提":{"docs":{},"升":{"docs":{},"执":{"docs":{},"行":{"docs":{},"效":{"docs":{},"率":{"docs":{},"、":{"docs":{},"减":{"docs":{},"少":{"docs":{},"数":{"docs":{},"据":{"docs":{},"读":{"docs":{},"取":{"docs":{},"以":{"docs":{},"及":{"docs":{},"执":{"docs":{},"行":{"docs":{},"计":{"docs":{},"划":{"docs":{},"的":{"docs":{},"优":{"docs":{},"化":{"docs":{},"。":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"：":{"docs":{},"分":{"docs":{},"布":{"docs":{},"式":{"docs":{},"的":{"docs":{},"对":{"docs":{},"象":{"docs":{},"的":{"docs":{},"集":{"docs":{},"合":{"docs":{},"，":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"并":{"docs":{},"不":{"docs":{},"知":{"docs":{},"道":{"docs":{},"对":{"docs":{},"象":{"docs":{},"的":{"docs":{},"详":{"docs":{},"细":{"docs":{},"模":{"docs":{},"式":{"docs":{},"信":{"docs":{},"息":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"i":{"docs":{},"c":{"docs":{},"h":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}},".":{"docs":{},"a":{"docs":{},"g":{"docs":{},"e":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0008158270446665307},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}},"d":{"docs":{},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"i":{"docs":{},"d":{"docs":{},",":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}},"c":{"docs":{},"m":{"docs":{},"s":{"docs":{},"_":{"docs":{},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0008158270446665307},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}},"s":{"docs":{},"e":{"docs":{},"g":{"docs":{},"i":{"docs":{},"d":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}}}}}}}}}}},"f":{"docs":{},"i":{"docs":{},"n":{"docs":{},"a":{"docs":{},"l":{"docs":{},"_":{"docs":{},"g":{"docs":{},"e":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"c":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0008158270446665307},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}}}}}},"o":{"docs":{},"c":{"docs":{},"c":{"docs":{},"u":{"docs":{},"p":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"]":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}}},",":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"p":{"docs":{},"p":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0008158270446665307},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}}},"i":{"docs":{},"d":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"n":{"docs":{},"e":{"docs":{},"w":{"docs":{},"_":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"c":{"docs":{},"l":{"docs":{},"a":{"docs":{},"s":{"docs":{},"s":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}}}}}}}},"p":{"docs":{},"r":{"docs":{},"i":{"docs":{},"c":{"docs":{},"e":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},",":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"i":{"docs":{},"d":{"docs":{},",":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}},"u":{"2":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.01606425702811245}}},"docs":{},"e":{"docs":{},"(":{"docs":{},"前":{"docs":{},"端":{"docs":{},"界":{"docs":{},"面":{"docs":{},")":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}}}}}}}},"i":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}},"d":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0056925996204933585},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.004629629629629629}},")":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}}},",":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.015437392795883362},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.011385199240986717},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.006944444444444444},"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842},"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}},"i":{"docs":{},"i":{"docs":{},"d":{"docs":{},",":{"docs":{},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"l":{"docs":{},"_":{"docs":{},"r":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}}}}}}}},":":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.006493506493506494}}},"s":{"docs":{},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0017152658662092624}}}}},"中":{"docs":{},"看":{"docs":{},"到":{"docs":{},"当":{"docs":{},"前":{"docs":{},"的":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"作":{"docs":{},"业":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}}}}}}}}},"观":{"docs":{},"察":{"docs":{},"执":{"docs":{},"行":{"docs":{},"情":{"docs":{},"况":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00851063829787234}}}}}}}}},"查":{"docs":{},"看":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"集":{"docs":{},"群":{"docs":{},"及":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}}}}}}}}}}}}}}}},"s":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.008032128514056224},"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0084985835694051},"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.01702127659574468},"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981}},"e":{"docs":{},"r":{"1":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147}}},"2":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147}}},"3":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147}}},"4":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147}}},"5":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147}}},"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.012711864406779662},"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.0016233766233766235},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0056925996204933585},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147},"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842},"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907},"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.008032128514056224},"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.004405286343612335},"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}},"_":{"docs":{},"s":{"docs":{},"i":{"docs":{},"m":{"docs":{},"i":{"docs":{},"l":{"docs":{},"a":{"docs":{},"r":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.007062146892655367},"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.0016233766233766235}},".":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{},":":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}},"l":{"docs":{},"o":{"docs":{},"c":{"docs":{},"[":{"docs":{},"i":{"docs":{},"]":{"docs":{},".":{"docs":{},"d":{"docs":{},"r":{"docs":{},"o":{"docs":{},"p":{"docs":{},"(":{"docs":{},"[":{"docs":{},"i":{"docs":{},"]":{"docs":{},")":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}}}}},")":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}},":":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.006493506493506494}}}},":":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.00487012987012987}}},"[":{"1":{"docs":{},"]":{"docs":{},".":{"docs":{},"d":{"docs":{},"r":{"docs":{},"o":{"docs":{},"p":{"docs":{},"(":{"docs":{},"[":{"1":{"docs":{},"]":{"docs":{},")":{"docs":{},".":{"docs":{},"d":{"docs":{},"r":{"docs":{},"o":{"docs":{},"p":{"docs":{},"n":{"docs":{},"a":{"docs":{},"(":{"docs":{},")":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.0016233766233766235}}}}}}}}}}}}}},"docs":{}}}}}}}}}},"docs":{},"u":{"docs":{},"i":{"docs":{},"d":{"docs":{},"]":{"docs":{},".":{"docs":{},"d":{"docs":{},"r":{"docs":{},"o":{"docs":{},"p":{"docs":{},"(":{"docs":{},"[":{"docs":{},"u":{"docs":{},"i":{"docs":{},"d":{"docs":{},"]":{"docs":{},")":{"docs":{},".":{"docs":{},"d":{"docs":{},"r":{"docs":{},"o":{"docs":{},"p":{"docs":{},"n":{"docs":{},"a":{"docs":{},"(":{"docs":{},")":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"r":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"_":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}}}}}}},"p":{"docs":{},"r":{"docs":{},"o":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.015789473684210527},"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}},"e":{"docs":{},"[":{"docs":{},"u":{"docs":{},"i":{"docs":{},"d":{"docs":{},"]":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}}}}},".":{"docs":{},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"m":{"docs":{},"s":{"docs":{},"(":{"docs":{},")":{"docs":{},":":{"docs":{"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}}}}}}}}}},"c":{"docs":{},"s":{"docs":{},"v":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.010526315789473684}}}}}},"_":{"docs":{},"d":{"docs":{},"f":{"2":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},".":{"docs":{},"p":{"docs":{},"r":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"s":{"docs":{},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},"m":{"docs":{},"a":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}},"3":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},".":{"docs":{},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}},"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0008158270446665307},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289}},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}},"d":{"docs":{},"r":{"docs":{},"o":{"docs":{},"p":{"docs":{},"n":{"docs":{},"a":{"docs":{},"(":{"docs":{},"s":{"docs":{},"u":{"docs":{},"b":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"=":{"docs":{},"[":{"docs":{},"\"":{"docs":{},"n":{"docs":{},"e":{"docs":{},"w":{"docs":{},"_":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"c":{"docs":{},"l":{"docs":{},"a":{"docs":{},"s":{"docs":{},"s":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"\"":{"docs":{},"]":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},".":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"(":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"p":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"\"":{"docs":{},"]":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},".":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"(":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}},"u":{"docs":{},"n":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"a":{"docs":{},"l":{"docs":{},"l":{"docs":{},"(":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},".":{"docs":{},"c":{"docs":{},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"(":{"docs":{},"p":{"docs":{},"d":{"docs":{},"f":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"：":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"b":{"docs":{},"y":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"a":{"docs":{},"g":{"docs":{},"e":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"c":{"docs":{},"m":{"docs":{},"s":{"docs":{},"_":{"docs":{},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"s":{"docs":{},"e":{"docs":{},"g":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"f":{"docs":{},"i":{"docs":{},"n":{"docs":{},"a":{"docs":{},"l":{"docs":{},"_":{"docs":{},"g":{"docs":{},"e":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"c":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"n":{"docs":{},"e":{"docs":{},"w":{"docs":{},"_":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"c":{"docs":{},"l":{"docs":{},"a":{"docs":{},"s":{"docs":{},"s":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},".":{"docs":{},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}},"m":{"docs":{},"i":{"docs":{},"n":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"n":{"docs":{},"u":{"docs":{},"c":{"docs":{},"l":{"docs":{},"_":{"docs":{},"o":{"docs":{},"n":{"docs":{},"e":{"docs":{},"h":{"docs":{},"o":{"docs":{},"t":{"docs":{},"_":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"e":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"o":{"docs":{},"c":{"docs":{},"c":{"docs":{},"u":{"docs":{},"p":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"p":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},".":{"docs":{},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}},"m":{"docs":{},"i":{"docs":{},"n":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"p":{"docs":{},"l":{"docs":{},"_":{"docs":{},"o":{"docs":{},"n":{"docs":{},"e":{"docs":{},"h":{"docs":{},"o":{"docs":{},"t":{"docs":{},"_":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"e":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"p":{"docs":{},"p":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"n":{"docs":{},"a":{"docs":{},".":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"l":{"docs":{},"(":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0008158270446665307}}}}}}}}},"e":{"docs":{},"w":{"docs":{},"_":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"c":{"docs":{},"l":{"docs":{},"a":{"docs":{},"s":{"docs":{},"s":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},".":{"docs":{},"c":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"(":{"docs":{},"s":{"docs":{},"t":{"docs":{},"r":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"t":{"docs":{},"y":{"docs":{},"p":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"p":{"docs":{},"r":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"s":{"docs":{},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},"m":{"docs":{},"a":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},".":{"docs":{},"c":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"(":{"docs":{},"s":{"docs":{},"t":{"docs":{},"r":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"t":{"docs":{},"y":{"docs":{},"p":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{},")":{"docs":{},"\\":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}}}}}},"w":{"docs":{},"i":{"docs":{},"t":{"docs":{},"h":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"p":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}},"f":{"docs":{},"o":{"docs":{},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"c":{"docs":{},"h":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{},"i":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"(":{"docs":{},"f":{"docs":{},"o":{"docs":{},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"c":{"docs":{},"h":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{},"i":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"2":{"docs":{},")":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"a":{"docs":{},"c":{"docs":{},"t":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.01020408163265306}},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"s":{"docs":{},"(":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}},";":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0045351473922902496}}}}}}}}}},"i":{"docs":{},"d":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0022675736961451248}},",":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"_":{"docs":{},"l":{"docs":{},"i":{"docs":{},"s":{"docs":{},"t":{"docs":{},"(":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{},"i":{"docs":{},"c":{"docs":{},"l":{"docs":{},"e":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},")":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}}}}}}}}}}}}}},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"(":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{},"i":{"docs":{},"c":{"docs":{},"l":{"docs":{},"e":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},")":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}}}}}}}}}}}}}}}}}}}}},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{},"w":{"docs":{},"o":{"docs":{},"r":{"docs":{},"d":{"docs":{},",":{"docs":{},"w":{"docs":{},"e":{"docs":{},"i":{"docs":{},"g":{"docs":{},"h":{"docs":{},"t":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}}}}}}}}}}}},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"_":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{},"s":{"docs":{},"(":{"docs":{},"w":{"docs":{},"m":{"docs":{},")":{"docs":{},",":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"_":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"s":{"docs":{},"(":{"docs":{},"w":{"docs":{},"m":{"docs":{},")":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"s":{"docs":{},"o":{"docs":{},"r":{"docs":{},"t":{"docs":{},"_":{"docs":{},"a":{"docs":{},"r":{"docs":{},"r":{"docs":{},"a":{"docs":{},"y":{"docs":{},"(":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"_":{"docs":{},"l":{"docs":{},"i":{"docs":{},"s":{"docs":{},"t":{"docs":{},"(":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{},"i":{"docs":{},"c":{"docs":{},"l":{"docs":{},"e":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},")":{"docs":{},")":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},";":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.003401360544217687}}},"：":{"docs":{},"脱":{"docs":{},"敏":{"docs":{},"过":{"docs":{},"的":{"docs":{},"用":{"docs":{},"户":{"docs":{},"i":{"docs":{},"d":{"docs":{},"；":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}}}}}}}},"k":{"docs":{},"w":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0022675736961451248}},"s":{"docs":{},";":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0022675736961451248}}}}}},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}}},"c":{"docs":{},"f":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}},"s":{"docs":{},"_":{"docs":{},"r":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"s":{"docs":{},".":{"docs":{},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"t":{"docs":{},"u":{"docs":{},"p":{"docs":{},"l":{"docs":{},"e":{"docs":{},"s":{"docs":{},"(":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{},"=":{"docs":{},"t":{"docs":{},"r":{"docs":{},"u":{"docs":{},"e":{"docs":{},")":{"docs":{},":":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"i":{"docs":{},"d":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289}},"：":{"docs":{},"脱":{"docs":{},"敏":{"docs":{},"过":{"docs":{},"的":{"docs":{},"用":{"docs":{},"户":{"docs":{},"i":{"docs":{},"d":{"docs":{},"；":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}}}}}},":":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0024469820554649264},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0008158270446665307},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.001193792280143255}}},"、":{"docs":{},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"i":{"docs":{},"d":{"docs":{},"的":{"docs":{},"d":{"docs":{},"f":{"docs":{},"，":{"docs":{},"对":{"docs":{},"应":{"docs":{},"预":{"docs":{},"测":{"docs":{},"值":{"docs":{},"进":{"docs":{},"行":{"docs":{},"排":{"docs":{},"序":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}}}}}}}},")":{"docs":{},")":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}},"：":{"docs":{},"脱":{"docs":{},"敏":{"docs":{},"过":{"docs":{},"的":{"docs":{},"用":{"docs":{},"户":{"docs":{},"i":{"docs":{},"d":{"docs":{},"；":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}}}}}},":":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}},"|":{"docs":{},"t":{"docs":{},"i":{"docs":{},"m":{"docs":{},"e":{"docs":{},"_":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"m":{"docs":{},"p":{"docs":{},"|":{"docs":{},"b":{"docs":{},"t":{"docs":{},"a":{"docs":{},"g":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}},"a":{"docs":{},"d":{"docs":{},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"s":{"docs":{},"=":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"g":{"docs":{},"e":{"docs":{},"(":{"1":{"docs":{},",":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}},"2":{"docs":{},")":{"docs":{},",":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}},"d":{"docs":{},"t":{"docs":{},"y":{"docs":{},"p":{"docs":{},"e":{"docs":{},"=":{"docs":{},"{":{"docs":{},"\"":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},":":{"docs":{"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}}}}}}}}}}}}}}}}}}}}},"3":{"docs":{},")":{"docs":{},")":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0017152658662092624},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.003795066413662239},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}},"docs":{}}}}}}}}}}}},":":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}},"f":{"docs":{},"u":{"docs":{},"l":{"docs":{},"_":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}},"}":{"docs":{},"s":{"docs":{},"i":{"docs":{},"m":{"docs":{},"(":{"docs":{},"u":{"docs":{},",":{"docs":{},"v":{"docs":{},")":{"docs":{},"*":{"docs":{},"r":{"docs":{},"_":{"docs":{},"{":{"docs":{},"v":{"docs":{},"i":{"docs":{},"}":{"docs":{},"}":{"docs":{},"{":{"docs":{},"\\":{"docs":{},"s":{"docs":{},"u":{"docs":{},"m":{"docs":{},"_":{"docs":{},"{":{"docs":{},"v":{"docs":{},"\\":{"docs":{},"i":{"docs":{},"n":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734},"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"|":{"docs":{},"s":{"docs":{},"i":{"docs":{},"m":{"docs":{},"(":{"docs":{},"u":{"docs":{},",":{"docs":{},"v":{"docs":{},")":{"docs":{},"|":{"docs":{},"}":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734},"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}}}}}}}}}}}}}},"g":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}},"c":{"docs":{"day02_推荐算法/07_基于内容的推荐算法.html":{"ref":"day02_推荐算法/07_基于内容的推荐算法.html","tf":0.03125}}}},"t":{"docs":{},"i":{"docs":{},"l":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282}},".":{"docs":{},"n":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{},"c":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"l":{"docs":{},"o":{"docs":{},"a":{"docs":{},"d":{"docs":{},"e":{"docs":{},"r":{"docs":{},":":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}}}}}}}}}}}}}}}}}},"r":{"docs":{},"i":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.0125}}},"l":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}},"p":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"\\":{"docs":{},"i":{"docs":{},"n":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"t":{"docs":{},"\\":{"docs":{},"d":{"docs":{},"e":{"docs":{},"l":{"docs":{},"e":{"docs":{},"t":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}}}}}},"f":{"docs":{},"u":{"docs":{},"n":{"docs":{},"c":{"docs":{},"(":{"docs":{},"n":{"docs":{},"e":{"docs":{},"w":{"docs":{},"_":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"s":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}}}}}}}}}},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"b":{"docs":{},"y":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.007633587786259542}}}}}}}}}}}}}}}}},"(":{"docs":{},"f":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}},";":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}},"d":{"docs":{},"a":{"docs":{},"f":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.008032128514056224}},":":{"docs":{},"就":{"docs":{},"是":{"docs":{},"一":{"docs":{},"个":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{},"e":{"docs":{},"r":{"docs":{},"，":{"docs":{},"把":{"docs":{},"一":{"docs":{},"组":{"docs":{},"输":{"docs":{},"入":{"docs":{},"数":{"docs":{},"据":{"docs":{},"映":{"docs":{},"射":{"docs":{},"为":{"docs":{},"一":{"docs":{},"条":{"docs":{},"(":{"docs":{},"或":{"docs":{},"多":{"docs":{},"条":{"docs":{},")":{"docs":{},"输":{"docs":{},"出":{"docs":{},"数":{"docs":{},"据":{"docs":{},"。":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"f":{"1":{"docs":{},".":{"docs":{},"p":{"docs":{},"y":{"docs":{},"'":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}},"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.01606425702811245},"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.006993006993006993}},".":{"docs":{},"p":{"docs":{},"i":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}},"示":{"docs":{},"例":{"docs":{},"(":{"docs":{},"运":{"docs":{},"行":{"docs":{},"j":{"docs":{},"a":{"docs":{},"v":{"docs":{},"a":{"docs":{},"已":{"docs":{},"经":{"docs":{},"编":{"docs":{},"写":{"docs":{},"好":{"docs":{},"的":{"docs":{},"u":{"docs":{},"d":{"docs":{},"f":{"docs":{},")":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}}}}}}}}}}}}}}}},"：":{"docs":{},"就":{"docs":{},"是":{"docs":{},"做":{"docs":{},"一":{"docs":{},"个":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"p":{"docs":{},"e":{"docs":{},"r":{"docs":{},"，":{"docs":{},"对":{"docs":{},"每":{"docs":{},"一":{"docs":{},"条":{"docs":{},"输":{"docs":{},"入":{"docs":{},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"映":{"docs":{},"射":{"docs":{},"为":{"docs":{},"一":{"docs":{},"条":{"docs":{},"输":{"docs":{},"出":{"docs":{},"数":{"docs":{},"据":{"docs":{},"。":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"自":{"docs":{},"定":{"docs":{},"义":{"docs":{},"函":{"docs":{},"数":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}},"(":{"docs":{},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"u":{"docs":{},"l":{"docs":{},"d":{"docs":{},"_":{"docs":{},"r":{"docs":{},"e":{"docs":{},"m":{"docs":{},"o":{"docs":{},"v":{"docs":{},"e":{"docs":{},",":{"docs":{},"s":{"docs":{},"t":{"docs":{},"r":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"t":{"docs":{},"y":{"docs":{},"p":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}}}},"n":{"docs":{},"a":{"docs":{},"b":{"docs":{},"l":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}},"t":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}},"v":{"docs":{},"统":{"docs":{},"计":{"docs":{},"案":{"docs":{},"例":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}}},"：":{"docs":{},"网":{"docs":{},"站":{"docs":{},"的":{"docs":{},"独":{"docs":{},"立":{"docs":{},"用":{"docs":{},"户":{"docs":{},"访":{"docs":{},"问":{"docs":{},"量":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}}}}}}}}}}}},"业":{"docs":{},"务":{"docs":{},"知":{"docs":{},"识":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}}}}},"估":{"docs":{},"计":{"docs":{},"用":{"docs":{},"户":{"docs":{},"是":{"docs":{},"否":{"docs":{},"会":{"docs":{},"点":{"docs":{},"击":{"docs":{},"某":{"docs":{},"个":{"docs":{},"商":{"docs":{},"品":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}}}}}}}}}}}}}},"使":{"docs":{},"用":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938},"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842},"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}},"l":{"docs":{},"r":{"docs":{},"算":{"docs":{},"法":{"docs":{},")":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}}}}}},"协":{"docs":{},"同":{"docs":{},"过":{"docs":{},"滤":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"算":{"docs":{},"法":{"docs":{},"对":{"docs":{},"用":{"docs":{},"户":{"docs":{},"进":{"docs":{},"行":{"docs":{},"评":{"docs":{},"分":{"docs":{},"预":{"docs":{},"测":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}}}}},"用":{"docs":{},"户":{"docs":{},"行":{"docs":{},"为":{"docs":{},"数":{"docs":{},"据":{"docs":{},"描":{"docs":{},"述":{"docs":{},"商":{"docs":{},"品":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}},"其":{"docs":{},"它":{"docs":{},"站":{"docs":{},"点":{"docs":{},"的":{"docs":{},"行":{"docs":{},"为":{"docs":{},"数":{"docs":{},"据":{"docs":{},",":{"docs":{"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html":{"ref":"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html","tf":0.024390243902439025}}}}}}}}}}}},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"i":{"docs":{},"n":{"docs":{},"e":{"docs":{},"的":{"docs":{},"算":{"docs":{},"法":{"docs":{},"思":{"docs":{},"想":{"docs":{},"预":{"docs":{},"测":{"docs":{},"评":{"docs":{},"分":{"docs":{},"的":{"docs":{},"步":{"docs":{},"骤":{"docs":{},"如":{"docs":{},"下":{"docs":{},"：":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}}}}}}}}}}}}},"交":{"docs":{},"替":{"docs":{},"最":{"docs":{},"小":{"docs":{},"二":{"docs":{},"乘":{"docs":{},"法":{"docs":{},"优":{"docs":{},"化":{"docs":{},"算":{"docs":{},"法":{"docs":{},"预":{"docs":{},"测":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"i":{"docs":{},"n":{"docs":{},"e":{"docs":{},"偏":{"docs":{},"置":{"docs":{},"值":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}}}}}}}}}}}}}},"随":{"docs":{},"机":{"docs":{},"梯":{"docs":{},"度":{"docs":{},"下":{"docs":{},"降":{"docs":{},"优":{"docs":{},"化":{"docs":{},"算":{"docs":{},"法":{"docs":{},"预":{"docs":{},"测":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"i":{"docs":{},"n":{"docs":{},"e":{"docs":{},"偏":{"docs":{},"置":{"docs":{},"值":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}}}}}}},"，":{"docs":{},"优":{"docs":{},"化":{"docs":{},"结":{"docs":{},"果":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}}}}}}},"t":{"docs":{},"f":{"docs":{},"i":{"docs":{},"d":{"docs":{},"f":{"docs":{},"，":{"docs":{},"分":{"docs":{},"析":{"docs":{},"提":{"docs":{},"取":{"docs":{},"t":{"docs":{},"o":{"docs":{},"p":{"docs":{},"n":{"docs":{},"关":{"docs":{},"键":{"docs":{},"词":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876}}}}}}}}}}}}}}}}}}},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"s":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"类":{"docs":{},"统":{"docs":{},"计":{"docs":{},"列":{"docs":{},"表":{"docs":{},"元":{"docs":{},"素":{"docs":{},"出":{"docs":{},"现":{"docs":{},"次":{"docs":{},"数":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"方":{"docs":{},"法":{"docs":{},"：":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.03125}}}}}}}}}}},"p":{"docs":{},"i":{"docs":{},"p":{"docs":{},"安":{"docs":{},"装":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}},"y":{"docs":{},"t":{"docs":{},"h":{"docs":{},"o":{"docs":{},"n":{"docs":{},"开":{"docs":{},"发":{"docs":{},"在":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},"上":{"docs":{},"运":{"docs":{},"行":{"docs":{},"的":{"docs":{},"程":{"docs":{},"序":{"docs":{},",":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}}}}}}}}}}}}}}}},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"中":{"docs":{},"的":{"docs":{},"a":{"docs":{},"l":{"docs":{},"s":{"docs":{},"矩":{"docs":{},"阵":{"docs":{},"分":{"docs":{},"解":{"docs":{},"方":{"docs":{},"法":{"docs":{},"实":{"docs":{},"现":{"docs":{},"c":{"docs":{},"f":{"docs":{},"评":{"docs":{},"分":{"docs":{},"预":{"docs":{},"测":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0024469820554649264}}}}}}}}}}}}}}}}}}}}}}}}}}}},"动":{"docs":{},"态":{"docs":{},"分":{"docs":{},"区":{"docs":{},"需":{"docs":{},"要":{"docs":{},"设":{"docs":{},"置":{"docs":{},"参":{"docs":{},"数":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}}}}}},"语":{"docs":{},"法":{"docs":{},"为":{"docs":{},"：":{"docs":{},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{},"_":{"docs":{},"w":{"docs":{},"s":{"docs":{},"(":{"docs":{},"s":{"docs":{},"e":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"a":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{},",":{"docs":{},"s":{"docs":{},"t":{"docs":{},"r":{"1":{"docs":{},",":{"docs":{},"s":{"docs":{},"t":{"docs":{},"r":{"2":{"docs":{},",":{"docs":{},"…":{"docs":{},")":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}},"docs":{}}}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}},"多":{"docs":{},"线":{"docs":{},"程":{"docs":{},"模":{"docs":{},"型":{"docs":{},"来":{"docs":{},"减":{"docs":{},"少":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"k":{"docs":{},"启":{"docs":{},"动":{"docs":{},"开":{"docs":{},"销":{"docs":{},"，":{"docs":{},"s":{"docs":{},"h":{"docs":{},"u":{"docs":{},"f":{"docs":{},"f":{"docs":{},"l":{"docs":{},"e":{"docs":{},"过":{"docs":{},"程":{"docs":{},"中":{"docs":{},"避":{"docs":{},"免":{"docs":{},"不":{"docs":{},"必":{"docs":{},"要":{"docs":{},"的":{"docs":{},"s":{"docs":{},"o":{"docs":{},"r":{"docs":{},"t":{"docs":{},"操":{"docs":{},"作":{"docs":{},"以":{"docs":{},"及":{"docs":{},"减":{"docs":{},"少":{"docs":{},"磁":{"docs":{},"盘":{"docs":{},"i":{"docs":{},"o":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"有":{"docs":{},"状":{"docs":{},"态":{"docs":{},"的":{"docs":{},"t":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"s":{"docs":{},"f":{"docs":{},"o":{"docs":{},"r":{"docs":{},"m":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"，":{"docs":{},"需":{"docs":{},"要":{"docs":{},"开":{"docs":{},"启":{"docs":{},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},"c":{"docs":{},"k":{"docs":{},"p":{"docs":{},"o":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},".":{"docs":{},"w":{"docs":{},"i":{"docs":{},"t":{"docs":{},"h":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{},"更":{"docs":{},"改":{"docs":{},"d":{"docs":{},"f":{"docs":{},"列":{"docs":{},"数":{"docs":{},"据":{"docs":{},"结":{"docs":{},"构":{"docs":{},"；":{"docs":{},"使":{"docs":{},"用":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},".":{"docs":{},"w":{"docs":{},"i":{"docs":{},"t":{"docs":{},"h":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{},"r":{"docs":{},"e":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"d":{"docs":{},"更":{"docs":{},"改":{"docs":{},"列":{"docs":{},"名":{"docs":{},"称":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"热":{"docs":{},"独":{"docs":{},"编":{"docs":{},"码":{"docs":{},"转":{"docs":{},"换":{"docs":{},"p":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"的":{"docs":{},"一":{"docs":{},"维":{"docs":{},"数":{"docs":{},"据":{"docs":{},"为":{"docs":{},"多":{"docs":{},"维":{"docs":{},"，":{"docs":{},"其":{"docs":{},"中":{"docs":{},"缺":{"docs":{},"失":{"docs":{},"值":{"docs":{},"单":{"docs":{},"独":{"docs":{},"作":{"docs":{},"为":{"docs":{},"一":{"docs":{},"个":{"docs":{},"特":{"docs":{},"征":{"docs":{},"值":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"编":{"docs":{},"码":{"docs":{},"转":{"docs":{},"换":{"docs":{},"n":{"docs":{},"e":{"docs":{},"w":{"docs":{},"_":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"c":{"docs":{},"l":{"docs":{},"a":{"docs":{},"s":{"docs":{},"s":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"的":{"docs":{},"一":{"docs":{},"维":{"docs":{},"数":{"docs":{},"据":{"docs":{},"为":{"docs":{},"多":{"docs":{},"维":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"p":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"的":{"docs":{},"一":{"docs":{},"维":{"docs":{},"数":{"docs":{},"据":{"docs":{},"为":{"docs":{},"多":{"docs":{},"维":{"docs":{},"，":{"docs":{},"增":{"docs":{},"加":{"docs":{},"n":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"⽤":{"docs":{},"历":{"docs":{},"史":{"docs":{},"⾏":{"docs":{},"为":{"docs":{},"预":{"docs":{},"测":{"docs":{},"⽤":{"docs":{},"户":{"docs":{},"对":{"docs":{},"某":{"docs":{},"个":{"docs":{},"物":{"docs":{},"品":{"docs":{},"的":{"docs":{},"喜":{"docs":{},"爱":{"docs":{},"程":{"docs":{},"度":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}}}}}}}}}}}}}}}},"单":{"docs":{},"独":{"docs":{},"的":{"docs":{},"特":{"docs":{},"征":{"docs":{},"和":{"docs":{},"模":{"docs":{},"型":{"docs":{},"预":{"docs":{},"估":{"docs":{"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html":{"ref":"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html","tf":0.024390243902439025}}}}}}}}}}}}}},"决":{"docs":{},"定":{"docs":{},"了":{"docs":{},"最":{"docs":{},"终":{"docs":{},"的":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"效":{"docs":{},"果":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}}}}}}}}}}},"几":{"docs":{},"分":{"docs":{},"钟":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}},"~":{"docs":{},"几":{"docs":{},"小":{"docs":{},"时":{"docs":{},"(":{"docs":{},"计":{"docs":{},"算":{"docs":{},"量":{"docs":{},"和":{"docs":{},"数":{"docs":{},"据":{"docs":{},"量":{"docs":{},"不":{"docs":{},"同":{"docs":{},")":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}}}}}}}}}}}}}}}}}}},"协":{"docs":{},"同":{"docs":{},"过":{"docs":{},"滤":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863},"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734},"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}},"推":{"docs":{},"荐":{"docs":{},"算":{"docs":{},"法":{"docs":{},"代":{"docs":{},"码":{"docs":{},"实":{"docs":{},"现":{"docs":{},"：":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}},"算":{"docs":{},"法":{"docs":{"day02_推荐算法/01_基于模型的协同过滤推荐.html":{"ref":"day02_推荐算法/01_基于模型的协同过滤推荐.html","tf":0.07692307692307693}}}},"召":{"docs":{},"回":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}},"召":{"docs":{},"回":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}},"决":{"docs":{},"定":{"docs":{},"了":{"docs":{},"最":{"docs":{},"终":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"结":{"docs":{},"果":{"docs":{},"的":{"docs":{},"天":{"docs":{},"花":{"docs":{},"板":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}},",":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}}}}}}}}}}}}}}},"阶":{"docs":{},"段":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}}},"率":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}},"到":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"i":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}}}}}}},"可":{"docs":{},"水":{"docs":{},"平":{"docs":{},"扩":{"docs":{},"展":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}}}}},"进":{"docs":{},"行":{"docs":{},"任":{"docs":{},"何":{"docs":{},"计":{"docs":{},"算":{"docs":{},",":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}}}}}}}},"以":{"docs":{},"看":{"docs":{},"到":{"docs":{"day03_Hadoop/ha2.1.html":{"ref":"day03_Hadoop/ha2.1.html","tf":0.0273972602739726}},"与":{"docs":{},"用":{"docs":{},"户":{"1":{"docs":{},"最":{"docs":{},"相":{"docs":{},"似":{"docs":{},"的":{"docs":{},"是":{"docs":{},"用":{"docs":{},"户":{"2":{"docs":{},"和":{"docs":{},"用":{"docs":{},"户":{"3":{"docs":{},"；":{"docs":{},"与":{"docs":{},"物":{"docs":{},"品":{"docs":{},"a":{"docs":{},"最":{"docs":{},"相":{"docs":{},"似":{"docs":{},"的":{"docs":{},"物":{"docs":{},"品":{"docs":{},"分":{"docs":{},"别":{"docs":{},"是":{"docs":{},"物":{"docs":{},"品":{"docs":{},"e":{"docs":{},"和":{"docs":{},"物":{"docs":{},"品":{"docs":{},"d":{"docs":{},"。":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{}}}}},"docs":{}}}}}}}}},"docs":{}}}}}},"把":{"docs":{},"运":{"docs":{},"行":{"docs":{},"日":{"docs":{},"志":{"docs":{},",":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}}}}},"快":{"docs":{},"速":{"docs":{},"查":{"docs":{},"找":{"docs":{},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},"中":{"docs":{},"数":{"docs":{},"据":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}}}}}}}},"理":{"docs":{},"解":{"docs":{},"为":{"docs":{},"一":{"docs":{},"个":{"docs":{},"键":{"docs":{},"值":{"docs":{},"对":{"docs":{},"(":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{"Hive&HBase/06_hbase数据模型.html":{"ref":"Hive&HBase/06_hbase数据模型.html","tf":0.020833333333333332}}}}}}}}}}}}}},"通":{"docs":{},"过":{"docs":{},"参":{"docs":{},"数":{"docs":{},"禁":{"docs":{},"止":{"docs":{},"自":{"docs":{},"动":{"docs":{},"链":{"docs":{},"接":{"docs":{},",":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}}}}}}},"广":{"docs":{},"播":{"docs":{},"变":{"docs":{},"量":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}}}}},"启":{"docs":{},"动":{"docs":{},"多":{"docs":{},"个":{"docs":{},"h":{"docs":{},"m":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"，":{"docs":{},"通":{"docs":{},"过":{"docs":{},"z":{"docs":{},"o":{"docs":{},"o":{"docs":{},"k":{"docs":{},"e":{"docs":{},"e":{"docs":{},"p":{"docs":{},"e":{"docs":{},"r":{"docs":{},"的":{"docs":{},"m":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"在":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}}},"缓":{"docs":{},"存":{"docs":{},"在":{"docs":{},"内":{"docs":{},"存":{"docs":{},"中":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}},"扩":{"docs":{},"展":{"docs":{},"性":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008},"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}},":":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}},"的":{"docs":{},"(":{"docs":{},"s":{"docs":{},"c":{"docs":{},"a":{"docs":{},"l":{"docs":{},"a":{"docs":{},"b":{"docs":{},"l":{"docs":{},"e":{"docs":{},")":{"docs":{},"分":{"docs":{},"布":{"docs":{},"式":{"docs":{},"计":{"docs":{},"算":{"docs":{},"框":{"docs":{},"架":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}}}}}}}}}}}}}}}}}}},"能":{"docs":{},"导":{"docs":{},"致":{"docs":{},"用":{"docs":{},"户":{"docs":{},"流":{"docs":{},"失":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}}}}},"理":{"docs":{},"解":{"docs":{},"为":{"docs":{},"解":{"docs":{},"压":{"docs":{},"，":{"docs":{},"返":{"docs":{},"回":{"docs":{},"二":{"docs":{},"维":{"docs":{},"矩":{"docs":{},"阵":{"docs":{},"式":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}}},"迭":{"docs":{},"代":{"docs":{},"对":{"docs":{},"象":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}}}},"选":{"docs":{},"，":{"docs":{},"初":{"docs":{},"始":{"docs":{},"参":{"docs":{},"数":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}}}}}},"靠":{"docs":{},"的":{"docs":{},"(":{"docs":{},"r":{"docs":{},"e":{"docs":{},"l":{"docs":{},"i":{"docs":{},"a":{"docs":{},"b":{"docs":{},"l":{"docs":{},"e":{"docs":{},")":{"docs":{},",":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}}}}}}}}}},":":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}},"构":{"docs":{},"建":{"docs":{},"在":{"docs":{},"廉":{"docs":{},"价":{"docs":{},"机":{"docs":{},"器":{"docs":{},"上":{"docs":{"day03_Hadoop/ha2.4.html":{"ref":"day03_Hadoop/ha2.4.html","tf":0.03333333333333333}}}}}}}}}},"用":{"docs":{},"性":{"docs":{},"(":{"docs":{},"保":{"docs":{},"证":{"docs":{},"每":{"docs":{},"个":{"docs":{},"请":{"docs":{},"求":{"docs":{},"不":{"docs":{},"管":{"docs":{},"成":{"docs":{},"功":{"docs":{},"或":{"docs":{},"失":{"docs":{},"败":{"docs":{},"都":{"docs":{},"有":{"docs":{},"响":{"docs":{},"应":{"docs":{},",":{"docs":{},"但":{"docs":{},"不":{"docs":{},"保":{"docs":{},"证":{"docs":{},"获":{"docs":{},"取":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"的":{"docs":{},"正":{"docs":{},"确":{"docs":{},"性":{"docs":{},")":{"docs":{"Hive&HBase/06_hbase数据模型.html":{"ref":"Hive&HBase/06_hbase数据模型.html","tf":0.020833333333333332}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"分":{"docs":{},"区":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}},"视":{"docs":{},"化":{"docs":{},"查":{"docs":{},"看":{"docs":{},"效":{"docs":{},"果":{"docs":{},"：":{"docs":{},"h":{"docs":{},"t":{"docs":{},"t":{"docs":{},"p":{"docs":{},":":{"docs":{},"/":{"docs":{},"/":{"1":{"9":{"2":{"docs":{},".":{"1":{"6":{"8":{"docs":{},".":{"1":{"9":{"docs":{},".":{"1":{"3":{"7":{"docs":{},":":{"4":{"0":{"4":{"0":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}}}}},"通":{"docs":{},"过":{"docs":{},"该":{"docs":{},"方":{"docs":{},"法":{"docs":{},"获":{"docs":{},"得":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}},"大":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}},"数":{"docs":{},"据":{"docs":{},"l":{"docs":{},"a":{"docs":{},"m":{"docs":{},"b":{"docs":{},"d":{"docs":{},"a":{"docs":{},"架":{"docs":{},"构":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}}}}}}}}},"提":{"docs":{},"高":{"docs":{},"数":{"docs":{},"据":{"docs":{},"存":{"docs":{},"储":{"docs":{},"能":{"docs":{},"力":{"docs":{},",":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}}}}}}}},"资":{"docs":{},"源":{"docs":{},"管":{"docs":{},"理":{"docs":{},"产":{"docs":{},"品":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}}}},"产":{"docs":{},"品":{"docs":{},"与":{"docs":{},"互":{"docs":{},"联":{"docs":{},"网":{"docs":{},"产":{"docs":{},"品":{"docs":{},"结":{"docs":{},"合":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}}}},"存":{"docs":{},"储":{"docs":{},"与":{"docs":{},"计":{"docs":{},"算":{"docs":{},"的":{"docs":{},"核":{"docs":{},"心":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}},"平":{"docs":{},"台":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}},"(":{"docs":{},"互":{"docs":{},"联":{"docs":{},"网":{"docs":{},"企":{"docs":{},"业":{"docs":{},")":{"docs":{},"运":{"docs":{},"行":{"docs":{},"的":{"docs":{},"绝":{"docs":{},"大":{"docs":{},"多":{"docs":{},"数":{"docs":{},"大":{"docs":{},"数":{"docs":{},"据":{"docs":{},"计":{"docs":{},"算":{"docs":{},"都":{"docs":{},"是":{"docs":{},"关":{"docs":{},"于":{"docs":{},"数":{"docs":{},"据":{"docs":{},"分":{"docs":{},"析":{"docs":{},"的":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"应":{"docs":{},"用":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}},"量":{"docs":{},"的":{"docs":{},"清":{"docs":{},"洗":{"docs":{},",":{"docs":{},"转":{"docs":{},"化":{"docs":{},"处":{"docs":{},"理":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}},"数":{"docs":{},"据":{"docs":{},"需":{"docs":{},"要":{"docs":{},"长":{"docs":{},"期":{"docs":{},"保":{"docs":{},"存":{"docs":{},",":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}}}}}}},"致":{"docs":{},"查":{"docs":{},"看":{"docs":{},"一":{"docs":{},"下":{"docs":{},"数":{"docs":{},"据":{"docs":{},"类":{"docs":{},"型":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}},"存":{"docs":{},"储":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}},"和":{"docs":{},"分":{"docs":{},"析":{"docs":{},"某":{"docs":{},"个":{"docs":{},"窗":{"docs":{},"口":{"docs":{},"期":{"docs":{},"内":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"（":{"docs":{},"一":{"docs":{},"段":{"docs":{},"时":{"docs":{},"间":{"docs":{},"的":{"docs":{},"热":{"docs":{},"销":{"docs":{},"排":{"docs":{},"行":{"docs":{},"，":{"docs":{},"实":{"docs":{},"时":{"docs":{},"热":{"docs":{},"搜":{"docs":{},"等":{"docs":{},"）":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"推":{"docs":{},"荐":{"docs":{},"结":{"docs":{},"果":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}},"的":{"docs":{},"b":{"docs":{},"l":{"docs":{},"o":{"docs":{},"c":{"docs":{},"k":{"docs":{},"i":{"docs":{},"d":{"docs":{},"分":{"docs":{},"别":{"docs":{},"为":{"1":{"docs":{},"、":{"3":{"docs":{},"。":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282}}}},"docs":{}}},"docs":{}}}}}}}}}}}},"/":{"docs":{},"计":{"docs":{},"算":{"docs":{},"资":{"docs":{},"源":{"docs":{},"不":{"docs":{},"够":{"docs":{},"时":{"docs":{},"，":{"docs":{},"可":{"docs":{},"以":{"docs":{},"横":{"docs":{},"向":{"docs":{},"的":{"docs":{},"线":{"docs":{},"性":{"docs":{},"扩":{"docs":{},"展":{"docs":{},"机":{"docs":{},"器":{"docs":{"day03_Hadoop/ha1.3.html":{"ref":"day03_Hadoop/ha1.3.html","tf":0.07692307692307693}}}}}}}}}}}}}}}}}}}}}},"用":{"docs":{},"户":{"docs":{},"的":{"docs":{},"文":{"docs":{},"件":{"docs":{},"对":{"docs":{},"应":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"块":{"docs":{},"(":{"docs":{},"b":{"docs":{},"l":{"docs":{},"o":{"docs":{},"c":{"docs":{},"k":{"docs":{},")":{"docs":{"day03_Hadoop/ha2.4.html":{"ref":"day03_Hadoop/ha2.4.html","tf":0.03333333333333333}}}}}}}}}}}}}}}}}},"召":{"docs":{},"回":{"docs":{},"，":{"docs":{},"使":{"docs":{},"用":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"i":{"docs":{},"s":{"docs":{},"第":{"9":{"docs":{},"号":{"docs":{},"数":{"docs":{},"据":{"docs":{},"库":{"docs":{},"，":{"docs":{},"类":{"docs":{},"型":{"docs":{},"：":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"s":{"docs":{},"类":{"docs":{},"型":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}}},"docs":{}}}}}}}}}}}}}},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"利":{"docs":{},"用":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}},"实":{"docs":{},"时":{"docs":{},"处":{"docs":{},"理":{"docs":{},"层":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}}}},"数":{"docs":{},"据":{"docs":{},"分":{"docs":{},"析":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}}},"收":{"docs":{},"集":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}}}}},"性":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}},"计":{"docs":{},"算":{"docs":{"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}},"框":{"docs":{},"架":{"docs":{},"对":{"docs":{},"比":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}}}}}},"流":{"docs":{},"式":{"docs":{},"计":{"docs":{},"算":{"docs":{"day03_Hadoop/ha3.2.html":{"ref":"day03_Hadoop/ha3.2.html","tf":0.02040816326530612}}}}}},"商":{"docs":{},"品":{"docs":{},"类":{"docs":{},"别":{"docs":{},"/":{"docs":{},"品":{"docs":{},"牌":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}}},"广":{"docs":{},"告":{"docs":{},"召":{"docs":{},"回":{"docs":{},"集":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}},"特":{"docs":{},"征":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}},"行":{"docs":{},"为":{"docs":{},"日":{"docs":{},"志":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.010526315789473684}}}}}},"产":{"docs":{},"生":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"结":{"docs":{},"果":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}}},"现":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}},"协":{"docs":{},"同":{"docs":{},"过":{"docs":{},"滤":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"有":{"docs":{},"以":{"docs":{},"下":{"docs":{},"几":{"docs":{},"个":{"docs":{},"步":{"docs":{},"骤":{"docs":{},"：":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}},"复":{"docs":{},"杂":{"docs":{},"查":{"docs":{},"询":{"docs":{},"逻":{"docs":{},"辑":{"docs":{},"开":{"docs":{},"发":{"docs":{},"难":{"docs":{},"度":{"docs":{},"太":{"docs":{},"大":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}}}}},"并":{"docs":{},"开":{"docs":{},"源":{"docs":{},"，":{"docs":{},"是":{"docs":{},"基":{"docs":{},"于":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}},"，":{"docs":{},"与":{"docs":{},"传":{"docs":{},"统":{"docs":{},"数":{"docs":{},"据":{"docs":{},"库":{"docs":{},"j":{"docs":{},"d":{"docs":{},"b":{"docs":{},"c":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}}}},"将":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"作":{"docs":{},"业":{"docs":{},"分":{"docs":{},"解":{"docs":{},"成":{"docs":{},"一":{"docs":{},"到":{"docs":{},"多":{"docs":{},"个":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"g":{"docs":{},"e":{"docs":{},"，":{"docs":{},"每":{"docs":{},"个":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"g":{"docs":{},"e":{"docs":{},"根":{"docs":{},"据":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"的":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{},"i":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"个":{"docs":{},"数":{"docs":{},"决":{"docs":{},"定":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"k":{"docs":{},"的":{"docs":{},"个":{"docs":{},"数":{"docs":{},"，":{"docs":{},"然":{"docs":{},"后":{"docs":{},"生":{"docs":{},"成":{"docs":{},"相":{"docs":{},"应":{"docs":{},"的":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"k":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"际":{"docs":{},"上":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}},"也":{"docs":{},"是":{"docs":{},"余":{"docs":{},"弦":{"docs":{},"相":{"docs":{},"似":{"docs":{},"度":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}},"践":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}},":":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}},"战":{"docs":{},"案":{"docs":{},"例":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}}},"常":{"docs":{},"用":{"docs":{},"算":{"docs":{},"法":{"docs":{},":":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}}}},"评":{"docs":{},"估":{"docs":{},"指":{"docs":{},"标":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}},"操":{"docs":{},"作":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}},"批":{"docs":{},"处":{"docs":{},"理":{"docs":{},"层":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}},"：":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{},"e":{"docs":{},"、":{"docs":{},"h":{"docs":{},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{},"、":{"docs":{},"p":{"docs":{},"i":{"docs":{},"g":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}}}}}}}}}}}}}}}}}},"计":{"docs":{},"算":{"docs":{},"框":{"docs":{},"架":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}}}}}}},"排":{"docs":{},"序":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734},"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}},"逼":{"docs":{},"近":{"docs":{},"这":{"docs":{},"个":{"docs":{},"极":{"docs":{},"限":{"docs":{},",":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}}}}}}}},"阶":{"docs":{},"段":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}}}}},"支":{"docs":{},"持":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.006172839506172839},"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}},"随":{"docs":{},"机":{"docs":{},"读":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}}}},"包":{"docs":{},"括":{"docs":{},"t":{"docs":{},"f":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}},"多":{"docs":{},"级":{"docs":{},"分":{"docs":{},"区":{"docs":{},",":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}},"特":{"docs":{},"定":{"docs":{},"场":{"docs":{},"景":{"docs":{},"下":{"docs":{},"的":{"docs":{"Hive&HBase/06_hbase数据模型.html":{"ref":"Hive&HBase/06_hbase数据模型.html","tf":0.020833333333333332}}}}}}}},"压":{"docs":{},"缩":{"docs":{},"文":{"docs":{},"件":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}},"整":{"docs":{},"个":{"docs":{},"目":{"docs":{},"录":{"docs":{},"、":{"docs":{},"多":{"docs":{},"文":{"docs":{},"件":{"docs":{},"、":{"docs":{},"通":{"docs":{},"配":{"docs":{},"符":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}}}}}}}}},"两":{"docs":{},"种":{"docs":{},"类":{"docs":{},"型":{"docs":{},"的":{"docs":{},"操":{"docs":{},"作":{"docs":{},"：":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}},"m":{"docs":{},"i":{"docs":{},"c":{"docs":{},"r":{"docs":{},"o":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}}}}}},"付":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}},"数":{"docs":{},"据":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}},"不":{"docs":{},"可":{"docs":{},"变":{"docs":{},",":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}}}}},"来":{"docs":{},"源":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}},"清":{"docs":{},"洗":{"docs":{},"/":{"docs":{},"数":{"docs":{},"据":{"docs":{},"处":{"docs":{},"理":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}},"量":{"docs":{},"/":{"docs":{},"数":{"docs":{},"据":{"docs":{},"能":{"docs":{},"否":{"docs":{},"满":{"docs":{},"足":{"docs":{},"要":{"docs":{},"求":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}},"集":{"docs":{},"：":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}},"下":{"docs":{},"载":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.0016233766233766235}}}},"中":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683}}}}}}},"路":{"docs":{},"径":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}},"介":{"docs":{},"绍":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}},"来":{"docs":{},"源":{"docs":{},"：":{"docs":{},"天":{"docs":{},"池":{"docs":{},"竞":{"docs":{},"赛":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}}}},"初":{"docs":{},"始":{"docs":{},"化":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}},"加":{"docs":{},"载":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}},"初":{"docs":{},"始":{"docs":{},"化":{"docs":{},"与":{"docs":{},"之":{"docs":{},"前":{"docs":{},"完":{"docs":{},"全":{"docs":{},"相":{"docs":{},"同":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}},"仓":{"docs":{},"库":{"docs":{},"时":{"docs":{},"代":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}}},"挖":{"docs":{},"掘":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009},"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}},"时":{"docs":{},"代":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}}},"切":{"docs":{},"分":{"docs":{},"、":{"docs":{},"多":{"docs":{},"副":{"docs":{},"本":{"docs":{},"、":{"docs":{},"容":{"docs":{},"错":{"docs":{},"等":{"docs":{},"操":{"docs":{},"作":{"docs":{},"对":{"docs":{},"用":{"docs":{},"户":{"docs":{},"是":{"docs":{},"透":{"docs":{},"明":{"docs":{},"的":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282}}}}}}}}}}}}}}}}}}}}},"块":{"docs":{},"多":{"docs":{},"副":{"docs":{},"本":{"docs":{"day03_Hadoop/ha1.3.html":{"ref":"day03_Hadoop/ha1.3.html","tf":0.07692307692307693}}}}}},"存":{"docs":{},"储":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}},":":{"docs":{"day03_Hadoop/ha1.3.html":{"ref":"day03_Hadoop/ha1.3.html","tf":0.07692307692307693}}},"故":{"docs":{},"障":{"docs":{},"容":{"docs":{},"错":{"docs":{"day03_Hadoop/ha4.2.html":{"ref":"day03_Hadoop/ha4.2.html","tf":0.0196078431372549}}}}}}},"入":{"docs":{},"m":{"docs":{},"e":{"docs":{},"m":{"docs":{},"s":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{},"e":{"docs":{},"，":{"docs":{},"一":{"docs":{},"直":{"docs":{},"到":{"docs":{},"m":{"docs":{},"e":{"docs":{},"m":{"docs":{},"s":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{},"e":{"docs":{},"满":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}}}}}}}}}}}}}}}}}}},"计":{"docs":{},"算":{"docs":{},":":{"docs":{"day03_Hadoop/ha1.3.html":{"ref":"day03_Hadoop/ha1.3.html","tf":0.07692307692307693}}}}},"冗":{"docs":{},"余":{"docs":{"day03_Hadoop/ha2.4.html":{"ref":"day03_Hadoop/ha2.4.html","tf":0.03333333333333333}}}},"分":{"docs":{},"析":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}},"师":{"docs":{},"分":{"docs":{},"析":{"docs":{},"可":{"docs":{},"能":{"docs":{},"性":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}},"案":{"docs":{},"例":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}},"布":{"docs":{},"式":{"docs":{},"也":{"docs":{},"是":{"docs":{},"弹":{"docs":{},"性":{"docs":{},"的":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}}}}},"同":{"docs":{},"步":{"docs":{},"后":{"docs":{},"导":{"docs":{},"入":{"docs":{},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}},"处":{"docs":{},"理":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}},"部":{"docs":{},"分":{"docs":{},"：":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}},"库":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}},",":{"docs":{},"日":{"docs":{},"志":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}},"同":{"docs":{},"步":{"docs":{},":":{"docs":{},"s":{"docs":{},"q":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}},"大":{"docs":{},"小":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}},"规":{"docs":{},"模":{"docs":{},"大":{"docs":{},",":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}},"输":{"docs":{},"出":{"docs":{},"与":{"docs":{},"展":{"docs":{},"示":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}},"采":{"docs":{},"集":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}},"需":{"docs":{},"要":{"docs":{},"写":{"docs":{},"入":{"docs":{},"数":{"docs":{},"据":{"docs":{},"库":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}},"驱":{"docs":{},"动":{"docs":{},"运":{"docs":{},"营":{"docs":{},":":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}},"模":{"docs":{},"型":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}},"保":{"docs":{},"存":{"docs":{},"位":{"docs":{},"置":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}},"文":{"docs":{},"件":{"docs":{},"中":{"docs":{},"没":{"docs":{},"有":{"docs":{},"对":{"docs":{},"应":{"docs":{},"的":{"docs":{},"列":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}}}}},"管":{"docs":{},"理":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}},"上":{"docs":{},"传":{"docs":{},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}}},"类":{"docs":{},"型":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}},"能":{"docs":{},"正":{"docs":{},"常":{"docs":{},"读":{"docs":{},"写":{"docs":{},",":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}},"示":{"docs":{},"例":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}},"也":{"docs":{},"是":{"docs":{},"只":{"docs":{},"需":{"docs":{},"要":{"docs":{},"进":{"docs":{},"行":{"docs":{},"查":{"docs":{},"询":{"docs":{},"操":{"docs":{},"作":{"docs":{},"的":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}}}}}}}}}}},"源":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}},"：":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"、":{"docs":{},"c":{"docs":{},"s":{"docs":{},"v":{"docs":{},"、":{"docs":{},"j":{"docs":{},"s":{"docs":{},"o":{"docs":{},"n":{"docs":{},"、":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"q":{"docs":{},"u":{"docs":{},"e":{"docs":{},"t":{"docs":{},"、":{"docs":{},"o":{"docs":{},"r":{"docs":{},"c":{"docs":{},"、":{"docs":{},"j":{"docs":{},"d":{"docs":{},"b":{"docs":{},"c":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"去":{"docs":{},"重":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}},"条":{"docs":{},"数":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}},"量":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}},"钱":{"docs":{},"实":{"docs":{},"例":{"docs":{},"：":{"docs":{},"一":{"docs":{},"堆":{"docs":{},"钞":{"docs":{},"票":{"docs":{},"，":{"docs":{},"各":{"docs":{},"种":{"docs":{},"面":{"docs":{},"值":{"docs":{},"分":{"docs":{},"别":{"docs":{},"是":{"docs":{},"多":{"docs":{},"少":{"docs":{"day03_Hadoop/ha3.2.html":{"ref":"day03_Hadoop/ha3.2.html","tf":0.02040816326530612}}}}}}}}}}}}}}}}}}}},"组":{"docs":{},"的":{"docs":{},"元":{"docs":{},"素":{"docs":{},"之":{"docs":{},"间":{"docs":{},"用":{"docs":{},"'":{"docs":{},"|":{"docs":{},"'":{"docs":{},"分":{"docs":{},"割":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}}}}}}}},"数":{"docs":{},"据":{"docs":{},"类":{"docs":{},"型":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}}}},"千":{"docs":{},"写":{"docs":{},"入":{"docs":{},"/":{"docs":{},"秒":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}}},"日":{"docs":{},"志":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}},"收":{"docs":{},"集":{"docs":{},"：":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}}}},"分":{"docs":{},"析":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}},"同":{"docs":{},"步":{"docs":{},":":{"docs":{},"f":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"e":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}}},"开":{"docs":{},"始":{"docs":{},"，":{"docs":{},"网":{"docs":{},"站":{"docs":{},"的":{"docs":{},"订":{"docs":{},"单":{"docs":{},"量":{"docs":{},"连":{"docs":{},"续":{"docs":{},"四":{"docs":{},"天":{"docs":{},"明":{"docs":{},"显":{"docs":{},"下":{"docs":{},"跌":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}}}}}}}}}}},"当":{"docs":{},"天":{"docs":{},"发":{"docs":{},"布":{"docs":{},"记":{"docs":{},"录":{"docs":{},",":{"docs":{},"发":{"docs":{},"现":{"docs":{},"有":{"docs":{},"消":{"docs":{},"息":{"docs":{},"队":{"docs":{},"列":{"docs":{},"s":{"docs":{},"d":{"docs":{},"k":{"docs":{},"更":{"docs":{},"新":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}}}}}}}}}}}}},"活":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}},"订":{"docs":{},"单":{"docs":{},"量":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}},"期":{"docs":{},"函":{"docs":{},"数":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}},"服":{"docs":{},"务":{"docs":{},"层":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}},"提":{"docs":{},"供":{"docs":{},"方":{"docs":{},",":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}},"设":{"docs":{},"定":{"docs":{},"的":{"docs":{},"属":{"docs":{},"性":{"docs":{},"（":{"docs":{},"服":{"docs":{},"务":{"docs":{},"提":{"docs":{},"供":{"docs":{},"方":{"docs":{},"为":{"docs":{},"物":{"docs":{},"品":{"docs":{},"附":{"docs":{},"加":{"docs":{},"的":{"docs":{},"属":{"docs":{},"性":{"docs":{},"）":{"docs":{},"：":{"docs":{},"如":{"docs":{},"短":{"docs":{},"视":{"docs":{},"频":{"docs":{},"话":{"docs":{},"题":{"docs":{},"、":{"docs":{},"微":{"docs":{},"博":{"docs":{},"话":{"docs":{},"题":{"docs":{},"（":{"docs":{},"平":{"docs":{},"台":{"docs":{},"拟":{"docs":{},"定":{"docs":{},"）":{"docs":{"day02_推荐算法/07_基于内容的推荐算法.html":{"ref":"day02_推荐算法/07_基于内容的推荐算法.html","tf":0.03125}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"器":{"docs":{},"集":{"docs":{},"群":{"docs":{},"资":{"docs":{},"源":{"docs":{},"调":{"docs":{},"度":{"docs":{},"管":{"docs":{},"理":{"docs":{},"和":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{},"e":{"docs":{},"执":{"docs":{},"行":{"docs":{},"过":{"docs":{},"程":{"docs":{},"耦":{"docs":{},"合":{"docs":{},"在":{"docs":{},"一":{"docs":{},"起":{"docs":{},"带":{"docs":{},"来":{"docs":{},"的":{"docs":{},"问":{"docs":{},"题":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"策":{"docs":{},"略":{"docs":{},"调":{"docs":{},"整":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}}}}},"算":{"docs":{},"法":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}},"思":{"docs":{},"想":{"docs":{},"：":{"docs":{},"物":{"docs":{},"以":{"docs":{},"类":{"docs":{},"聚":{"docs":{},"，":{"docs":{},"人":{"docs":{},"以":{"docs":{},"群":{"docs":{},"分":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}},"实":{"docs":{},"现":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}},"举":{"docs":{},"例":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}},"原":{"docs":{},"理":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}},"术":{"docs":{},"运":{"docs":{},"算":{"docs":{},"符":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}},"视":{"docs":{},"图":{"docs":{},"存":{"docs":{},"储":{"docs":{},"数":{"docs":{},"据":{"docs":{},"库":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}}}}}}}},"读":{"docs":{},"取":{"docs":{},"批":{"docs":{},"处":{"docs":{},"理":{"docs":{},"层":{"docs":{},"和":{"docs":{},"实":{"docs":{},"时":{"docs":{},"处":{"docs":{},"理":{"docs":{},"层":{"docs":{},"结":{"docs":{},"果":{"docs":{},"并":{"docs":{},"对":{"docs":{},"其":{"docs":{},"归":{"docs":{},"并":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}}}}}}}}}}}}}}}}}},"数":{"docs":{},"据":{"docs":{},"的":{"docs":{},"时":{"docs":{},"候":{"docs":{},",":{"docs":{"day03_Hadoop/ha4.2.html":{"ref":"day03_Hadoop/ha4.2.html","tf":0.0196078431372549}}}}}},"进":{"docs":{},"行":{"docs":{},"计":{"docs":{},"算":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}},"一":{"docs":{},"个":{"docs":{},"j":{"docs":{},"s":{"docs":{},"o":{"docs":{},"n":{"docs":{},"文":{"docs":{},"件":{"docs":{},"可":{"docs":{},"以":{"docs":{},"用":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"s":{"docs":{},"e":{"docs":{},"s":{"docs":{},"s":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},".":{"docs":{},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"d":{"docs":{},".":{"docs":{},"j":{"docs":{},"s":{"docs":{},"o":{"docs":{},"n":{"docs":{},"方":{"docs":{},"法":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"写":{"docs":{},"流":{"docs":{},"程":{"docs":{},"&":{"docs":{"day03_Hadoop/ha4.2.html":{"ref":"day03_Hadoop/ha4.2.html","tf":0.0196078431372549}}}}}},"模":{"docs":{},"式":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}},"需":{"docs":{},"要":{"docs":{},"在":{"docs":{},"非":{"docs":{},"常":{"docs":{},"短":{"docs":{},"的":{"docs":{},"时":{"docs":{},"间":{"docs":{},"内":{"docs":{},"返":{"docs":{},"回":{"docs":{},"结":{"docs":{},"果":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}}}}}}}}}}}}},"用":{"docs":{},"户":{"docs":{},"的":{"docs":{},"点":{"docs":{},"击":{"docs":{},"数":{"docs":{},"据":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}}}}}}}},"与":{"docs":{},"n":{"docs":{},"m":{"docs":{},"通":{"docs":{},"信":{"docs":{},"：":{"docs":{},"启":{"docs":{},"动":{"docs":{},"/":{"docs":{},"停":{"docs":{},"止":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"k":{"docs":{},"，":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"k":{"docs":{},"是":{"docs":{},"运":{"docs":{},"行":{"docs":{},"在":{"docs":{},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"t":{"docs":{},"a":{"docs":{},"i":{"docs":{},"n":{"docs":{},"e":{"docs":{},"r":{"docs":{},"里":{"docs":{},"面":{"docs":{},"，":{"docs":{},"a":{"docs":{},"m":{"docs":{},"也":{"docs":{},"是":{"docs":{},"运":{"docs":{},"行":{"docs":{},"在":{"docs":{},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"t":{"docs":{},"a":{"docs":{},"i":{"docs":{},"n":{"docs":{},"e":{"docs":{},"r":{"docs":{},"里":{"docs":{},"面":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"进":{"docs":{},"行":{"docs":{},"资":{"docs":{},"源":{"docs":{},"调":{"docs":{},"度":{"docs":{},"管":{"docs":{},"理":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}}}},"缓":{"docs":{},"存":{"docs":{},"的":{"docs":{},"特":{"docs":{},"征":{"docs":{},"值":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}},"通":{"docs":{},"过":{"docs":{},"大":{"docs":{},"数":{"docs":{},"据":{"docs":{},"实":{"docs":{},"现":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}},"h":{"docs":{},"q":{"docs":{},"l":{"docs":{},"添":{"docs":{},"加":{"docs":{},"分":{"docs":{},"区":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}}}}},"一":{"docs":{},"种":{"docs":{},"灵":{"docs":{},"活":{"docs":{},"的":{"docs":{},"框":{"docs":{},"架":{"docs":{},"可":{"docs":{},"同":{"docs":{},"时":{"docs":{},"进":{"docs":{},"行":{"docs":{},"批":{"docs":{},"处":{"docs":{},"理":{"docs":{},"、":{"docs":{},"流":{"docs":{},"式":{"docs":{},"计":{"docs":{},"算":{"docs":{},"、":{"docs":{},"交":{"docs":{},"互":{"docs":{},"式":{"docs":{},"计":{"docs":{},"算":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}}}}}}}}}}}}}}}}}}}}}}}}},"先":{"docs":{},"将":{"docs":{},"缺":{"docs":{},"失":{"docs":{},"值":{"docs":{},"全":{"docs":{},"部":{"docs":{},"替":{"docs":{},"换":{"docs":{},"为":{"docs":{},"数":{"docs":{},"值":{"docs":{},"，":{"docs":{},"与":{"docs":{},"原":{"docs":{},"有":{"docs":{},"特":{"docs":{},"征":{"docs":{},"一":{"docs":{},"起":{"docs":{},"处":{"docs":{},"理":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}},"便":{"docs":{},"于":{"docs":{},"处":{"docs":{},"理":{"docs":{},"，":{"docs":{},"否":{"docs":{},"则":{"docs":{},"会":{"docs":{},"抛":{"docs":{},"出":{"docs":{},"异":{"docs":{},"常":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}}}}}}}},"求":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624},"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}},"：":{"docs":{},"监":{"docs":{},"听":{"docs":{},"某":{"docs":{},"个":{"docs":{},"端":{"docs":{},"口":{"docs":{},"上":{"docs":{},"的":{"docs":{},"网":{"docs":{},"络":{"docs":{},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"实":{"docs":{},"时":{"docs":{},"统":{"docs":{},"计":{"docs":{},"出":{"docs":{},"现":{"docs":{},"的":{"docs":{},"不":{"docs":{},"同":{"docs":{},"单":{"docs":{},"词":{"docs":{},"个":{"docs":{},"数":{"docs":{},"。":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099}}}}}}}}}}}}}}}}}}}}}}}}}}},"网":{"docs":{},"络":{"docs":{},"端":{"docs":{},"口":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"获":{"docs":{},"取":{"docs":{},"到":{"docs":{},"每":{"docs":{},"个":{"docs":{},"批":{"docs":{},"次":{"docs":{},"的":{"docs":{},"出":{"docs":{},"现":{"docs":{},"的":{"docs":{},"单":{"docs":{},"词":{"docs":{},"数":{"docs":{},"量":{"docs":{},"，":{"docs":{},"并":{"docs":{},"且":{"docs":{},"需":{"docs":{},"要":{"docs":{},"把":{"docs":{},"每":{"docs":{},"个":{"docs":{},"批":{"docs":{},"次":{"docs":{},"的":{"docs":{},"信":{"docs":{},"息":{"docs":{},"保":{"docs":{},"留":{"docs":{},"下":{"docs":{},"来":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"想":{"docs":{},"要":{"docs":{},"将":{"docs":{},"一":{"docs":{},"个":{"docs":{},"大":{"docs":{},"时":{"docs":{},"间":{"docs":{},"段":{"docs":{},"（":{"1":{"docs":{},"天":{"docs":{},"）":{"docs":{},"，":{"docs":{},"即":{"docs":{},"多":{"docs":{},"个":{"docs":{},"小":{"docs":{},"时":{"docs":{},"间":{"docs":{},"段":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"内":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"持":{"docs":{},"续":{"docs":{},"进":{"docs":{},"行":{"docs":{},"累":{"docs":{},"积":{"docs":{},"操":{"docs":{},"作":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{}}}}}}}}}}}}}},"（":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}},"精":{"docs":{},"选":{"docs":{},"）":{"docs":{"day01_推荐系统介绍/02_推荐系统架构设计.html":{"ref":"day01_推荐系统介绍/02_推荐系统架构设计.html","tf":0.0136986301369863}}}}},"以":{"docs":{},"用":{"docs":{},"户":{"1":{"docs":{},"对":{"docs":{},"电":{"docs":{},"影":{"1":{"docs":{},"评":{"docs":{},"分":{"docs":{},"为":{"docs":{},"例":{"docs":{},"）":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}}}}}}}},"docs":{}}}}},"docs":{}}}},"注":{"docs":{},"：":{"docs":{},"w":{"docs":{},"a":{"docs":{},"l":{"docs":{},"，":{"docs":{},"w":{"docs":{},"r":{"docs":{},"i":{"docs":{},"t":{"docs":{"day03_Hadoop/ha4.2.html":{"ref":"day03_Hadoop/ha4.2.html","tf":0.0196078431372549}}}}}}}}}},"发":{"docs":{},"送":{"docs":{},"完":{"docs":{},"成":{"docs":{},"信":{"docs":{},"号":{"docs":{},"的":{"docs":{},"时":{"docs":{},"机":{"docs":{},"取":{"docs":{},"决":{"docs":{},"于":{"docs":{},"集":{"docs":{},"群":{"docs":{},"是":{"docs":{},"强":{"docs":{},"一":{"docs":{},"致":{"docs":{},"性":{"docs":{},"还":{"docs":{},"是":{"docs":{},"最":{"docs":{},"终":{"docs":{},"一":{"docs":{},"致":{"docs":{},"性":{"docs":{},"，":{"docs":{},"强":{"docs":{},"一":{"docs":{},"致":{"docs":{},"性":{"docs":{},"则":{"docs":{},"需":{"docs":{},"要":{"docs":{},"所":{"docs":{},"有":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"n":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"写":{"docs":{},"完":{"docs":{},"后":{"docs":{},"才":{"docs":{},"向":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"n":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"汇":{"docs":{},"报":{"docs":{},"。":{"docs":{},"最":{"docs":{},"终":{"docs":{},"一":{"docs":{},"致":{"docs":{},"性":{"docs":{},"则":{"docs":{},"其":{"docs":{},"中":{"docs":{},"任":{"docs":{},"意":{"docs":{},"一":{"docs":{},"个":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"n":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"写":{"docs":{},"完":{"docs":{},"后":{"docs":{},"就":{"docs":{},"能":{"docs":{},"单":{"docs":{},"独":{"docs":{},"向":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"n":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"汇":{"docs":{},"报":{"docs":{},"，":{"docs":{},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},"一":{"docs":{},"般":{"docs":{},"情":{"docs":{},"况":{"docs":{},"下":{"docs":{},"都":{"docs":{},"是":{"docs":{},"强":{"docs":{},"调":{"docs":{},"强":{"docs":{},"一":{"docs":{},"致":{"docs":{},"性":{"docs":{},"）":{"docs":{"day03_Hadoop/ha4.2.html":{"ref":"day03_Hadoop/ha4.2.html","tf":0.0196078431372549}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"并":{"docs":{},"不":{"docs":{},"是":{"docs":{},"写":{"docs":{},"好":{"docs":{},"一":{"docs":{},"个":{"docs":{},"块":{"docs":{},"或":{"docs":{},"一":{"docs":{},"整":{"docs":{},"个":{"docs":{},"文":{"docs":{},"件":{"docs":{},"后":{"docs":{},"才":{"docs":{},"向":{"docs":{},"后":{"docs":{},"分":{"docs":{},"发":{"docs":{},"）":{"docs":{"day03_Hadoop/ha4.2.html":{"ref":"day03_Hadoop/ha4.2.html","tf":0.0196078431372549}}}}}}}}}}}}}}}}}}}},"每":{"docs":{},"写":{"docs":{},"完":{"docs":{},"一":{"docs":{},"个":{"docs":{},"p":{"docs":{},"a":{"docs":{},"c":{"docs":{},"k":{"docs":{},"e":{"docs":{},"t":{"docs":{},"后":{"docs":{},"就":{"docs":{},"返":{"docs":{},"回":{"docs":{},"确":{"docs":{},"认":{"docs":{},"信":{"docs":{},"息":{"docs":{},"，":{"docs":{},"个":{"docs":{},"人":{"docs":{},"觉":{"docs":{},"得":{"docs":{},"因":{"docs":{},"为":{"docs":{},"p":{"docs":{},"a":{"docs":{},"c":{"docs":{},"k":{"docs":{},"e":{"docs":{},"t":{"docs":{},"中":{"docs":{},"的":{"docs":{},"每":{"docs":{},"个":{"docs":{},"c":{"docs":{},"h":{"docs":{},"u":{"docs":{},"n":{"docs":{},"k":{"docs":{},"都":{"docs":{},"携":{"docs":{},"带":{"docs":{},"校":{"docs":{},"验":{"docs":{},"信":{"docs":{},"息":{"docs":{},"，":{"docs":{},"没":{"docs":{},"必":{"docs":{},"要":{"docs":{},"每":{"docs":{},"写":{"docs":{},"一":{"docs":{},"个":{"docs":{},"就":{"docs":{},"汇":{"docs":{},"报":{"docs":{},"一":{"docs":{},"下":{"docs":{},"，":{"docs":{},"这":{"docs":{},"样":{"docs":{},"效":{"docs":{},"率":{"docs":{},"太":{"docs":{},"慢":{"docs":{},"。":{"docs":{},"正":{"docs":{},"确":{"docs":{},"的":{"docs":{},"做":{"docs":{},"法":{"docs":{},"是":{"docs":{},"写":{"docs":{},"完":{"docs":{},"一":{"docs":{},"个":{"docs":{},"b":{"docs":{},"l":{"docs":{},"o":{"docs":{},"c":{"docs":{},"k":{"docs":{},"块":{"docs":{},"后":{"docs":{},"，":{"docs":{},"对":{"docs":{},"校":{"docs":{},"验":{"docs":{},"信":{"docs":{},"息":{"docs":{},"进":{"docs":{},"行":{"docs":{},"汇":{"docs":{},"总":{"docs":{},"分":{"docs":{},"析":{"docs":{},"，":{"docs":{},"就":{"docs":{},"能":{"docs":{},"得":{"docs":{},"出":{"docs":{},"是":{"docs":{},"否":{"docs":{},"有":{"docs":{},"块":{"docs":{},"写":{"docs":{},"错":{"docs":{},"的":{"docs":{},"情":{"docs":{},"况":{"docs":{},"发":{"docs":{},"生":{"docs":{},"）":{"docs":{"day03_Hadoop/ha4.2.html":{"ref":"day03_Hadoop/ha4.2.html","tf":0.0196078431372549}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"默":{"docs":{},"认":{"docs":{},"：":{"docs":{},"/":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"/":{"docs":{},"h":{"docs":{},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{},"/":{"docs":{},"w":{"docs":{},"a":{"docs":{},"r":{"docs":{},"e":{"docs":{},"h":{"docs":{},"o":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"）":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}}}}}}}}}}}}}}}}}}}}},"\"":{"0":{"1":{"0":{"0":{"1":{"docs":{},"\"":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.007692307692307693}}}}},"2":{"docs":{},"\"":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.007692307692307693}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"1":{"9":{"2":{"docs":{},".":{"1":{"6":{"8":{"docs":{},".":{"1":{"9":{"docs":{},".":{"1":{"3":{"7":{"docs":{},"\"":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"4":{"3":{"0":{"5":{"4":{"8":{"docs":{},"_":{"1":{"0":{"0":{"7":{"docs":{},"\"":{"docs":{},")":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}},",":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{},"\"":{"docs":{},")":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}},"6":{"docs":{},"g":{"docs":{},"\"":{"docs":{},")":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}},"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.0738255033557047},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289}},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"m":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.02824858757062147}}}}},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}},"d":{"docs":{},"\"":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.015384615384615385}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"2":{"docs":{},"\"":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.007062146892655367}}}}},"3":{"docs":{},"\"":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.007062146892655367}}}}},"4":{"docs":{},"\"":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.007062146892655367}}}}},"5":{"docs":{},"\"":{"docs":{},"]":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.007062146892655367}}}}},"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},")":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}},".":{"docs":{},"\\":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}},",":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289}}}}}}}}}},".":{"docs":{},"/":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"s":{"docs":{},"/":{"docs":{},"m":{"docs":{},"l":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}}}}}}}}}}}}}}},"m":{"docs":{},"o":{"docs":{},"v":{"docs":{},"i":{"docs":{},"e":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},":":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416},"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842},"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}}},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}}}},"z":{"docs":{},"i":{"docs":{},"l":{"docs":{},"l":{"docs":{},"a":{"docs":{},"/":{"4":{"docs":{},".":{"0":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}},"docs":{}}},"5":{"docs":{},".":{"0":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.006711409395973154}}},"docs":{}}},"docs":{}}}}}}}},"a":{"docs":{},"e":{"docs":{},"\"":{"docs":{},":":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}},":":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}},"\"":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.015384615384615385}}}}},"r":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"\"":{"docs":{},":":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}},"]":{"docs":{},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}},":":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}}}}},"m":{"docs":{},"s":{"docs":{},"e":{"docs":{},"\"":{"docs":{},":":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}},")":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.003795066413662239},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.004629629629629629},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683},"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102},"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.004545454545454545},"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577},"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}},".":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"(":{"docs":{},"l":{"docs":{},"a":{"docs":{},"m":{"docs":{},"b":{"docs":{},"d":{"docs":{},"a":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}}}}}}}}}}}},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"(":{"docs":{},"l":{"docs":{},"a":{"docs":{},"m":{"docs":{},"b":{"docs":{},"d":{"docs":{},"a":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}}}}}}}}}}}},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683},"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0012237405669997961}}},"动":{"docs":{},"作":{"docs":{},"\"":{"docs":{},"、":{"docs":{},"\"":{"docs":{},"吴":{"docs":{},"京":{"docs":{},"\"":{"docs":{},"、":{"docs":{},"\"":{"docs":{},"吴":{"docs":{},"刚":{"docs":{},"\"":{"docs":{},"、":{"docs":{},"\"":{"docs":{},"张":{"docs":{},"翰":{"docs":{},"\"":{"docs":{},"、":{"docs":{},"\"":{"docs":{},"大":{"docs":{},"陆":{"docs":{},"电":{"docs":{},"影":{"docs":{},"\"":{"docs":{},"、":{"docs":{},"\"":{"docs":{},"国":{"docs":{},"产":{"docs":{},"\"":{"docs":{},"、":{"docs":{},"\"":{"docs":{},"爱":{"docs":{},"国":{"docs":{},"\"":{"docs":{},"、":{"docs":{},"\"":{"docs":{},"军":{"docs":{},"事":{"docs":{},"\"":{"docs":{},"等":{"docs":{},"等":{"docs":{},"一":{"docs":{},"系":{"docs":{},"列":{"docs":{},"标":{"docs":{},"签":{"docs":{},"是":{"docs":{},"不":{"docs":{},"是":{"docs":{},"都":{"docs":{},"可":{"docs":{},"以":{"docs":{},"贴":{"docs":{},"上":{"docs":{"day02_推荐算法/07_基于内容的推荐算法.html":{"ref":"day02_推荐算法/07_基于内容的推荐算法.html","tf":0.03125}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"g":{"docs":{},"e":{"docs":{},"n":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"docs":{},"t":{"docs":{},"a":{"docs":{},"g":{"docs":{},"s":{"docs":{},"\"":{"docs":{},"]":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}},"t":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.010067114093959731}}}}},"p":{"docs":{},"r":{"docs":{},"o":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"e":{"docs":{},"\"":{"docs":{},",":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}},"b":{"docs":{},"a":{"docs":{},"b":{"docs":{},"i":{"docs":{},"l":{"docs":{},"i":{"docs":{},"t":{"docs":{},"y":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}}}}}}}}}}},"e":{"docs":{},"p":{"docs":{},"r":{"docs":{},"o":{"docs":{},"c":{"docs":{},"e":{"docs":{},"s":{"docs":{},"s":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"b":{"docs":{},"e":{"docs":{},"h":{"docs":{},"a":{"docs":{},"v":{"docs":{},"i":{"docs":{},"o":{"docs":{},"r":{"docs":{},"l":{"docs":{},"o":{"docs":{},"g":{"docs":{},"\"":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}},"d":{"docs":{},"i":{"docs":{},"c":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"s":{"docs":{},"o":{"docs":{},"r":{"docs":{},"t":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"p":{"docs":{},"r":{"docs":{},"o":{"docs":{},"b":{"docs":{},"a":{"docs":{},"b":{"docs":{},"i":{"docs":{},"l":{"docs":{},"i":{"docs":{},"t":{"docs":{},"y":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"1":{"0":{"0":{"docs":{},")":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"i":{"docs":{},"c":{"docs":{},"e":{"docs":{},"\"":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}},",":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.001193792280143255},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}},":":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}},"e":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"\"":{"docs":{},",":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.02631578947368421}}}}}}}},"o":{"docs":{},"p":{"docs":{},"\"":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.015384615384615385}}}}},"l":{"docs":{},"_":{"docs":{},"o":{"docs":{},"n":{"docs":{},"e":{"docs":{},"h":{"docs":{},"o":{"docs":{},"t":{"docs":{},"_":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}},"i":{"docs":{},"d":{"docs":{},"_":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}},":":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}},"t":{"docs":{},"i":{"docs":{},"t":{"docs":{},"l":{"docs":{},"e":{"docs":{},"\"":{"docs":{},",":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876}}}}}}},"m":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"m":{"docs":{},"p":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"\\":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}},",":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}},"r":{"docs":{},"u":{"docs":{},"e":{"docs":{},"\"":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}},"w":{"docs":{},"e":{"docs":{},"i":{"docs":{},"g":{"docs":{},"h":{"docs":{},"t":{"docs":{},"s":{"docs":{},"\"":{"docs":{},"]":{"docs":{},")":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}},"l":{"docs":{},"l":{"docs":{},"\"":{"docs":{},",":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.02631578947368421}}},"]":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}}}}},"a":{"docs":{},"r":{"docs":{},"n":{"docs":{},"\"":{"docs":{},".":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}}},"b":{"docs":{},"o":{"docs":{},"b":{"docs":{},"\"":{"docs":{},",":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.031578947368421054}},"\"":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"n":{"docs":{},"l":{"docs":{},"e":{"docs":{},"y":{"docs":{},"\"":{"docs":{},",":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}}}}}}}}}}}}}},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"\\":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289}}}}}}}}}}}}},"l":{"docs":{},"i":{"docs":{},"l":{"docs":{},"y":{"docs":{},"\"":{"docs":{},",":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.010526315789473684}}}}}},"c":{"docs":{},"e":{"docs":{},"n":{"docs":{},"s":{"docs":{},"e":{"docs":{},"\"":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}}}}},"\"":{"docs":{},":":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}},"\"":{"docs":{},"{":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.015384615384615385}}}}},"表":{"docs":{},"名":{"docs":{},"称":{"docs":{},"\"":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}},"_":{"docs":{},"_":{"docs":{},"m":{"docs":{},"a":{"docs":{},"i":{"docs":{},"n":{"docs":{},"_":{"docs":{},"_":{"docs":{},"\"":{"docs":{},":":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017},"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}}}},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{},"p":{"docs":{},"r":{"docs":{},"e":{"docs":{},"f":{"docs":{},"i":{"docs":{},"x":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"(":{"docs":{},"'":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"'":{"docs":{},")":{"docs":{},"\"":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0056657223796034}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"p":{"docs":{},"y":{"docs":{},"r":{"docs":{},"i":{"docs":{},"g":{"docs":{},"h":{"docs":{},"t":{"docs":{},"\"":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}}}}}}},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"i":{"docs":{},"t":{"docs":{},"s":{"docs":{},"\"":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}}}},"i":{"docs":{},"t":{"docs":{},"y":{"docs":{},"\"":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.015384615384615385}}}}}},"u":{"docs":{},"s":{"docs":{},"h":{"docs":{},"m":{"docs":{},"a":{"docs":{},"n":{"docs":{},"\"":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.007692307692307693}}}}}}}},"t":{"docs":{},"o":{"docs":{},"m":{"docs":{},"e":{"docs":{},"r":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"\\":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289}}}}}}}}}}}}}}},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}},")":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}},".":{"docs":{},"\\":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289}}}}}}}}}},"m":{"docs":{},"p":{"docs":{},"a":{"docs":{},"i":{"docs":{},"g":{"docs":{},"n":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"\\":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289}}}}}}}}}}}}}}},"l":{"docs":{},"k":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}},"m":{"docs":{},"s":{"docs":{},"_":{"docs":{},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.004228329809725159}}},":":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}},"s":{"docs":{},"e":{"docs":{},"g":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}},"h":{"docs":{},"e":{"docs":{},"l":{"docs":{},"p":{"docs":{},"\"":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}},"a":{"docs":{},"d":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.013422818791946308}}}}},"t":{"docs":{},"t":{"docs":{},"p":{"docs":{},":":{"docs":{},"/":{"docs":{},"/":{"docs":{},"c":{"docs":{},"o":{"docs":{},"s":{"docs":{},".":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"/":{"docs":{},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"g":{"docs":{},"o":{"docs":{},"r":{"docs":{},"y":{"docs":{},"/":{"docs":{},"s":{"docs":{},"o":{"docs":{},"f":{"docs":{},"t":{"docs":{},"w":{"docs":{},"a":{"docs":{},"r":{"docs":{},"e":{"docs":{},"/":{"docs":{},"p":{"docs":{},"a":{"docs":{},"c":{"docs":{},"k":{"docs":{},"a":{"docs":{},"g":{"docs":{},"e":{"docs":{},"s":{"docs":{},"/":{"docs":{},"\"":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"w":{"docs":{},"w":{"docs":{},"w":{"docs":{},".":{"docs":{},"a":{"docs":{},"n":{"docs":{},"g":{"docs":{},"u":{"docs":{},"l":{"docs":{},"a":{"docs":{},"r":{"docs":{},"j":{"docs":{},"s":{"docs":{},".":{"docs":{},"c":{"docs":{},"n":{"docs":{},"/":{"docs":{},"a":{"0":{"0":{"docs":{},"n":{"docs":{},"\"":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}},"docs":{}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}},"d":{"docs":{},"n":{"docs":{},"s":{"docs":{},"p":{"docs":{},"o":{"docs":{},"d":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.013422818791946308}}}}}}}},"a":{"docs":{},"g":{"docs":{},"a":{"docs":{},"w":{"docs":{},"a":{"docs":{},"m":{"docs":{},"\"":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.007692307692307693}}}}}}}},"e":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.004228329809725159}}},":":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}},"d":{"docs":{},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"\\":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289}}}}},",":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}}}}}}},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"\"":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.015384615384615385}}}}}}},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},":":{"docs":{},"/":{"docs":{},"/":{"1":{"9":{"2":{"docs":{},".":{"1":{"6":{"8":{"docs":{},".":{"1":{"9":{"docs":{},".":{"1":{"3":{"7":{"docs":{},":":{"7":{"0":{"7":{"7":{"docs":{},"\"":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}}}}}}},"h":{"docs":{},"o":{"docs":{},"p":{"docs":{},"p":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.004228329809725159}}},":":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}}}},"/":{"docs":{},"m":{"docs":{},"i":{"docs":{},"n":{"docs":{},"i":{"docs":{},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"d":{"docs":{},"a":{"2":{"docs":{},"/":{"docs":{},"e":{"docs":{},"n":{"docs":{},"v":{"docs":{},"s":{"docs":{},"/":{"docs":{},"p":{"docs":{},"y":{"3":{"6":{"5":{"docs":{},"/":{"docs":{},"b":{"docs":{},"i":{"docs":{},"n":{"docs":{},"/":{"docs":{},"p":{"docs":{},"y":{"docs":{},"t":{"docs":{},"h":{"docs":{},"o":{"docs":{},"n":{"docs":{},"\"":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.007633587786259542},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}},"docs":{}}}}}}}}}},"r":{"docs":{},"o":{"docs":{},"o":{"docs":{},"t":{"docs":{},"/":{"docs":{},"b":{"docs":{},"i":{"docs":{},"g":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"/":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"\"":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.007633587786259542},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}},"n":{"docs":{},"u":{"docs":{},"c":{"docs":{},"l":{"docs":{},"_":{"docs":{},"o":{"docs":{},"n":{"docs":{},"e":{"docs":{},"h":{"docs":{},"o":{"docs":{},"t":{"docs":{},"_":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"\"":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}},"]":{"docs":{},")":{"docs":{},".":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"o":{"docs":{},"u":{"docs":{},"t":{"docs":{},"p":{"docs":{},"u":{"docs":{},"t":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"t":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"s":{"docs":{},"f":{"docs":{},"o":{"docs":{},"r":{"docs":{},"m":{"docs":{},"(":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"p":{"docs":{},"r":{"docs":{},"o":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"e":{"docs":{},"_":{"docs":{},"d":{"docs":{},"f":{"3":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"e":{"docs":{},"w":{"docs":{},"_":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"c":{"docs":{},"l":{"docs":{},"a":{"docs":{},"s":{"docs":{},"s":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"\"":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}},":":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}}}}}}}}}},"上":{"docs":{},"海":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}},"华":{"docs":{},"为":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}},"女":{"docs":{},"\"":{"docs":{},"]":{"docs":{},"[":{"0":{"docs":{},",":{"1":{"docs":{},"]":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}}},"docs":{}}}}},"小":{"docs":{},"米":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}},"广":{"docs":{},"州":{"docs":{},"\"":{"docs":{},"]":{"docs":{},"[":{"0":{"docs":{},",":{"1":{"docs":{},",":{"2":{"docs":{},"]":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}}},"docs":{}}},"docs":{}}}}}},"微":{"docs":{},"软":{"docs":{},"\"":{"docs":{},"]":{"docs":{},"[":{"0":{"docs":{},",":{"1":{"docs":{},",":{"2":{"docs":{},",":{"3":{"docs":{},"]":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}}}}},"空":{"docs":{},"值":{"docs":{},"占":{"docs":{},"比":{"docs":{},"：":{"docs":{},"%":{"0":{"docs":{},".":{"2":{"docs":{},"f":{"docs":{},"%":{"docs":{},"%":{"docs":{},"\"":{"docs":{},"%":{"docs":{},"(":{"docs":{},"n":{"docs":{},"u":{"docs":{},"l":{"docs":{},"_":{"docs":{},"n":{"docs":{},"a":{"docs":{},"_":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"/":{"docs":{},"t":{"docs":{},"_":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"*":{"1":{"0":{"0":{"docs":{},")":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}}}}}}}}}}}},"p":{"docs":{},"l":{"docs":{},"_":{"docs":{},"n":{"docs":{},"a":{"docs":{},"_":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"/":{"docs":{},"t":{"docs":{},"_":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"*":{"1":{"0":{"0":{"docs":{},")":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{}}},"docs":{}}}}}}},"f":{"docs":{},"i":{"docs":{},"n":{"docs":{},"a":{"docs":{},"l":{"docs":{},"_":{"docs":{},"g":{"docs":{},"e":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"c":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.004228329809725159}}},":":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}}}}}},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"\"":{"docs":{},"]":{"docs":{},")":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}}}}}}},"o":{"docs":{},"c":{"docs":{},"c":{"docs":{},"u":{"docs":{},"p":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.004228329809725159}}},":":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}},"u":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"\"":{"docs":{},")":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}},"#":{"0":{"0":{"0":{"0":{"0":{"0":{"0":{"0":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"1":{"1":{"0":{"1":{"1":{"1":{"1":{"1":{"0":{"0":{"0":{"0":{"0":{"0":{"0":{"0":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"1":{"1":{"1":{"1":{"0":{"0":{"1":{"1":{"0":{"0":{"0":{"0":{"0":{"0":{"0":{"0":{"0":{"0":{"0":{"0":{"0":{"0":{"0":{"0":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.04519774011299435},"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.08441558441558442},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.03201829616923957},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.04174573055028463},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.011574074074074073},"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.05058365758754864},"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.042105263157894736},"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.11235955056179775},"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269},"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676},"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.056657223796033995},"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391},"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.05944055944055944},"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.04230769230769231},"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.1050228310502283},"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.02197802197802198},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.026717557251908396},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.08238172920065252},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.01223740566999796},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.019896538002387585},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.05226480836236934},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.03171247357293869}},"s":{"docs":{},"o":{"docs":{},"r":{"docs":{},"t":{"docs":{},"_":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}},"t":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{},"r":{"docs":{},"o":{"docs":{},"w":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}},"r":{"docs":{},"u":{"docs":{},"c":{"docs":{},"t":{"docs":{},"t":{"docs":{},"y":{"docs":{},"p":{"docs":{},"e":{"docs":{},"：":{"docs":{},"s":{"docs":{},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},"m":{"docs":{},"a":{"docs":{},"的":{"docs":{},"整":{"docs":{},"体":{"docs":{},"结":{"docs":{},"构":{"docs":{},"，":{"docs":{},"表":{"docs":{},"示":{"docs":{},"j":{"docs":{},"s":{"docs":{},"o":{"docs":{},"n":{"docs":{},"的":{"docs":{},"对":{"docs":{},"象":{"docs":{},"结":{"docs":{},"构":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"e":{"docs":{},"e":{"docs":{},"d":{"docs":{},"：":{"docs":{},"随":{"docs":{},"机":{"docs":{},"种":{"docs":{},"子":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}},"遍":{"docs":{},"历":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}},"所":{"docs":{},"有":{"docs":{},"的":{"docs":{},"最":{"docs":{},"相":{"docs":{},"似":{"docs":{},"用":{"docs":{},"户":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}},"计":{"docs":{},"算":{"docs":{},"用":{"docs":{},"户":{"docs":{},"之":{"docs":{},"间":{"docs":{},"相":{"docs":{},"似":{"docs":{},"度":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}}}}}}}}},"损":{"docs":{},"失":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}},"某":{"docs":{},"一":{"docs":{},"列":{"docs":{},"的":{"docs":{},"描":{"docs":{},"述":{"docs":{},"信":{"docs":{},"息":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}},"n":{"docs":{},"u":{"docs":{},"m":{"docs":{},"b":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"e":{"docs":{},"p":{"docs":{},"o":{"docs":{},"c":{"docs":{},"h":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}},"#":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}},"梯":{"docs":{},"度":{"docs":{},"下":{"docs":{},"降":{"docs":{},"优":{"docs":{},"化":{"docs":{},"损":{"docs":{},"失":{"docs":{},"函":{"docs":{},"数":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}}}}}}},"物":{"docs":{},"品":{"docs":{},"向":{"docs":{},"量":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}},"用":{"docs":{},"户":{"docs":{},"向":{"docs":{},"量":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}},"到":{"docs":{},"i":{"docs":{},"n":{"docs":{},"v":{"docs":{},"e":{"docs":{},"r":{"docs":{},"t":{"docs":{},"e":{"docs":{},"d":{"docs":{},"_":{"docs":{},"t":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}},"将":{"docs":{},"修":{"docs":{},"改":{"docs":{},"后":{"docs":{},"的":{"docs":{},"值":{"docs":{},"设":{"docs":{},"置":{"docs":{},"回":{"docs":{},"去":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}},"电":{"docs":{},"影":{"docs":{},"的":{"docs":{},"i":{"docs":{},"d":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}},"i":{"docs":{},"p":{"docs":{},"转":{"docs":{},"换":{"docs":{},"为":{"docs":{},"特":{"docs":{},"殊":{"docs":{},"的":{"docs":{},"数":{"docs":{},"字":{"docs":{},"形":{"docs":{},"式":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}}}}}}}}}},"单":{"docs":{},"词":{"docs":{},"转":{"docs":{},"换":{"docs":{},"为":{"docs":{},"(":{"docs":{},"单":{"docs":{},"词":{"docs":{},"，":{"1":{"docs":{},")":{"docs":{},"的":{"docs":{},"形":{"docs":{},"式":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099}}}}}}},"docs":{}}}}}}}}}},"数":{"docs":{},"据":{"docs":{},"按":{"docs":{},"空":{"docs":{},"格":{"docs":{},"进":{"docs":{},"行":{"docs":{},"拆":{"docs":{},"分":{"docs":{},"为":{"docs":{},"多":{"docs":{},"个":{"docs":{},"单":{"docs":{},"词":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099}}}}}}}}}}}}}}}}},"保":{"docs":{},"存":{"docs":{},"退":{"docs":{},"出":{"docs":{},"后":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428}}}}}}},"找":{"docs":{},"到":{"docs":{},"下":{"docs":{},"面":{"docs":{},"内":{"docs":{},"容":{"docs":{},"添":{"docs":{},"加":{"docs":{},"j":{"docs":{},"a":{"docs":{},"v":{"docs":{},"a":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428}}}}}}}}}}}}}},"介":{"docs":{},"于":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"p":{"docs":{},"e":{"docs":{},"r":{"docs":{},"和":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{},"e":{"docs":{},"r":{"docs":{},"之":{"docs":{},"间":{"docs":{},"，":{"docs":{},"用":{"docs":{},"于":{"docs":{},"临":{"docs":{},"时":{"docs":{},"的":{"docs":{},"将":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"p":{"docs":{},"e":{"docs":{},"r":{"docs":{},"输":{"docs":{},"出":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"进":{"docs":{},"行":{"docs":{},"统":{"docs":{},"计":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"传":{"docs":{},"入":{"docs":{},"两":{"docs":{},"个":{"docs":{},"s":{"docs":{},"t":{"docs":{},"e":{"docs":{},"p":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}}}},"利":{"docs":{},"用":{"docs":{},"h":{"docs":{},"e":{"docs":{},"a":{"docs":{},"p":{"docs":{},"q":{"docs":{},"将":{"docs":{},"数":{"docs":{},"据":{"docs":{},"进":{"docs":{},"行":{"docs":{},"排":{"docs":{},"序":{"docs":{},"，":{"docs":{},"将":{"docs":{},"最":{"docs":{},"大":{"docs":{},"的":{"2":{"docs":{},"个":{"docs":{},"取":{"docs":{},"出":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}},"docs":{}}}}}}}}}}}}}}}}}}}},"实":{"docs":{},"现":{"docs":{},"s":{"docs":{},"t":{"docs":{},"e":{"docs":{},"p":{"docs":{},"s":{"docs":{},"方":{"docs":{},"法":{"docs":{},"用":{"docs":{},"于":{"docs":{},"指":{"docs":{},"定":{"docs":{},"自":{"docs":{},"定":{"docs":{},"义":{"docs":{},"的":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"p":{"docs":{},"e":{"docs":{},"r":{"docs":{},"，":{"docs":{},"c":{"docs":{},"o":{"docs":{},"m":{"docs":{},"n":{"docs":{},"b":{"docs":{},"i":{"docs":{},"n":{"docs":{},"e":{"docs":{},"r":{"docs":{},"和":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{},"e":{"docs":{},"r":{"docs":{},"方":{"docs":{},"法":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"每":{"docs":{},"一":{"docs":{},"行":{"docs":{},"从":{"docs":{},"l":{"docs":{},"i":{"docs":{},"n":{"docs":{},"e":{"docs":{},"中":{"docs":{},"输":{"docs":{},"入":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}}}}}},"条":{"docs":{},"数":{"docs":{},"据":{"docs":{},"代":{"docs":{},"表":{"docs":{},"一":{"docs":{},"次":{"docs":{},"访":{"docs":{},"问":{"docs":{},"记":{"docs":{},"录":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}}}}}}}}}}},"含":{"docs":{},"有":{"docs":{},"o":{"docs":{},"u":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}}}}},"h":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"中":{"docs":{},"一":{"docs":{},"般":{"docs":{},"存":{"docs":{},"储":{"docs":{},"数":{"docs":{},"据":{"docs":{},"量":{"docs":{},"都":{"docs":{},"很":{"docs":{},"大":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}}}}}}}}},"l":{"docs":{},"i":{"docs":{},"m":{"docs":{},"i":{"docs":{},"t":{"docs":{},"=":{"docs":{},">":{"2":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}},"docs":{}}}}}}}},"显":{"docs":{},"示":{"docs":{},"结":{"docs":{},"果":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}},"前":{"1":{"0":{"docs":{},"条":{"docs":{},"数":{"docs":{},"据":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}},"docs":{}},"docs":{}},"数":{"docs":{},"据":{"docs":{},"结":{"docs":{},"构":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}},"返":{"docs":{},"回":{"docs":{},"结":{"docs":{},"果":{"docs":{},"如":{"docs":{},"下":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.004405286343612335}}}},"：":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}},"a":{"docs":{},"p":{"docs":{},"i":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0084985835694051}}}}},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"n":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"h":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}}}}}}}}}}},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"t":{"docs":{},"a":{"docs":{},"b":{"docs":{},"l":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}}}}}}}}}}},"d":{"docs":{},"e":{"docs":{},"l":{"docs":{},"e":{"docs":{},"t":{"docs":{},"e":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"(":{"docs":{},")":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}}}},"t":{"docs":{},"a":{"docs":{},"b":{"docs":{},"l":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}}}}}}}}}}},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0056657223796034}}}}}}},"r":{"docs":{},"a":{"docs":{},"c":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"：":{"docs":{},"采":{"docs":{},"样":{"docs":{},"比":{"docs":{},"例":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}},"g":{"docs":{},"e":{"docs":{},"t":{"docs":{},"q":{"docs":{},"u":{"docs":{},"e":{"docs":{},"r":{"docs":{},"y":{"docs":{},"(":{"docs":{},")":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}}}}}}}},"i":{"docs":{},"n":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"t":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"(":{"docs":{},")":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}}}}}}}}}},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"u":{"docs":{},"l":{"docs":{},"t":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0056657223796034}}}}}}},"o":{"docs":{},"w":{"docs":{},"_":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0056657223796034}}}}}}}}}}},"全":{"docs":{},"表":{"docs":{},"查":{"docs":{},"询":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}},"关":{"docs":{},"闭":{"docs":{},"连":{"docs":{},"接":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0056657223796034}}}}}},"函":{"docs":{},"数":{"docs":{},"封":{"docs":{},"装":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}},"创":{"docs":{},"建":{"docs":{},"和":{"docs":{},"h":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"的":{"docs":{},"连":{"docs":{},"接":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0056657223796034}}}}}}}}}}},"一":{"docs":{},"个":{"docs":{},"广":{"docs":{},"播":{"docs":{},"变":{"docs":{},"量":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}}}},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}},"u":{"docs":{},"d":{"docs":{},"f":{"docs":{},"，":{"docs":{},"u":{"docs":{},"d":{"docs":{},"f":{"docs":{},"函":{"docs":{},"数":{"docs":{},"需":{"docs":{},"要":{"docs":{},"两":{"docs":{},"个":{"docs":{},"参":{"docs":{},"数":{"docs":{},"：":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}}}},"封":{"docs":{},"装":{"docs":{},"函":{"docs":{},"数":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}},"获":{"docs":{},"取":{"docs":{},"h":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"中":{"docs":{},"的":{"docs":{},"所":{"docs":{},"有":{"docs":{},"表":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0056657223796034}}}}}}}}}}}},"s":{"docs":{},"t":{"docs":{},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"m":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"t":{"docs":{},"e":{"docs":{},"x":{"docs":{},"t":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}}}}}}}}}}}},"通":{"docs":{},"过":{"docs":{},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"n":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"找":{"docs":{},"到":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"表":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0056657223796034}}}}}}}}}}}}}}}}}}}}},"取":{"docs":{},"出":{"docs":{},"序":{"docs":{},"列":{"docs":{},"数":{"docs":{},"据":{"docs":{},"中":{"docs":{},"的":{"docs":{},"前":{"docs":{},"n":{"docs":{},"个":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}}}}}}}}}},"对":{"docs":{},"数":{"docs":{},"据":{"docs":{},"进":{"docs":{},"行":{"docs":{},"累":{"docs":{},"加":{"docs":{},"，":{"docs":{},"按":{"docs":{},"照":{"docs":{},"u":{"docs":{},"r":{"docs":{},"l":{"docs":{},"出":{"docs":{},"现":{"docs":{},"次":{"docs":{},"数":{"docs":{},"的":{"docs":{},"降":{"docs":{},"序":{"docs":{},"排":{"docs":{},"列":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}}}}}}}}}}}}}}}}}}}},"每":{"docs":{},"一":{"docs":{},"行":{"docs":{},"按":{"docs":{},"照":{"docs":{},"空":{"docs":{},"格":{"docs":{},"拆":{"docs":{},"分":{"docs":{},"，":{"docs":{},"将":{"docs":{},"i":{"docs":{},"p":{"docs":{},"地":{"docs":{},"址":{"docs":{},"取":{"docs":{},"出":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}}}}},"u":{"docs":{},"r":{"docs":{},"l":{"docs":{},"数":{"docs":{},"据":{"docs":{},"取":{"docs":{},"出":{"docs":{},"，":{"docs":{},"把":{"docs":{},"每":{"docs":{},"个":{"docs":{},"u":{"docs":{},"r":{"docs":{},"l":{"docs":{},"记":{"docs":{},"为":{"1":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"把":{"docs":{},"每":{"docs":{},"一":{"docs":{},"行":{"docs":{},"数":{"docs":{},"据":{"docs":{},"记":{"docs":{},"为":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"p":{"docs":{},"v":{"docs":{},"\"":{"docs":{},",":{"1":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}},"docs":{}}}}}}}}}}}}},"个":{"docs":{},"u":{"docs":{},"r":{"docs":{},"记":{"docs":{},"为":{"1":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}},"docs":{}}}}}}}},"(":{"docs":{},"(":{"docs":{},"纬":{"docs":{},"度":{"docs":{},",":{"docs":{},"精":{"docs":{},"度":{"docs":{},")":{"docs":{},",":{"1":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.013245033112582781}}}},"docs":{}}}}}}}}}},"‭":{"1":{"1":{"0":{"1":{"1":{"1":{"1":{"1":{"docs":{},"‬":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}},"根":{"docs":{},"据":{"docs":{},"单":{"docs":{},"个":{"docs":{},"i":{"docs":{},"p":{"docs":{},"获":{"docs":{},"取":{"docs":{},"对":{"docs":{},"应":{"docs":{},"经":{"docs":{},"纬":{"docs":{},"度":{"docs":{},"信":{"docs":{},"息":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}}}}}}}}}}},"取":{"docs":{},"出":{"docs":{},"对":{"docs":{},"应":{"docs":{},"的":{"docs":{},"位":{"docs":{},"置":{"docs":{},"信":{"docs":{},"息":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}}}}}}}}},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}},"=":{"docs":{},"=":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}},"=":{"docs":{},"=":{"docs":{},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{},"拆":{"docs":{},"成":{"docs":{},"两":{"docs":{},"部":{"docs":{},"分":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"w":{"docs":{},"i":{"docs":{},"t":{"docs":{},"h":{"docs":{},"r":{"docs":{},"e":{"docs":{},"p":{"docs":{},"l":{"docs":{},"a":{"docs":{},"c":{"docs":{},"e":{"docs":{},"m":{"docs":{},"e":{"docs":{},"n":{"docs":{},"t":{"docs":{},"：":{"docs":{},"是":{"docs":{},"否":{"docs":{},"有":{"docs":{},"放":{"docs":{},"回":{"docs":{},"的":{"docs":{},"采":{"docs":{},"样":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}}}}}}}}}},"为":{"docs":{},"数":{"docs":{},"据":{"docs":{},"添":{"docs":{},"加":{"docs":{},"列":{"docs":{},"名":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}},"列":{"docs":{},"名":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}},"删":{"docs":{},"除":{"docs":{},"一":{"docs":{},"列":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}},"加":{"docs":{},"载":{"docs":{},"c":{"docs":{},"s":{"docs":{},"v":{"docs":{},"类":{"docs":{},"型":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"并":{"docs":{},"转":{"docs":{},"换":{"docs":{},"为":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}}}}}}}},"在":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"中":{"docs":{},"需":{"docs":{},"要":{"docs":{},"通":{"docs":{},"过":{"docs":{},"u":{"docs":{},"d":{"docs":{},"f":{"docs":{},"将":{"docs":{},"自":{"docs":{},"定":{"docs":{},"义":{"docs":{},"函":{"docs":{},"数":{"docs":{},"封":{"docs":{},"装":{"docs":{},"成":{"docs":{},"u":{"docs":{},"d":{"docs":{},"f":{"docs":{},"函":{"docs":{},"数":{"docs":{},"再":{"docs":{},"交":{"docs":{},"给":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"进":{"docs":{},"行":{"docs":{},"调":{"docs":{},"用":{"docs":{},"执":{"docs":{},"行":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"中":{"docs":{},"可":{"docs":{},"以":{"docs":{},"直":{"docs":{},"接":{"docs":{},"定":{"docs":{},"义":{"docs":{},"函":{"docs":{},"数":{"docs":{},"，":{"docs":{},"交":{"docs":{},"给":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"的":{"docs":{},"t":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"s":{"docs":{},"f":{"docs":{},"o":{"docs":{},"r":{"docs":{},"m":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"i":{"docs":{},"n":{"docs":{},"s":{"docs":{},"方":{"docs":{},"法":{"docs":{},"进":{"docs":{},"行":{"docs":{},"执":{"docs":{},"行":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"如":{"docs":{},"果":{"docs":{},"操":{"docs":{},"作":{"docs":{},"的":{"docs":{},"是":{"docs":{},"原":{"docs":{},"有":{"docs":{},"列":{"docs":{},"，":{"docs":{},"可":{"docs":{},"以":{"docs":{},"替":{"docs":{},"换":{"docs":{},"原":{"docs":{},"有":{"docs":{},"列":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}}}}}},"定":{"docs":{},"义":{"docs":{},"一":{"docs":{},"个":{"docs":{},"新":{"docs":{},"的":{"docs":{},"列":{"docs":{},"，":{"docs":{},"数":{"docs":{},"据":{"docs":{},"为":{"docs":{},"其":{"docs":{},"他":{"docs":{},"某":{"docs":{},"列":{"docs":{},"数":{"docs":{},"据":{"docs":{},"的":{"docs":{},"两":{"docs":{},"倍":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}},"方":{"docs":{},"法":{"docs":{},"，":{"docs":{},"用":{"docs":{},"于":{"docs":{},"检":{"docs":{},"测":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}},"结":{"docs":{},"构":{"docs":{},"类":{"docs":{},"型":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"更":{"docs":{},"新":{"docs":{},"函":{"docs":{},"数":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}}},"处":{"docs":{},"理":{"docs":{},"的":{"docs":{},"时":{"docs":{},"间":{"docs":{},"间":{"docs":{},"隔":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}},"滑":{"docs":{},"动":{"docs":{},"时":{"docs":{},"间":{"docs":{},"间":{"docs":{},"隔":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}},"窗":{"docs":{},"口":{"docs":{},"长":{"docs":{},"度":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}},"查":{"docs":{},"看":{"docs":{},"两":{"docs":{},"个":{"docs":{},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{},"在":{"docs":{},"类":{"docs":{},"别":{"docs":{},"上":{"docs":{},"的":{"docs":{},"差":{"docs":{},"异":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}},"统":{"docs":{},"计":{"docs":{},"总":{"docs":{},"量":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}},"单":{"docs":{},"词":{"docs":{},"个":{"docs":{},"数":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099}}}}}}}},"设":{"docs":{},"置":{"docs":{},"数":{"docs":{},"据":{"docs":{},"比":{"docs":{},"例":{"docs":{},"将":{"docs":{},"数":{"docs":{},"据":{"docs":{},"划":{"docs":{},"分":{"docs":{},"为":{"docs":{},"两":{"docs":{},"部":{"docs":{},"分":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}},"调":{"docs":{},"用":{"docs":{},"函":{"docs":{},"数":{"docs":{},"并":{"docs":{},"起":{"docs":{},"一":{"docs":{},"个":{"docs":{},"别":{"docs":{},"名":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{},"e":{"docs":{},"b":{"docs":{},"y":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"w":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"o":{"docs":{},"w":{"docs":{},"，":{"docs":{},"来":{"docs":{},"进":{"docs":{},"行":{"docs":{},"窗":{"docs":{},"口":{"docs":{},"函":{"docs":{},"数":{"docs":{},"的":{"docs":{},"调":{"docs":{},"用":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"首":{"docs":{},"先":{"docs":{},"找":{"docs":{},"到":{"docs":{},"这":{"docs":{},"些":{"docs":{},"类":{"docs":{},"，":{"docs":{},"整":{"docs":{},"理":{"docs":{},"到":{"docs":{},"一":{"docs":{},"个":{"docs":{},"列":{"docs":{},"表":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}},"x":{"docs":{},"x":{"docs":{},"x":{"docs":{},"s":{"docs":{},"t":{"docs":{},"y":{"docs":{},"p":{"docs":{},"e":{"docs":{},":":{"docs":{},"指":{"docs":{},"的":{"docs":{},"是":{"docs":{},"某":{"docs":{},"一":{"docs":{},"列":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"类":{"docs":{},"型":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}}}}}}}}}}}}}}}}}},"使":{"docs":{},"用":{"docs":{},"内":{"docs":{},"部":{"docs":{},"的":{"docs":{},"s":{"docs":{},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},"m":{"docs":{},"a":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}}}}}}}}},"依":{"docs":{},"照":{"docs":{},"已":{"docs":{},"有":{"docs":{},"的":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"，":{"docs":{},"创":{"docs":{},"建":{"docs":{},"一":{"docs":{},"个":{"docs":{},"临":{"docs":{},"时":{"docs":{},"的":{"docs":{},"表":{"docs":{},"(":{"docs":{},"相":{"docs":{},"当":{"docs":{},"于":{"docs":{},"m":{"docs":{},"y":{"docs":{},"s":{"docs":{},"q":{"docs":{},"l":{"docs":{},"数":{"docs":{},"据":{"docs":{},"库":{"docs":{},"中":{"docs":{},"的":{"docs":{},"一":{"docs":{},"个":{"docs":{},"表":{"docs":{},")":{"docs":{},"，":{"docs":{},"这":{"docs":{},"样":{"docs":{},"就":{"docs":{},"可":{"docs":{},"以":{"docs":{},"用":{"docs":{},"纯":{"docs":{},"s":{"docs":{},"q":{"docs":{},"l":{"docs":{},"语":{"docs":{},"句":{"docs":{},"进":{"docs":{},"行":{"docs":{},"数":{"docs":{},"据":{"docs":{},"操":{"docs":{},"作":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"只":{"docs":{},"有":{"docs":{},"被":{"docs":{},"压":{"docs":{},"缩":{"docs":{},"后":{"docs":{},"的":{"docs":{},"j":{"docs":{},"s":{"docs":{},"o":{"docs":{},"n":{"docs":{},"文":{"docs":{},"件":{"docs":{},"内":{"docs":{},"容":{"docs":{},"，":{"docs":{},"才":{"docs":{},"能":{"docs":{},"被":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}}}}}}}}}}}}}}}}}}}}}},"指":{"docs":{},"定":{"docs":{},"s":{"docs":{},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},"m":{"docs":{},"a":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}}}}}},"无":{"docs":{},"意":{"docs":{},"义":{"docs":{},"重":{"docs":{},"复":{"docs":{},"数":{"docs":{},"据":{"docs":{},"去":{"docs":{},"重":{"docs":{},"：":{"docs":{},"数":{"docs":{},"据":{"docs":{},"中":{"docs":{},"行":{"docs":{},"与":{"docs":{},"行":{"docs":{},"完":{"docs":{},"全":{"docs":{},"重":{"docs":{},"复":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}}}}}}}}}}}}},"有":{"docs":{},"意":{"docs":{},"义":{"docs":{},"去":{"docs":{},"重":{"docs":{},"：":{"docs":{},"删":{"docs":{},"除":{"docs":{},"除":{"docs":{},"去":{"docs":{},"无":{"docs":{},"意":{"docs":{},"义":{"docs":{},"字":{"docs":{},"段":{"docs":{},"之":{"docs":{},"外":{"docs":{},"的":{"docs":{},"完":{"docs":{},"全":{"docs":{},"重":{"docs":{},"复":{"docs":{},"的":{"docs":{},"行":{"docs":{},"数":{"docs":{},"据":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}}}}}}}}}}}}}}}}}}},"参":{"docs":{},"数":{"2":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},"：":{"docs":{},"指":{"docs":{},"定":{"docs":{},"执":{"docs":{},"行":{"docs":{},"计":{"docs":{},"算":{"docs":{},"的":{"docs":{},"时":{"docs":{},"间":{"docs":{},"间":{"docs":{},"隔":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099}}}}}}}}}}}}}}},"3":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}},"4":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}},"docs":{}}},"启":{"docs":{},"动":{"docs":{},"s":{"docs":{},"t":{"docs":{},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"m":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"t":{"docs":{},"e":{"docs":{},"x":{"docs":{},"t":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099}}}}}}}}}}}}}}}}}}}},"打":{"docs":{},"印":{"docs":{},"结":{"docs":{},"果":{"docs":{},"信":{"docs":{},"息":{"docs":{},"，":{"docs":{},"会":{"docs":{},"使":{"docs":{},"得":{"docs":{},"前":{"docs":{},"面":{"docs":{},"的":{"docs":{},"t":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"s":{"docs":{},"f":{"docs":{},"o":{"docs":{},"r":{"docs":{},"m":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"操":{"docs":{},"作":{"docs":{},"执":{"docs":{},"行":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"监":{"docs":{},"听":{"docs":{},"i":{"docs":{},"p":{"docs":{},"，":{"docs":{},"端":{"docs":{},"口":{"docs":{},"上":{"docs":{},"的":{"docs":{},"上":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099}}}}}}}}}}}}}}},"等":{"docs":{},"待":{"docs":{},"计":{"docs":{},"算":{"docs":{},"结":{"docs":{},"束":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099}}}}}}}},"开":{"docs":{},"启":{"docs":{},"检":{"docs":{},"查":{"docs":{},"点":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}},"输":{"docs":{},"出":{"docs":{},"处":{"docs":{},"理":{"docs":{},"结":{"docs":{},"果":{"docs":{},"信":{"docs":{},"息":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}},"需":{"docs":{},"要":{"docs":{},"设":{"docs":{},"置":{"docs":{},"检":{"docs":{},"查":{"docs":{},"点":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{},"i":{"docs":{},"t":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}},"注":{"docs":{},"意":{"docs":{},"：":{"docs":{},"b":{"docs":{},"e":{"docs":{},"h":{"docs":{},"a":{"docs":{},"v":{"docs":{},"i":{"docs":{},"o":{"docs":{},"r":{"docs":{},"_":{"docs":{},"l":{"docs":{},"o":{"docs":{},"g":{"docs":{},"_":{"docs":{},"d":{"docs":{},"f":{"docs":{},".":{"docs":{},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"b":{"docs":{},"y":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"准":{"docs":{},"备":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}},"*":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734},"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.006493506493506494},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.010291595197255575},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.011385199240986717},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.023148148148148147},"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.007407407407407408},"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0022675736961451248},"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965},"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.011538461538461539},"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.0045662100456621},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.007633587786259542},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}},"*":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147},"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876}},"*":{"docs":{},"*":{"docs":{},"*":{"docs":{},"*":{"docs":{},"*":{"docs":{},"*":{"docs":{},"*":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}},"/":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}},"[":{"docs":{},"f":{"docs":{},"n":{"docs":{},".":{"docs":{},"m":{"docs":{},"e":{"docs":{},"a":{"docs":{},"n":{"docs":{},"(":{"docs":{},"c":{"docs":{},")":{"docs":{},".":{"docs":{},"a":{"docs":{},"l":{"docs":{},"i":{"docs":{},"a":{"docs":{},"s":{"docs":{},"(":{"docs":{},"c":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}}}}}}}}}}}}},"r":{"docs":{},"e":{"docs":{},"t":{"docs":{},")":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}},"=":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0748587570621469},"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.08766233766233766},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.07432818753573471},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.08349146110056926},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.07407407407407407},"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.07003891050583658},"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.07368421052631578},"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.0898876404494382},"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.01680672268907563},"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.012048192771084338},"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.006802721088435374},"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.08498583569405099},"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.020202020202020204},"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.03404255319148936},"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.05909090909090909},"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.06711409395973154},"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.10596026490066225},"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.06643356643356643},"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.07692307692307693},"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.0365296803652968},"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.12087912087912088},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.11450381679389313},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.048939641109298535},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.022639200489496226},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.030242737763629127},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.04355400696864112},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.06765327695560254}},"\\":{"docs":{},"c":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"c":{"docs":{},"{":{"0":{"docs":{},".":{"8":{"5":{"docs":{},"*":{"3":{"docs":{},"+":{"0":{"docs":{},".":{"7":{"1":{"docs":{},"*":{"5":{"docs":{},"}":{"docs":{},"{":{"0":{"docs":{},".":{"8":{"5":{"docs":{},"+":{"0":{"docs":{},".":{"7":{"1":{"docs":{},"}":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}},"docs":{}},"docs":{}}},"docs":{}}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"docs":{}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}},"docs":{}}},"docs":{}}}}}}}},"=":{"1":{"docs":{},":":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}},"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.006493506493506494},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.004574042309891366},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147},"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269},"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017},"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577},"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391},"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.015267175572519083}},"=":{"docs":{},">":{"docs":{"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.02247191011235955},"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.010526315789473684}}},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"从":{"docs":{},"c":{"docs":{},"s":{"docs":{},"v":{"docs":{},"读":{"docs":{},"取":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"基":{"docs":{},"本":{"docs":{},"统":{"docs":{},"计":{"docs":{},"功":{"docs":{},"能":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.007692307692307693}}}}}}}}}}}}}}}}}}}}}}}}}}}},"交":{"docs":{},"叉":{"docs":{},"表":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}},"直":{"docs":{},"接":{"docs":{},"创":{"docs":{},"建":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"采":{"docs":{},"样":{"docs":{},"数":{"docs":{},"据":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}},"增":{"docs":{},"加":{"docs":{},"一":{"docs":{},"列":{"docs":{},"(":{"docs":{},"或":{"docs":{},"者":{"docs":{},"替":{"docs":{},"换":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}},"提":{"docs":{},"取":{"docs":{},"部":{"docs":{},"分":{"docs":{},"列":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}},"删":{"docs":{},"除":{"docs":{},"一":{"docs":{},"列":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}},">":{"7":{"docs":{},"维":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}},"docs":{"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.033707865168539325},"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.09473684210526316},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.012684989429175475}}}},">":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.05066079295154185}},"[":{"docs":{},"'":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"_":{"docs":{},"i":{"docs":{},"n":{"docs":{},"f":{"docs":{},"o":{"docs":{},"'":{"docs":{},"]":{"docs":{},",":{"docs":{},"l":{"docs":{},"i":{"docs":{},"m":{"docs":{},"i":{"docs":{},"t":{"docs":{},"=":{"docs":{},">":{"2":{"docs":{},",":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{},"r":{"docs":{},"o":{"docs":{},"w":{"docs":{},"=":{"docs":{},">":{"docs":{},"'":{"docs":{},"r":{"docs":{},"o":{"docs":{},"w":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{},"_":{"1":{"6":{"docs":{},"'":{"docs":{},"}":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}},"docs":{}},"docs":{}}}}}}}}}}}}}}}}}}}},"}":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}},"[":{"0":{"docs":{},",":{"1":{"docs":{},",":{"1":{"docs":{},",":{"0":{"docs":{},",":{"0":{"docs":{},",":{"1":{"docs":{},",":{"0":{"docs":{},",":{"0":{"docs":{},",":{"0":{"docs":{},"]":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"1":{"docs":{},",":{"0":{"docs":{},",":{"0":{"docs":{},"]":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}}},"docs":{}}},"docs":{}}},">":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.023391812865497075},"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.037037037037037035},"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.004545454545454545}},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"(":{"docs":{},"特":{"docs":{},"征":{"docs":{},")":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}},"m":{"docs":{},"l":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}},"p":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"i":{"docs":{},"c":{"docs":{},"t":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}},">":{"docs":{},">":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002858776443682104},"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.005836575875486381},"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.010526315789473684},"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.03829787234042553},"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.11363636363636363},"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}},"d":{"docs":{},"e":{"docs":{},"f":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938},"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}}}}},"(":{"docs":{},"o":{"docs":{},"u":{"docs":{},"t":{"docs":{},"_":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{},",":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"m":{"docs":{},"e":{"docs":{},"d":{"docs":{},"i":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"_":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},")":{"docs":{"day03_Hadoop/ha3.2.html":{"ref":"day03_Hadoop/ha3.2.html","tf":0.02040816326530612}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"o":{"docs":{},"u":{"docs":{},"t":{"docs":{},"_":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{"day03_Hadoop/ha3.2.html":{"ref":"day03_Hadoop/ha3.2.html","tf":0.02040816326530612}}}}}}}}}},"写":{"docs":{},"入":{"docs":{},"数":{"docs":{},"据":{"docs":{"day03_Hadoop/ha3.5.html":{"ref":"day03_Hadoop/ha3.5.html","tf":0.030303030303030304}}}}}},"处":{"docs":{},"理":{"docs":{},"数":{"docs":{},"据":{"docs":{"day03_Hadoop/ha3.5.html":{"ref":"day03_Hadoop/ha3.5.html","tf":0.030303030303030304}}}}}},"读":{"docs":{},"取":{"docs":{},"数":{"docs":{},"据":{"docs":{"day03_Hadoop/ha3.5.html":{"ref":"day03_Hadoop/ha3.5.html","tf":0.030303030303030304}}}}}},"输":{"docs":{},"出":{"docs":{},"数":{"docs":{},"据":{"docs":{"day03_Hadoop/ha3.5.html":{"ref":"day03_Hadoop/ha3.5.html","tf":0.030303030303030304}}}}}},".":{"docs":{},"c":{"docs":{},"l":{"docs":{},"a":{"docs":{},"s":{"docs":{},"s":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}},"j":{"docs":{},"a":{"docs":{},"r":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}},"j":{"docs":{},"v":{"docs":{},"m":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}},"r":{"docs":{},"d":{"docs":{},"d":{"2":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}},"docs":{}}}},"t":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"s":{"docs":{},"f":{"docs":{},"o":{"docs":{},"r":{"docs":{},"m":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}}},"=":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"[":{"0":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.016913319238900635}},",":{"1":{"docs":{},",":{"0":{"docs":{},",":{"1":{"docs":{},",":{"1":{"docs":{},"]":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.00423728813559322}}}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}},".":{"0":{"docs":{},".":{"0":{"docs":{},".":{"0":{"docs":{},"]":{"docs":{"day03_Hadoop/ha2.1.html":{"ref":"day03_Hadoop/ha2.1.html","tf":0.0136986301369863}}}},"docs":{}}},"docs":{}}},"2":{"5":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}},"docs":{}},"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"1":{"5":{"5":{"8":{"3":{"2":{"3":{"1":{"3":{"9":{"7":{"3":{"2":{"docs":{},",":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"9":{"0":{"4":{"1":{"3":{"0":{"docs":{},",":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"8":{"docs":{},"/":{"docs":{},"s":{"docs":{},"e":{"docs":{},"p":{"docs":{},"/":{"2":{"0":{"1":{"3":{"docs":{},":":{"0":{"6":{"docs":{},":":{"4":{"9":{"docs":{},":":{"1":{"8":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}},"docs":{}},"2":{"3":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}},"docs":{}},"3":{"3":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}},"6":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}},"docs":{}},"4":{"2":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}},"5":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}},"8":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}},"docs":{}},"5":{"7":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}},"docs":{}},"docs":{}}},"docs":{}},"5":{"0":{"docs":{},":":{"0":{"8":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}}}},"docs":{},",":{"0":{"docs":{},",":{"0":{"docs":{},",":{"1":{"docs":{},",":{"1":{"docs":{},"]":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.00423728813559322}}}}},"docs":{}}},"docs":{}}},"1":{"docs":{},",":{"0":{"docs":{},",":{"0":{"docs":{},"]":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.00423728813559322}}}}},"docs":{}}},"1":{"docs":{},",":{"0":{"docs":{},"]":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.00423728813559322}}}}},"docs":{}}},"docs":{}}},"docs":{}}},"1":{"docs":{},",":{"1":{"docs":{},",":{"0":{"docs":{},",":{"1":{"docs":{},"]":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.00423728813559322}}}}},"docs":{}}},"docs":{}}},"docs":{}}},"2":{"docs":{},",":{"3":{"docs":{},"]":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}},",":{"4":{"docs":{},",":{"5":{"docs":{},"]":{"docs":{},")":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938},"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.010526315789473684}}}}},"docs":{}}},"docs":{}}},"docs":{}}},"5":{"docs":{},",":{"5":{"docs":{},",":{"2":{"docs":{},",":{"1":{"docs":{},"]":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208},"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.007782101167315175},"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.01276595744680851},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}},".":{"0":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0008158270446665307}}}},"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"2":{"docs":{},",":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938},"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.011363636363636364}}}},"3":{"docs":{},",":{"1":{"docs":{},",":{"2":{"docs":{},",":{"3":{"docs":{},",":{"3":{"docs":{},"]":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}},"docs":{}}},"docs":{}}},"docs":{}}},"3":{"docs":{},",":{"1":{"docs":{},",":{"5":{"docs":{},",":{"4":{"docs":{},"]":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}},".":{"0":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}}},"docs":{}}},"4":{"docs":{},",":{"3":{"docs":{},",":{"4":{"docs":{},",":{"3":{"docs":{},",":{"5":{"docs":{},"]":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}},"docs":{}}},"docs":{}}},"docs":{}}},"5":{"docs":{},",":{"6":{"docs":{},",":{"7":{"docs":{},",":{"8":{"docs":{},"]":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}},"docs":{}}},"docs":{}},"]":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}},"docs":{}}},"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}},".":{"4":{"9":{"9":{"9":{"9":{"9":{"9":{"9":{"9":{"9":{"9":{"9":{"9":{"9":{"9":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"5":{"docs":{},",":{"3":{"docs":{},",":{"4":{"docs":{},",":{"4":{"docs":{},",":{"docs":{},"n":{"docs":{},"o":{"docs":{},"n":{"docs":{},"e":{"docs":{},"]":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}},"docs":{}}},"docs":{}}},"docs":{}}},"6":{"3":{"1":{"2":{"0":{"4":{"docs":{},",":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}},"9":{"1":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}},".":{"6":{"9":{"9":{"9":{"9":{"9":{"9":{"9":{"9":{"9":{"9":{"9":{"9":{"9":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.007062146892655367},"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269},"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.007692307692307693},"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.0045662100456621},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}},"\"":{"1":{"0":{"1":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"1":{"0":{"1":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"1":{"0":{"1":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"1":{"0":{"4":{"docs":{},"\"":{"docs":{},"]":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}},"docs":{}},"docs":{}},"docs":{}}}}},"docs":{}},"docs":{}},"docs":{}}}}},"2":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"1":{"0":{"3":{"docs":{},"\"":{"docs":{},"]":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}},"docs":{}},"docs":{}},"docs":{}}}}},"4":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"1":{"0":{"1":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"1":{"0":{"1":{"docs":{},"\"":{"docs":{},"]":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}},"docs":{}},"docs":{}},"docs":{}}}}},"docs":{}},"docs":{}},"docs":{}}},"]":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}},"docs":{}},"docs":{}},"docs":{}}}}},"2":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"1":{"0":{"3":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"1":{"0":{"3":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"1":{"0":{"4":{"docs":{},"\"":{"docs":{},"]":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}},"docs":{}},"docs":{}},"docs":{}}}}},"4":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"1":{"0":{"3":{"docs":{},"\"":{"docs":{},"]":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}},"docs":{}},"docs":{}},"docs":{}}},"]":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}},"docs":{}},"docs":{}},"docs":{}}}}},"5":{"docs":{},"\"":{"docs":{},"]":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0045351473922902496}}}}},"docs":{}},"docs":{}},"docs":{}}}}},"3":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"1":{"0":{"2":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"1":{"0":{"1":{"docs":{},"\"":{"docs":{},"]":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0022675736961451248}}}}},"docs":{}},"docs":{}},"docs":{}}}}},"4":{"docs":{},"\"":{"docs":{},"]":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.003401360544217687}}}}},"docs":{}},"docs":{}},"docs":{}}}}},"5":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"1":{"0":{"2":{"docs":{},"\"":{"docs":{},"]":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0022675736961451248}}}}},"docs":{}},"docs":{}},"docs":{}}}}},"docs":{}},"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"1":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"1":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"1":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"1":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"2":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"1":{"docs":{},"\"":{"docs":{},"]":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}},"docs":{}}}}},"docs":{}}},"]":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0022675736961451248}}}}},"docs":{}}}}},"docs":{}}}}},"docs":{}}}}},"docs":{},"k":{"docs":{},"w":{"3":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"docs":{},"k":{"docs":{},"w":{"6":{"docs":{},"\"":{"docs":{},"]":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0022675736961451248}}}}},"docs":{}}}}}}},"docs":{}}}}}}},"4":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"1":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"1":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"3":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"1":{"docs":{},"\"":{"docs":{},"]":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}},"docs":{}}}}},"docs":{}}}}},"docs":{}}}}},"docs":{}}}}},"docs":{},"b":{"docs":{},"u":{"docs":{},"y":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"docs":{},"b":{"docs":{},"u":{"docs":{},"y":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"docs":{},"b":{"docs":{},"u":{"docs":{},"y":{"docs":{},"\"":{"docs":{},",":{"docs":{},"n":{"docs":{},"o":{"docs":{},"n":{"docs":{},"e":{"docs":{},",":{"docs":{},"\"":{"docs":{},"b":{"docs":{},"u":{"docs":{},"y":{"docs":{},"\"":{"docs":{},"]":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}}}}}}}}}}}},"n":{"docs":{},"o":{"docs":{},"n":{"docs":{},"e":{"docs":{},",":{"docs":{},"\"":{"docs":{},"b":{"docs":{},"u":{"docs":{},"y":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"docs":{},"b":{"docs":{},"u":{"docs":{},"y":{"docs":{},"\"":{"docs":{},",":{"docs":{},"n":{"docs":{},"o":{"docs":{},"n":{"docs":{},"e":{"docs":{},"]":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}},"n":{"docs":{},"o":{"docs":{},"n":{"docs":{},"e":{"docs":{},",":{"docs":{},"n":{"docs":{},"o":{"docs":{},"n":{"docs":{},"e":{"docs":{},"]":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}}}}},"n":{"docs":{},"o":{"docs":{},"n":{"docs":{},"e":{"docs":{},",":{"docs":{},"\"":{"docs":{},"b":{"docs":{},"u":{"docs":{},"y":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"docs":{},"b":{"docs":{},"u":{"docs":{},"y":{"docs":{},"\"":{"docs":{},"]":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"m":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.007062146892655367}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"1":{"docs":{},"\"":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.007062146892655367}}}}},"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}}}}},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"n":{"docs":{},"l":{"docs":{},"e":{"docs":{},"y":{"docs":{},"\"":{"docs":{},",":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}}}}}}}}},"k":{"docs":{},"w":{"1":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"docs":{},"k":{"docs":{},"w":{"3":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"docs":{},"k":{"docs":{},"w":{"4":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"docs":{},"k":{"docs":{},"w":{"5":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"docs":{},"k":{"docs":{},"w":{"6":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"docs":{},"k":{"docs":{},"w":{"7":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"docs":{},"k":{"docs":{},"w":{"9":{"docs":{},"\"":{"docs":{},"]":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}},"docs":{}}}}}}},"docs":{}}}}}}},"docs":{}}}}}}},"docs":{}}}}}}},"6":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"docs":{},"k":{"docs":{},"w":{"7":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"docs":{},"k":{"docs":{},"w":{"8":{"docs":{},"\"":{"docs":{},"]":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}},"docs":{}}}}}}},"docs":{}}}}}}},"docs":{}}}}}}},"4":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"docs":{},"k":{"docs":{},"w":{"5":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"docs":{},"k":{"docs":{},"w":{"7":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"docs":{},"k":{"docs":{},"w":{"9":{"docs":{},"\"":{"docs":{},"]":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}},"docs":{}}}}}}},"8":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"docs":{},"k":{"docs":{},"w":{"9":{"docs":{},"\"":{"docs":{},"]":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}},"docs":{}}}}}}},"docs":{}}}}}}},"docs":{}}}}}}},"docs":{}}}}}}},"5":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"docs":{},"k":{"docs":{},"w":{"1":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"docs":{},"k":{"docs":{},"w":{"4":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"docs":{},"k":{"docs":{},"w":{"9":{"docs":{},"\"":{"docs":{},"]":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}},"docs":{}}}}}}},"docs":{}}}}}}},"docs":{}}}}}}},"6":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"docs":{},"k":{"docs":{},"w":{"3":{"docs":{},"\"":{"docs":{},"]":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}},"docs":{}}}}}}},"7":{"docs":{},"\"":{"docs":{},"]":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}},"8":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"docs":{},"k":{"docs":{},"w":{"1":{"docs":{},"\"":{"docs":{},"]":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}},"docs":{}}}}}}},"docs":{}}},"北":{"docs":{},"京":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}},"女":{"docs":{},"“":{"docs":{},"，":{"docs":{},"”":{"docs":{},"北":{"docs":{},"京":{"docs":{},"“":{"docs":{},"，":{"docs":{},"”":{"docs":{},"苹":{"docs":{},"果":{"docs":{},"“":{"docs":{},"]":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}}}}}}}}}}}}}},"男":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"“":{"docs":{},"，":{"docs":{},"”":{"docs":{},"上":{"docs":{},"海":{"docs":{},"“":{"docs":{},"，":{"docs":{},"”":{"docs":{},"小":{"docs":{},"米":{"docs":{},"“":{"docs":{},"]":{"docs":{},"=":{"docs":{},"[":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}}}}}}}}}}}}}}}},"苹":{"docs":{},"果":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}},"n":{"docs":{},"o":{"docs":{},"n":{"docs":{},"e":{"docs":{},",":{"docs":{},"\"":{"docs":{},"b":{"docs":{},"u":{"docs":{},"y":{"docs":{},"\"":{"docs":{},",":{"docs":{},"n":{"docs":{},"o":{"docs":{},"n":{"docs":{},"e":{"docs":{},",":{"docs":{},"\"":{"docs":{},"b":{"docs":{},"u":{"docs":{},"y":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"docs":{},"b":{"docs":{},"u":{"docs":{},"y":{"docs":{},"\"":{"docs":{},"]":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"u":{"docs":{},"l":{"docs":{},"l":{"docs":{},",":{"docs":{},"\"":{"1":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"1":{"docs":{},"\"":{"docs":{},"]":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0022675736961451248}}}}},"docs":{}}}}},"docs":{}}}}}}},"(":{"1":{"docs":{},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416},"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}},"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}},"\"":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0017152658662092624},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.003795066413662239},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}}}}},"r":{"docs":{},"_":{"docs":{},"{":{"docs":{},"u":{"docs":{},"i":{"docs":{},"}":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}},"w":{"docs":{},",":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"d":{"docs":{},"(":{"docs":{},"c":{"docs":{},"/":{"docs":{},"m":{"docs":{},"a":{"docs":{},"x":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},",":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}}}}}}}}}}}}}}}}}}},"'":{"1":{"docs":{},"'":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.004545454545454545}}}}},"docs":{},"p":{"docs":{},"y":{"docs":{},"t":{"docs":{},"h":{"docs":{},"o":{"docs":{},"n":{"docs":{},"'":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}}}}}}},"a":{"docs":{},"'":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.00909090909090909}}}},"n":{"docs":{},"k":{"docs":{},"i":{"docs":{},"t":{"docs":{},"'":{"docs":{},",":{"2":{"5":{"docs":{},")":{"docs":{},",":{"docs":{},"(":{"docs":{},"'":{"docs":{},"j":{"docs":{},"a":{"docs":{},"l":{"docs":{},"f":{"docs":{},"a":{"docs":{},"i":{"docs":{},"z":{"docs":{},"y":{"docs":{},"'":{"docs":{},",":{"2":{"2":{"docs":{},")":{"docs":{},",":{"docs":{},"(":{"docs":{},"'":{"docs":{},"s":{"docs":{},"a":{"docs":{},"u":{"docs":{},"r":{"docs":{},"a":{"docs":{},"b":{"docs":{},"h":{"docs":{},"'":{"docs":{},",":{"2":{"0":{"docs":{},")":{"docs":{},",":{"docs":{},"(":{"docs":{},"'":{"docs":{},"b":{"docs":{},"a":{"docs":{},"l":{"docs":{},"a":{"docs":{},"'":{"docs":{},",":{"2":{"6":{"docs":{},")":{"docs":{},"]":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}},"docs":{}},"docs":{}}}}}}}}}}}},"docs":{}},"docs":{}}}}}}}}}}}}}}},"docs":{}},"docs":{}}}}}}}}}}}}}}}},"docs":{}},"docs":{}}}}}}}},"b":{"docs":{},"'":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}},"c":{"docs":{},"'":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}},"m":{"docs":{},"a":{"docs":{},"r":{"docs":{},"y":{"docs":{},"'":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}},"]":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147},"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938},"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}},")":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938},"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}},",":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}},"\\":{"docs":{},"s":{"docs":{},"u":{"docs":{},"m":{"docs":{},"_":{"docs":{},"{":{"docs":{},"u":{"docs":{},",":{"docs":{},"i":{"docs":{},"\\":{"docs":{},"i":{"docs":{},"n":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}}}}}}}},"d":{"docs":{},"c":{"docs":{},"t":{"docs":{},".":{"docs":{},"d":{"docs":{},"o":{"docs":{},"c":{"2":{"docs":{},"b":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"docs":{},"l":{"docs":{},"i":{"docs":{},"n":{"docs":{},"e":{"docs":{},")":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876}}}}}}}}}}}},"docs":{}}}}}}}},"i":{"docs":{},"[":{"0":{"docs":{},"]":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}},"docs":{}},".":{"docs":{},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"i":{"docs":{},"d":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}},"a":{"docs":{},"d":{"docs":{},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"i":{"docs":{},"d":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}}}}}}},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"0":{"0":{"docs":{},"]":{"docs":{"day03_Hadoop/ha2.1.html":{"ref":"day03_Hadoop/ha2.1.html","tf":0.0136986301369863}}}},"docs":{}},"docs":{},"@":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"0":{"0":{"0":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}},"docs":{"day03_Hadoop/ha2.1.html":{"ref":"day03_Hadoop/ha2.1.html","tf":0.0273972602739726}}},"docs":{}},"docs":{}}}}}}}}}}}}}},"u":{"docs":{},"r":{"docs":{},"i":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.0125}}}}},"'":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"_":{"docs":{},"i":{"docs":{},"n":{"docs":{},"f":{"docs":{},"o":{"docs":{},":":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"'":{"docs":{},",":{"docs":{},"'":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"_":{"docs":{},"i":{"docs":{},"n":{"docs":{},"f":{"docs":{},"o":{"docs":{},":":{"docs":{},"s":{"docs":{},"e":{"docs":{},"x":{"docs":{},"'":{"docs":{},"]":{"docs":{},"}":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"列":{"docs":{},"族":{"docs":{},"名":{"1":{"docs":{},"'":{"docs":{},",":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}},"docs":{}}}},"f":{"docs":{},"o":{"docs":{},"o":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}},"a":{"docs":{},"'":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}},"d":{"docs":{},"'":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}},"h":{"docs":{},"'":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}},"i":{"docs":{},"d":{"docs":{},"'":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00684931506849315}}}}}},"w":{"docs":{},"e":{"docs":{},"i":{"docs":{},"g":{"docs":{},"h":{"docs":{},"t":{"docs":{},"'":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}},"g":{"docs":{},"c":{"docs":{},"c":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}},"[":{"docs":{},"'":{"docs":{},"a":{"docs":{},"'":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}},"c":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}},"r":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"docs":{},"b":{"docs":{},"t":{"docs":{},"a":{"docs":{},"g":{"docs":{},"=":{"docs":{},"'":{"docs":{},"b":{"docs":{},"u":{"docs":{},"y":{"docs":{},"'":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"i":{"docs":{},"d":{"docs":{},"=":{"1":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}},"docs":{}}}}}}}},"c":{"docs":{},"l":{"docs":{},"k":{"docs":{},"=":{"docs":{},"'":{"0":{"docs":{},"'":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}},"docs":{}}}}}},"p":{"docs":{},"i":{"docs":{},"d":{"docs":{},"=":{"docs":{},"'":{"4":{"3":{"0":{"5":{"4":{"8":{"docs":{},"_":{"1":{"0":{"0":{"7":{"docs":{},"'":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}}},"a":{"docs":{},"d":{"docs":{},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"i":{"docs":{},"d":{"docs":{},"=":{"6":{"3":{"1":{"2":{"0":{"4":{"docs":{},")":{"docs":{},",":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}}}},".":{"docs":{},"c":{"docs":{},"m":{"docs":{},"s":{"docs":{},"_":{"docs":{},"s":{"docs":{},"e":{"docs":{},"g":{"docs":{},"i":{"docs":{},"d":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}}}}}}}}}}}},"a":{"docs":{},"w":{"docs":{},"_":{"docs":{},"s":{"docs":{},"a":{"docs":{},"m":{"docs":{},"p":{"docs":{},"l":{"docs":{},"e":{"docs":{},"_":{"docs":{},"d":{"docs":{},"f":{"docs":{},".":{"docs":{},"a":{"docs":{},"d":{"docs":{},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"i":{"docs":{},"d":{"docs":{},"=":{"docs":{},"=":{"docs":{},"a":{"docs":{},"d":{"docs":{},"_":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"e":{"docs":{},"_":{"docs":{},"d":{"docs":{},"f":{"docs":{},".":{"docs":{},"a":{"docs":{},"d":{"docs":{},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"i":{"docs":{},"d":{"docs":{},"]":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"_":{"docs":{},".":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"i":{"docs":{},"d":{"docs":{},"=":{"docs":{},"=":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"p":{"docs":{},"r":{"docs":{},"o":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"e":{"docs":{},"_":{"docs":{},"d":{"docs":{},"f":{"docs":{},".":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"i":{"docs":{},"d":{"docs":{},"]":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"\\":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.030303030303030304},"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.010067114093959731},"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.006993006993006993},"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.03076923076923077},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.011450381679389313}},"c":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"c":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}},"{":{"docs":{},"\\":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{},"i":{"docs":{},"a":{"docs":{},"l":{"docs":{},"}":{"docs":{},"{":{"docs":{},"\\":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{},"i":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}}}}}}}}}}}},"\\":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683}},"&":{"docs":{},"=":{"2":{"docs":{},"\\":{"docs":{},"s":{"docs":{},"u":{"docs":{},"m":{"docs":{},"_":{"docs":{},"{":{"docs":{},"u":{"docs":{},",":{"docs":{},"i":{"docs":{},"\\":{"docs":{},"i":{"docs":{},"n":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}}},"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0017152658662092624}},"\\":{"docs":{},"s":{"docs":{},"u":{"docs":{},"m":{"docs":{},"_":{"docs":{},"{":{"docs":{},"u":{"docs":{},",":{"docs":{},"i":{"docs":{},"\\":{"docs":{},"i":{"docs":{},"n":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}}}}}}},"m":{"docs":{},"u":{"docs":{"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}},"{":{"docs":{},"\\":{"docs":{},"s":{"docs":{},"u":{"docs":{},"m":{"docs":{},"_":{"docs":{},"{":{"docs":{},"k":{"docs":{},"=":{"1":{"docs":{},"}":{"docs":{},"}":{"docs":{},"^":{"docs":{},"k":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}},"docs":{}}}}}}}}}}},"q":{"docs":{},"_":{"docs":{},"{":{"docs":{},"i":{"docs":{},"k":{"docs":{},"}":{"docs":{},":":{"docs":{},"=":{"docs":{},"q":{"docs":{},"_":{"docs":{},"{":{"docs":{},"i":{"docs":{},"k":{"docs":{},"}":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}}}}}}}}}}},"+":{"docs":{"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}},"a":{"docs":{},"l":{"docs":{},"p":{"docs":{},"h":{"docs":{},"a":{"docs":{},"*":{"docs":{},"(":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}},"(":{"docs":{},"r":{"docs":{},"_":{"docs":{},"{":{"docs":{},"u":{"docs":{},"i":{"docs":{},"}":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}}},"\\":{"docs":{},"s":{"docs":{},"u":{"docs":{},"m":{"docs":{},"_":{"docs":{},"{":{"docs":{},"u":{"docs":{},",":{"docs":{},"i":{"docs":{},"\\":{"docs":{},"i":{"docs":{},"n":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}}}}}}}},"e":{"docs":{},"r":{"docs":{},"r":{"docs":{},"o":{"docs":{},"r":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}}},"\\":{"docs":{},"c":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"c":{"docs":{},"{":{"docs":{},"\\":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{},"i":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}}},"[":{"docs":{},"(":{"docs":{},"r":{"docs":{},"_":{"docs":{},"{":{"docs":{},"u":{"docs":{},"i":{"docs":{},"}":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.006944444444444444}}}}}}}}},"\\":{"docs":{},"s":{"docs":{},"u":{"docs":{},"m":{"docs":{},"_":{"docs":{},"{":{"docs":{},"u":{"docs":{},",":{"docs":{},"i":{"docs":{},"\\":{"docs":{},"i":{"docs":{},"n":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.006944444444444444}}}}}}}}}}}}}}}}}}}},"b":{"docs":{},"e":{"docs":{},"g":{"docs":{},"i":{"docs":{},"n":{"docs":{},"{":{"docs":{},"s":{"docs":{},"p":{"docs":{},"l":{"docs":{},"i":{"docs":{},"t":{"docs":{},"}":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.003430531732418525},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.009487666034155597},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.011574074074074073}}}}}}}}}}}}}},"e":{"docs":{},"n":{"docs":{},"d":{"docs":{},"{":{"docs":{},"s":{"docs":{},"p":{"docs":{},"l":{"docs":{},"i":{"docs":{},"t":{"docs":{},"}":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.003430531732418525},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.009487666034155597},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.011574074074074073}}}}}}}}}}}},"h":{"docs":{},"a":{"docs":{},"t":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}},"{":{"docs":{},"r":{"docs":{},"}":{"docs":{},"_":{"docs":{},"{":{"docs":{},"u":{"docs":{},"i":{"docs":{},"}":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}},")":{"docs":{},"^":{"2":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}},"docs":{}}}}}}}}}}}}}},"l":{"docs":{},"a":{"docs":{},"m":{"docs":{},"b":{"docs":{},"d":{"docs":{},"a":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.003795066413662239},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.009259259259259259}},"*":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}},"(":{"docs":{},"\\":{"docs":{},"s":{"docs":{},"u":{"docs":{},"m":{"docs":{},"_":{"docs":{},"u":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0017152658662092624}}}}}}}}},"b":{"docs":{},"_":{"docs":{},"i":{"docs":{},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}},"\\":{"docs":{},"\\":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}},"u":{"docs":{},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}},"(":{"docs":{},"\\":{"docs":{},"s":{"docs":{},"u":{"docs":{},"m":{"docs":{},"_":{"docs":{},"u":{"docs":{},"{":{"docs":{},"p":{"docs":{},"_":{"docs":{},"{":{"docs":{},"u":{"docs":{},"k":{"docs":{},"}":{"docs":{},"}":{"docs":{},"^":{"2":{"docs":{},"+":{"docs":{},"\\":{"docs":{},"s":{"docs":{},"u":{"docs":{},"m":{"docs":{},"_":{"docs":{},"i":{"docs":{},"{":{"docs":{},"q":{"docs":{},"_":{"docs":{},"{":{"docs":{},"i":{"docs":{},"k":{"docs":{},"}":{"docs":{},"}":{"docs":{},"^":{"2":{"docs":{},")":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}},"docs":{}}}}}}}}}}}}}}}}}},"docs":{}}}}}}}}},"b":{"docs":{},"_":{"docs":{},"u":{"docs":{},"}":{"docs":{},"^":{"2":{"docs":{},"+":{"docs":{},"\\":{"docs":{},"s":{"docs":{},"u":{"docs":{},"m":{"docs":{},"_":{"docs":{},"i":{"docs":{},"{":{"docs":{},"b":{"docs":{},"_":{"docs":{},"i":{"docs":{},"}":{"docs":{},"^":{"2":{"docs":{},"+":{"docs":{},"\\":{"docs":{},"s":{"docs":{},"u":{"docs":{},"m":{"docs":{},"_":{"docs":{},"u":{"docs":{},"{":{"docs":{},"p":{"docs":{},"_":{"docs":{},"{":{"docs":{},"u":{"docs":{},"k":{"docs":{},"}":{"docs":{},"}":{"docs":{},"^":{"2":{"docs":{},"+":{"docs":{},"\\":{"docs":{},"s":{"docs":{},"u":{"docs":{},"m":{"docs":{},"_":{"docs":{},"i":{"docs":{},"{":{"docs":{},"q":{"docs":{},"_":{"docs":{},"{":{"docs":{},"i":{"docs":{},"k":{"docs":{},"}":{"docs":{},"}":{"docs":{},"^":{"2":{"docs":{},")":{"docs":{"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}},"docs":{}}}}}}}}}}}}}}}}}},"docs":{}}}}}}}}}}}}}}}}}},"docs":{}}}}}}}}}}}}}}},"docs":{}}}}}}}}}}}}}},"_":{"1":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}},"2":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}},"3":{"docs":{"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}},"4":{"docs":{"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}},"docs":{}}}}}}}},"m":{"docs":{},"u":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.009148084619782733},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.023148148148148147}}}},"s":{"docs":{},"u":{"docs":{},"m":{"docs":{},"_":{"docs":{},"i":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683}}},"{":{"docs":{},"u":{"docs":{},",":{"docs":{},"i":{"docs":{},"\\":{"docs":{},"i":{"docs":{},"n":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0017152658662092624},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.003795066413662239},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.004629629629629629}}}}}}}}}}}}},"t":{"docs":{},"h":{"docs":{},"e":{"docs":{},"t":{"docs":{},"a":{"docs":{},"_":{"docs":{},"j":{"docs":{},":":{"docs":{},"=":{"docs":{},"\\":{"docs":{},"t":{"docs":{},"h":{"docs":{},"e":{"docs":{},"t":{"docs":{},"a":{"docs":{},"_":{"docs":{},"j":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}},"}":{"docs":{},"j":{"docs":{},"(":{"docs":{},"\\":{"docs":{},"t":{"docs":{},"h":{"docs":{},"e":{"docs":{},"t":{"docs":{},"a":{"docs":{},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}}}}}}},"v":{"docs":{},"e":{"docs":{},"c":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.004629629629629629}},"{":{"docs":{},"q":{"docs":{},"_":{"docs":{},"{":{"docs":{},"k":{"docs":{},",":{"1":{"docs":{},"}":{"docs":{},"}":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}},"docs":{}}}}}}}}}},"/":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00851063829787234}}}},"]":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.007062146892655367},"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269},"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.007692307692307693},"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464},"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.0045662100456621},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0024469820554649264},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.0045662100456621}}}},"_":{"1":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"2":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"3":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"4":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938},"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775},"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00851063829787234},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289}},"d":{"docs":{},"f":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147}},".":{"docs":{},"s":{"docs":{},"o":{"docs":{},"r":{"docs":{},"t":{"docs":{},"_":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"s":{"docs":{},"(":{"docs":{},"a":{"docs":{},"s":{"docs":{},"c":{"docs":{},"e":{"docs":{},"n":{"docs":{},"d":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"=":{"docs":{},"f":{"docs":{},"a":{"docs":{},"l":{"docs":{},"s":{"docs":{},"e":{"docs":{},")":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"_":{"docs":{},"s":{"docs":{},"o":{"docs":{},"r":{"docs":{},"t":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147}}}}}}}}},"_":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"_":{"docs":{},"_":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.006493506493506494},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147},"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269},"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017},"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577},"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391},"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}},"=":{"docs":{},"=":{"docs":{},"'":{"docs":{},"_":{"docs":{},"_":{"docs":{},"m":{"docs":{},"a":{"docs":{},"i":{"docs":{},"n":{"docs":{},"_":{"docs":{},"_":{"docs":{},"'":{"docs":{},":":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}}}}}}}}}}}}}}},"i":{"docs":{},"n":{"docs":{},"i":{"docs":{},"t":{"docs":{},"_":{"docs":{},"_":{"docs":{},"(":{"docs":{},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"f":{"docs":{},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}}}}}}}},"/":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}},"_":{"docs":{},"_":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}},"_":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}},"_":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}},"_":{"docs":{},"/":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}},"i":{"docs":{},"t":{"docs":{},"_":{"docs":{},"m":{"docs":{},"a":{"docs":{},"t":{"docs":{},"r":{"docs":{},"i":{"docs":{},"x":{"docs":{},"(":{"docs":{},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"f":{"docs":{},")":{"docs":{},":":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}}}}}}}}}}}}}},"m":{"docs":{},"a":{"docs":{},"e":{"docs":{},"_":{"docs":{},"s":{"docs":{},"u":{"docs":{},"m":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.004574042309891366}}}}}}}},"o":{"docs":{},"v":{"docs":{},"i":{"docs":{},"e":{"docs":{},"_":{"docs":{},"p":{"docs":{},"r":{"docs":{},"o":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}},"e":{"docs":{},".":{"docs":{},"a":{"docs":{},"p":{"docs":{},"p":{"docs":{},"e":{"docs":{},"n":{"docs":{},"d":{"docs":{},"(":{"docs":{},"(":{"docs":{},"m":{"docs":{},"i":{"docs":{},"d":{"docs":{},",":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}}}}}}}}}}}}},"r":{"docs":{},"m":{"docs":{},"s":{"docs":{},"e":{"docs":{},"_":{"docs":{},"s":{"docs":{},"u":{"docs":{},"m":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.004574042309891366}}}}}}}}},"a":{"docs":{},"w":{"docs":{},"_":{"docs":{},"s":{"docs":{},"a":{"docs":{},"m":{"docs":{},"p":{"docs":{},"l":{"docs":{},"e":{"docs":{},"_":{"docs":{},"d":{"docs":{},"f":{"1":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}},".":{"docs":{},"\\":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}},"a":{"docs":{},"d":{"docs":{},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},".":{"docs":{},"c":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"(":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"e":{"docs":{},"g":{"docs":{},"e":{"docs":{},"r":{"docs":{},"t":{"docs":{},"y":{"docs":{},"p":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{},")":{"docs":{},".":{"docs":{},"w":{"docs":{},"i":{"docs":{},"t":{"docs":{},"h":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{},"r":{"docs":{},"e":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"d":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"a":{"docs":{},"d":{"docs":{},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"c":{"docs":{},"l":{"docs":{},"k":{"docs":{},".":{"docs":{},"c":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"(":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"e":{"docs":{},"g":{"docs":{},"e":{"docs":{},"r":{"docs":{},"t":{"docs":{},"y":{"docs":{},"p":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{},")":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}}}}}},"n":{"docs":{},"o":{"docs":{},"n":{"docs":{},"c":{"docs":{},"l":{"docs":{},"k":{"docs":{},".":{"docs":{},"c":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"(":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"e":{"docs":{},"g":{"docs":{},"e":{"docs":{},"r":{"docs":{},"t":{"docs":{},"y":{"docs":{},"p":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{},")":{"docs":{},".":{"docs":{},"\\":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"p":{"docs":{},"i":{"docs":{},"d":{"docs":{},".":{"docs":{},"c":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"(":{"docs":{},"s":{"docs":{},"t":{"docs":{},"r":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"t":{"docs":{},"y":{"docs":{},"p":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{},")":{"docs":{},".":{"docs":{},"\\":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}}}}}}},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}},"t":{"docs":{},"i":{"docs":{},"m":{"docs":{},"e":{"docs":{},"_":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"m":{"docs":{},"p":{"docs":{},".":{"docs":{},"c":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"(":{"docs":{},"l":{"docs":{},"o":{"docs":{},"n":{"docs":{},"g":{"docs":{},"t":{"docs":{},"y":{"docs":{},"p":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{},")":{"docs":{},".":{"docs":{},"w":{"docs":{},"i":{"docs":{},"t":{"docs":{},"h":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{},"r":{"docs":{},"e":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"d":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"t":{"docs":{},"i":{"docs":{},"m":{"docs":{},"e":{"docs":{},"_":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"m":{"docs":{},"p":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},".":{"docs":{},"c":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"(":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"e":{"docs":{},"g":{"docs":{},"e":{"docs":{},"r":{"docs":{},"t":{"docs":{},"y":{"docs":{},"p":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{},")":{"docs":{},".":{"docs":{},"w":{"docs":{},"i":{"docs":{},"t":{"docs":{},"h":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{},"r":{"docs":{},"e":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"d":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"2":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}},".":{"docs":{},"p":{"docs":{},"r":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"s":{"docs":{},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},"m":{"docs":{},"a":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}},"docs":{}}}}}}}}}}}}}},"s":{"docs":{},"u":{"docs":{},"m":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.010291595197255575}}}}},")":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938},"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}}},".":{"docs":{},"a":{"docs":{},"p":{"docs":{},"p":{"docs":{},"e":{"docs":{},"n":{"docs":{},"d":{"docs":{},"(":{"docs":{},"(":{"docs":{},"m":{"docs":{},"i":{"docs":{},"d":{"docs":{},",":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{},"_":{"docs":{},"w":{"docs":{},"e":{"docs":{},"i":{"docs":{},"g":{"docs":{},"h":{"docs":{},"t":{"docs":{},")":{"docs":{"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}}},"*":{"docs":{},"r":{"docs":{},"e":{"docs":{},"l":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"d":{"docs":{},"_":{"docs":{},"w":{"docs":{},"e":{"docs":{},"i":{"docs":{},"g":{"docs":{},"h":{"docs":{},"t":{"docs":{},")":{"docs":{"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"r":{"docs":{},"e":{"docs":{},"l":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"d":{"docs":{},"_":{"docs":{},"w":{"docs":{},"e":{"docs":{},"i":{"docs":{},"g":{"docs":{},"h":{"docs":{},"t":{"docs":{},")":{"docs":{"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}}}}}}}}}}}}}}}}}}}}}}}},"j":{"docs":{},"o":{"docs":{},"i":{"docs":{},"n":{"docs":{},"(":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"p":{"docs":{},"r":{"docs":{},"o":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"e":{"docs":{},"_":{"docs":{},"d":{"docs":{},"f":{"docs":{},",":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}}},"t":{"docs":{},"o":{"docs":{},"p":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"a":{"docs":{},"s":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}},"t":{"docs":{},"a":{"docs":{},"g":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}},"s":{"docs":{},".":{"docs":{},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"b":{"docs":{},"y":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"m":{"docs":{},"o":{"docs":{},"v":{"docs":{},"i":{"docs":{},"e":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"a":{"docs":{},"g":{"docs":{},"g":{"docs":{},"(":{"docs":{},"l":{"docs":{},"i":{"docs":{},"s":{"docs":{},"t":{"docs":{},")":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},",":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.011695906432748537}}},"\\":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}},"a":{"docs":{},"d":{"docs":{},"_":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"e":{"docs":{},"_":{"docs":{},"d":{"docs":{},"f":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289}},".":{"docs":{},"\\":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}},"a":{"docs":{},"d":{"docs":{},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},".":{"docs":{},"c":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"(":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"e":{"docs":{},"g":{"docs":{},"e":{"docs":{},"r":{"docs":{},"t":{"docs":{},"y":{"docs":{},"p":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{},")":{"docs":{},".":{"docs":{},"w":{"docs":{},"i":{"docs":{},"t":{"docs":{},"h":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{},"r":{"docs":{},"e":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"d":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"a":{"docs":{},"d":{"docs":{},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"b":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},".":{"docs":{},"c":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"(":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"e":{"docs":{},"g":{"docs":{},"e":{"docs":{},"r":{"docs":{},"t":{"docs":{},"y":{"docs":{},"p":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{},")":{"docs":{},".":{"docs":{},"w":{"docs":{},"i":{"docs":{},"t":{"docs":{},"h":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{},"r":{"docs":{},"e":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"d":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"b":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"c":{"docs":{},"a":{"docs":{},"m":{"docs":{},"p":{"docs":{},"a":{"docs":{},"i":{"docs":{},"g":{"docs":{},"n":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},".":{"docs":{},"c":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"(":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"e":{"docs":{},"g":{"docs":{},"e":{"docs":{},"r":{"docs":{},"t":{"docs":{},"y":{"docs":{},"p":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{},")":{"docs":{},".":{"docs":{},"w":{"docs":{},"i":{"docs":{},"t":{"docs":{},"h":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{},"r":{"docs":{},"e":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"d":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"c":{"docs":{},"a":{"docs":{},"m":{"docs":{},"p":{"docs":{},"a":{"docs":{},"i":{"docs":{},"g":{"docs":{},"n":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"t":{"docs":{},"e":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},".":{"docs":{},"c":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"(":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"e":{"docs":{},"g":{"docs":{},"e":{"docs":{},"r":{"docs":{},"t":{"docs":{},"y":{"docs":{},"p":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{},")":{"docs":{},".":{"docs":{},"w":{"docs":{},"i":{"docs":{},"t":{"docs":{},"h":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{},"r":{"docs":{},"e":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"d":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"u":{"docs":{},"s":{"docs":{},"t":{"docs":{},"o":{"docs":{},"m":{"docs":{},"e":{"docs":{},"r":{"docs":{},".":{"docs":{},"c":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"(":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"e":{"docs":{},"g":{"docs":{},"e":{"docs":{},"r":{"docs":{},"t":{"docs":{},"y":{"docs":{},"p":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{},")":{"docs":{},".":{"docs":{},"w":{"docs":{},"i":{"docs":{},"t":{"docs":{},"h":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{},"r":{"docs":{},"e":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"d":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"c":{"docs":{},"u":{"docs":{},"s":{"docs":{},"t":{"docs":{},"o":{"docs":{},"m":{"docs":{},"e":{"docs":{},"r":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"p":{"docs":{},"r":{"docs":{},"i":{"docs":{},"c":{"docs":{},"e":{"docs":{},".":{"docs":{},"c":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"(":{"docs":{},"f":{"docs":{},"l":{"docs":{},"o":{"docs":{},"a":{"docs":{},"t":{"docs":{},"t":{"docs":{},"y":{"docs":{},"p":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{},")":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}}}}}}}}}}}},"r":{"docs":{},"e":{"docs":{},"p":{"docs":{},"l":{"docs":{},"a":{"docs":{},"c":{"docs":{},"e":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"n":{"docs":{},"u":{"docs":{},"l":{"docs":{},"l":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"p":{"docs":{},"r":{"docs":{},"o":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"e":{"docs":{},"_":{"docs":{},"d":{"docs":{},"f":{"1":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}},".":{"docs":{},"n":{"docs":{},"a":{"docs":{},".":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"l":{"docs":{},"(":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}},"2":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}},".":{"docs":{},"n":{"docs":{},"e":{"docs":{},"w":{"docs":{},"_":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"c":{"docs":{},"l":{"docs":{},"a":{"docs":{},"s":{"docs":{},"s":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},".":{"docs":{},"c":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"(":{"docs":{},"s":{"docs":{},"t":{"docs":{},"r":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"t":{"docs":{},"y":{"docs":{},"p":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{},")":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"p":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},".":{"docs":{},"c":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"(":{"docs":{},"s":{"docs":{},"t":{"docs":{},"r":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"t":{"docs":{},"y":{"docs":{},"p":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{},")":{"docs":{},"\\":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}},"w":{"docs":{},"i":{"docs":{},"t":{"docs":{},"h":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"p":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"3":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}},".":{"docs":{},"p":{"docs":{},"r":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"s":{"docs":{},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},"m":{"docs":{},"a":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}},"4":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}},".":{"docs":{},"p":{"docs":{},"r":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"s":{"docs":{},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},"m":{"docs":{},"a":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}},"docs":{}}}}}}}}}}}}}}}},"和":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"p":{"docs":{},"r":{"docs":{},"o":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"e":{"docs":{},"_":{"docs":{},"d":{"docs":{},"f":{"docs":{},"合":{"docs":{},"并":{"docs":{},"条":{"docs":{},"件":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}}},"a":{"1":{"docs":{},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}},"2":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}},"docs":{},"\"":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.007062146892655367}}},"]":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}},"l":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0032626427406199023}},"g":{"docs":{},"o":{"docs":{},"r":{"docs":{},"i":{"docs":{},"t":{"docs":{},"h":{"docs":{},"m":{"docs":{},"(":{"docs":{},"选":{"docs":{},"择":{"docs":{},"算":{"docs":{},"法":{"docs":{},"训":{"docs":{},"练":{"docs":{},"模":{"docs":{},"型":{"docs":{},")":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}},"s":{"docs":{},".":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}},"p":{"docs":{},"h":{"docs":{},"a":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147},"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}},"学":{"docs":{},"习":{"docs":{},"率":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}},"s":{"docs":{},"(":{"docs":{},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"f":{"docs":{},")":{"docs":{},":":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"=":{"docs":{},"'":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"i":{"docs":{},"d":{"docs":{},"'":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}}}}}}}}}}}}}}}}}},".":{"docs":{},"f":{"docs":{},"i":{"docs":{},"t":{"docs":{},"(":{"docs":{},"b":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"_":{"docs":{},"r":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"_":{"docs":{},"d":{"docs":{},"f":{"docs":{},")":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"_":{"docs":{},"r":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"_":{"docs":{},"d":{"docs":{},"f":{"docs":{},")":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}},"_":{"docs":{},"m":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"l":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}},".":{"docs":{},"r":{"docs":{},"e":{"docs":{},"c":{"docs":{},"o":{"docs":{},"m":{"docs":{},"m":{"docs":{},"e":{"docs":{},"n":{"docs":{},"d":{"docs":{},"f":{"docs":{},"o":{"docs":{},"r":{"docs":{},"a":{"docs":{},"l":{"docs":{},"l":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"s":{"docs":{},"(":{"3":{"docs":{},")":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}},"t":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"s":{"docs":{},"f":{"docs":{},"o":{"docs":{},"r":{"docs":{},"m":{"docs":{},"(":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},".":{"docs":{},"c":{"docs":{},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"(":{"docs":{},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"i":{"docs":{},"d":{"docs":{},"_":{"docs":{},"d":{"docs":{},"f":{"docs":{},")":{"docs":{},")":{"docs":{},".":{"docs":{},"s":{"docs":{},"o":{"docs":{},"r":{"docs":{},"t":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"p":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"i":{"docs":{},"c":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"f":{"docs":{},"a":{"docs":{},"c":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}},"s":{"docs":{},".":{"docs":{},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},":":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"m":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"l":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}},".":{"docs":{},"l":{"docs":{},"o":{"docs":{},"a":{"docs":{},"d":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},":":{"docs":{},"/":{"docs":{},"/":{"docs":{},"l":{"docs":{},"o":{"docs":{},"c":{"docs":{},"a":{"docs":{},"l":{"docs":{},"h":{"docs":{},"o":{"docs":{},"s":{"docs":{},"t":{"docs":{},":":{"9":{"0":{"0":{"0":{"docs":{},"/":{"docs":{},"m":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"l":{"docs":{},"s":{"docs":{},"/":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"b":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"r":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"m":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"l":{"docs":{},".":{"docs":{},"o":{"docs":{},"b":{"docs":{},"j":{"docs":{},"\"":{"docs":{},")":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"a":{"docs":{},"l":{"docs":{},"s":{"docs":{},"m":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"l":{"docs":{},".":{"docs":{},"o":{"docs":{},"b":{"docs":{},"j":{"docs":{},"\"":{"docs":{},")":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"模":{"docs":{},"型":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}},"的":{"docs":{},"意":{"docs":{},"思":{"docs":{},"是":{"docs":{},"交":{"docs":{},"替":{"docs":{},"最":{"docs":{},"小":{"docs":{},"二":{"docs":{},"乘":{"docs":{},"法":{"docs":{},"（":{"docs":{},"a":{"docs":{},"l":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"n":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}}}}}}}}}}}}}}}}}}},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.007407407407407408},"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.006607929515418502}}}}},"l":{"docs":{},"o":{"docs":{},"w":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.008032128514056224}}}}},"i":{"docs":{},"_":{"docs":{},"d":{"docs":{},"i":{"docs":{},"s":{"docs":{},"p":{"docs":{},"l":{"docs":{},"a":{"docs":{},"y":{"docs":{},"_":{"docs":{},"a":{"docs":{},"d":{"docs":{},"_":{"docs":{},"c":{"docs":{},"l":{"docs":{},"i":{"docs":{},"c":{"docs":{},"k":{"docs":{},"是":{"docs":{},"阿":{"docs":{},"里":{"docs":{},"巴":{"docs":{},"巴":{"docs":{},"提":{"docs":{},"供":{"docs":{},"的":{"docs":{},"一":{"docs":{},"个":{"docs":{},"淘":{"docs":{},"宝":{"docs":{},"展":{"docs":{},"示":{"docs":{},"广":{"docs":{},"告":{"docs":{},"点":{"docs":{},"击":{"docs":{},"率":{"docs":{},"预":{"docs":{},"估":{"docs":{},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"/":{"docs":{},"b":{"docs":{},"测":{"docs":{},"试":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}},"b":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}},"s":{"docs":{},"(":{"docs":{},"p":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"_":{"docs":{},"r":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683}}}}}}}}}},"c":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}},"c":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}},"c":{"docs":{},"u":{"docs":{},"r":{"docs":{},"a":{"docs":{},"y":{"docs":{},"(":{"docs":{},"p":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"_":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"u":{"docs":{},"l":{"docs":{},"t":{"docs":{},"s":{"docs":{},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}}}}},"i":{"docs":{},"c":{"docs":{},"t":{"docs":{},"_":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"u":{"docs":{},"l":{"docs":{},"t":{"docs":{},"s":{"docs":{},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}}}}}}}}}}}}}}}}},"e":{"docs":{},"s":{"docs":{},"s":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282}}}}}},"t":{"docs":{},"u":{"docs":{},"a":{"docs":{},"l":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}},"算":{"docs":{},"子":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}},"：":{"docs":{},"立":{"docs":{},"即":{"docs":{},"操":{"docs":{},"作":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}},"操":{"docs":{},"作":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}},"i":{"docs":{},"d":{"docs":{"Hive&HBase/06_hbase数据模型.html":{"ref":"Hive&HBase/06_hbase数据模型.html","tf":0.020833333333333332}},"只":{"docs":{},"支":{"docs":{},"持":{"docs":{},"单":{"docs":{},"个":{"docs":{},"r":{"docs":{},"o":{"docs":{},"w":{"docs":{},"级":{"docs":{},"别":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}}}}}}},"，":{"docs":{},"即":{"docs":{},"对":{"docs":{},"行":{"docs":{},"级":{"docs":{},"别":{"docs":{},"的":{"docs":{"Hive&HBase/06_hbase数据模型.html":{"ref":"Hive&HBase/06_hbase数据模型.html","tf":0.020833333333333332}}}}}}}}}}}},"g":{"docs":{},"g":{"docs":{},"（":{"docs":{},"a":{"docs":{},"g":{"docs":{},"g":{"docs":{},"r":{"docs":{},"e":{"docs":{},"g":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"聚":{"docs":{},"合":{"docs":{},"）":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}}}}}}}}}}}},"r":{"docs":{},"e":{"docs":{},"g":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.008032128514056224}},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},".":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.008032128514056224}}}}}}}}}}}},"e":{"docs":{},"=":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},"x":{"docs":{},"[":{"1":{"docs":{},"]":{"docs":{},")":{"docs":{},")":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}},"docs":{}}}}}}}},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"：":{"docs":{},"年":{"docs":{},"龄":{"docs":{},"层":{"docs":{},"次":{"docs":{},"；":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}},":":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0010197838058331635},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}},"_":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.004228329809725159}},"e":{"docs":{},"[":{"docs":{},"a":{"docs":{},"g":{"docs":{},"e":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"_":{"docs":{},"r":{"docs":{},"e":{"docs":{},"l":{"docs":{},"a":{"docs":{},"[":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"[":{"docs":{},"\"":{"docs":{},"a":{"docs":{},"g":{"docs":{},"e":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"\"":{"docs":{},"]":{"docs":{},")":{"docs":{},"]":{"docs":{},"]":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"p":{"docs":{},"i":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0056657223796034},"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}},"丰":{"docs":{},"富":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}},"实":{"docs":{},"现":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}},"或":{"docs":{},"s":{"docs":{},"q":{"docs":{},"l":{"docs":{},"处":{"docs":{},"理":{"docs":{},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"会":{"docs":{},"自":{"docs":{},"动":{"docs":{},"经":{"docs":{},"过":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}}}}},"（":{"docs":{},"如":{"docs":{},"d":{"docs":{},"f":{"docs":{},".":{"docs":{},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{},"和":{"docs":{},"s":{"docs":{},"q":{"docs":{},"l":{"docs":{},"(":{"docs":{},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}}}}}}}}}}},"：":{"docs":{},"比":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}},"，":{"2":{"docs":{},".":{"2":{"docs":{},".":{"2":{"docs":{},"版":{"docs":{},"本":{"docs":{},"中":{"docs":{},"无":{"docs":{},"法":{"docs":{},"使":{"docs":{},"用":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}},"docs":{}}},"docs":{}}},"docs":{}},".":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"a":{"docs":{},"c":{"docs":{},"h":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.02702702702702703},"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907},"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017},"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}},"e":{"docs":{},"™":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}}}},"p":{"docs":{},"l":{"docs":{},"i":{"docs":{},"c":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282},"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017},"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617},"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"m":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}},"e":{"docs":{},"r":{"docs":{},":":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}}}}}}}}}}}},"e":{"docs":{},"w":{"docs":{},"e":{"docs":{},"b":{"docs":{},"k":{"docs":{},"i":{"docs":{},"t":{"docs":{},"/":{"5":{"3":{"7":{"docs":{},".":{"3":{"6":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.006711409395973154}}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}},"/":{"docs":{},"w":{"docs":{},"e":{"docs":{},"b":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}},"r":{"docs":{},"o":{"docs":{},"x":{"docs":{},"q":{"docs":{},"u":{"docs":{},"a":{"docs":{},"n":{"docs":{},"t":{"docs":{},"i":{"docs":{},"l":{"docs":{},"e":{"docs":{},"方":{"docs":{},"法":{"docs":{},"接":{"docs":{},"收":{"docs":{},"三":{"docs":{},"个":{"docs":{},"参":{"docs":{},"数":{"docs":{},"：":{"docs":{},"参":{"docs":{},"数":{"1":{"docs":{},"，":{"docs":{},"列":{"docs":{},"名":{"docs":{},"；":{"docs":{},"参":{"docs":{},"数":{"2":{"docs":{},"：":{"docs":{},"想":{"docs":{},"要":{"docs":{},"计":{"docs":{},"算":{"docs":{},"的":{"docs":{},"分":{"docs":{},"位":{"docs":{},"点":{"docs":{},"，":{"docs":{},"可":{"docs":{},"以":{"docs":{},"是":{"docs":{},"一":{"docs":{},"个":{"docs":{},"点":{"docs":{},"，":{"docs":{},"也":{"docs":{},"可":{"docs":{},"以":{"docs":{},"是":{"docs":{},"一":{"docs":{},"个":{"docs":{},"列":{"docs":{},"表":{"docs":{},"（":{"0":{"docs":{},"和":{"1":{"docs":{},"之":{"docs":{},"间":{"docs":{},"的":{"docs":{},"小":{"docs":{},"数":{"docs":{},"）":{"docs":{},"，":{"docs":{},"第":{"docs":{},"三":{"docs":{},"个":{"docs":{},"参":{"docs":{},"数":{"docs":{},"是":{"docs":{},"能":{"docs":{},"容":{"docs":{},"忍":{"docs":{},"的":{"docs":{},"误":{"docs":{},"差":{"docs":{},"，":{"docs":{},"如":{"docs":{},"果":{"docs":{},"是":{"0":{"docs":{},"，":{"docs":{},"代":{"docs":{},"表":{"docs":{},"百":{"docs":{},"分":{"docs":{},"百":{"docs":{},"精":{"docs":{},"确":{"docs":{},"计":{"docs":{},"算":{"docs":{},"。":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{}}}}}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}},"d":{"docs":{},"d":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.007407407407407408},"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.012048192771084338}},"(":{"docs":{},"x":{"docs":{},",":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}},")":{"docs":{},":":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}},"f":{"docs":{},"u":{"docs":{},"n":{"docs":{},"c":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}},"j":{"docs":{},"u":{"docs":{},"s":{"docs":{},"t":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}},"_":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}},"e":{"docs":{},".":{"docs":{},"c":{"docs":{},"s":{"docs":{},"v":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.010526315789473684}}}}}},"_":{"docs":{},"d":{"docs":{},"f":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.005226480836236934}},".":{"docs":{},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"b":{"docs":{},"y":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"b":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}},"c":{"docs":{},"a":{"docs":{},"m":{"docs":{},"p":{"docs":{},"a":{"docs":{},"i":{"docs":{},"g":{"docs":{},"n":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}},"t":{"docs":{},"e":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}},"u":{"docs":{},"s":{"docs":{},"t":{"docs":{},"o":{"docs":{},"m":{"docs":{},"e":{"docs":{},"r":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"p":{"docs":{},"r":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"s":{"docs":{},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},"m":{"docs":{},"a":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"p":{"docs":{},"r":{"docs":{},"i":{"docs":{},"c":{"docs":{},"e":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"p":{"docs":{},"r":{"docs":{},"i":{"docs":{},"c":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},"e":{"docs":{},">":{"1":{"0":{"0":{"0":{"0":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}}}}}}}}}}}}},"a":{"docs":{},"d":{"docs":{},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}}}}}},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"o":{"docs":{},"r":{"docs":{},"t":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"p":{"docs":{},"r":{"docs":{},"i":{"docs":{},"c":{"docs":{},"e":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}},"f":{"docs":{},"o":{"docs":{},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"c":{"docs":{},"h":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{},"i":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"(":{"docs":{},"f":{"docs":{},"o":{"docs":{},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"c":{"docs":{},"h":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{},"i":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},")":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},"：":{"docs":{},"脱":{"docs":{},"敏":{"docs":{},"过":{"docs":{},"的":{"docs":{},"广":{"docs":{},"告":{"docs":{},"i":{"docs":{},"d":{"docs":{},"；":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}},"单":{"docs":{},"元":{"docs":{},"i":{"docs":{},"d":{"docs":{},"；":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}}}}}}}},":":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981}}},"总":{"docs":{},"数":{"docs":{},"：":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}},"i":{"docs":{},"d":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.004228329809725159}},":":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0015917230401910067}}},",":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}},")":{"docs":{},")":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}}}}}},"v":{"docs":{},"a":{"docs":{},"i":{"docs":{},"l":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.018018018018018018},"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}},"a":{"docs":{},"b":{"docs":{},"i":{"docs":{},"l":{"docs":{},"i":{"docs":{},"t":{"docs":{},"y":{"docs":{},")":{"docs":{},"，":{"docs":{},"而":{"docs":{},"是":{"docs":{},"在":{"docs":{},"应":{"docs":{},"用":{"docs":{},"层":{"docs":{},"检":{"docs":{},"测":{"docs":{},"和":{"docs":{},"处":{"docs":{},"理":{"docs":{},"故":{"docs":{},"障":{"docs":{},"，":{"docs":{},"从":{"docs":{},"而":{"docs":{},"在":{"docs":{},"计":{"docs":{},"算":{"docs":{},"机":{"docs":{},"集":{"docs":{},"群":{"docs":{},"之":{"docs":{},"上":{"docs":{},"提":{"docs":{},"供":{"docs":{},"高":{"docs":{},"可":{"docs":{},"用":{"docs":{},"服":{"docs":{},"务":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"g":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112},"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}},"(":{"docs":{},")":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}},"n":{"docs":{},"o":{"docs":{},"t":{"docs":{},"h":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282},"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.017094017094017096}}}}},"s":{"docs":{},"i":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}},"h":{"docs":{},"e":{"docs":{},"a":{"docs":{},"d":{"docs":{"day03_Hadoop/ha4.2.html":{"ref":"day03_Hadoop/ha4.2.html","tf":0.0196078431372549}}}}}},"r":{"docs":{},"r":{"docs":{},"a":{"docs":{},"y":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196},"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0022675736961451248}},"t":{"docs":{},"y":{"docs":{},"p":{"docs":{},"e":{"docs":{},"(":{"docs":{},"d":{"docs":{},"o":{"docs":{},"u":{"docs":{},"b":{"docs":{},"l":{"docs":{},"e":{"docs":{},"t":{"docs":{},"y":{"docs":{},"p":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{},")":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}}}}}}}}}}}}}}}}}},"]":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}},"t":{"docs":{},"i":{"docs":{},"c":{"docs":{},"a":{"docs":{},"l":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},",":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{},"i":{"docs":{},"c":{"docs":{},"a":{"docs":{},"l":{"docs":{},"_":{"docs":{},"u":{"docs":{},"r":{"docs":{},"l":{"docs":{},",":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{},"i":{"docs":{},"c":{"docs":{},"a":{"docs":{},"l":{"docs":{},"_":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{},"w":{"docs":{},"o":{"docs":{},"r":{"docs":{},"d":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"l":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.009070294784580499}},"e":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.003401360544217687}},",":{"docs":{},"k":{"docs":{},"w":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.009070294784580499}}}}}}}},"s":{"docs":{},"(":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}},";":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.003401360544217687}}}}}}}}}},".":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},",":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.006802721088435374}},"b":{"docs":{},".":{"docs":{},"k":{"docs":{},"w":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0045351473922902496}},";":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}}},"w":{"docs":{},"e":{"docs":{},"i":{"docs":{},"g":{"docs":{},"h":{"docs":{},"t":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}}}}},";":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}}}}}}},"u":{"docs":{},"t":{"docs":{},"o":{"docs":{},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"n":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"=":{"docs":{},"f":{"docs":{},"a":{"docs":{},"l":{"docs":{},"s":{"docs":{},"e":{"docs":{},")":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}}}}}}}}}}}},"m":{"docs":{},"a":{"docs":{},"t":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}}}},",":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102},"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617},"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577},"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}},"b":{"docs":{},":":{"docs":{},"a":{"docs":{},"+":{"docs":{},"b":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.006711409395973154}},".":{"docs":{},"s":{"docs":{},"o":{"docs":{},"r":{"docs":{},"t":{"docs":{},"b":{"docs":{},"y":{"docs":{},"(":{"docs":{},"l":{"docs":{},"a":{"docs":{},"m":{"docs":{},"b":{"docs":{},"d":{"docs":{},"a":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}}}}}}}}}}}}}}}}}}}},"s":{"docs":{},"s":{"docs":{},"u":{"docs":{},"m":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}},"o":{"docs":{},"c":{"docs":{},"i":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}},"c":{"docs":{},"e":{"docs":{},"n":{"docs":{},"d":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"=":{"docs":{},"f":{"docs":{},"a":{"docs":{},"l":{"docs":{},"s":{"docs":{},"e":{"docs":{},")":{"docs":{},".":{"docs":{},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}}}}}}},"n":{"docs":{},"a":{"docs":{},".":{"docs":{},"d":{"docs":{},"r":{"docs":{},"o":{"docs":{},"p":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}},".":{"docs":{},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"d":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147}},"\"":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.007062146892655367}}}},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938},"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009},"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282},"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907},"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.011111111111111112},"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.01276595744680851},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289}},"(":{"docs":{},"数":{"docs":{},"据":{"docs":{},")":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.007062146892655367},"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.0016233766233766235},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.004002287021154946},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0056925996204933585},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.004629629629629629},"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876},"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}},")":{"docs":{},":":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}},".":{"docs":{},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"b":{"docs":{},"y":{"docs":{},"(":{"docs":{},"'":{"docs":{},"m":{"docs":{},"o":{"docs":{},"v":{"docs":{},"i":{"docs":{},"e":{"docs":{},"i":{"docs":{},"d":{"docs":{},"'":{"docs":{},")":{"docs":{},".":{"docs":{},"a":{"docs":{},"g":{"docs":{},"g":{"docs":{},"(":{"docs":{},"[":{"docs":{},"l":{"docs":{},"i":{"docs":{},"s":{"docs":{},"t":{"docs":{},"]":{"docs":{},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}}}}}}}}}}}}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"i":{"docs":{},"d":{"docs":{},"'":{"docs":{},")":{"docs":{},".":{"docs":{},"a":{"docs":{},"g":{"docs":{},"g":{"docs":{},"(":{"docs":{},"[":{"docs":{},"l":{"docs":{},"i":{"docs":{},"s":{"docs":{},"t":{"docs":{},"]":{"docs":{},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}}}}}}}}}}}}}}}}}},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"f":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{},"s":{"docs":{},"[":{"0":{"docs":{},"]":{"docs":{},")":{"docs":{},".":{"docs":{},"a":{"docs":{},"g":{"docs":{},"g":{"docs":{},"(":{"docs":{},"[":{"docs":{},"l":{"docs":{},"i":{"docs":{},"s":{"docs":{},"t":{"docs":{},"]":{"docs":{},")":{"docs":{},"[":{"docs":{},"[":{"docs":{},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"f":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{},"s":{"docs":{},"[":{"1":{"docs":{},"]":{"docs":{},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"1":{"docs":{},"]":{"docs":{},")":{"docs":{},".":{"docs":{},"a":{"docs":{},"g":{"docs":{},"g":{"docs":{},"(":{"docs":{},"[":{"docs":{},"l":{"docs":{},"i":{"docs":{},"s":{"docs":{},"t":{"docs":{},"]":{"docs":{},")":{"docs":{},"[":{"docs":{},"[":{"docs":{},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"f":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{},"s":{"docs":{},"[":{"0":{"docs":{},"]":{"docs":{},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"t":{"docs":{},"u":{"docs":{},"p":{"docs":{},"l":{"docs":{},"e":{"docs":{},"s":{"docs":{},"(":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}},"=":{"docs":{},"f":{"docs":{},"a":{"docs":{},"l":{"docs":{},"s":{"docs":{},"e":{"docs":{},")":{"docs":{},":":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}}}}}}}}}}}}}},"w":{"docs":{},"i":{"docs":{},"t":{"docs":{},"h":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{},"r":{"docs":{},"e":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"d":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"_":{"1":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}},":":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}},"一":{"docs":{},"个":{"docs":{},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{},"，":{"docs":{},"简":{"docs":{},"单":{"docs":{},"的":{"docs":{},"理":{"docs":{},"解":{"docs":{},"为":{"docs":{},"集":{"docs":{},"合":{"docs":{},"，":{"docs":{},"用":{"docs":{},"于":{"docs":{},"存":{"docs":{},"放":{"docs":{},"数":{"docs":{},"据":{"docs":{},"的":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}}}}}}}}}}}}}}}}}}}},"[":{"docs":{},"'":{"docs":{},"r":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"'":{"docs":{},"]":{"docs":{},".":{"docs":{},"m":{"docs":{},"e":{"docs":{},"a":{"docs":{},"n":{"docs":{},"(":{"docs":{},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}}}}}}}}}}}}}},"]":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876}}},"）":{"docs":{},"叫":{"docs":{},"做":{"docs":{},"弹":{"docs":{},"性":{"docs":{},"分":{"docs":{},"布":{"docs":{},"式":{"docs":{},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{},"，":{"docs":{},"是":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"中":{"docs":{},"最":{"docs":{},"基":{"docs":{},"本":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"抽":{"docs":{},"象":{"docs":{},"，":{"docs":{},"它":{"docs":{},"代":{"docs":{},"表":{"docs":{},"一":{"docs":{},"个":{"docs":{},"不":{"docs":{},"可":{"docs":{},"变":{"docs":{},"、":{"docs":{},"可":{"docs":{},"分":{"docs":{},"区":{"docs":{},"、":{"docs":{},"里":{"docs":{},"面":{"docs":{},"的":{"docs":{},"元":{"docs":{},"素":{"docs":{},"可":{"docs":{},"并":{"docs":{},"行":{"docs":{},"计":{"docs":{},"算":{"docs":{},"的":{"docs":{},"集":{"docs":{},"合":{"docs":{},".":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"s":{"docs":{},".":{"docs":{},"p":{"docs":{},"r":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"s":{"docs":{},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},"m":{"docs":{},"a":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},"*":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"f":{"docs":{},"u":{"docs":{},"l":{"docs":{},"_":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"s":{"docs":{},")":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}}},"_":{"1":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.001193792280143255}},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}},"d":{"docs":{},"r":{"docs":{},"o":{"docs":{},"p":{"docs":{},"n":{"docs":{},"a":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"(":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"s":{"docs":{},"_":{"1":{"docs":{},".":{"docs":{},"t":{"docs":{},"i":{"docs":{},"m":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"m":{"docs":{},"p":{"docs":{},"(":{"1":{"4":{"9":{"4":{"6":{"9":{"1":{"1":{"8":{"6":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}}},"docs":{}}}}}}}}}}}}}}}}}}},"docs":{}}}}}},"_":{"docs":{},"p":{"docs":{},"a":{"docs":{},"t":{"docs":{},"h":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}},":":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}},"s":{"docs":{},"p":{"docs":{},"l":{"docs":{},"i":{"docs":{},"t":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"s":{"docs":{},"/":{"docs":{},"m":{"docs":{},"l":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}}}}}}}},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"_":{"docs":{},"p":{"docs":{},"a":{"docs":{},"t":{"docs":{},"h":{"docs":{},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}}}}}}}}}}}},"b":{"docs":{},"l":{"docs":{},"o":{"docs":{},"c":{"docs":{},"k":{"docs":{},"_":{"docs":{},"e":{"docs":{},"n":{"docs":{},"c":{"docs":{},"o":{"docs":{},"d":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}}}}},"[":{"0":{"docs":{},"]":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}},"1":{"docs":{},"]":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}},"2":{"docs":{},"]":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}},"docs":{}},".":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}},"(":{"docs":{},"分":{"docs":{},"布":{"docs":{},"式":{"docs":{},"文":{"docs":{},"件":{"docs":{},"系":{"docs":{},"统":{"docs":{},")":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282}}}}}}}}}}}},"n":{"docs":{},"o":{"docs":{},"d":{"docs":{"day03_Hadoop/ha2.1.html":{"ref":"day03_Hadoop/ha2.1.html","tf":0.0410958904109589},"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428},"day03_Hadoop/ha4.2.html":{"ref":"day03_Hadoop/ha4.2.html","tf":0.0392156862745098}},"e":{"docs":{},",":{"docs":{"day03_Hadoop/ha2.1.html":{"ref":"day03_Hadoop/ha2.1.html","tf":0.0136986301369863}}},"s":{"docs":{},"界":{"docs":{},"面":{"docs":{},"查":{"docs":{},"看":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"n":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"的":{"docs":{},"情":{"docs":{},"况":{"docs":{"day03_Hadoop/ha2.1.html":{"ref":"day03_Hadoop/ha2.1.html","tf":0.0136986301369863}}}}}}}}}}}}}}}}}},"启":{"docs":{},"动":{"docs":{},"的":{"docs":{},"日":{"docs":{},"志":{"docs":{},"信":{"docs":{},"息":{"docs":{"day03_Hadoop/ha2.1.html":{"ref":"day03_Hadoop/ha2.1.html","tf":0.0136986301369863}}}}}}}}},"(":{"docs":{},"d":{"docs":{},"n":{"docs":{},")":{"docs":{"day03_Hadoop/ha2.4.html":{"ref":"day03_Hadoop/ha2.4.html","tf":0.03333333333333333}}}}}},"/":{"docs":{},"d":{"docs":{},"n":{"docs":{},"(":{"docs":{},"s":{"docs":{},"l":{"docs":{},"a":{"docs":{},"v":{"docs":{},"e":{"docs":{},"s":{"docs":{},")":{"docs":{"day03_Hadoop/ha2.4.html":{"ref":"day03_Hadoop/ha2.4.html","tf":0.03333333333333333}}}}}}}}}}}}},"故":{"docs":{},"障":{"docs":{},"容":{"docs":{},"错":{"docs":{"day03_Hadoop/ha4.2.html":{"ref":"day03_Hadoop/ha4.2.html","tf":0.0196078431372549}}}}}}}}}},"：":{"docs":{},"输":{"docs":{},"入":{"docs":{},"数":{"docs":{},"据":{"docs":{"day03_Hadoop/ha3.5.html":{"ref":"day03_Hadoop/ha3.5.html","tf":0.030303030303030304}}}}}}},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}},"e":{"docs":{},"s":{"docs":{},";":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872},"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.013986013986013986},"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.007692307692307693},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},"e":{"docs":{},"引":{"docs":{},"入":{"docs":{},"s":{"docs":{},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},"m":{"docs":{},"a":{"docs":{},"和":{"docs":{},"o":{"docs":{},"f":{"docs":{},"f":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}}}}}}}}}}}}}},"、":{"docs":{},"l":{"docs":{},"i":{"docs":{},"s":{"docs":{},"t":{"docs":{},"、":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}},"和":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"统":{"docs":{},"一":{"docs":{},"，":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"只":{"docs":{},"是":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"[":{"docs":{},"r":{"docs":{},"o":{"docs":{},"w":{"docs":{},"]":{"docs":{},"的":{"docs":{},"类":{"docs":{},"型":{"docs":{},"别":{"docs":{},"名":{"docs":{},"。":{"docs":{},"由":{"docs":{},"于":{"docs":{},"p":{"docs":{},"y":{"docs":{},"t":{"docs":{},"h":{"docs":{},"o":{"docs":{},"n":{"docs":{},"是":{"docs":{},"弱":{"docs":{},"类":{"docs":{},"型":{"docs":{},"语":{"docs":{},"言":{"docs":{},"，":{"docs":{},"只":{"docs":{},"能":{"docs":{},"使":{"docs":{},"用":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"普":{"docs":{},"通":{"docs":{},"的":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"的":{"docs":{},"逻":{"docs":{},"辑":{"docs":{},"框":{"docs":{},"架":{"docs":{},"区":{"docs":{},"别":{"docs":{},"如":{"docs":{},"下":{"docs":{},"所":{"docs":{},"示":{"docs":{},"：":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}}}}},"的":{"docs":{},"抽":{"docs":{},"象":{"docs":{},"后":{"docs":{},"，":{"docs":{},"我":{"docs":{},"们":{"docs":{},"处":{"docs":{},"理":{"docs":{},"数":{"docs":{},"据":{"docs":{},"更":{"docs":{},"加":{"docs":{},"简":{"docs":{},"单":{"docs":{},"了":{"docs":{},"，":{"docs":{},"甚":{"docs":{},"至":{"docs":{},"可":{"docs":{},"以":{"docs":{},"用":{"docs":{},"s":{"docs":{},"q":{"docs":{},"l":{"docs":{},"来":{"docs":{},"处":{"docs":{},"理":{"docs":{},"数":{"docs":{},"据":{"docs":{},"了":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"相":{"docs":{},"当":{"docs":{},"于":{"docs":{},"是":{"docs":{},"一":{"docs":{},"个":{"docs":{},"带":{"docs":{},"着":{"docs":{},"s":{"docs":{},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},"m":{"docs":{},"a":{"docs":{},"的":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}}}},"还":{"docs":{},"引":{"docs":{},"入":{"docs":{},"了":{"docs":{},"o":{"docs":{},"f":{"docs":{},"f":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}},"配":{"docs":{},"套":{"docs":{},"了":{"docs":{},"新":{"docs":{},"的":{"docs":{},"操":{"docs":{},"作":{"docs":{},"数":{"docs":{},"据":{"docs":{},"的":{"docs":{},"方":{"docs":{},"法":{"docs":{},"，":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}}}}}}}},"：":{"docs":{},"分":{"docs":{},"布":{"docs":{},"式":{"docs":{},"的":{"docs":{},"r":{"docs":{},"o":{"docs":{},"w":{"docs":{},"对":{"docs":{},"象":{"docs":{},"的":{"docs":{},"集":{"docs":{},"合":{"docs":{},"，":{"docs":{},"其":{"docs":{},"提":{"docs":{},"供":{"docs":{},"了":{"docs":{},"由":{"docs":{},"列":{"docs":{},"组":{"docs":{},"成":{"docs":{},"的":{"docs":{},"详":{"docs":{},"细":{"docs":{},"模":{"docs":{},"式":{"docs":{},"信":{"docs":{},"息":{"docs":{},"，":{"docs":{},"使":{"docs":{},"得":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"[":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"i":{"docs":{},"d":{"docs":{},":":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}},"i":{"docs":{},"d":{"docs":{},":":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}},"来":{"docs":{},"处":{"docs":{},"理":{"docs":{},"，":{"docs":{},"因":{"docs":{},"为":{"docs":{},"方":{"docs":{},"便":{"docs":{},"，":{"docs":{},"但":{"docs":{},"注":{"docs":{},"意":{"docs":{},"如":{"docs":{},"果":{"docs":{},"数":{"docs":{},"据":{"docs":{},"量":{"docs":{},"较":{"docs":{},"大":{"docs":{},"不":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"，":{"docs":{},"因":{"docs":{},"为":{"docs":{},"这":{"docs":{},"样":{"docs":{},"会":{"docs":{},"把":{"docs":{},"全":{"docs":{},"部":{"docs":{},"数":{"docs":{},"据":{"docs":{},"加":{"docs":{},"载":{"docs":{},"到":{"docs":{},"内":{"docs":{},"存":{"docs":{},"中":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"把":{"docs":{},"数":{"docs":{},"据":{"docs":{},"载":{"docs":{},"入":{"docs":{},"内":{"docs":{},"存":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}},"数":{"docs":{},"据":{"docs":{},"合":{"docs":{},"并":{"docs":{},"：":{"docs":{},"p":{"docs":{},"y":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},".":{"docs":{},"s":{"docs":{},"q":{"docs":{},"l":{"docs":{},".":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},".":{"docs":{},"j":{"docs":{},"o":{"docs":{},"i":{"docs":{},"n":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"e":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}},"t":{"docs":{},"i":{"docs":{},"m":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}},"e":{"docs":{},".":{"docs":{},"f":{"docs":{},"r":{"docs":{},"o":{"docs":{},"m":{"docs":{},"t":{"docs":{},"i":{"docs":{},"m":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"m":{"docs":{},"p":{"docs":{},"(":{"1":{"4":{"9":{"4":{"6":{"9":{"1":{"1":{"8":{"6":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}}}}}}}}}}}},"e":{"docs":{},"m":{"docs":{},"o":{"docs":{},"n":{"docs":{},".":{"docs":{},"s":{"docs":{},"h":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}}}}},"g":{"docs":{},"引":{"docs":{},"擎":{"docs":{},"，":{"docs":{},"较":{"docs":{},"少":{"docs":{},"多":{"docs":{},"次":{"docs":{},"计":{"docs":{},"算":{"docs":{},"之":{"docs":{},"间":{"docs":{},"中":{"docs":{},"间":{"docs":{},"结":{"docs":{},"果":{"docs":{},"写":{"docs":{},"到":{"docs":{},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},"的":{"docs":{},"开":{"docs":{},"销":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}}}}}}}}}}}}}}}}}}}}}}},"s":{"docs":{},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"l":{"docs":{},"e":{"docs":{},"r":{"docs":{},"：":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}}}}}}}}}}},"f":{"docs":{},"a":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"对":{"docs":{},"象":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}},"f":{"2":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}},"d":{"docs":{},"r":{"docs":{},"o":{"docs":{},"p":{"docs":{},"d":{"docs":{},"u":{"docs":{},"p":{"docs":{},"l":{"docs":{},"i":{"docs":{},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"s":{"docs":{},"(":{"docs":{},"s":{"docs":{},"u":{"docs":{},"b":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}}}}}}}}}}}}}}}},"3":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}},".":{"docs":{},"a":{"docs":{},"g":{"docs":{},"g":{"docs":{},"(":{"docs":{},"f":{"docs":{},"n":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},"'":{"docs":{},"i":{"docs":{},"d":{"docs":{},"'":{"docs":{},")":{"docs":{},".":{"docs":{},"a":{"docs":{},"l":{"docs":{},"i":{"docs":{},"a":{"docs":{},"s":{"docs":{},"(":{"docs":{},"'":{"docs":{},"i":{"docs":{},"d":{"docs":{},"_":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"'":{"docs":{},")":{"docs":{},",":{"docs":{},"f":{"docs":{},"n":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"d":{"docs":{},"i":{"docs":{},"s":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},"'":{"docs":{},"i":{"docs":{},"d":{"docs":{},"'":{"docs":{},")":{"docs":{},".":{"docs":{},"a":{"docs":{},"l":{"docs":{},"i":{"docs":{},"a":{"docs":{},"s":{"docs":{},"(":{"docs":{},"'":{"docs":{},"d":{"docs":{},"i":{"docs":{},"s":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"c":{"docs":{},"t":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},"_":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"'":{"docs":{},")":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"w":{"docs":{},"i":{"docs":{},"t":{"docs":{},"h":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{},"(":{"docs":{},"'":{"docs":{},"n":{"docs":{},"e":{"docs":{},"w":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},"'":{"docs":{},",":{"docs":{},"f":{"docs":{},"n":{"docs":{},".":{"docs":{},"m":{"docs":{},"o":{"docs":{},"n":{"docs":{},"o":{"docs":{},"t":{"docs":{},"o":{"docs":{},"n":{"docs":{},"i":{"docs":{},"c":{"docs":{},"a":{"docs":{},"l":{"docs":{},"l":{"docs":{},"y":{"docs":{},"_":{"docs":{},"i":{"docs":{},"n":{"docs":{},"c":{"docs":{},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"s":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{},".":{"docs":{},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.005649717514124294},"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965},"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0008158270446665307},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.005226480836236934}},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"r":{"docs":{},"r":{"docs":{},"(":{"docs":{},")":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}},"r":{"docs":{},"o":{"docs":{},"s":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"b":{"docs":{},"(":{"docs":{},"'":{"docs":{},"c":{"docs":{},"l":{"docs":{},"s":{"docs":{},"'":{"docs":{},",":{"docs":{},"'":{"docs":{},"s":{"docs":{},"e":{"docs":{},"p":{"docs":{},"a":{"docs":{},"l":{"docs":{},"l":{"docs":{},"e":{"docs":{},"n":{"docs":{},"g":{"docs":{},"t":{"docs":{},"h":{"docs":{},"'":{"docs":{},")":{"docs":{},".":{"docs":{},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"a":{"docs":{},"m":{"docs":{},"p":{"docs":{},"a":{"docs":{},"i":{"docs":{},"g":{"docs":{},"n":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},".":{"docs":{},"c":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"(":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"e":{"docs":{},"g":{"docs":{},"e":{"docs":{},"r":{"docs":{},"t":{"docs":{},"y":{"docs":{},"p":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{},")":{"docs":{},".":{"docs":{},"w":{"docs":{},"i":{"docs":{},"t":{"docs":{},"h":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{},"r":{"docs":{},"e":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"d":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"c":{"docs":{},"a":{"docs":{},"m":{"docs":{},"p":{"docs":{},"a":{"docs":{},"i":{"docs":{},"g":{"docs":{},"n":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"t":{"docs":{},"e":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},".":{"docs":{},"c":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"(":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"e":{"docs":{},"g":{"docs":{},"e":{"docs":{},"r":{"docs":{},"t":{"docs":{},"y":{"docs":{},"p":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{},")":{"docs":{},".":{"docs":{},"w":{"docs":{},"i":{"docs":{},"t":{"docs":{},"h":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{},"r":{"docs":{},"e":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"d":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"l":{"docs":{},"k":{"docs":{},".":{"docs":{},"c":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"(":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"e":{"docs":{},"g":{"docs":{},"e":{"docs":{},"r":{"docs":{},"t":{"docs":{},"y":{"docs":{},"p":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}},"u":{"docs":{},"s":{"docs":{},"t":{"docs":{},"o":{"docs":{},"m":{"docs":{},"e":{"docs":{},"r":{"docs":{},".":{"docs":{},"c":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"(":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"e":{"docs":{},"g":{"docs":{},"e":{"docs":{},"r":{"docs":{},"t":{"docs":{},"y":{"docs":{},"p":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{},")":{"docs":{},".":{"docs":{},"w":{"docs":{},"i":{"docs":{},"t":{"docs":{},"h":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{},"r":{"docs":{},"e":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"d":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"c":{"docs":{},"u":{"docs":{},"s":{"docs":{},"t":{"docs":{},"o":{"docs":{},"m":{"docs":{},"e":{"docs":{},"r":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{},":":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}},"x":{"docs":{},"[":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"]":{"docs":{},".":{"docs":{},"r":{"docs":{},"e":{"docs":{},"p":{"docs":{},"l":{"docs":{},"a":{"docs":{},"c":{"docs":{},"e":{"docs":{},"(":{"0":{"docs":{},",":{"docs":{},"n":{"docs":{},"p":{"docs":{},".":{"docs":{},"n":{"docs":{},"a":{"docs":{},"n":{"docs":{},")":{"docs":{},".":{"docs":{},"d":{"docs":{},"r":{"docs":{},"o":{"docs":{},"p":{"docs":{},"n":{"docs":{},"a":{"docs":{},"(":{"docs":{},")":{"docs":{},".":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{},":":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{}}}}}}}}}}}}}}}}}},"t":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"r":{"docs":{},"r":{"docs":{},"(":{"docs":{},")":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}},"i":{"docs":{},"m":{"docs":{},"e":{"docs":{},"_":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"m":{"docs":{},"p":{"docs":{},".":{"docs":{},"c":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"(":{"docs":{},"l":{"docs":{},"o":{"docs":{},"n":{"docs":{},"g":{"docs":{},"t":{"docs":{},"y":{"docs":{},"p":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{},")":{"docs":{},".":{"docs":{},"w":{"docs":{},"i":{"docs":{},"t":{"docs":{},"h":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{},"r":{"docs":{},"e":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"d":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"t":{"docs":{},"i":{"docs":{},"m":{"docs":{},"e":{"docs":{},"_":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"m":{"docs":{},"p":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"a":{"docs":{},"g":{"docs":{},"g":{"docs":{},"(":{"docs":{},"f":{"docs":{},"n":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},"'":{"docs":{},"s":{"docs":{},"e":{"docs":{},"p":{"docs":{},"a":{"docs":{},"l":{"docs":{},"w":{"docs":{},"i":{"docs":{},"d":{"docs":{},"t":{"docs":{},"h":{"docs":{},"'":{"docs":{},")":{"docs":{},".":{"docs":{},"a":{"docs":{},"l":{"docs":{},"i":{"docs":{},"a":{"docs":{},"s":{"docs":{},"(":{"docs":{},"'":{"docs":{},"w":{"docs":{},"i":{"docs":{},"d":{"docs":{},"t":{"docs":{},"h":{"docs":{},"_":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"'":{"docs":{},")":{"docs":{},",":{"docs":{},"f":{"docs":{},"n":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"d":{"docs":{},"i":{"docs":{},"s":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},"'":{"docs":{},"c":{"docs":{},"l":{"docs":{},"s":{"docs":{},"'":{"docs":{},")":{"docs":{},".":{"docs":{},"a":{"docs":{},"l":{"docs":{},"i":{"docs":{},"a":{"docs":{},"s":{"docs":{},"(":{"docs":{},"'":{"docs":{},"d":{"docs":{},"i":{"docs":{},"s":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"c":{"docs":{},"t":{"docs":{},"_":{"docs":{},"c":{"docs":{},"l":{"docs":{},"s":{"docs":{},"_":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"'":{"docs":{},")":{"docs":{},")":{"docs":{},".":{"docs":{},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"d":{"docs":{},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},".":{"docs":{},"c":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"(":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"e":{"docs":{},"g":{"docs":{},"e":{"docs":{},"r":{"docs":{},"t":{"docs":{},"y":{"docs":{},"p":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{},")":{"docs":{},".":{"docs":{},"w":{"docs":{},"i":{"docs":{},"t":{"docs":{},"h":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{},"r":{"docs":{},"e":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"d":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"a":{"docs":{},"d":{"docs":{},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"d":{"docs":{},"e":{"docs":{},"s":{"docs":{},"c":{"docs":{},"r":{"docs":{},"i":{"docs":{},"b":{"docs":{},"e":{"docs":{},"(":{"docs":{},"'":{"docs":{},"c":{"docs":{},"l":{"docs":{},"s":{"docs":{},"'":{"docs":{},")":{"docs":{},".":{"docs":{},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}},")":{"docs":{},".":{"docs":{},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}},"r":{"docs":{},"o":{"docs":{},"p":{"docs":{},"(":{"docs":{},"'":{"docs":{},"c":{"docs":{},"l":{"docs":{},"s":{"docs":{},"'":{"docs":{},")":{"docs":{},".":{"docs":{},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}},"d":{"docs":{},"u":{"docs":{},"p":{"docs":{},"l":{"docs":{},"i":{"docs":{},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"s":{"docs":{},"(":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}}}}}}}}},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"b":{"docs":{},"y":{"docs":{},"(":{"docs":{},"'":{"docs":{},"c":{"docs":{},"l":{"docs":{},"s":{"docs":{},"'":{"docs":{},")":{"docs":{},".":{"docs":{},"a":{"docs":{},"g":{"docs":{},"g":{"docs":{},"(":{"docs":{},"{":{"docs":{},"'":{"docs":{},"s":{"docs":{},"e":{"docs":{},"p":{"docs":{},"a":{"docs":{},"l":{"docs":{},"w":{"docs":{},"i":{"docs":{},"d":{"docs":{},"t":{"docs":{},"h":{"docs":{},"'":{"docs":{},":":{"docs":{},"'":{"docs":{},"m":{"docs":{},"e":{"docs":{},"a":{"docs":{},"n":{"docs":{},"'":{"docs":{},",":{"docs":{},"'":{"docs":{},"s":{"docs":{},"e":{"docs":{},"p":{"docs":{},"a":{"docs":{},"l":{"docs":{},"l":{"docs":{},"e":{"docs":{},"n":{"docs":{},"g":{"docs":{},"t":{"docs":{},"h":{"docs":{},"'":{"docs":{},":":{"docs":{},"'":{"docs":{},"m":{"docs":{},"a":{"docs":{},"x":{"docs":{},"'":{"docs":{},"}":{"docs":{},")":{"docs":{},".":{"docs":{},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"\"":{"docs":{},"a":{"docs":{},"d":{"docs":{},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"c":{"docs":{},"l":{"docs":{},"k":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}},"p":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"p":{"docs":{},"r":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"s":{"docs":{},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},"m":{"docs":{},"a":{"docs":{},"(":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981}}}}}}}}}}}},"c":{"docs":{},"e":{"docs":{},".":{"docs":{},"c":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"(":{"docs":{},"f":{"docs":{},"l":{"docs":{},"o":{"docs":{},"a":{"docs":{},"t":{"docs":{},"t":{"docs":{},"y":{"docs":{},"p":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}}}}}}}}}}},"i":{"docs":{},"d":{"docs":{},".":{"docs":{},"c":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"(":{"docs":{},"s":{"docs":{},"t":{"docs":{},"r":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"t":{"docs":{},"y":{"docs":{},"p":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{},")":{"docs":{},".":{"docs":{},"\\":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"o":{"docs":{},"m":{"docs":{},"s":{"docs":{},"p":{"docs":{},"l":{"docs":{},"i":{"docs":{},"t":{"docs":{},"(":{"docs":{},"[":{"0":{"docs":{},".":{"6":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}},"9":{"9":{"docs":{},",":{"0":{"docs":{},".":{"0":{"1":{"docs":{},"]":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}},"docs":{}},"docs":{}}},"docs":{}}},"docs":{}},"docs":{}}},"docs":{}}}}}}}}}}}}},"e":{"docs":{},"p":{"docs":{},"l":{"docs":{},"a":{"docs":{},"c":{"docs":{},"e":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"n":{"docs":{},"u":{"docs":{},"l":{"docs":{},"l":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}}},"s":{"docs":{},"a":{"docs":{},"m":{"docs":{},"p":{"docs":{},"l":{"docs":{},"e":{"docs":{},"(":{"docs":{},"f":{"docs":{},"a":{"docs":{},"l":{"docs":{},"s":{"docs":{},"e":{"docs":{},",":{"0":{"docs":{},".":{"2":{"docs":{},",":{"1":{"0":{"0":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}},"docs":{}}}}}}}}}}}}},"e":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},"'":{"docs":{},"c":{"docs":{},"l":{"docs":{},"s":{"docs":{},"'":{"docs":{},")":{"docs":{},".":{"docs":{},"d":{"docs":{},"i":{"docs":{},"s":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}}}}}}}}}},"s":{"docs":{},"e":{"docs":{},"p":{"docs":{},"a":{"docs":{},"l":{"docs":{},"l":{"docs":{},"e":{"docs":{},"n":{"docs":{},"g":{"docs":{},"t":{"docs":{},"h":{"docs":{},"'":{"docs":{},",":{"docs":{},"'":{"docs":{},"s":{"docs":{},"e":{"docs":{},"p":{"docs":{},"a":{"docs":{},"l":{"docs":{},"w":{"docs":{},"i":{"docs":{},"d":{"docs":{},"t":{"docs":{},"h":{"docs":{},"'":{"docs":{},")":{"docs":{},".":{"docs":{},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"1":{"0":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}},"docs":{}},"docs":{},")":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981}}}}}}}},"w":{"docs":{},"i":{"docs":{},"t":{"docs":{},"h":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{},"(":{"docs":{},"'":{"docs":{},"n":{"docs":{},"e":{"docs":{},"w":{"docs":{},"w":{"docs":{},"i":{"docs":{},"d":{"docs":{},"t":{"docs":{},"h":{"docs":{},"'":{"docs":{},",":{"docs":{},"d":{"docs":{},"f":{"docs":{},".":{"docs":{},"s":{"docs":{},"e":{"docs":{},"p":{"docs":{},"a":{"docs":{},"l":{"docs":{},"w":{"docs":{},"i":{"docs":{},"d":{"docs":{},"t":{"docs":{},"h":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"\\":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}},"b":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},".":{"docs":{},"c":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"(":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"e":{"docs":{},"g":{"docs":{},"e":{"docs":{},"r":{"docs":{},"t":{"docs":{},"y":{"docs":{},"p":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{},")":{"docs":{},".":{"docs":{},"w":{"docs":{},"i":{"docs":{},"t":{"docs":{},"h":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{},"r":{"docs":{},"e":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"d":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"b":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"n":{"docs":{},"o":{"docs":{},"n":{"docs":{},"c":{"docs":{},"l":{"docs":{},"k":{"docs":{},".":{"docs":{},"c":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"(":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"e":{"docs":{},"g":{"docs":{},"e":{"docs":{},"r":{"docs":{},"t":{"docs":{},"y":{"docs":{},"p":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{},")":{"docs":{},".":{"docs":{},"\\":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},".":{"docs":{},"c":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"(":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"e":{"docs":{},"g":{"docs":{},"e":{"docs":{},"r":{"docs":{},"t":{"docs":{},"y":{"docs":{},"p":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{},")":{"docs":{},".":{"docs":{},"w":{"docs":{},"i":{"docs":{},"t":{"docs":{},"h":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{},"r":{"docs":{},"e":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"d":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"[":{"docs":{},"\"":{"docs":{},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"m":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}},"s":{"docs":{},".":{"docs":{},"s":{"docs":{},"h":{"docs":{"day03_Hadoop/ha2.1.html":{"ref":"day03_Hadoop/ha2.1.html","tf":0.0273972602739726},"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428}}}},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"n":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},".":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},".":{"docs":{},"d":{"docs":{},"i":{"docs":{},"r":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428}}}}}}}}}}}}}}}}}}},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"n":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},".":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},".":{"docs":{},"d":{"docs":{},"i":{"docs":{},"r":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428}}}}}}}}}}}}}}}}}}},"r":{"docs":{},"e":{"docs":{},"p":{"docs":{},"l":{"docs":{},"i":{"docs":{},"c":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428}}}}}}}}}},"_":{"docs":{},"m":{"docs":{},"i":{"docs":{},"s":{"docs":{},"s":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}},".":{"docs":{},"a":{"docs":{},"g":{"docs":{},"g":{"docs":{},"(":{"docs":{},"*":{"docs":{},"[":{"docs":{},"(":{"1":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}},"docs":{}}}}}}}},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}},"s":{"docs":{},"]":{"docs":{},")":{"docs":{},".":{"docs":{},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}}}}}}}}},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},".":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"(":{"docs":{},"l":{"docs":{},"a":{"docs":{},"m":{"docs":{},"b":{"docs":{},"d":{"docs":{},"a":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}}}}}}},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},"[":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}},"_":{"docs":{},"n":{"docs":{},"o":{"docs":{},"_":{"docs":{},"i":{"docs":{},"n":{"docs":{},"c":{"docs":{},"o":{"docs":{},"m":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}},"e":{"docs":{},".":{"docs":{},"a":{"docs":{},"g":{"docs":{},"g":{"docs":{},"(":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}},"d":{"docs":{},"r":{"docs":{},"o":{"docs":{},"p":{"docs":{},"n":{"docs":{},"a":{"docs":{},"(":{"docs":{},"t":{"docs":{},"h":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"h":{"docs":{},"=":{"3":{"docs":{},")":{"docs":{},".":{"docs":{},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}},"docs":{}}}}}}}}}}}}}}},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"l":{"docs":{},"n":{"docs":{},"a":{"docs":{},"(":{"docs":{},"m":{"docs":{},"e":{"docs":{},"a":{"docs":{},"n":{"docs":{},"s":{"docs":{},")":{"docs":{},".":{"docs":{},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"o":{"docs":{},"u":{"docs":{},"t":{"docs":{},"l":{"docs":{},"i":{"docs":{},"e":{"docs":{},"r":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.0045662100456621}},"s":{"docs":{},".":{"docs":{},"a":{"docs":{},"p":{"docs":{},"p":{"docs":{},"r":{"docs":{},"o":{"docs":{},"x":{"docs":{},"q":{"docs":{},"u":{"docs":{},"a":{"docs":{},"n":{"docs":{},"t":{"docs":{},"i":{"docs":{},"l":{"docs":{},"e":{"docs":{},"(":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}}}}}}}}}}}},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"(":{"docs":{},"'":{"docs":{},"a":{"docs":{},"g":{"docs":{},"e":{"docs":{},"_":{"docs":{},"o":{"docs":{},"'":{"docs":{},")":{"docs":{},".":{"docs":{},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},"'":{"docs":{},"i":{"docs":{},"d":{"docs":{},"'":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}}}}}}}}}}}}},"w":{"docs":{},"e":{"docs":{},"i":{"docs":{},"g":{"docs":{},"h":{"docs":{},"t":{"docs":{},"_":{"docs":{},"o":{"docs":{},"'":{"docs":{},")":{"docs":{},".":{"docs":{},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},"'":{"docs":{},"i":{"docs":{},"d":{"docs":{},"'":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"j":{"docs":{},"o":{"docs":{},"i":{"docs":{},"n":{"docs":{},"(":{"docs":{},"o":{"docs":{},"u":{"docs":{},"t":{"docs":{},"l":{"docs":{},"i":{"docs":{},"e":{"docs":{},"r":{"docs":{},"s":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}}}}}}},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},"*":{"docs":{},"[":{"docs":{},"'":{"docs":{},"i":{"docs":{},"d":{"docs":{},"'":{"docs":{},"]":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}}}}}}}}}}}}}}}}},"，":{"docs":{},"且":{"docs":{},"值":{"docs":{},"很":{"docs":{},"多":{"docs":{},"时":{"docs":{},"，":{"docs":{},"需":{"docs":{},"要":{"docs":{},"修":{"docs":{},"改":{"docs":{},"，":{"docs":{},"默":{"docs":{},"认":{"docs":{},"是":{"1":{"0":{"0":{"0":{"0":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}}}}}},"的":{"docs":{},"基":{"docs":{},"础":{"docs":{},"上":{"docs":{},"直":{"docs":{},"接":{"docs":{},"替":{"docs":{},"换":{"docs":{},"掉":{"docs":{},"列":{"docs":{},"数":{"docs":{},"据":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}},":":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"e":{"docs":{},"f":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.00974025974025974},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.016580903373356205},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.013282732447817837},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.011574074074074073},"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.007782101167315175},"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842},"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.04678362573099415},"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.036827195467422094},"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726},"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.026490066225165563},"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.007633587786259542},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0024469820554649264},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}},"i":{"docs":{},"n":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.012048192771084338}}}},"a":{"docs":{},"u":{"docs":{},"l":{"docs":{},"t":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}},"n":{"docs":{},"o":{"docs":{},"m":{"docs":{},"i":{"docs":{},"n":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.012987012987012988}}}}}},"s":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}},"(":{"docs":{},"[":{"docs":{},"p":{"docs":{},"r":{"docs":{},"i":{"docs":{},"c":{"docs":{},"e":{"docs":{},"]":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}}}}}}}}}}}}},"c":{"docs":{},"i":{"docs":{},"m":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}},"r":{"docs":{},"b":{"docs":{},"i":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.006172839506172839}}}}},"v":{"docs":{},"i":{"docs":{},"c":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}},"e":{"docs":{},"l":{"docs":{},"o":{"docs":{},"p":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0056657223796034}}}}}}},"l":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.005226480836236934}},"i":{"docs":{},"m":{"docs":{},"i":{"docs":{},"t":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.018518518518518517},"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0022675736961451248}}}}}},"e":{"docs":{},"t":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.004405286343612335}},"e":{"docs":{},"_":{"docs":{},"t":{"docs":{},"a":{"docs":{},"b":{"docs":{},"l":{"docs":{},"e":{"docs":{},"(":{"docs":{},"t":{"docs":{},"a":{"docs":{},"b":{"docs":{},"l":{"docs":{},"e":{"docs":{},"_":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},")":{"docs":{},":":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}}}}}}}}}}}}}}}}},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"(":{"docs":{},")":{"docs":{},":":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0056657223796034}}}}}}}}},"t":{"docs":{},"a":{"docs":{},"b":{"docs":{},"l":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{},":":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}}}}}}}}}},"s":{"docs":{},"c":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704},"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.008032128514056224},"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}},";":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}},"r":{"docs":{},"i":{"docs":{},"p":{"docs":{},"t":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}},"b":{"docs":{},"e":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}}}}}}},"i":{"docs":{},"g":{"docs":{},"n":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}},"t":{"docs":{},"_":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}},".":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{},"i":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"s":{"docs":{},"(":{"docs":{},"l":{"docs":{},"a":{"docs":{},"m":{"docs":{},"b":{"docs":{},"d":{"docs":{},"a":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}}}}}}}}}}}}}}}}}}}}}}},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}},".":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{},"e":{"docs":{},"b":{"docs":{},"y":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{},"(":{"docs":{},"l":{"docs":{},"a":{"docs":{},"m":{"docs":{},"b":{"docs":{},"d":{"docs":{},"a":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}}}}}}}}}}}}}}}}}}}}}}},"t":{"docs":{},"a":{"docs":{},"i":{"docs":{},"l":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}},"t":{"docs":{},"y":{"docs":{},"p":{"docs":{},"e":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002858776443682104},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.003795066413662239},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}},"=":{"docs":{},"d":{"docs":{},"t":{"docs":{},"y":{"docs":{},"p":{"docs":{},"e":{"docs":{},",":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}},"i":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},"d":{"docs":{},"t":{"docs":{},"y":{"docs":{},"p":{"docs":{},"e":{"docs":{},")":{"docs":{},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0017152658662092624},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.003795066413662239},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}}}}}}}},"{":{"docs":{},"\"":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},":":{"docs":{},"n":{"docs":{},"p":{"docs":{},".":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"3":{"2":{"docs":{},",":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}},"docs":{}},"docs":{}}}}}}}}}}}}}}}}}},":":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}},"i":{"docs":{},"c":{"docs":{},"t":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}},"(":{"docs":{},"z":{"docs":{},"i":{"docs":{},"p":{"docs":{},"(":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.003795066413662239},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.004629629629629629}},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"m":{"docs":{},"s":{"docs":{},"_":{"docs":{},"r":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"s":{"docs":{},".":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}},"n":{"docs":{},"p":{"docs":{},".":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"o":{"docs":{},"m":{"docs":{},".":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"(":{"docs":{},"l":{"docs":{},"e":{"docs":{},"n":{"docs":{},"(":{"docs":{},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"m":{"docs":{},"s":{"docs":{},"_":{"docs":{},"r":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"s":{"docs":{},")":{"docs":{},",":{"1":{"0":{"docs":{},")":{"docs":{},".":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"y":{"docs":{},"p":{"docs":{},"e":{"docs":{},"(":{"docs":{},"n":{"docs":{},"p":{"docs":{},".":{"docs":{},"f":{"docs":{},"l":{"docs":{},"o":{"docs":{},"a":{"docs":{},"t":{"3":{"2":{"docs":{},")":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}},"docs":{}},"docs":{}}}}}}}}}}}}}}}}}}},"docs":{}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"f":{"docs":{},".":{"docs":{},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"m":{"docs":{},"s":{"docs":{},"_":{"docs":{},"r":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"s":{"docs":{},".":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}}}}}}}}}}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"s":{"docs":{},"_":{"docs":{},"r":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"s":{"docs":{},".":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}}}}}}}}}}}}}}}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"s":{"docs":{},"_":{"docs":{},"r":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"s":{"docs":{},".":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}},"n":{"docs":{},"p":{"docs":{},".":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"o":{"docs":{},"m":{"docs":{},".":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"(":{"docs":{},"l":{"docs":{},"e":{"docs":{},"n":{"docs":{},"(":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"s":{"docs":{},"_":{"docs":{},"r":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"s":{"docs":{},")":{"docs":{},",":{"1":{"0":{"docs":{},")":{"docs":{},".":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"y":{"docs":{},"p":{"docs":{},"e":{"docs":{},"(":{"docs":{},"n":{"docs":{},"p":{"docs":{},".":{"docs":{},"f":{"docs":{},"l":{"docs":{},"o":{"docs":{},"a":{"docs":{},"t":{"3":{"2":{"docs":{},")":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}},"docs":{}},"docs":{}}}}}}}}}}}}}}}}}}},"docs":{}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"(":{"docs":{},"l":{"docs":{},"a":{"docs":{},"m":{"docs":{},"b":{"docs":{},"d":{"docs":{},"a":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876}}}}}}}}}}}},")":{"docs":{},"}":{"docs":{},")":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0056657223796034}}}}}},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"a":{"docs":{},"r":{"docs":{},"i":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876}}},"y":{"docs":{},"(":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},")":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876}}}}}}}}}}}}}}}}}}},"s":{"docs":{},"t":{"docs":{},"r":{"docs":{},"i":{"docs":{},"b":{"docs":{},"u":{"docs":{},"t":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009},"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.02564102564102564},"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907},"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112},"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}},"e":{"docs":{},"d":{"docs":{},"：":{"docs":{},"它":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"是":{"docs":{},"分":{"docs":{},"布":{"docs":{},"式":{"docs":{},"存":{"docs":{},"储":{"docs":{},"，":{"docs":{},"并":{"docs":{},"且":{"docs":{},"可":{"docs":{},"以":{"docs":{},"做":{"docs":{},"分":{"docs":{},"布":{"docs":{},"式":{"docs":{},"的":{"docs":{},"计":{"docs":{},"算":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}}}}}}}}}}}}}}}}}}},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"和":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"一":{"docs":{},"样":{"docs":{},"都":{"docs":{},"是":{"docs":{},"分":{"docs":{},"布":{"docs":{},"式":{"docs":{},"的":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"i":{"docs":{},"n":{"docs":{},"c":{"docs":{},"t":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112},"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.01702127659574468}},".":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{},"e":{"docs":{},"(":{"docs":{},"l":{"docs":{},"a":{"docs":{},"m":{"docs":{},"b":{"docs":{},"d":{"docs":{},"a":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}}}}}}}}}}}}}}}},"k":{"docs":{},"：":{"docs":{},"将":{"docs":{},"所":{"docs":{},"有":{"docs":{},"的":{"docs":{},"\"":{"docs":{},"小":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"\"":{"docs":{},"进":{"docs":{},"行":{"docs":{},"合":{"docs":{},"并":{"docs":{},"。":{"docs":{"day03_Hadoop/ha3.5.html":{"ref":"day03_Hadoop/ha3.5.html","tf":0.030303030303030304}}}}}}}}}}}}}}}}}}},"a":{"docs":{},"b":{"docs":{},"l":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.004405286343612335}}}}}},"f":{"docs":{},"f":{"docs":{},"_":{"docs":{},"i":{"docs":{},"n":{"docs":{},"_":{"docs":{},"t":{"docs":{},"r":{"docs":{},"a":{"docs":{},"i":{"docs":{},"n":{"docs":{},"_":{"docs":{},"t":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.006993006993006993}},".":{"docs":{},"d":{"docs":{},"i":{"docs":{},"s":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"o":{"docs":{},"c":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},"s":{"docs":{},"/":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"b":{"docs":{},"l":{"docs":{},"e":{"docs":{},"/":{"docs":{},"r":{"docs":{},"e":{"docs":{},"f":{"docs":{},"e":{"docs":{},"r":{"docs":{},"e":{"docs":{},"n":{"docs":{},"c":{"docs":{},"e":{"docs":{},"/":{"docs":{},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"b":{"docs":{},"y":{"docs":{},".":{"docs":{},"h":{"docs":{},"t":{"docs":{},"m":{"docs":{},"l":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"u":{"docs":{},"m":{"docs":{},"e":{"docs":{},"n":{"docs":{},"t":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876}}}}}}},"k":{"docs":{},"e":{"docs":{},"r":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.006172839506172839}}}}}},"u":{"docs":{},"g":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}},"b":{"docs":{},"l":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.001193792280143255}},"e":{"docs":{},"t":{"docs":{},"y":{"docs":{},"p":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}}}}},"]":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}}}}},"n":{"docs":{},"e":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112},"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}},"c":{"docs":{},"t":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876}}}},"s":{"docs":{},"i":{"docs":{},"t":{"docs":{},"r":{"docs":{},"i":{"docs":{},"b":{"docs":{},"u":{"docs":{},"t":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}}}}}},"t":{"docs":{},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"m":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}},"s":{"docs":{},"中":{"docs":{},"的":{"docs":{},"每":{"docs":{},"个":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"都":{"docs":{},"包":{"docs":{},"含":{"docs":{},"确":{"docs":{},"定":{"docs":{},"时":{"docs":{},"间":{"docs":{},"间":{"docs":{},"隔":{"docs":{},"内":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}}}}}}}}}}}}}}}}}}}}},"由":{"docs":{},"一":{"docs":{},"系":{"docs":{},"列":{"docs":{},"连":{"docs":{},"续":{"docs":{},"的":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"组":{"docs":{},"成":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}}}}}}}}}}}}}}}}}},"b":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}},"：":{"docs":{},"在":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}},"=":{"1":{"0":{"docs":{},")":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}},"docs":{}},"9":{"docs":{},")":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}},"docs":{}}},"r":{"docs":{},"o":{"docs":{},"p":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704},"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0022675736961451248},"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.004405286343612335}},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{},"=":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{},"r":{"docs":{},"：":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}},"和":{"docs":{},"p":{"docs":{},"y":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"运":{"docs":{},"行":{"docs":{},"时":{"docs":{},"，":{"docs":{},"所":{"docs":{},"使":{"docs":{},"用":{"docs":{},"的":{"docs":{},"p":{"docs":{},"y":{"docs":{},"t":{"docs":{},"h":{"docs":{},"o":{"docs":{},"n":{"docs":{},"解":{"docs":{},"释":{"docs":{},"器":{"docs":{},"路":{"docs":{},"径":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.007633587786259542},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"d":{"docs":{},"l":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}},"m":{"docs":{},"l":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}},"e":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147},"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.004545454545454545}},"\"":{"docs":{},"]":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.007062146892655367}}}},"v":{"docs":{},"e":{"docs":{},"n":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}},"t":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}},"_":{"docs":{},"t":{"docs":{},"i":{"docs":{},"m":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}}}}},"i":{"docs":{},"c":{"docs":{},"t":{"docs":{},"_":{"docs":{},"b":{"docs":{},"l":{"docs":{},"o":{"docs":{},"c":{"docs":{},"k":{"docs":{},"s":{"docs":{},"_":{"docs":{},"o":{"docs":{},"n":{"docs":{},"_":{"docs":{},"c":{"docs":{},"l":{"docs":{},"o":{"docs":{},"s":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}}}}}}}}}}},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"s":{"docs":{},":":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}},"：":{"docs":{},"只":{"docs":{},"有":{"docs":{},"a":{"docs":{},"c":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"才":{"docs":{},"会":{"docs":{},"触":{"docs":{},"发":{"docs":{},"t":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"s":{"docs":{},"f":{"docs":{},"o":{"docs":{},"r":{"docs":{},"m":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"的":{"docs":{},"执":{"docs":{},"行":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},":":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}},"l":{"docs":{},"s":{"docs":{},"e":{"docs":{},":":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.003430531732418525},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}},"i":{"docs":{},"f":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.007633587786259542},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"机":{"docs":{},"制":{"docs":{},"保":{"docs":{},"证":{"docs":{},"总":{"docs":{},"有":{"docs":{},"一":{"docs":{},"个":{"docs":{},"m":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"运":{"docs":{},"行":{"docs":{},"。":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}}}}}}}}}}}}}}}}}}}},"x":{"docs":{},"c":{"docs":{},"e":{"docs":{},"p":{"docs":{},"t":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.006493506493506494},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.003795066413662239}},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"用":{"docs":{},"户":{"docs":{},"没":{"docs":{},"有":{"docs":{},"相":{"docs":{},"似":{"docs":{},"的":{"docs":{},"用":{"docs":{},"户":{"docs":{},"\"":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}}}}}}}}}}}},"无":{"docs":{},"法":{"docs":{},"预":{"docs":{},"测":{"docs":{},"用":{"docs":{},"户":{"docs":{},"对":{"docs":{},"电":{"docs":{},"影":{"docs":{},"的":{"docs":{},"评":{"docs":{},"分":{"docs":{},"，":{"docs":{},"因":{"docs":{},"为":{"docs":{},"训":{"docs":{},"练":{"docs":{},"集":{"docs":{},"中":{"docs":{},"缺":{"docs":{},"失":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"\"":{"docs":{},".":{"docs":{},"f":{"docs":{},"o":{"docs":{},"r":{"docs":{},"m":{"docs":{},"a":{"docs":{},"t":{"docs":{},"(":{"docs":{},"u":{"docs":{},"i":{"docs":{},"d":{"docs":{},"=":{"docs":{},"u":{"docs":{},"i":{"docs":{},"d":{"docs":{},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"p":{"docs":{},"l":{"docs":{},"o":{"docs":{},"i":{"docs":{},"t":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"(":{"docs":{},"开":{"docs":{},"发":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}}}}}},"⼒":{"docs":{},"度":{"docs":{"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html":{"ref":"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html","tf":0.024390243902439025}}}}}},"r":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008},"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html":{"ref":"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html","tf":0.024390243902439025}},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"(":{"docs":{},"探":{"docs":{},"测":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}}}}}}},"d":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0022675736961451248}},"e":{"docs":{},"(":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{},"_":{"docs":{},"w":{"docs":{},"o":{"docs":{},"r":{"docs":{},"d":{"docs":{},"s":{"docs":{},")":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.01020408163265306}}}}}}}}}}}},"w":{"docs":{},"m":{"docs":{},")":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}},"函":{"docs":{},"数":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}},"把":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"中":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"转":{"docs":{},"换":{"docs":{},"成":{"docs":{},"多":{"docs":{},"列":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}}}}}}}}}}}}}}},"o":{"docs":{},"r":{"docs":{},"t":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.03571428571428571},"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.006172839506172839},"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.00881057268722467},"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.06451612903225806}},"_":{"docs":{},"j":{"docs":{},"a":{"docs":{},"v":{"docs":{},"a":{"docs":{},"_":{"docs":{},"h":{"docs":{},"o":{"docs":{},"m":{"docs":{},"e":{"docs":{},"=":{"docs":{},"/":{"docs":{},"r":{"docs":{},"o":{"docs":{},"o":{"docs":{},"t":{"docs":{},"/":{"docs":{},"b":{"docs":{},"i":{"docs":{},"g":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"/":{"docs":{},"j":{"docs":{},"d":{"docs":{},"k":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"e":{"docs":{},"c":{"docs":{},"u":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}},"：":{"docs":{},"即":{"docs":{},"真":{"docs":{},"正":{"docs":{},"执":{"docs":{},"行":{"docs":{},"作":{"docs":{},"业":{"docs":{},"的":{"docs":{},"地":{"docs":{},"方":{"docs":{},"，":{"docs":{},"一":{"docs":{},"个":{"docs":{},"集":{"docs":{},"群":{"docs":{},"一":{"docs":{},"般":{"docs":{},"包":{"docs":{},"含":{"docs":{},"多":{"docs":{},"个":{"docs":{},"e":{"docs":{},"x":{"docs":{},"e":{"docs":{},"c":{"docs":{},"u":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{},"，":{"docs":{},"每":{"docs":{},"个":{"docs":{},"e":{"docs":{},"x":{"docs":{},"e":{"docs":{},"c":{"docs":{},"u":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{},"接":{"docs":{},"收":{"docs":{},"d":{"docs":{},"r":{"docs":{},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{},"r":{"docs":{},"的":{"docs":{},"命":{"docs":{},"令":{"docs":{},"l":{"docs":{},"a":{"docs":{},"u":{"docs":{},"n":{"docs":{},"c":{"docs":{},"h":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"使":{"docs":{},"用":{"docs":{},"的":{"docs":{},"c":{"docs":{},"p":{"docs":{},"u":{"docs":{},"核":{"docs":{},"心":{"docs":{},"数":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"n":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196},"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704},"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0022675736961451248}}}},"n":{"docs":{},"d":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}},"i":{"docs":{},"s":{"docs":{},"t":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.007407407407407408},"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0022675736961451248}}}}},"a":{"docs":{},"c":{"docs":{},"t":{"docs":{},"l":{"docs":{},"i":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}}},"e":{"docs":{},"可":{"docs":{},"能":{"docs":{},"带":{"docs":{},"来":{"docs":{},"的":{"docs":{},"问":{"docs":{},"题":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}}}}},"问":{"docs":{},"题":{"docs":{},"实":{"docs":{},"践":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}}},"r":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}},"r":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}},"o":{"docs":{},"r":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}},"_":{"docs":{},"l":{"docs":{},"i":{"docs":{},"s":{"docs":{},"t":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}},".":{"docs":{},"a":{"docs":{},"p":{"docs":{},"p":{"docs":{},"e":{"docs":{},"n":{"docs":{},"d":{"docs":{},"(":{"docs":{},"e":{"docs":{},"r":{"docs":{},"r":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}}}}}}}}}}}}}}}},"n":{"docs":{},"u":{"docs":{},"m":{"docs":{},"e":{"docs":{},"r":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"(":{"docs":{},"m":{"docs":{},"o":{"docs":{},"v":{"docs":{},"i":{"docs":{},"e":{"docs":{},"_":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},".":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{},")":{"docs":{},":":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"t":{"docs":{},"u":{"docs":{},"p":{"docs":{},"l":{"docs":{},"e":{"docs":{},"s":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{},":":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"v":{"docs":{},".":{"docs":{},"s":{"docs":{},"h":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.017857142857142856},"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.006172839506172839},"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}},".":{"docs":{},"t":{"docs":{},"e":{"docs":{},"m":{"docs":{},"p":{"docs":{},"l":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"重":{"docs":{},"命":{"docs":{},"名":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}}}}}}}}}}}}},"中":{"docs":{},"指":{"docs":{},"定":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},"的":{"docs":{},"路":{"docs":{},"径":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}}}}},"(":{"docs":{},"需":{"docs":{},"要":{"docs":{},"将":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}}}}}}}}}}}}},"a":{"docs":{},"b":{"docs":{},"l":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}},"c":{"docs":{},"o":{"docs":{},"d":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.001193792280143255}},"e":{"docs":{},"r":{"docs":{},"]":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.001193792280143255}}}}}}}}}},"t":{"docs":{},"c":{"docs":{},"/":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428}}}}}}}}}}},"c":{"docs":{},"h":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}},"m":{"docs":{},"p":{"docs":{},"l":{"docs":{},"o":{"docs":{},"y":{"docs":{},"e":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.014814814814814815}},"e":{"2":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.007407407407407408}}},"docs":{},";":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.007407407407407408},"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}}}}},"a":{"docs":{},"c":{"docs":{},"h":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}},"i":{"docs":{},"_":{"5":{"docs":{},")":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147}}}},"docs":{},"{":{"docs":{},"r":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"d":{"docs":{},"}":{"docs":{},"}":{"docs":{},"s":{"docs":{},"i":{"docs":{},"m":{"docs":{},"(":{"docs":{},"i":{"docs":{},",":{"docs":{},"j":{"docs":{},")":{"docs":{},"*":{"docs":{},"r":{"docs":{},"_":{"docs":{},"{":{"docs":{},"u":{"docs":{},"j":{"docs":{},"}":{"docs":{},"}":{"docs":{},"{":{"docs":{},"\\":{"docs":{},"s":{"docs":{},"u":{"docs":{},"m":{"docs":{},"_":{"docs":{},"{":{"docs":{},"j":{"docs":{},"\\":{"docs":{},"i":{"docs":{},"n":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}}}}}}},"}":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}}}}}},"m":{"docs":{},"p":{"docs":{},"o":{"docs":{},"r":{"docs":{},"t":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.015536723163841809},"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.012987012987012988},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.005717552887364208},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.007590132827324478},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.009259259259259259},"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.023346303501945526},"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.03684210526315789},"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.023391812865497075},"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112},"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0056657223796034},"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.016778523489932886},"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391},"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.017482517482517484},"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.011538461538461539},"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105},"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.03296703296703297},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.026717557251908396},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.009787928221859706},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.004079135223332654},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.005173099880620772},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.024390243902439025},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.010570824524312896}}}}}},"m":{"docs":{},"u":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"b":{"docs":{},"l":{"docs":{},"e":{"docs":{},"：":{"docs":{},"一":{"docs":{},"旦":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"、":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"被":{"docs":{},"创":{"docs":{},"建":{"docs":{},"，":{"docs":{},"就":{"docs":{},"不":{"docs":{},"能":{"docs":{},"更":{"docs":{},"改":{"docs":{},"，":{"docs":{},"只":{"docs":{},"能":{"docs":{},"通":{"docs":{},"过":{"docs":{},"t":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"s":{"docs":{},"f":{"docs":{},"o":{"docs":{},"r":{"docs":{},"m":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"生":{"docs":{},"成":{"docs":{},"新":{"docs":{},"的":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"、":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"t":{"docs":{},"a":{"docs":{},"b":{"docs":{},"l":{"docs":{},"e":{"docs":{},"：":{"docs":{},"不":{"docs":{},"可":{"docs":{},"更":{"docs":{},"改":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683},"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}},"=":{"docs":{},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"m":{"docs":{},"s":{"docs":{},")":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147}}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"s":{"docs":{},")":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.00847457627118644}}}}}}}}},"_":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"=":{"docs":{},"\"":{"docs":{},"m":{"docs":{},"o":{"docs":{},"v":{"docs":{},"i":{"docs":{},"e":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},")":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}}},"p":{"docs":{},"e":{"docs":{},"n":{"docs":{},"d":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}}}}}}},"i":{"docs":{},"c":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}},"t":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}},"(":{"docs":{},"i":{"docs":{},"n":{"docs":{},"p":{"docs":{},"u":{"docs":{},"t":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"i":{"docs":{},"i":{"docs":{},"d":{"docs":{},":":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}},"u":{"docs":{},"i":{"docs":{},"d":{"docs":{},":":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}}}}}},"i":{"docs":{},"d":{"docs":{},")":{"docs":{},")":{"docs":{},")":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}},")":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}},"b":{"docs":{},"r":{"docs":{},"o":{"docs":{},"a":{"docs":{},"d":{"docs":{},"c":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"_":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"[":{"docs":{},"m":{"docs":{},"i":{"docs":{},"d":{"docs":{},"]":{"docs":{},"[":{"0":{"docs":{},"]":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}},"1":{"docs":{},"]":{"docs":{},")":{"docs":{},":":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}},"a":{"docs":{},"d":{"docs":{},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"i":{"docs":{},"d":{"docs":{},")":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}}}}}}},"e":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{},"_":{"docs":{},"w":{"docs":{},"o":{"docs":{},"r":{"docs":{},"d":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.015789473684210527},"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}},"s":{"docs":{},"[":{"0":{"docs":{},"]":{"docs":{},"[":{"1":{"docs":{},"]":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}},"docs":{}}}},"docs":{}},"]":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}},":":{"docs":{"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}}}},",":{"docs":{"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}}}}}},"e":{"docs":{},"i":{"docs":{},"g":{"docs":{},"h":{"docs":{},"t":{"docs":{"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}}}}}}}}}}}},"f":{"docs":{},"a":{"docs":{},"c":{"docs":{},"e":{"docs":{},")":{"docs":{},"为":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}},"a":{"docs":{},"c":{"docs":{},"t":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0056657223796034}}}}},"s":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}},"l":{"docs":{},"l":{"docs":{},"i":{"docs":{},"g":{"docs":{},"e":{"docs":{},"n":{"docs":{},"c":{"docs":{},"e":{"docs":{},"，":{"docs":{},"简":{"docs":{},"称":{"docs":{},"：":{"docs":{},"b":{"docs":{},"i":{"docs":{},")":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}}}}}}}}}}}}}},"g":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.00897226753670473},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.006526616357332245},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.009948269001193792}},"e":{"docs":{},"r":{"docs":{},"t":{"docs":{},"y":{"docs":{},"p":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}},"e":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0024469820554649264},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.009787928221859706},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0016316540893330613},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0031834460803820135},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.013937282229965157}}}}}},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.001193792280143255},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.005226480836236934}}}}}}}}}}},")":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.007407407407407408}}},",":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0156794425087108}}},"]":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"p":{"docs":{},"u":{"docs":{},"t":{"docs":{"day03_Hadoop/ha3.5.html":{"ref":"day03_Hadoop/ha3.5.html","tf":0.030303030303030304},"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}},"(":{"docs":{},"\"":{"docs":{},"i":{"docs":{},"i":{"docs":{},"d":{"docs":{},":":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}},"u":{"docs":{},"i":{"docs":{},"d":{"docs":{},":":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}},".":{"docs":{},"t":{"docs":{},"x":{"docs":{},"t":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.017543859649122806}}}}}},"f":{"docs":{},"o":{"docs":{},"r":{"docs":{},"m":{"docs":{},"a":{"docs":{},"t":{"docs":{},"：":{"docs":{},"对":{"docs":{},"数":{"docs":{},"据":{"docs":{},"进":{"docs":{},"行":{"docs":{},"切":{"docs":{},"分":{"docs":{},"，":{"docs":{},"格":{"docs":{},"式":{"docs":{},"化":{"docs":{},"处":{"docs":{},"理":{"docs":{"day03_Hadoop/ha3.5.html":{"ref":"day03_Hadoop/ha3.5.html","tf":0.030303030303030304}}}}}}}}}}}}}}}}}}}}}},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"=":{"docs":{},"'":{"docs":{},"n":{"docs":{},"u":{"docs":{},"c":{"docs":{},"l":{"docs":{},"_":{"docs":{},"o":{"docs":{},"n":{"docs":{},"e":{"docs":{},"h":{"docs":{},"o":{"docs":{},"t":{"docs":{},"_":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"e":{"docs":{},"'":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}}},"p":{"docs":{},"i":{"docs":{},"d":{"docs":{},"_":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"e":{"docs":{},"'":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}},"l":{"docs":{},"_":{"docs":{},"o":{"docs":{},"n":{"docs":{},"e":{"docs":{},"h":{"docs":{},"o":{"docs":{},"t":{"docs":{},"_":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"e":{"docs":{},"'":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}}}}}}}},"l":{"docs":{},"a":{"docs":{},"c":{"docs":{},"e":{"docs":{},"=":{"docs":{},"t":{"docs":{},"r":{"docs":{},"u":{"docs":{},"e":{"docs":{},")":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876}}}}}}}}}}}},"a":{"docs":{},"t":{"docs":{},"h":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.011111111111111112}}}}}},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"l":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938},"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269},"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017},"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099}}}}},"e":{"docs":{},"r":{"docs":{},"t":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196},"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704},"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.01606425702811245}},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"(":{"docs":{},")":{"docs":{},":":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0056657223796034}}}}}}}}}}}}},"v":{"docs":{},"e":{"docs":{},"r":{"docs":{},"t":{"docs":{},"e":{"docs":{},"d":{"docs":{},"_":{"docs":{},"t":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.005836575875486381}},"a":{"docs":{},"b":{"docs":{},"l":{"docs":{},"e":{"docs":{},".":{"docs":{},"g":{"docs":{},"e":{"docs":{},"t":{"docs":{},"(":{"docs":{},"t":{"docs":{},"a":{"docs":{},"g":{"docs":{},",":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"d":{"docs":{},"e":{"docs":{},"f":{"docs":{},"a":{"docs":{},"u":{"docs":{},"l":{"docs":{},"t":{"docs":{},"(":{"docs":{},"t":{"docs":{},"a":{"docs":{},"g":{"docs":{},",":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}}},"[":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{},"_":{"docs":{},"w":{"docs":{},"o":{"docs":{},"r":{"docs":{},"d":{"docs":{},"]":{"docs":{"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}}}}}}}}}}}}}}}}}}}}}}}}}}}},"a":{"docs":{},"d":{"docs":{},"d":{"docs":{},"f":{"docs":{},"u":{"docs":{},"n":{"docs":{},"c":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.007633587786259542}},",":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}},"f":{"docs":{},"u":{"docs":{},"n":{"docs":{},"c":{"docs":{},"：":{"docs":{},"反":{"docs":{},"向":{"docs":{},"操":{"docs":{},"作":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}}}},"i":{"docs":{},"t":{"docs":{},"i":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}},"a":{"docs":{},"l":{"docs":{},"i":{"docs":{},"z":{"docs":{},"e":{"docs":{},"r":{"docs":{},"]":{"docs":{},")":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}}}}}}}}}}},"c":{"docs":{},"l":{"docs":{},"u":{"docs":{},"d":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009},"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}},"l":{"docs":{},"i":{"docs":{},"n":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}},"e":{"docs":{},")":{"docs":{},"可":{"docs":{},"以":{"docs":{},"省":{"docs":{},"略":{"docs":{},"，":{"docs":{},"输":{"docs":{},"出":{"docs":{},"文":{"docs":{},"件":{"docs":{},"使":{"docs":{},"用":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}}}}}}},"方":{"docs":{},"式":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}}},"f":{"docs":{},"o":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.017391304347826087}},"r":{"docs":{},"m":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},".":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}}}}}},"e":{"docs":{},"r":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}},"_":{"docs":{},"m":{"docs":{},"e":{"docs":{},"m":{"docs":{},"o":{"docs":{},"r":{"docs":{},"i":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}},"t":{"docs":{},"e":{"docs":{},"m":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.02824858757062147},"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.0016233766233766235},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0056925996204933585},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147},"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0022675736961451248},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}},"_":{"docs":{},"s":{"docs":{},"i":{"docs":{},"m":{"docs":{},"i":{"docs":{},"l":{"docs":{},"a":{"docs":{},"r":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.007062146892655367},"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.0016233766233766235}},".":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{},":":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}},"l":{"docs":{},"o":{"docs":{},"c":{"docs":{},"[":{"docs":{},"i":{"docs":{},"]":{"docs":{},".":{"docs":{},"d":{"docs":{},"r":{"docs":{},"o":{"docs":{},"p":{"docs":{},"(":{"docs":{},"[":{"docs":{},"i":{"docs":{},"]":{"docs":{},")":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}}}}},")":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}},":":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}}}},":":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.0016233766233766235}}},"[":{"1":{"docs":{},"]":{"docs":{},".":{"docs":{},"d":{"docs":{},"r":{"docs":{},"o":{"docs":{},"p":{"docs":{},"(":{"docs":{},"[":{"1":{"docs":{},"]":{"docs":{},")":{"docs":{},".":{"docs":{},"d":{"docs":{},"r":{"docs":{},"o":{"docs":{},"p":{"docs":{},"n":{"docs":{},"a":{"docs":{},"(":{"docs":{},")":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.0016233766233766235}}}}}}}}}}}}}},"docs":{}}}}}}}}}},"docs":{}}}}}}}}},"i":{"docs":{},"d":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}},"s":{"docs":{},":":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}}}}}}},"s":{"docs":{},"_":{"docs":{},"r":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"s":{"docs":{},".":{"docs":{},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"t":{"docs":{},"u":{"docs":{},"p":{"docs":{},"l":{"docs":{},"e":{"docs":{},"s":{"docs":{},"(":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{},"=":{"docs":{},"t":{"docs":{},"r":{"docs":{},"u":{"docs":{},"e":{"docs":{},")":{"docs":{},":":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"的":{"docs":{},"评":{"docs":{},"分":{"docs":{},"矩":{"docs":{},"阵":{"docs":{},"（":{"docs":{},"稠":{"docs":{},"密":{"docs":{},"/":{"docs":{},"稀":{"docs":{},"疏":{"docs":{},"）":{"docs":{},"分":{"docs":{},"解":{"docs":{},"为":{"docs":{},"p":{"docs":{},"和":{"docs":{},"q":{"docs":{},"矩":{"docs":{},"阵":{"docs":{},"，":{"docs":{},"然":{"docs":{},"后":{"docs":{},"利":{"docs":{},"用":{"docs":{},"p":{"docs":{},"*":{"docs":{},"q":{"docs":{},"​":{"docs":{},"还":{"docs":{},"原":{"docs":{},"出":{"docs":{},"u":{"docs":{},"s":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"矩":{"docs":{},"阵":{"docs":{},"，":{"docs":{},"即":{"docs":{},"隐":{"docs":{},"含":{"docs":{},"特":{"docs":{},"征":{"docs":{},"和":{"docs":{},"物":{"docs":{},"品":{"docs":{},"的":{"docs":{},"矩":{"docs":{},"阵":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}}}}}}}},"有":{"docs":{},"p":{"docs":{},"*":{"docs":{},"q":{"docs":{},"得":{"docs":{},"来":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}}}}}},"评":{"docs":{},"分":{"docs":{},"矩":{"docs":{},"阵":{"docs":{},"r":{"docs":{},"​":{"docs":{},"。":{"docs":{},"整":{"docs":{},"个":{"docs":{},"过":{"docs":{},"程":{"docs":{},"相":{"docs":{},"当":{"docs":{},"于":{"docs":{},"降":{"docs":{},"维":{"docs":{},"处":{"docs":{},"理":{"docs":{},"，":{"docs":{},"其":{"docs":{},"中":{"docs":{},"：":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}}}}}}}}}}}}}}}}}}},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"=":{"docs":{},"'":{"docs":{},"b":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"i":{"docs":{},"d":{"docs":{},"'":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"i":{"docs":{},"d":{"docs":{},"'":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}},"打":{"docs":{},"分":{"docs":{},"数":{"docs":{},"据":{"docs":{},"应":{"docs":{},"该":{"docs":{},"是":{"docs":{},"通":{"docs":{},"过":{"docs":{},"一":{"docs":{},"下":{"docs":{},"方":{"docs":{},"式":{"docs":{},"进":{"docs":{},"行":{"docs":{},"处":{"docs":{},"理":{"docs":{},"转":{"docs":{},"换":{"docs":{},"为":{"docs":{},"u":{"docs":{},"s":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}},"r":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938},"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}},"a":{"docs":{},"b":{"docs":{},"l":{"docs":{},"e":{"docs":{},",":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}},"[":{"docs":{},",":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}}}}}}}}},"d":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.006493506493506494},"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}},"f":{"docs":{},"与":{"docs":{},"词":{"docs":{},"语":{"docs":{},"在":{"docs":{},"文":{"docs":{},"档":{"docs":{},"中":{"docs":{},"的":{"docs":{},"出":{"docs":{},"现":{"docs":{},"次":{"docs":{},"数":{"docs":{},"成":{"docs":{},"正":{"docs":{},"比":{"docs":{},"，":{"docs":{},"与":{"docs":{},"该":{"docs":{},"词":{"docs":{},"在":{"docs":{},"整":{"docs":{},"个":{"docs":{},"文":{"docs":{},"档":{"docs":{},"集":{"docs":{},"中":{"docs":{},"的":{"docs":{},"出":{"docs":{},"现":{"docs":{},"次":{"docs":{},"数":{"docs":{},"成":{"docs":{},"反":{"docs":{},"比":{"docs":{},"。":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"值":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876}},"。":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}},"作":{"docs":{},"为":{"docs":{},"它":{"docs":{},"们":{"docs":{},"的":{"docs":{},"权":{"docs":{},"重":{"docs":{},"按":{"docs":{},"照":{"docs":{},"对":{"docs":{},"应":{"docs":{},"的":{"docs":{},"顺":{"docs":{},"序":{"docs":{},"依":{"docs":{},"次":{"docs":{},"排":{"docs":{},"列":{"docs":{},"，":{"docs":{},"就":{"docs":{},"得":{"docs":{},"到":{"docs":{},"这":{"docs":{},"篇":{"docs":{},"影":{"docs":{},"评":{"docs":{},"的":{"docs":{},"特":{"docs":{},"征":{"docs":{},"向":{"docs":{},"量":{"docs":{},"，":{"docs":{},"我":{"docs":{},"们":{"docs":{},"就":{"docs":{},"用":{"docs":{},"这":{"docs":{},"个":{"docs":{},"向":{"docs":{},"量":{"docs":{},"来":{"docs":{},"代":{"docs":{},"表":{"docs":{},"这":{"docs":{},"篇":{"docs":{},"影":{"docs":{},"评":{"docs":{},"，":{"docs":{},"向":{"docs":{},"量":{"docs":{},"中":{"docs":{},"每":{"docs":{},"一":{"docs":{},"个":{"docs":{},"维":{"docs":{},"度":{"docs":{},"的":{"docs":{},"分":{"docs":{},"量":{"docs":{},"大":{"docs":{},"小":{"docs":{},"对":{"docs":{},"应":{"docs":{},"这":{"docs":{},"个":{"docs":{},"属":{"docs":{},"性":{"docs":{},"的":{"docs":{},"重":{"docs":{},"要":{"docs":{},"性":{"docs":{},"。":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"得":{"docs":{},"到":{"docs":{},"t":{"docs":{},"o":{"docs":{},"p":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}},"最":{"docs":{},"大":{"docs":{},"的":{"docs":{},"k":{"docs":{},"个":{"docs":{},"数":{"docs":{},"组":{"docs":{},"成":{"docs":{},"目":{"docs":{},"标":{"docs":{},"文":{"docs":{},"档":{"docs":{},"的":{"docs":{},"特":{"docs":{},"征":{"docs":{},"向":{"docs":{},"量":{"docs":{},"用":{"docs":{},"以":{"docs":{},"表":{"docs":{},"示":{"docs":{},"文":{"docs":{},"档":{"docs":{},"。":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}}}}}}}}}}}},"是":{"docs":{},"一":{"docs":{},"个":{"docs":{},"词":{"docs":{},"语":{"docs":{},"普":{"docs":{},"遍":{"docs":{},"重":{"docs":{},"要":{"docs":{},"性":{"docs":{},"的":{"docs":{},"度":{"docs":{},"量":{"docs":{},"。":{"docs":{},"表":{"docs":{},"示":{"docs":{},"某":{"docs":{},"一":{"docs":{},"词":{"docs":{},"语":{"docs":{},"在":{"docs":{},"整":{"docs":{},"个":{"docs":{},"文":{"docs":{},"档":{"docs":{},"集":{"docs":{},"中":{"docs":{},"出":{"docs":{},"现":{"docs":{},"的":{"docs":{},"频":{"docs":{},"率":{"docs":{},"，":{"docs":{},"由":{"docs":{},"它":{"docs":{},"计":{"docs":{},"算":{"docs":{},"的":{"docs":{},"结":{"docs":{},"果":{"docs":{},"取":{"docs":{},"对":{"docs":{},"数":{"docs":{},"得":{"docs":{},"到":{"docs":{},"关":{"docs":{},"键":{"docs":{},"词":{"docs":{},"k":{"docs":{},"_":{"docs":{},"i":{"docs":{},"的":{"docs":{},"逆":{"docs":{},"文":{"docs":{},"档":{"docs":{},"频":{"docs":{},"率":{"docs":{},"i":{"docs":{},"d":{"docs":{},"f":{"docs":{},"_":{"docs":{},"i":{"docs":{},"：":{"docs":{},"i":{"docs":{},"d":{"docs":{},"f":{"docs":{},"_":{"docs":{},"i":{"docs":{},"=":{"docs":{},"l":{"docs":{},"o":{"docs":{},"g":{"docs":{},"\\":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"c":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"模":{"docs":{},"型":{"docs":{},"，":{"docs":{},"即":{"docs":{},"计":{"docs":{},"算":{"docs":{},"t":{"docs":{},"f":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876}}}}}}}}}},"的":{"docs":{},"特":{"docs":{},"征":{"docs":{},"提":{"docs":{},"取":{"docs":{},"技":{"docs":{},"术":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}},"算":{"docs":{},"法":{"docs":{},"便":{"docs":{},"是":{"docs":{},"其":{"docs":{},"中":{"docs":{},"一":{"docs":{},"种":{"docs":{},"在":{"docs":{},"自":{"docs":{},"然":{"docs":{},"语":{"docs":{},"言":{"docs":{},"处":{"docs":{},"理":{"docs":{},"领":{"docs":{},"域":{"docs":{},"中":{"docs":{},"应":{"docs":{},"用":{"docs":{},"比":{"docs":{},"较":{"docs":{},"广":{"docs":{},"泛":{"docs":{},"的":{"docs":{},"一":{"docs":{},"种":{"docs":{},"算":{"docs":{},"法":{"docs":{},"。":{"docs":{},"可":{"docs":{},"用":{"docs":{},"来":{"docs":{},"提":{"docs":{},"取":{"docs":{},"目":{"docs":{},"标":{"docs":{},"文":{"docs":{},"档":{"docs":{},"中":{"docs":{},"，":{"docs":{},"并":{"docs":{},"得":{"docs":{},"到":{"docs":{},"关":{"docs":{},"键":{"docs":{},"词":{"docs":{},"用":{"docs":{},"于":{"docs":{},"计":{"docs":{},"算":{"docs":{},"对":{"docs":{},"于":{"docs":{},"目":{"docs":{},"标":{"docs":{},"文":{"docs":{},"档":{"docs":{},"的":{"docs":{},"权":{"docs":{},"重":{"docs":{},"，":{"docs":{},"并":{"docs":{},"将":{"docs":{},"这":{"docs":{},"些":{"docs":{},"权":{"docs":{},"重":{"docs":{},"组":{"docs":{},"合":{"docs":{},"到":{"docs":{},"一":{"docs":{},"起":{"docs":{},"得":{"docs":{},"到":{"docs":{},"特":{"docs":{},"征":{"docs":{},"向":{"docs":{},"量":{"docs":{},"。":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"基":{"docs":{},"于":{"docs":{},"一":{"docs":{},"个":{"docs":{},"这":{"docs":{},"样":{"docs":{},"的":{"docs":{},"假":{"docs":{},"设":{"docs":{},"：":{"docs":{},"若":{"docs":{},"一":{"docs":{},"个":{"docs":{},"词":{"docs":{},"语":{"docs":{},"在":{"docs":{},"目":{"docs":{},"标":{"docs":{},"文":{"docs":{},"档":{"docs":{},"中":{"docs":{},"出":{"docs":{},"现":{"docs":{},"的":{"docs":{},"频":{"docs":{},"率":{"docs":{},"高":{"docs":{},"而":{"docs":{},"在":{"docs":{},"其":{"docs":{},"他":{"docs":{},"文":{"docs":{},"档":{"docs":{},"中":{"docs":{},"出":{"docs":{},"现":{"docs":{},"的":{"docs":{},"频":{"docs":{},"率":{"docs":{},"低":{"docs":{},"，":{"docs":{},"那":{"docs":{},"么":{"docs":{},"这":{"docs":{},"个":{"docs":{},"词":{"docs":{},"语":{"docs":{},"就":{"docs":{},"可":{"docs":{},"以":{"docs":{},"用":{"docs":{},"来":{"docs":{},"区":{"docs":{},"分":{"docs":{},"出":{"docs":{},"目":{"docs":{},"标":{"docs":{},"文":{"docs":{},"档":{"docs":{},"。":{"docs":{},"这":{"docs":{},"个":{"docs":{},"假":{"docs":{},"设":{"docs":{},"需":{"docs":{},"要":{"docs":{},"掌":{"docs":{},"握":{"docs":{},"的":{"docs":{},"有":{"docs":{},"两":{"docs":{},"点":{"docs":{},"：":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"的":{"docs":{},"计":{"docs":{},"算":{"docs":{},"可":{"docs":{},"以":{"docs":{},"分":{"docs":{},"为":{"docs":{},"词":{"docs":{},"频":{"docs":{},"（":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"m":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}}},"结":{"docs":{},"果":{"docs":{},"，":{"docs":{},"“":{"docs":{},"海":{"docs":{},"盗":{"docs":{},"”":{"docs":{},"为":{"0":{"docs":{},"，":{"docs":{},"“":{"docs":{},"船":{"docs":{},"长":{"docs":{},"”":{"docs":{},"为":{"0":{"docs":{},".":{"0":{"2":{"2":{"5":{"docs":{},"，":{"docs":{},"“":{"docs":{},"自":{"docs":{},"由":{"docs":{},"”":{"docs":{},"为":{"0":{"docs":{},".":{"0":{"5":{"docs":{},"。":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}},"docs":{}},"docs":{}}},"docs":{}}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}}}}}},"docs":{}}}}}}}}},"自":{"docs":{},"然":{"docs":{},"语":{"docs":{},"言":{"docs":{},"处":{"docs":{},"理":{"docs":{},"领":{"docs":{},"域":{"docs":{},"中":{"docs":{},"计":{"docs":{},"算":{"docs":{},"文":{"docs":{},"档":{"docs":{},"中":{"docs":{},"词":{"docs":{},"或":{"docs":{},"短":{"docs":{},"语":{"docs":{},"的":{"docs":{},"权":{"docs":{},"值":{"docs":{},"的":{"docs":{},"方":{"docs":{},"法":{"docs":{},"，":{"docs":{},"是":{"docs":{},"词":{"docs":{},"频":{"docs":{},"（":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"m":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"计":{"docs":{},"算":{"docs":{},"出":{"docs":{},"来":{"docs":{},"并":{"docs":{},"进":{"docs":{},"行":{"docs":{},"对":{"docs":{},"比":{"docs":{},"，":{"docs":{},"取":{"docs":{},"其":{"docs":{},"中":{"docs":{},"t":{"docs":{},"f":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}},"，":{"docs":{},"w":{"docs":{},"o":{"docs":{},"r":{"docs":{},"d":{"2":{"docs":{},"v":{"docs":{},"e":{"docs":{},"c":{"docs":{},"在":{"docs":{},"内":{"docs":{},"的":{"docs":{},"多":{"docs":{},"种":{"docs":{},"主":{"docs":{},"题":{"docs":{},"模":{"docs":{},"型":{"docs":{},"算":{"docs":{},"法":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}},"docs":{}}}}},"以":{"docs":{},"电":{"docs":{},"影":{"docs":{},"“":{"docs":{},"加":{"docs":{},"勒":{"docs":{},"比":{"docs":{},"海":{"docs":{},"盗":{"docs":{},"：":{"docs":{},"黑":{"docs":{},"珍":{"docs":{},"珠":{"docs":{},"号":{"docs":{},"的":{"docs":{},"诅":{"docs":{},"咒":{"docs":{},"”":{"docs":{},"为":{"docs":{},"例":{"docs":{},"，":{"docs":{},"假":{"docs":{},"设":{"docs":{},"它":{"docs":{},"总":{"docs":{},"共":{"docs":{},"有":{"1":{"0":{"0":{"0":{"docs":{},"篇":{"docs":{},"影":{"docs":{},"评":{"docs":{},"，":{"docs":{},"其":{"docs":{},"中":{"docs":{},"一":{"docs":{},"篇":{"docs":{},"影":{"docs":{},"评":{"docs":{},"的":{"docs":{},"总":{"docs":{},"词":{"docs":{},"语":{"docs":{},"数":{"docs":{},"为":{"2":{"0":{"0":{"docs":{},"，":{"docs":{},"其":{"docs":{},"中":{"docs":{},"出":{"docs":{},"现":{"docs":{},"最":{"docs":{},"频":{"docs":{},"繁":{"docs":{},"的":{"docs":{},"词":{"docs":{},"语":{"docs":{},"为":{"docs":{},"“":{"docs":{},"海":{"docs":{},"盗":{"docs":{},"”":{"docs":{},"、":{"docs":{},"“":{"docs":{},"船":{"docs":{},"长":{"docs":{},"”":{"docs":{},"、":{"docs":{},"“":{"docs":{},"自":{"docs":{},"由":{"docs":{},"”":{"docs":{},"，":{"docs":{},"分":{"docs":{},"别":{"docs":{},"是":{"2":{"0":{"docs":{},"、":{"1":{"5":{"docs":{},"、":{"1":{"0":{"docs":{},"次":{"docs":{},"，":{"docs":{},"并":{"docs":{},"且":{"docs":{},"这":{"3":{"docs":{},"个":{"docs":{},"词":{"docs":{},"在":{"docs":{},"所":{"docs":{},"有":{"docs":{},"影":{"docs":{},"评":{"docs":{},"中":{"docs":{},"被":{"docs":{},"提":{"docs":{},"及":{"docs":{},"的":{"docs":{},"次":{"docs":{},"数":{"docs":{},"分":{"docs":{},"别":{"docs":{},"为":{"1":{"0":{"0":{"0":{"docs":{},"、":{"5":{"0":{"0":{"docs":{},"、":{"1":{"0":{"0":{"docs":{},"，":{"docs":{},"就":{"docs":{},"这":{"3":{"docs":{},"个":{"docs":{},"词":{"docs":{},"语":{"docs":{},"作":{"docs":{},"为":{"docs":{},"关":{"docs":{},"键":{"docs":{},"词":{"docs":{},"的":{"docs":{},"顺":{"docs":{},"序":{"docs":{},"计":{"docs":{},"算":{"docs":{},"如":{"docs":{},"下":{"docs":{},"。":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}}}},"docs":{}}}}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}}}}}}}}},"docs":{}}}}}}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}},"|":{"docs":{},"a":{"docs":{},"g":{"docs":{},"e":{"docs":{},"|":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}},"w":{"docs":{},"e":{"docs":{},"i":{"docs":{},"g":{"docs":{},"h":{"docs":{},"t":{"docs":{},"_":{"docs":{},"o":{"docs":{},"|":{"docs":{},"h":{"docs":{},"e":{"docs":{},"i":{"docs":{},"g":{"docs":{},"h":{"docs":{},"t":{"docs":{},"_":{"docs":{},"o":{"docs":{},"|":{"docs":{},"a":{"docs":{},"g":{"docs":{},"e":{"docs":{},"_":{"docs":{},"o":{"docs":{},"|":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}}}}}}}}}}},"|":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}},"i":{"docs":{},"d":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0056925996204933585},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.004629629629629629}},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}},")":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}},":":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002858776443682104},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.003795066413662239},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}},",":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.01461038961038961},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.013150371640937679},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.007590132827324478},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.004629629629629629}}},":":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}}},"=":{"docs":{},"i":{"docs":{},"i":{"docs":{},"d":{"docs":{},")":{"docs":{},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}},"s":{"docs":{},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0017152658662092624}}}}}},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.003430531732418525}}},",":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876}}},"p":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.013245033112582781}},".":{"docs":{},"s":{"docs":{},"p":{"docs":{},"l":{"docs":{},"i":{"docs":{},"t":{"docs":{},"(":{"docs":{},"\"":{"docs":{},".":{"docs":{},"\"":{"docs":{},")":{"docs":{},"#":{"docs":{},"[":{"2":{"2":{"3":{"docs":{},",":{"2":{"4":{"3":{"docs":{},",":{"0":{"docs":{},",":{"0":{"docs":{},"]":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}},"docs":{}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}}}},"_":{"docs":{},"n":{"docs":{},"u":{"docs":{},"m":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.033112582781456956}}}}},"t":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"s":{"docs":{},"f":{"docs":{},"o":{"docs":{},"r":{"docs":{},"m":{"docs":{},"(":{"docs":{},"i":{"docs":{},"p":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}},":":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}}}}}}}}}}}}},"s":{"docs":{},":":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}},"日":{"docs":{},"志":{"docs":{},"信":{"docs":{},"息":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}}},"q":{"docs":{},"r":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.0045662100456621}},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}},"j":{"docs":{},"a":{"docs":{},"c":{"docs":{},"c":{"docs":{},"a":{"docs":{},"r":{"docs":{},"d":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}},"_":{"docs":{},"s":{"docs":{},"i":{"docs":{},"m":{"docs":{},"i":{"docs":{},"l":{"docs":{},"a":{"docs":{},"r":{"docs":{},"i":{"docs":{},"t":{"docs":{},"y":{"docs":{},"_":{"docs":{},"s":{"docs":{},"c":{"docs":{},"o":{"docs":{},"r":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}}}}}}}}},"v":{"docs":{},"a":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907},"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.006172839506172839},"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}},"_":{"docs":{},"h":{"docs":{},"o":{"docs":{},"m":{"docs":{},"e":{"docs":{},"=":{"docs":{},"/":{"docs":{},"r":{"docs":{},"o":{"docs":{},"o":{"docs":{},"t":{"docs":{},"/":{"docs":{},"b":{"docs":{},"i":{"docs":{},"g":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"/":{"docs":{},"j":{"docs":{},"d":{"docs":{},"k":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428},"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}}}}}}}}}},"j":{"docs":{},"a":{"docs":{},"v":{"docs":{},"a":{"docs":{},"_":{"docs":{},"h":{"docs":{},"o":{"docs":{},"m":{"docs":{},"e":{"docs":{},"_":{"docs":{},"p":{"docs":{},"a":{"docs":{},"t":{"docs":{},"h":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}}}}}}}}}}}}}},"'":{"docs":{},"/":{"docs":{},"r":{"docs":{},"o":{"docs":{},"o":{"docs":{},"t":{"docs":{},"/":{"docs":{},"b":{"docs":{},"i":{"docs":{},"g":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"/":{"docs":{},"j":{"docs":{},"d":{"docs":{},"k":{"docs":{},"'":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.007633587786259542},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}}},"x":{"docs":{},".":{"docs":{},"j":{"docs":{},"d":{"docs":{},"o":{"docs":{},".":{"docs":{},"o":{"docs":{},"p":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"n":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"d":{"docs":{},"r":{"docs":{},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{},"r":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}},"p":{"docs":{},"a":{"docs":{},"s":{"docs":{},"s":{"docs":{},"w":{"docs":{},"o":{"docs":{},"r":{"docs":{},"d":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}},"u":{"docs":{},"r":{"docs":{},"l":{"docs":{},"m":{"docs":{},"y":{"docs":{},"s":{"docs":{},"q":{"docs":{},"l":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"r":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.008032128514056224}},"包":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}},"k":{"docs":{},"e":{"docs":{},",":{"1":{"1":{"0":{"0":{"0":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}},"(":{"docs":{},"\\":{"docs":{},"t":{"docs":{},"h":{"docs":{},"e":{"docs":{},"t":{"docs":{},"a":{"docs":{},")":{"docs":{},"&":{"docs":{},"=":{"docs":{},"\\":{"docs":{},"c":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"c":{"docs":{},"{":{"docs":{},"\\":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{},"i":{"docs":{},"a":{"docs":{},"l":{"docs":{},"}":{"docs":{},"{":{"docs":{},"\\":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{},"i":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}}}}}}}}}}}}}}},"=":{"docs":{},"\\":{"docs":{},"s":{"docs":{},"u":{"docs":{},"m":{"docs":{},"_":{"docs":{},"{":{"docs":{},"u":{"docs":{},",":{"docs":{},"i":{"docs":{},"\\":{"docs":{},"i":{"docs":{},"n":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}}}}}}}}}}},"o":{"docs":{},"b":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282},"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"f":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.017543859649122806}}}}}},"t":{"docs":{},"r":{"docs":{},"a":{"docs":{},"c":{"docs":{},"k":{"docs":{},"e":{"docs":{},"r":{"docs":{},":":{"docs":{},"负":{"docs":{},"责":{"docs":{},"接":{"docs":{},"收":{"docs":{},"客":{"docs":{},"户":{"docs":{},"作":{"docs":{},"业":{"docs":{},"提":{"docs":{},"交":{"docs":{},"，":{"docs":{},"负":{"docs":{},"责":{"docs":{},"任":{"docs":{},"务":{"docs":{},"到":{"docs":{},"作":{"docs":{},"业":{"docs":{},"节":{"docs":{},"点":{"docs":{},"上":{"docs":{},"运":{"docs":{},"行":{"docs":{},"，":{"docs":{},"检":{"docs":{},"查":{"docs":{},"作":{"docs":{},"业":{"docs":{},"的":{"docs":{},"状":{"docs":{},"态":{"docs":{"day03_Hadoop/ha3.5.html":{"ref":"day03_Hadoop/ha3.5.html","tf":0.030303030303030304}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"i":{"docs":{},"n":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.006802721088435374}}}}},"p":{"docs":{"day03_Hadoop/ha2.1.html":{"ref":"day03_Hadoop/ha2.1.html","tf":0.0273972602739726},"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}},"g":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}},"s":{"docs":{},"查":{"docs":{},"看":{"docs":{},"进":{"docs":{},"程":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}}}}}},"d":{"docs":{},"b":{"docs":{},"c":{"docs":{},"/":{"docs":{},"o":{"docs":{},"d":{"docs":{},"b":{"docs":{},"c":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}},":":{"docs":{},"m":{"docs":{},"y":{"docs":{},"s":{"docs":{},"q":{"docs":{},"l":{"docs":{},":":{"docs":{},"/":{"docs":{},"/":{"1":{"2":{"7":{"docs":{},".":{"0":{"docs":{},".":{"0":{"docs":{},".":{"1":{"docs":{},":":{"3":{"3":{"0":{"6":{"docs":{},"/":{"docs":{},"h":{"docs":{},"i":{"docs":{},"v":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}},"d":{"docs":{},"f":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}},"k":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}},"e":{"docs":{},"r":{"docs":{},"r":{"docs":{},"y":{"docs":{},",":{"1":{"2":{"0":{"0":{"0":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"i":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}},"\"":{"docs":{},"]":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.004545454545454545}}}}},"s":{"docs":{},"o":{"docs":{},"n":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.007692307692307693},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}},"d":{"docs":{},"f":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.006993006993006993},"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.03076923076923077}},".":{"docs":{},"c":{"docs":{},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"o":{"docs":{},"r":{"docs":{},"r":{"docs":{},"e":{"docs":{},"p":{"docs":{},"l":{"docs":{},"a":{"docs":{},"c":{"docs":{},"e":{"docs":{},"t":{"docs":{},"e":{"docs":{},"m":{"docs":{},"p":{"docs":{},"v":{"docs":{},"i":{"docs":{},"e":{"docs":{},"w":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"t":{"docs":{},"m":{"docs":{},"p":{"docs":{},"_":{"docs":{},"t":{"docs":{},"a":{"docs":{},"b":{"docs":{},"l":{"docs":{},"e":{"docs":{},"\"":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"(":{"docs":{},"j":{"docs":{},"s":{"docs":{},"o":{"docs":{},"n":{"docs":{},"d":{"docs":{},"f":{"docs":{},".":{"docs":{},"p":{"docs":{},"o":{"docs":{},"p":{"docs":{},">":{"4":{"0":{"0":{"0":{"docs":{},")":{"docs":{},".":{"docs":{},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"1":{"0":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.007692307692307693}}}},"docs":{}},"docs":{}}}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}}}}}}}}},"p":{"docs":{},"r":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"s":{"docs":{},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},"m":{"docs":{},"a":{"docs":{},"(":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.015384615384615385}}}}}}}}}}}}}}},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"2":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}},"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.011538461538461539}}}}}}}}}}},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.007692307692307693}}}}},"s":{"docs":{},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},"m":{"docs":{},"a":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.011538461538461539}}}}}}},"t":{"docs":{},"r":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.007692307692307693}}}}},"数":{"docs":{},"据":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}},".":{"docs":{},"d":{"docs":{},"u":{"docs":{},"m":{"docs":{},"p":{"docs":{},"s":{"docs":{},"(":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},")":{"docs":{},")":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289}}}}}}}}}}}}}},"l":{"docs":{},"o":{"docs":{},"a":{"docs":{},"d":{"docs":{},"s":{"docs":{},"(":{"docs":{},"c":{"docs":{},"l":{"docs":{},"i":{"docs":{},"e":{"docs":{},"n":{"docs":{},"t":{"docs":{},"_":{"docs":{},"o":{"docs":{},"f":{"docs":{},"_":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},".":{"docs":{},"h":{"docs":{},"g":{"docs":{},"e":{"docs":{},"t":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"a":{"docs":{},"d":{"docs":{},"_":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}}}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"o":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.011695906432748537}},"r":{"docs":{},"d":{"docs":{},"e":{"docs":{},"r":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734},"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0022675736961451248}}}}}},"u":{"docs":{},"t":{"docs":{},"p":{"docs":{},"u":{"docs":{},"t":{"1":{"docs":{},".":{"docs":{},"t":{"docs":{},"x":{"docs":{},"t":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}},"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.011695906432748537},"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112},"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.015267175572519083}},"(":{"docs":{},"预":{"docs":{},"测":{"docs":{},"输":{"docs":{},"出":{"docs":{},")":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}},".":{"docs":{},"t":{"docs":{},"x":{"docs":{},"t":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.011695906432748537}}}}}},"f":{"docs":{},"o":{"docs":{},"r":{"docs":{},"m":{"docs":{},"a":{"docs":{},"t":{"docs":{},"：":{"docs":{},"格":{"docs":{},"式":{"docs":{},"化":{"docs":{},"输":{"docs":{},"出":{"docs":{},"数":{"docs":{},"据":{"docs":{"day03_Hadoop/ha3.5.html":{"ref":"day03_Hadoop/ha3.5.html","tf":0.030303030303030304}}}}}}}}}}}}}}}},":":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"=":{"docs":{},"'":{"docs":{},"n":{"docs":{},"u":{"docs":{},"c":{"docs":{},"l":{"docs":{},"_":{"docs":{},"o":{"docs":{},"n":{"docs":{},"e":{"docs":{},"h":{"docs":{},"o":{"docs":{},"t":{"docs":{},"_":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"e":{"docs":{},"'":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"'":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}},"p":{"docs":{},"i":{"docs":{},"d":{"docs":{},"_":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"e":{"docs":{},"'":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"'":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}},"l":{"docs":{},"_":{"docs":{},"o":{"docs":{},"n":{"docs":{},"e":{"docs":{},"h":{"docs":{},"o":{"docs":{},"t":{"docs":{},"_":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"e":{"docs":{},"'":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"'":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}}}}}}},"e":{"docs":{},"r":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.01473922902494331}}}},"l":{"docs":{},"i":{"docs":{},"e":{"docs":{},"r":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}},"s":{"docs":{},".":{"docs":{},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}}}}}}},"s":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247},"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.007633587786259542},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}},"系":{"docs":{},"统":{"docs":{},"上":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}},".":{"docs":{},"e":{"docs":{},"n":{"docs":{},"v":{"docs":{},"i":{"docs":{},"r":{"docs":{},"o":{"docs":{},"n":{"docs":{},"[":{"docs":{},"\"":{"docs":{},"p":{"docs":{},"y":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"_":{"docs":{},"d":{"docs":{},"r":{"docs":{},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"p":{"docs":{},"y":{"docs":{},"t":{"docs":{},"h":{"docs":{},"o":{"docs":{},"n":{"docs":{},"\"":{"docs":{},"]":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.007633587786259542},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}},"p":{"docs":{},"y":{"docs":{},"t":{"docs":{},"h":{"docs":{},"o":{"docs":{},"n":{"docs":{},"\"":{"docs":{},"]":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.007633587786259542},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"_":{"docs":{},"h":{"docs":{},"o":{"docs":{},"m":{"docs":{},"e":{"docs":{},"\"":{"docs":{},"]":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.007633587786259542},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}},"'":{"docs":{},"j":{"docs":{},"a":{"docs":{},"v":{"docs":{},"a":{"docs":{},"_":{"docs":{},"h":{"docs":{},"o":{"docs":{},"m":{"docs":{},"e":{"docs":{},"'":{"docs":{},"]":{"docs":{},"=":{"docs":{},"j":{"docs":{},"a":{"docs":{},"v":{"docs":{},"a":{"docs":{},"_":{"docs":{},"h":{"docs":{},"o":{"docs":{},"m":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.007633587786259542},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"v":{"docs":{},"e":{"docs":{},"r":{"docs":{},"v":{"docs":{},"i":{"docs":{},"e":{"docs":{},"w":{"docs":{},"界":{"docs":{},"面":{"docs":{},"查":{"docs":{},"看":{"docs":{},"整":{"docs":{},"体":{"docs":{},"情":{"docs":{},"况":{"docs":{"day03_Hadoop/ha2.1.html":{"ref":"day03_Hadoop/ha2.1.html","tf":0.0136986301369863}}}}}}}}}}}}}},"w":{"docs":{},"r":{"docs":{},"i":{"docs":{},"t":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}},"e":{"docs":{},"\\":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"o":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}}}}}},"o":{"docs":{},"z":{"docs":{},"i":{"docs":{},"e":{"docs":{},":":{"docs":{},"工":{"docs":{},"作":{"docs":{},"流":{"docs":{},"引":{"docs":{},"擎":{"docs":{},"，":{"docs":{},"管":{"docs":{},"理":{"docs":{},"作":{"docs":{},"业":{"docs":{},"执":{"docs":{},"行":{"docs":{},"顺":{"docs":{},"序":{"docs":{"day03_Hadoop/ha4.1.html":{"ref":"day03_Hadoop/ha4.1.html","tf":0.023809523809523808}}}}}}}}}}}}}}}}}}}}},"n":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.008032128514056224}},"=":{"docs":{},"'":{"docs":{},"i":{"docs":{},"d":{"docs":{},"'":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}},"e":{"docs":{},"h":{"docs":{},"o":{"docs":{},"t":{"docs":{},"e":{"docs":{},"n":{"docs":{},"c":{"docs":{},"o":{"docs":{},"d":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}},"e":{"docs":{},"r":{"docs":{},"(":{"docs":{},"d":{"docs":{},"r":{"docs":{},"o":{"docs":{},"p":{"docs":{},"l":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"=":{"docs":{},"f":{"docs":{},"a":{"docs":{},"l":{"docs":{},"s":{"docs":{},"e":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.001193792280143255}}}}}}}}}}}}}}}}}},"：":{"docs":{},"对":{"docs":{},"特":{"docs":{},"征":{"docs":{},"列":{"docs":{},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"进":{"docs":{},"行":{"docs":{},"热":{"docs":{},"编":{"docs":{},"码":{"docs":{},"，":{"docs":{},"通":{"docs":{},"常":{"docs":{},"需":{"docs":{},"结":{"docs":{},"合":{"docs":{},"s":{"docs":{},"t":{"docs":{},"r":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{},"e":{"docs":{},"r":{"docs":{},"一":{"docs":{},"起":{"docs":{},"使":{"docs":{},"用":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"f":{"docs":{},"f":{"docs":{},"e":{"docs":{},"r":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}},"p":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"h":{"docs":{},"e":{"docs":{},"a":{"docs":{},"d":{"docs":{},"e":{"docs":{},"r":{"docs":{},"\"":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}},"c":{"docs":{},"c":{"docs":{},"u":{"docs":{},"p":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"：":{"docs":{},"是":{"docs":{},"否":{"docs":{},"大":{"docs":{},"学":{"docs":{},"生":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}},":":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0010197838058331635},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}},"作":{"docs":{},"为":{"docs":{},"特":{"docs":{},"征":{"docs":{},"值":{"docs":{},"，":{"docs":{},"p":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"作":{"docs":{},"为":{"docs":{},"目":{"docs":{},"标":{"docs":{},"值":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}},"_":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.004228329809725159}},"e":{"docs":{},"[":{"docs":{},"o":{"docs":{},"c":{"docs":{},"c":{"docs":{},"u":{"docs":{},"p":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"_":{"docs":{},"r":{"docs":{},"e":{"docs":{},"l":{"docs":{},"a":{"docs":{},"[":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"[":{"docs":{},"\"":{"docs":{},"o":{"docs":{},"c":{"docs":{},"c":{"docs":{},"u":{"docs":{},"p":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"\"":{"docs":{},"]":{"docs":{},")":{"docs":{},"]":{"docs":{},"]":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"p":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0056925996204933585},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147},"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625}},"a":{"docs":{},"g":{"docs":{},"e":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734},"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"k":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}}}}},"i":{"docs":{},"r":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099}},"w":{"docs":{},"i":{"docs":{},"s":{"docs":{},"e":{"docs":{},"_":{"docs":{},"d":{"docs":{},"i":{"docs":{},"s":{"docs":{},"t":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.00423728813559322}},"a":{"docs":{},"n":{"docs":{},"c":{"docs":{},"e":{"docs":{},"s":{"docs":{},"(":{"docs":{},"d":{"docs":{},"f":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147}}},".":{"docs":{},"t":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147}}}}}}}}}}}}}}}}}}}}}},"s":{"docs":{},".":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{},"e":{"docs":{},"b":{"docs":{},"y":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{},"(":{"docs":{},"l":{"docs":{},"a":{"docs":{},"m":{"docs":{},"b":{"docs":{},"d":{"docs":{},"a":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099}}}}}}}}}}}}}}}}}}}}}}}},"n":{"docs":{},"d":{"docs":{},"a":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.00423728813559322},"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.004002287021154946},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.003795066413662239},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147},"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.005836575875486381},"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842},"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.006993006993006993},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}},"s":{"docs":{},"中":{"docs":{},"c":{"docs":{},"o":{"docs":{},"r":{"docs":{},"r":{"docs":{},"方":{"docs":{},"法":{"docs":{},"可":{"docs":{},"直":{"docs":{},"接":{"docs":{},"用":{"docs":{},"于":{"docs":{},"计":{"docs":{},"算":{"docs":{},"皮":{"docs":{},"尔":{"docs":{},"逊":{"docs":{},"相":{"docs":{},"关":{"docs":{},"系":{"docs":{},"数":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}}}}}}}}},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"分":{"docs":{},"批":{"docs":{},"读":{"docs":{},"取":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}},"s":{"docs":{},"s":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}},"w":{"docs":{},"o":{"docs":{},"r":{"docs":{},"d":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}},"r":{"docs":{},"a":{"docs":{},"l":{"docs":{},"l":{"docs":{},"e":{"docs":{},"l":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282},"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.01276595744680851}},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"[":{"0":{"docs":{},"]":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}},"docs":{}}}}}}}}}}}}}}},"：":{"docs":{},"集":{"docs":{},"群":{"docs":{},"并":{"docs":{},"行":{"docs":{},"执":{"docs":{},"行":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}},"t":{"docs":{},"i":{"docs":{},"t":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.011111111111111112},"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"s":{"docs":{},"：":{"docs":{},"在":{"docs":{},"磁":{"docs":{},"盘":{"docs":{},"上":{"docs":{},"有":{"docs":{},"很":{"docs":{},"多":{"docs":{},"\"":{"docs":{},"小":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"\"":{"docs":{},"，":{"docs":{},"将":{"docs":{},"这":{"docs":{},"些":{"docs":{},"数":{"docs":{},"据":{"docs":{},"进":{"docs":{},"行":{"docs":{},"归":{"docs":{},"并":{"docs":{},"排":{"docs":{},"序":{"docs":{},"。":{"docs":{"day03_Hadoop/ha3.5.html":{"ref":"day03_Hadoop/ha3.5.html","tf":0.030303030303030304}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"：":{"docs":{},"在":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}},"(":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"1":{"docs":{},")":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}},"=":{"docs":{},"'":{"2":{"0":{"1":{"8":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.011111111111111112}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}},"docs":{}}}}}},":":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289}}}}}}}}},"q":{"docs":{},"u":{"docs":{},"e":{"docs":{},"t":{"docs":{},"d":{"docs":{},"f":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}},"t":{"docs":{},"h":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}},"=":{"docs":{},"$":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},"_":{"docs":{},"h":{"docs":{},"o":{"docs":{},"m":{"docs":{},"e":{"docs":{},"/":{"docs":{},"b":{"docs":{},"i":{"docs":{},"n":{"docs":{},":":{"docs":{},"$":{"docs":{},"p":{"docs":{},"a":{"docs":{},"t":{"docs":{},"h":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428}}}}}}}}}}}}}}}}}}}}}},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{},"_":{"docs":{},"h":{"docs":{},"o":{"docs":{},"m":{"docs":{},"e":{"docs":{},"/":{"docs":{},"b":{"docs":{},"i":{"docs":{},"n":{"docs":{},":":{"docs":{},"$":{"docs":{},"p":{"docs":{},"a":{"docs":{},"t":{"docs":{},"h":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}}}}}}}}}}},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"_":{"docs":{},"h":{"docs":{},"o":{"docs":{},"m":{"docs":{},"e":{"docs":{},"/":{"docs":{},"b":{"docs":{},"i":{"docs":{},"n":{"docs":{},":":{"docs":{},"$":{"docs":{},"p":{"docs":{},"a":{"docs":{},"t":{"docs":{},"h":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}}}}}}}}}}}}},"j":{"docs":{},"a":{"docs":{},"v":{"docs":{},"a":{"docs":{},"_":{"docs":{},"h":{"docs":{},"o":{"docs":{},"m":{"docs":{},"e":{"docs":{},"/":{"docs":{},"b":{"docs":{},"i":{"docs":{},"n":{"docs":{},":":{"docs":{},"$":{"docs":{},"p":{"docs":{},"a":{"docs":{},"t":{"docs":{},"h":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428}}}}}}}}}}}}}}}}}}}}},"p":{"docs":{},"a":{"docs":{},"t":{"docs":{},"h":{"docs":{},":":{"docs":{},"$":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"_":{"docs":{},"h":{"docs":{},"o":{"docs":{},"m":{"docs":{},"e":{"docs":{},"/":{"docs":{},"b":{"docs":{},"i":{"docs":{},"n":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}}}}}}}}}}}}}}}}}}}}}},":":{"docs":{},"/":{"docs":{},"h":{"docs":{},"o":{"docs":{},"m":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}},"e":{"docs":{},"/":{"docs":{},"p":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}}}}}}},"c":{"docs":{},"k":{"docs":{},"a":{"docs":{},"g":{"docs":{},"e":{"docs":{},"s":{"docs":{},"目":{"docs":{},"录":{"docs":{},"下":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}}}}}}}}},"d":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.00423728813559322},"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002858776443682104},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.003795066413662239},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147},"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.005836575875486381},"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}},".":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"(":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"s":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.005649717514124294}}}},")":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}}}},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"m":{"docs":{},"_":{"docs":{},"s":{"docs":{},"i":{"docs":{},"m":{"docs":{},"i":{"docs":{},"l":{"docs":{},"a":{"docs":{},"r":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147}}}}}}}}}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"s":{"docs":{},"i":{"docs":{},"m":{"docs":{},"i":{"docs":{},"l":{"docs":{},"a":{"docs":{},"r":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147}}}}}}}}}}}}}}},"_":{"docs":{},"m":{"docs":{},"o":{"docs":{},"v":{"docs":{},"i":{"docs":{},"e":{"docs":{},"_":{"docs":{},"p":{"docs":{},"r":{"docs":{},"o":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"e":{"docs":{},",":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}},"p":{"docs":{},"d":{"docs":{},"f":{"docs":{},".":{"docs":{},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"i":{"docs":{},"d":{"docs":{},".":{"docs":{},"u":{"docs":{},"n":{"docs":{},"i":{"docs":{},"q":{"docs":{},"u":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{},",":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{},"s":{"docs":{},"=":{"docs":{},"[":{"docs":{},"\"":{"docs":{},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},"]":{"docs":{},")":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"c":{"docs":{},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"_":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"s":{"docs":{},"(":{"8":{"docs":{},",":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"d":{"docs":{},"_":{"docs":{},"c":{"docs":{},"s":{"docs":{},"v":{"docs":{},"(":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"_":{"docs":{},"p":{"docs":{},"a":{"docs":{},"t":{"docs":{},"h":{"docs":{},",":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}}}}}},"\"":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"s":{"docs":{},"/":{"docs":{},"m":{"docs":{},"l":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147},"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876},"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842},"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}}}}}}}}}}}}},"m":{"docs":{},"l":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}},"'":{"docs":{},"b":{"docs":{},"e":{"docs":{},"h":{"docs":{},"a":{"docs":{},"v":{"docs":{},"i":{"docs":{},"o":{"docs":{},"r":{"docs":{},"_":{"docs":{},"l":{"docs":{},"o":{"docs":{},"g":{"docs":{},".":{"docs":{},"c":{"docs":{},"s":{"docs":{},"v":{"docs":{},"'":{"docs":{},",":{"docs":{},"c":{"docs":{},"h":{"docs":{},"u":{"docs":{},"n":{"docs":{},"k":{"docs":{},"s":{"docs":{},"i":{"docs":{},"z":{"docs":{},"e":{"docs":{},"=":{"1":{"0":{"0":{"docs":{},",":{"docs":{},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"a":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{},"=":{"docs":{},"t":{"docs":{},"r":{"docs":{},"u":{"docs":{},"e":{"docs":{},")":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"f":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}},"[":{"docs":{},"\"":{"docs":{},"p":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"\"":{"docs":{},"]":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}},".":{"docs":{},"w":{"docs":{},"h":{"docs":{},"e":{"docs":{},"r":{"docs":{},"e":{"docs":{},"(":{"docs":{},"p":{"docs":{},"d":{"docs":{},"f":{"docs":{},".":{"docs":{},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"i":{"docs":{},"d":{"docs":{},"=":{"docs":{},"=":{"1":{"1":{"1":{"5":{"6":{"docs":{},")":{"docs":{},".":{"docs":{},"d":{"docs":{},"r":{"docs":{},"o":{"docs":{},"p":{"docs":{},"n":{"docs":{},"a":{"docs":{},"(":{"docs":{},")":{"docs":{},".":{"docs":{},"a":{"docs":{},"d":{"docs":{},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"i":{"docs":{},"d":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}}}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}}}}}}}}}}}},"p":{"docs":{},"r":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.005649717514124294},"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.006493506493506494},"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.007782101167315175},"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.010526315789473684}},"(":{"docs":{},"r":{"docs":{},"s":{"docs":{},"_":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"u":{"docs":{},"l":{"docs":{},"t":{"docs":{},"s":{"docs":{},")":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147}}}},")":{"docs":{"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}}}}}}}}}}},"e":{"docs":{},"s":{"docs":{},"u":{"docs":{},"l":{"docs":{},"t":{"docs":{},")":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}}}}}}}}},"t":{"docs":{},"o":{"docs":{},"p":{"docs":{},"n":{"docs":{},"_":{"docs":{},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"m":{"docs":{},"s":{"docs":{},")":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"s":{"docs":{},")":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}},"c":{"docs":{},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"_":{"docs":{},"m":{"docs":{},"o":{"docs":{},"v":{"docs":{},"i":{"docs":{},"e":{"docs":{},"_":{"docs":{},"p":{"docs":{},"r":{"docs":{},"o":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"e":{"docs":{},"(":{"docs":{},"m":{"docs":{},"o":{"docs":{},"v":{"docs":{},"i":{"docs":{},"e":{"docs":{},"_":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},")":{"docs":{},")":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"i":{"docs":{},"n":{"docs":{},"v":{"docs":{},"e":{"docs":{},"r":{"docs":{},"t":{"docs":{},"e":{"docs":{},"d":{"docs":{},"_":{"docs":{},"t":{"docs":{},"a":{"docs":{},"b":{"docs":{},"l":{"docs":{},"e":{"docs":{},")":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"p":{"docs":{},"r":{"docs":{},"o":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"e":{"docs":{},")":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}}}}}}}}}}}}}}}}}}},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"(":{"docs":{},"u":{"docs":{},",":{"docs":{},"i":{"docs":{},")":{"docs":{},"=":{"docs":{},"\\":{"docs":{},"h":{"docs":{},"a":{"docs":{},"t":{"docs":{},"{":{"docs":{},"r":{"docs":{},"}":{"docs":{},"_":{"docs":{},"{":{"docs":{},"u":{"docs":{},"i":{"docs":{},"}":{"docs":{},"=":{"docs":{},"\\":{"docs":{},"c":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"c":{"docs":{},"{":{"docs":{},"\\":{"docs":{},"s":{"docs":{},"u":{"docs":{},"m":{"docs":{},"_":{"docs":{},"{":{"docs":{},"j":{"docs":{},"\\":{"docs":{},"i":{"docs":{},"n":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}},"v":{"docs":{},"\\":{"docs":{},"i":{"docs":{},"n":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734},"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"_":{"1":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147}}}},"docs":{}}}},"i":{"docs":{},"c":{"docs":{},"t":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}},"(":{"docs":{},"u":{"docs":{},"i":{"docs":{},"d":{"docs":{},",":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.006493506493506494},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"f":{"docs":{},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.003795066413662239},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}},"_":{"docs":{},"a":{"docs":{},"l":{"docs":{},"l":{"docs":{},"(":{"1":{"docs":{},",":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.006493506493506494}}}},"docs":{},"u":{"docs":{},"i":{"docs":{},"d":{"docs":{},",":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}}}}}}}}}},"r":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.006493506493506494},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.005717552887364208}},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},")":{"docs":{},")":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.006493506493506494}}}}}}}}},"e":{"docs":{},"s":{"docs":{},"u":{"docs":{},"l":{"docs":{},"t":{"docs":{},"s":{"docs":{},":":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.008004574042309892}}}}}}}}}}},"s":{"2":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},".":{"docs":{},"t":{"docs":{},"a":{"docs":{},"k":{"docs":{},"e":{"docs":{},"(":{"2":{"0":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}}}}}}}},"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"(":{"docs":{},"l":{"docs":{},"a":{"docs":{},"m":{"docs":{},"b":{"docs":{},"d":{"docs":{},"a":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},".":{"docs":{},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"a":{"docs":{},"d":{"docs":{},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"h":{"docs":{},"e":{"docs":{},"a":{"docs":{},"d":{"docs":{},"(":{"2":{"0":{"docs":{},")":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}},"]":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}},"docs":{}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}}}}}}}}},"_":{"docs":{},"r":{"docs":{},"a":{"docs":{},"t":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.005717552887364208},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.003795066413662239}}}},"e":{"docs":{},"s":{"docs":{},"u":{"docs":{},"l":{"docs":{},"t":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}}}},"t":{"docs":{},"t":{"docs":{},"y":{"docs":{},"_":{"docs":{},"p":{"docs":{},"r":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},"'":{"docs":{},"d":{"docs":{},"e":{"docs":{},"l":{"docs":{},"e":{"docs":{},"t":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}}}}}}}}}}}}}}},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}},"(":{"docs":{},"\"":{"docs":{},"t":{"docs":{},"o":{"docs":{},"p":{"2":{"docs":{},"相":{"docs":{},"似":{"docs":{},"物":{"docs":{},"品":{"docs":{},"：":{"docs":{},"\"":{"docs":{},")":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}},"用":{"docs":{},"户":{"docs":{},"：":{"docs":{},"\"":{"docs":{},")":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}},"docs":{}}}},"最":{"docs":{},"终":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"结":{"docs":{},"果":{"docs":{},"：":{"docs":{},"\"":{"docs":{},")":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147}}}}}}}}}}},"物":{"docs":{},"品":{"docs":{},"之":{"docs":{},"间":{"docs":{},"的":{"docs":{},"两":{"docs":{},"两":{"docs":{},"相":{"docs":{},"似":{"docs":{},"度":{"docs":{},"：":{"docs":{},"\"":{"docs":{},")":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.00423728813559322}}}}}}}}}}}}}}},"用":{"docs":{},"户":{"docs":{},"之":{"docs":{},"间":{"docs":{},"的":{"docs":{},"两":{"docs":{},"两":{"docs":{},"相":{"docs":{},"似":{"docs":{},"度":{"docs":{},"：":{"docs":{},"\"":{"docs":{},")":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.00423728813559322}}}}}}}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"总":{"docs":{},"数":{"docs":{},"：":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}},"开":{"docs":{},"始":{"docs":{},"预":{"docs":{},"测":{"docs":{},"用":{"docs":{},"户":{"docs":{},"对":{"docs":{},"电":{"docs":{},"影":{"docs":{},"的":{"docs":{},"评":{"docs":{},"分":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"\"":{"docs":{},"%":{"docs":{},"(":{"docs":{},"u":{"docs":{},"i":{"docs":{},"d":{"docs":{},",":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}}}}}}}}}}}}}}}}}}}}}},"切":{"docs":{},"分":{"docs":{},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"\"":{"docs":{},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}}}}}}}},"预":{"docs":{},"测":{"docs":{},"出":{"docs":{},"用":{"docs":{},"户":{"docs":{},"对":{"docs":{},"电":{"docs":{},"影":{"docs":{},"的":{"docs":{},"评":{"docs":{},"分":{"docs":{},"：":{"docs":{},"%":{"0":{"docs":{},".":{"2":{"docs":{},"f":{"docs":{},"\"":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.006493506493506494}}}}},"docs":{}}},"docs":{}}}}}}}}}}}},"值":{"docs":{},"总":{"docs":{},"数":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"%":{"docs":{},"d":{"docs":{},"\"":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.003430531732418525}},"%":{"docs":{},"i":{"docs":{},")":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}}}}}},"r":{"docs":{},"m":{"docs":{},"s":{"docs":{},"e":{"docs":{},":":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}},"完":{"docs":{},"成":{"docs":{},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{},"切":{"docs":{},"分":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"\"":{"docs":{},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}}}}}}}},"%":{"docs":{},"s":{"docs":{},":":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}},"u":{"docs":{},"s":{"docs":{},"a":{"docs":{},"g":{"docs":{},"e":{"docs":{},":":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}}}}},"判":{"docs":{},"断":{"docs":{},"数":{"docs":{},"据":{"docs":{},"是":{"docs":{},"否":{"docs":{},"有":{"docs":{},"空":{"docs":{},"值":{"docs":{},"：":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}},"查":{"docs":{},"看":{"docs":{},"b":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"i":{"docs":{},"d":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"情":{"docs":{},"况":{"docs":{},"：":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}},"t":{"docs":{},"a":{"docs":{},"g":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"情":{"docs":{},"况":{"docs":{},"：":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"i":{"docs":{},"d":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"情":{"docs":{},"况":{"docs":{},"：":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"i":{"docs":{},"d":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"情":{"docs":{},"况":{"docs":{},"：":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}},"*":{"docs":{},"*":{"docs":{},"*":{"docs":{},"*":{"docs":{},"*":{"docs":{},"*":{"docs":{},"*":{"docs":{},"*":{"docs":{},"*":{"docs":{},"\"":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}},"a":{"docs":{},"g":{"docs":{},"e":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},":":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}},"b":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"i":{"docs":{},"d":{"docs":{},"数":{"docs":{},"值":{"docs":{},"个":{"docs":{},"数":{"docs":{},"：":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}},"c":{"docs":{},"a":{"docs":{},"m":{"docs":{},"p":{"docs":{},"a":{"docs":{},"i":{"docs":{},"g":{"docs":{},"n":{"docs":{},"i":{"docs":{},"d":{"docs":{},"数":{"docs":{},"值":{"docs":{},"个":{"docs":{},"数":{"docs":{},"：":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}},"t":{"docs":{},"e":{"docs":{},"i":{"docs":{},"d":{"docs":{},"数":{"docs":{},"值":{"docs":{},"个":{"docs":{},"数":{"docs":{},"：":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}},"m":{"docs":{},"s":{"docs":{},"_":{"docs":{},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},":":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}},"s":{"docs":{},"e":{"docs":{},"g":{"docs":{},"i":{"docs":{},"d":{"docs":{},":":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}},"u":{"docs":{},"s":{"docs":{},"t":{"docs":{},"o":{"docs":{},"m":{"docs":{},"e":{"docs":{},"r":{"docs":{},"i":{"docs":{},"d":{"docs":{},"数":{"docs":{},"值":{"docs":{},"个":{"docs":{},"数":{"docs":{},"：":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}},"f":{"docs":{},"i":{"docs":{},"n":{"docs":{},"a":{"docs":{},"l":{"docs":{},"_":{"docs":{},"g":{"docs":{},"e":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"c":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},":":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}},"n":{"docs":{},"e":{"docs":{},"w":{"docs":{},"_":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"c":{"docs":{},"l":{"docs":{},"a":{"docs":{},"s":{"docs":{},"s":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"的":{"docs":{},"空":{"docs":{},"值":{"docs":{},"情":{"docs":{},"况":{"docs":{},"：":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"o":{"docs":{},"c":{"docs":{},"c":{"docs":{},"u":{"docs":{},"p":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},":":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}},"p":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"的":{"docs":{},"空":{"docs":{},"值":{"docs":{},"情":{"docs":{},"况":{"docs":{},"：":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"p":{"docs":{},"p":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},":":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}},"价":{"docs":{},"格":{"docs":{},"低":{"docs":{},"于":{"1":{"docs":{},"的":{"docs":{},"条":{"docs":{},"目":{"docs":{},"个":{"docs":{},"数":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}},"docs":{}}},"高":{"docs":{},"于":{"1":{"docs":{},"w":{"docs":{},"的":{"docs":{},"条":{"docs":{},"目":{"docs":{},"个":{"docs":{},"数":{"docs":{},"：":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}},"docs":{}}}}},"分":{"docs":{},"类":{"docs":{},"特":{"docs":{},"征":{"docs":{},"值":{"docs":{},"个":{"docs":{},"数":{"docs":{},"情":{"docs":{},"况":{"docs":{},":":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}},"含":{"docs":{},"缺":{"docs":{},"失":{"docs":{},"值":{"docs":{},"的":{"docs":{},"特":{"docs":{},"征":{"docs":{},"情":{"docs":{},"况":{"docs":{},":":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}},"广":{"docs":{},"告":{"docs":{},"i":{"docs":{},"d":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"展":{"docs":{},"示":{"docs":{},"位":{"docs":{},"p":{"docs":{},"i":{"docs":{},"d":{"docs":{},"情":{"docs":{},"况":{"docs":{},"：":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}},"点":{"docs":{},"击":{"docs":{},"数":{"docs":{},"据":{"docs":{},"情":{"docs":{},"况":{"docs":{},"c":{"docs":{},"l":{"docs":{},"k":{"docs":{},"：":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}},"总":{"docs":{},"广":{"docs":{},"告":{"docs":{},"条":{"docs":{},"数":{"docs":{},"：":{"docs":{},"\"":{"docs":{},",":{"docs":{},"d":{"docs":{},"f":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}},"样":{"docs":{},"本":{"docs":{},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{},"总":{"docs":{},"条":{"docs":{},"目":{"docs":{},"数":{"docs":{},"：":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}},"测":{"docs":{},"试":{"docs":{},"样":{"docs":{},"本":{"docs":{},"个":{"docs":{},"数":{"docs":{},"：":{"docs":{},"\"":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}},"该":{"docs":{},"时":{"docs":{},"间":{"docs":{},"之":{"docs":{},"前":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"为":{"docs":{},"训":{"docs":{},"练":{"docs":{},"样":{"docs":{},"本":{"docs":{},"，":{"docs":{},"该":{"docs":{},"时":{"docs":{},"间":{"docs":{},"以":{"docs":{},"后":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"为":{"docs":{},"测":{"docs":{},"试":{"docs":{},"样":{"docs":{},"本":{"docs":{},"：":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"剔":{"docs":{},"除":{"docs":{},"空":{"docs":{},"值":{"docs":{},"数":{"docs":{},"据":{"docs":{},"后":{"docs":{},"，":{"docs":{},"还":{"docs":{},"剩":{"docs":{},"：":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}},"d":{"docs":{},"f":{"docs":{},")":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"s":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"m":{"docs":{},"_":{"docs":{},"s":{"docs":{},"i":{"docs":{},"m":{"docs":{},"i":{"docs":{},"l":{"docs":{},"a":{"docs":{},"r":{"docs":{},")":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147}}},".":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"d":{"docs":{},"(":{"4":{"docs":{},")":{"docs":{},")":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}},"docs":{}}}}}}}}}}}}}}}}}}}},"j":{"docs":{},"a":{"docs":{},"c":{"docs":{},"c":{"docs":{},"a":{"docs":{},"r":{"docs":{},"d":{"docs":{},"_":{"docs":{},"s":{"docs":{},"i":{"docs":{},"m":{"docs":{},"i":{"docs":{},"l":{"docs":{},"a":{"docs":{},"r":{"docs":{},"i":{"docs":{},"t":{"docs":{},"y":{"docs":{},"_":{"docs":{},"s":{"docs":{},"c":{"docs":{},"o":{"docs":{},"r":{"docs":{},"e":{"docs":{},"(":{"docs":{},"d":{"docs":{},"f":{"docs":{},"[":{"docs":{},"\"":{"docs":{},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"m":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"s":{"docs":{},"i":{"docs":{},"m":{"docs":{},"i":{"docs":{},"l":{"docs":{},"a":{"docs":{},"r":{"docs":{},")":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147}}},".":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"d":{"docs":{},"(":{"4":{"docs":{},")":{"docs":{},")":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}},"docs":{}}}}}}}}}}}}}}}}}}},"i":{"docs":{},"d":{"docs":{},")":{"docs":{"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}}}}}},"e":{"docs":{},")":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}},"b":{"docs":{},"c":{"docs":{},"f":{"docs":{},".":{"docs":{},"p":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"i":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},"u":{"docs":{},"i":{"docs":{},"d":{"docs":{},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}}}}}}}}}}},"s":{"docs":{},"v":{"docs":{},"d":{"docs":{},".":{"docs":{},"p":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"i":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},"u":{"docs":{},"i":{"docs":{},"d":{"docs":{},")":{"docs":{},",":{"docs":{"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}}}}}}}}}}}}}}}}}},"'":{"docs":{},"*":{"docs":{},"'":{"docs":{},"*":{"1":{"0":{"docs":{},",":{"docs":{},"i":{"docs":{},")":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}},"docs":{}},"docs":{}}}}},"l":{"docs":{},"f":{"docs":{},"m":{"docs":{},".":{"docs":{},"p":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"i":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},"u":{"docs":{},"i":{"docs":{},"d":{"docs":{},")":{"docs":{},",":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}}}}}}}}}}}}}}}}}},"n":{"docs":{},"p":{"docs":{},".":{"docs":{},"s":{"docs":{},"q":{"docs":{},"r":{"docs":{},"t":{"docs":{},"(":{"docs":{},"n":{"docs":{},"p":{"docs":{},".":{"docs":{},"m":{"docs":{},"e":{"docs":{},"a":{"docs":{},"n":{"docs":{},"(":{"docs":{},"e":{"docs":{},"r":{"docs":{},"r":{"docs":{},"o":{"docs":{},"r":{"docs":{},"_":{"docs":{},"l":{"docs":{},"i":{"docs":{},"s":{"docs":{},"t":{"docs":{},")":{"docs":{},")":{"docs":{},")":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"e":{"docs":{},"w":{"docs":{},"_":{"docs":{},"d":{"docs":{},"f":{"docs":{},".":{"docs":{},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"p":{"docs":{},"i":{"docs":{},"d":{"docs":{},"_":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"\"":{"docs":{},")":{"docs":{},".":{"docs":{},"f":{"docs":{},"i":{"docs":{},"r":{"docs":{},"s":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}},".":{"docs":{},"p":{"docs":{},"i":{"docs":{},"d":{"docs":{},"_":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},".":{"docs":{},"t":{"docs":{},"o":{"docs":{},"a":{"docs":{},"r":{"docs":{},"r":{"docs":{},"a":{"docs":{},"y":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"c":{"docs":{},"l":{"docs":{},"a":{"docs":{},"s":{"docs":{},"s":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"_":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},")":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"m":{"docs":{},"o":{"docs":{},"v":{"docs":{},"i":{"docs":{},"e":{"docs":{},"_":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},")":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}},"w":{"docs":{},"a":{"docs":{},"t":{"docs":{},"c":{"docs":{},"h":{"docs":{},"_":{"docs":{},"r":{"docs":{},"e":{"docs":{},"c":{"docs":{},"o":{"docs":{},"r":{"docs":{},"d":{"docs":{},")":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}}}}}}}}}}}}},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"n":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},".":{"docs":{},"t":{"docs":{},"a":{"docs":{},"b":{"docs":{},"l":{"docs":{},"e":{"docs":{},"s":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0113314447592068}}}}}}}}}}}}}}}}}}}}},"m":{"docs":{},"s":{"docs":{},"_":{"docs":{},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},"_":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},")":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}}}}}}}}}}}}}}},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{},",":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},")":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0169971671388102}}}}}}}}}}}},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"u":{"docs":{},"l":{"docs":{},"t":{"docs":{},")":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0056657223796034}}},"_":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}}}}}}}}}}}}}}}}}}},"p":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"i":{"docs":{},"c":{"docs":{},"t":{"docs":{},"s":{"docs":{},".":{"docs":{},"t":{"docs":{},"a":{"docs":{},"k":{"docs":{},"e":{"docs":{},"(":{"2":{"0":{"docs":{},")":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}},"docs":{}},"docs":{}}}}}}}}}}}}}},"i":{"docs":{},"d":{"docs":{},"_":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},")":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}}}}},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"_":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},")":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}}}}}}}}}}}}}}},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"s":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{},"(":{"4":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}}},"docs":{}}}}}}}}}}}}},"h":{"docs":{},"o":{"docs":{},"p":{"docs":{},"p":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"_":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},")":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}}}}}}}}}}}}}}}}},"t":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{},"_":{"docs":{},"s":{"docs":{},"a":{"docs":{},"m":{"docs":{},"p":{"docs":{},"l":{"docs":{},"e":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}},"a":{"docs":{},"g":{"docs":{},"e":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"_":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},")":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}}}}}}}}}}}},"f":{"docs":{},"i":{"docs":{},"n":{"docs":{},"a":{"docs":{},"l":{"docs":{},"_":{"docs":{},"g":{"docs":{},"e":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"c":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"_":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},")":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}}}}}}}}}}}}}}}}}}}},"o":{"docs":{},"c":{"docs":{},"c":{"docs":{},"u":{"docs":{},"p":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"_":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},")":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}}}}}}}}}}}}}}}},"c":{"docs":{},"e":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}},":":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}},"c":{"docs":{},"m":{"docs":{},"s":{"docs":{},"_":{"docs":{},"s":{"docs":{},"e":{"docs":{},"g":{"docs":{},"i":{"docs":{},"d":{"docs":{},"|":{"docs":{},"c":{"docs":{},"m":{"docs":{},"s":{"docs":{},"_":{"docs":{},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},"|":{"docs":{},"f":{"docs":{},"i":{"docs":{},"n":{"docs":{},"a":{"docs":{},"l":{"docs":{},"_":{"docs":{},"g":{"docs":{},"e":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"c":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"|":{"docs":{},"a":{"docs":{},"g":{"docs":{},"e":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"|":{"docs":{},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"p":{"docs":{},"p":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"|":{"docs":{},"o":{"docs":{},"c":{"docs":{},"c":{"docs":{},"u":{"docs":{},"p":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"|":{"docs":{},"p":{"docs":{},"l":{"docs":{},"_":{"docs":{},"o":{"docs":{},"n":{"docs":{},"e":{"docs":{},"h":{"docs":{},"o":{"docs":{},"t":{"docs":{},"_":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"|":{"docs":{},"n":{"docs":{},"u":{"docs":{},"c":{"docs":{},"l":{"docs":{},"_":{"docs":{},"o":{"docs":{},"n":{"docs":{},"e":{"docs":{},"h":{"docs":{},"o":{"docs":{},"t":{"docs":{},"_":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"o":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"e":{"docs":{},"画":{"docs":{},"像":{"docs":{},"建":{"docs":{},"立":{"docs":{},"：":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}}}}}}}}},"c":{"docs":{},"e":{"docs":{},"s":{"docs":{},"s":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009},"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282}},"_":{"docs":{},"r":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"docs":{},"r":{"docs":{},")":{"docs":{},":":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}}}}}}}}}}}},"v":{"docs":{},"i":{"docs":{},"d":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282}}}}},"j":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"t":{"docs":{},"u":{"docs":{},"n":{"docs":{},"g":{"docs":{},"s":{"docs":{},"t":{"docs":{},"e":{"docs":{},"n":{"docs":{},"：":{"docs":{},"钨":{"docs":{},"丝":{"docs":{},"计":{"docs":{},"划":{"docs":{},"，":{"docs":{},"为":{"docs":{},"了":{"docs":{},"提":{"docs":{},"高":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"的":{"docs":{},"效":{"docs":{},"率":{"docs":{},"而":{"docs":{},"制":{"docs":{},"定":{"docs":{},"的":{"docs":{},"计":{"docs":{},"划":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"b":{"docs":{},"a":{"docs":{},"b":{"docs":{},"i":{"docs":{},"l":{"docs":{},"i":{"docs":{},"t":{"docs":{},"y":{"docs":{},"|":{"docs":{},"p":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"i":{"docs":{},"c":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}}}}}}}}}}}}}}}}}},",":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0056925996204933585},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.006944444444444444}}},"[":{"docs":{},"u":{"docs":{},"i":{"docs":{},"d":{"docs":{},"]":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.007590132827324478},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.004629629629629629}}}}}}},"_":{"docs":{},"u":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.003795066413662239},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}},"{":{"docs":{},"u":{"docs":{},"k":{"docs":{},"}":{"docs":{},"&":{"docs":{},":":{"docs":{},"=":{"docs":{},"p":{"docs":{},"_":{"docs":{},"{":{"docs":{},"u":{"docs":{},"k":{"docs":{},"}":{"docs":{},"+":{"docs":{},"\\":{"docs":{},"a":{"docs":{},"l":{"docs":{},"p":{"docs":{},"h":{"docs":{},"a":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}}}}}}}}}}}},"]":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.003795066413662239},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.004629629629629629}}},"q":{"docs":{},"_":{"docs":{},"{":{"docs":{},"i":{"docs":{},"k":{"docs":{},"}":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}},")":{"docs":{"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.009259259259259259}},"^":{"2":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.003795066413662239},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.004629629629629629}}},"docs":{}},"p":{"docs":{},"_":{"docs":{},"{":{"docs":{},"u":{"docs":{},"k":{"docs":{},"}":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.003795066413662239},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.004629629629629629}}}}}}}},"q":{"docs":{},"_":{"docs":{},"{":{"docs":{},"i":{"docs":{},"k":{"docs":{},"}":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.003795066413662239},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.004629629629629629}}}}}}}}}}}}}}}}}}}},"矩":{"docs":{},"阵":{"docs":{},"是":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}},"正":{"docs":{},"则":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}},"g":{"docs":{},"c":{"docs":{"day02_推荐算法/07_基于内容的推荐算法.html":{"ref":"day02_推荐算法/07_基于内容的推荐算法.html","tf":0.03125}}}},"i":{"docs":{},"p":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938},"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269},"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}},"e":{"docs":{},"l":{"docs":{},"i":{"docs":{},"n":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0010197838058331635},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0019896538002387586}},"e":{"docs":{},"(":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"g":{"docs":{},"e":{"docs":{},"s":{"docs":{},"=":{"docs":{},"[":{"docs":{},"s":{"docs":{},"t":{"docs":{},"r":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{},"e":{"docs":{},"r":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.001193792280143255}}}}}}}}}}}}}}}}}}}}}}}}},".":{"docs":{},"f":{"docs":{},"i":{"docs":{},"t":{"docs":{},"(":{"docs":{},"r":{"docs":{},"a":{"docs":{},"w":{"docs":{},"_":{"docs":{},"s":{"docs":{},"a":{"docs":{},"m":{"docs":{},"p":{"docs":{},"l":{"docs":{},"e":{"docs":{},"_":{"docs":{},"d":{"docs":{},"f":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"p":{"docs":{},"r":{"docs":{},"o":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"e":{"docs":{},"_":{"docs":{},"d":{"docs":{},"f":{"2":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}},"_":{"docs":{},"r":{"docs":{},"a":{"docs":{},"w":{"docs":{},"_":{"docs":{},"s":{"docs":{},"a":{"docs":{},"m":{"docs":{},"p":{"docs":{},"l":{"docs":{},"e":{"docs":{},"_":{"docs":{},"d":{"docs":{},"f":{"2":{"docs":{},")":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}}}}}}}}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"p":{"docs":{},"r":{"docs":{},"o":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"e":{"docs":{},"_":{"docs":{},"d":{"docs":{},"f":{"3":{"docs":{},")":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"4":{"docs":{},")":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}},"_":{"docs":{},"f":{"docs":{},"i":{"docs":{},"t":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.001193792280143255}},".":{"docs":{},"t":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"s":{"docs":{},"f":{"docs":{},"o":{"docs":{},"r":{"docs":{},"m":{"docs":{},"(":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"p":{"docs":{},"r":{"docs":{},"o":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"e":{"docs":{},"_":{"docs":{},"d":{"docs":{},"f":{"2":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}},"_":{"docs":{},"r":{"docs":{},"a":{"docs":{},"w":{"docs":{},"_":{"docs":{},"s":{"docs":{},"a":{"docs":{},"m":{"docs":{},"p":{"docs":{},"l":{"docs":{},"e":{"docs":{},"_":{"docs":{},"d":{"docs":{},"f":{"2":{"docs":{},")":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}}}}}}}}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"p":{"docs":{},"r":{"docs":{},"o":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"e":{"docs":{},"_":{"docs":{},"d":{"docs":{},"f":{"3":{"docs":{},")":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"4":{"docs":{},")":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"m":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"l":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},".":{"docs":{},"t":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"s":{"docs":{},"f":{"docs":{},"o":{"docs":{},"r":{"docs":{},"m":{"docs":{},"(":{"docs":{},"r":{"docs":{},"a":{"docs":{},"w":{"docs":{},"_":{"docs":{},"s":{"docs":{},"a":{"docs":{},"m":{"docs":{},"p":{"docs":{},"l":{"docs":{},"e":{"docs":{},"_":{"docs":{},"d":{"docs":{},"f":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"：":{"docs":{},"让":{"docs":{},"数":{"docs":{},"据":{"docs":{},"按":{"docs":{},"顺":{"docs":{},"序":{"docs":{},"依":{"docs":{},"次":{"docs":{},"被":{"docs":{},"处":{"docs":{},"理":{"docs":{},"，":{"docs":{},"将":{"docs":{},"前":{"docs":{},"一":{"docs":{},"次":{"docs":{},"的":{"docs":{},"处":{"docs":{},"理":{"docs":{},"结":{"docs":{},"果":{"docs":{},"作":{"docs":{},"为":{"docs":{},"下":{"docs":{},"一":{"docs":{},"次":{"docs":{},"的":{"docs":{},"输":{"docs":{},"入":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"g":{"docs":{},"：":{"docs":{},"脚":{"docs":{},"本":{"docs":{},"语":{"docs":{},"言":{"docs":{},"，":{"docs":{},"跟":{"docs":{},"h":{"docs":{},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{},"类":{"docs":{},"似":{"docs":{"day03_Hadoop/ha4.1.html":{"ref":"day03_Hadoop/ha4.1.html","tf":0.023809523809523808}}}}}}}}}}}}}}}},"d":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},"：":{"docs":{},"资":{"docs":{},"源":{"docs":{},"位":{"docs":{},"；":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}},":":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}},"_":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.004228329809725159}},"e":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}},"p":{"docs":{},"r":{"docs":{},"i":{"docs":{},"c":{"docs":{},"e":{"docs":{},"|":{"docs":{},"c":{"docs":{},"m":{"docs":{},"s":{"docs":{},"_":{"docs":{},"s":{"docs":{},"e":{"docs":{},"g":{"docs":{},"i":{"docs":{},"d":{"docs":{},"|":{"docs":{},"c":{"docs":{},"m":{"docs":{},"s":{"docs":{},"_":{"docs":{},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},"|":{"docs":{},"f":{"docs":{},"i":{"docs":{},"n":{"docs":{},"a":{"docs":{},"l":{"docs":{},"_":{"docs":{},"g":{"docs":{},"e":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"c":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"|":{"docs":{},"a":{"docs":{},"g":{"docs":{},"e":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"|":{"docs":{},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"p":{"docs":{},"p":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"|":{"docs":{},"o":{"docs":{},"c":{"docs":{},"c":{"docs":{},"u":{"docs":{},"p":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"|":{"docs":{},"p":{"docs":{},"l":{"docs":{},"_":{"docs":{},"o":{"docs":{},"n":{"docs":{},"e":{"docs":{},"h":{"docs":{},"o":{"docs":{},"t":{"docs":{},"_":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"|":{"docs":{},"n":{"docs":{},"u":{"docs":{},"c":{"docs":{},"l":{"docs":{},"_":{"docs":{},"o":{"docs":{},"n":{"docs":{},"e":{"docs":{},"h":{"docs":{},"o":{"docs":{},"t":{"docs":{},"_":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},":":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}},"[":{"docs":{},"p":{"docs":{},"i":{"docs":{},"d":{"docs":{},"_":{"docs":{},"r":{"docs":{},"e":{"docs":{},"l":{"docs":{},"a":{"docs":{},"[":{"docs":{},"p":{"docs":{},"i":{"docs":{},"d":{"docs":{},"]":{"docs":{},"]":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}}}}}}}}}}}}}}}},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"e":{"docs":{},":":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}},"|":{"docs":{},"n":{"docs":{},"o":{"docs":{},"n":{"docs":{},"c":{"docs":{},"l":{"docs":{},"k":{"docs":{},"|":{"docs":{},"c":{"docs":{},"l":{"docs":{},"k":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}},"p":{"docs":{},"i":{"docs":{},"d":{"docs":{},"_":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"e":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}}}}}},")":{"docs":{},":":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}},"v":{"docs":{},"o":{"docs":{},"t":{"docs":{},"透":{"docs":{},"视":{"docs":{},"操":{"docs":{},"作":{"docs":{},"，":{"docs":{},"把":{"docs":{},"某":{"docs":{},"列":{"docs":{},"里":{"docs":{},"的":{"docs":{},"字":{"docs":{},"段":{"docs":{},"值":{"docs":{},"转":{"docs":{},"换":{"docs":{},"成":{"docs":{},"行":{"docs":{},"并":{"docs":{},"进":{"docs":{},"行":{"docs":{},"聚":{"docs":{},"合":{"docs":{},"运":{"docs":{},"算":{"docs":{},"(":{"docs":{},"p":{"docs":{},"y":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},".":{"docs":{},"s":{"docs":{},"q":{"docs":{},"l":{"docs":{},".":{"docs":{},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"e":{"docs":{},"d":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},".":{"docs":{},"p":{"docs":{},"i":{"docs":{},"v":{"docs":{},"o":{"docs":{},"t":{"docs":{},")":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"y":{"docs":{},"t":{"docs":{},"h":{"docs":{},"o":{"docs":{},"n":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.005836575875486381},"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.029239766081871343},"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112},"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0084985835694051},"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.01276595744680851},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}},"操":{"docs":{},"作":{"docs":{},"s":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{},"m":{"docs":{"day03_Hadoop/ha4.1.html":{"ref":"day03_Hadoop/ha4.1.html","tf":0.023809523809523808}}}}}}},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"，":{"docs":{},"转":{"docs":{},"换":{"docs":{},"为":{"docs":{},"可":{"docs":{},"执":{"docs":{},"行":{"docs":{},"代":{"docs":{},"码":{"docs":{},"，":{"docs":{},"运":{"docs":{},"行":{"docs":{},"在":{"docs":{},"j":{"docs":{},"a":{"docs":{},"v":{"docs":{},"a":{"docs":{},"虚":{"docs":{},"拟":{"docs":{},"机":{"docs":{},"，":{"docs":{},"涉":{"docs":{},"及":{"docs":{},"两":{"docs":{},"个":{"docs":{},"不":{"docs":{},"同":{"docs":{},"语":{"docs":{},"言":{"docs":{},"引":{"docs":{},"擎":{"docs":{},"之":{"docs":{},"间":{"docs":{},"的":{"docs":{},"切":{"docs":{},"换":{"docs":{},"，":{"docs":{},"进":{"docs":{},"行":{"docs":{},"进":{"docs":{},"程":{"docs":{},"间":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"'":{"docs":{},"]":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},".":{"docs":{},"s":{"docs":{},"c":{"docs":{},"a":{"docs":{},"l":{"docs":{},"a":{"docs":{},":":{"1":{"7":{"5":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}}}}},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617},"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}},"可":{"docs":{},"以":{"docs":{},"从":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},"支":{"docs":{},"持":{"docs":{},"的":{"docs":{},"任":{"docs":{},"何":{"docs":{},"存":{"docs":{},"储":{"docs":{},"源":{"docs":{},"创":{"docs":{},"建":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"，":{"docs":{},"包":{"docs":{},"括":{"docs":{},"本":{"docs":{},"地":{"docs":{},"文":{"docs":{},"件":{"docs":{},"系":{"docs":{},"统":{"docs":{},"，":{"docs":{},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},"，":{"docs":{},"c":{"docs":{},"a":{"docs":{},"s":{"docs":{},"s":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"r":{"docs":{},"a":{"docs":{},"，":{"docs":{},"h":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"，":{"docs":{},"a":{"docs":{},"m":{"docs":{},"a":{"docs":{},"z":{"docs":{},"o":{"docs":{},"n":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},".":{"docs":{},"s":{"docs":{},"q":{"docs":{},"l":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.013422818791946308},"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391},"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.006993006993006993},"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}},".":{"docs":{},"f":{"docs":{},"u":{"docs":{},"n":{"docs":{},"c":{"docs":{},"t":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.006993006993006993},"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}},"t":{"docs":{},"y":{"docs":{},"p":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965},"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.007692307692307693},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0024469820554649264},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0008158270446665307},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0015917230401910067},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.005226480836236934}}}}},"s":{"docs":{},"e":{"docs":{},"s":{"docs":{},"s":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.007633587786259542}}}}}}}}},"t":{"docs":{},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"m":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.007633587786259542}}}}}}}},"m":{"docs":{},"l":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}},".":{"docs":{},"r":{"docs":{},"e":{"docs":{},"c":{"docs":{},"o":{"docs":{},"m":{"docs":{},"m":{"docs":{},"e":{"docs":{},"n":{"docs":{},"d":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.004893964110929853},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0010197838058331635},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0019896538002387586}}}}}}}},"l":{"docs":{},"i":{"docs":{},"n":{"docs":{},"a":{"docs":{},"l":{"docs":{},"g":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}},".":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"s":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}},"c":{"docs":{},"l":{"docs":{},"a":{"docs":{},"s":{"docs":{},"s":{"docs":{},"i":{"docs":{},"f":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}}}},"l":{"docs":{},"i":{"docs":{},"b":{"docs":{},".":{"docs":{},"l":{"docs":{},"i":{"docs":{},"n":{"docs":{},"a":{"docs":{},"l":{"docs":{},"g":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}},"r":{"docs":{},"e":{"docs":{},"g":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"s":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981}}}}}}}}},"t":{"docs":{},"r":{"docs":{},"e":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}}}}}}}}}}},"_":{"docs":{},"p":{"docs":{},"y":{"docs":{},"t":{"docs":{},"h":{"docs":{},"o":{"docs":{},"n":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.03296703296703297},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.022900763358778626},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0024469820554649264}},"=":{"docs":{},"/":{"docs":{},"x":{"docs":{},"x":{"docs":{},"/":{"docs":{},"p":{"docs":{},"y":{"docs":{},"t":{"docs":{},"h":{"docs":{},"o":{"docs":{},"n":{"docs":{},"x":{"docs":{},"_":{"docs":{},"h":{"docs":{},"o":{"docs":{},"m":{"docs":{},"e":{"docs":{},"/":{"docs":{},"b":{"docs":{},"i":{"docs":{},"n":{"docs":{},"/":{"docs":{},"p":{"docs":{},"y":{"docs":{},"t":{"docs":{},"h":{"docs":{},"o":{"docs":{},"n":{"docs":{},"x":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"b":{"docs":{},"级":{"docs":{},"数":{"docs":{},"据":{"docs":{},"的":{"docs":{},"存":{"docs":{},"储":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}}}},"别":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}},"u":{"docs":{},"t":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.04375},"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.008032128514056224},"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.05066079295154185}}}},"l":{"docs":{},"a":{"docs":{},"t":{"docs":{},"f":{"docs":{},"o":{"docs":{},"r":{"docs":{},"m":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}},".":{"docs":{},".":{"docs":{},".":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}}}}}},"u":{"docs":{},"g":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}},"_":{"docs":{},"n":{"docs":{},"a":{"docs":{},"_":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}},"d":{"docs":{},"f":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},".":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},".":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"(":{"docs":{},"r":{"docs":{},"o":{"docs":{},"w":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"1":{"0":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}}}}}},"t":{"docs":{},"o":{"docs":{},"p":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"a":{"docs":{},"s":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}},"o":{"docs":{},"n":{"docs":{},"e":{"docs":{},"h":{"docs":{},"o":{"docs":{},"t":{"docs":{},"_":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"e":{"docs":{},":":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},":":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}},"列":{"docs":{},"的":{"docs":{},"值":{"docs":{},"为":{"docs":{},"稀":{"docs":{},"疏":{"docs":{},"向":{"docs":{},"量":{"docs":{},"，":{"docs":{},"存":{"docs":{},"储":{"docs":{},"热":{"docs":{},"独":{"docs":{},"编":{"docs":{},"码":{"docs":{},"的":{"docs":{},"结":{"docs":{},"果":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}},"矩":{"docs":{},"阵":{"docs":{},"，":{"docs":{},"存":{"docs":{},"储":{"docs":{},"热":{"docs":{},"编":{"docs":{},"码":{"docs":{},"的":{"docs":{},"结":{"docs":{},"果":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"v":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907},"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}},"：":{"docs":{},"网":{"docs":{},"站":{"docs":{},"的":{"docs":{},"总":{"docs":{},"访":{"docs":{},"问":{"docs":{},"量":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}}}}}}},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},"：":{"docs":{},"消":{"docs":{},"费":{"docs":{},"档":{"docs":{},"次":{"docs":{},"，":{"1":{"docs":{},":":{"docs":{},"低":{"docs":{},"档":{"docs":{},"，":{"2":{"docs":{},":":{"docs":{},"中":{"docs":{},"档":{"docs":{},"，":{"3":{"docs":{},":":{"docs":{},"高":{"docs":{},"档":{"docs":{},"；":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}},"docs":{}}}}}},"docs":{}}}}}},"docs":{}}}}}}},":":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}},"的":{"docs":{},"空":{"docs":{},"值":{"docs":{},"情":{"docs":{},"况":{"docs":{},"：":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}},"_":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.004228329809725159}},"e":{"docs":{},"[":{"docs":{},"p":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"_":{"docs":{},"r":{"docs":{},"e":{"docs":{},"l":{"docs":{},"a":{"docs":{},"[":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"[":{"docs":{},"\"":{"docs":{},"p":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"\"":{"docs":{},"]":{"docs":{},")":{"docs":{},"]":{"docs":{},"]":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},":":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0032626427406199023}}},"=":{"2":{"3":{"2":{"6":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"|":{"1":{"1":{"8":{"0":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.03181076672104405}}}},"e":{"docs":{},"r":{"docs":{},"f":{"docs":{},"o":{"docs":{},"r":{"docs":{},"m":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112},"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}}}}}},"s":{"docs":{},"i":{"docs":{},"s":{"docs":{},"t":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}},"操":{"docs":{},"作":{"docs":{},"用":{"docs":{},"于":{"docs":{},"将":{"docs":{},"数":{"docs":{},"据":{"docs":{},"缓":{"docs":{},"存":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}}}}}},"o":{"docs":{},"n":{"docs":{},"类":{"docs":{},"的":{"docs":{},"内":{"docs":{},"部":{"docs":{},"结":{"docs":{},"构":{"docs":{},"。":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}},"o":{"docs":{},"p":{"docs":{},"l":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}},"o":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},"s":{"docs":{},"s":{"docs":{},"i":{"docs":{},"b":{"docs":{},"l":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}},"i":{"docs":{},"t":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}},"p":{"docs":{},">":{"4":{"0":{"0":{"0":{"docs":{},"\"":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"o":{"docs":{},"l":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}},"r":{"docs":{},"t":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}},"=":{"6":{"3":{"7":{"9":{"docs":{},",":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.005226480836236934},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.004228329809725159}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{},"p":{"docs":{},"o":{"docs":{},"r":{"docs":{},"t":{"docs":{},")":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0014276973281664286}}}}}}},"t":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.01020408163265306}},"h":{"docs":{},"r":{"docs":{},"u":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}},"o":{"docs":{},"u":{"docs":{},"g":{"docs":{},"h":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.010526315789473684}},"p":{"docs":{},"u":{"docs":{},"t":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282}}}}}}}}},"i":{"docs":{},"f":{"docs":{},"t":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.014164305949008499}},",":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}}},"e":{"docs":{},"r":{"docs":{},"e":{"docs":{},"f":{"docs":{},"o":{"docs":{},"r":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}}}},"o":{"docs":{},"p":{"2":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.005649717514124294}}},"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.015789473684210527},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.004078303425774877},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.003671221700999388},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0031834460803820135},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.010570824524312896}},"n":{"docs":{},"_":{"docs":{},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"m":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}},"s":{"docs":{},"[":{"docs":{},"i":{"docs":{},"]":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}},"u":{"docs":{},"s":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}},"e":{"docs":{},"r":{"docs":{},"s":{"docs":{},".":{"docs":{},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"m":{"docs":{},"s":{"docs":{},"(":{"docs":{},")":{"docs":{},":":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}},"[":{"docs":{},"i":{"docs":{},"]":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}},"t":{"docs":{},"a":{"docs":{},"g":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}},"s":{"docs":{},",":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}},"_":{"docs":{},"w":{"docs":{},"e":{"docs":{},"i":{"docs":{},"g":{"docs":{},"h":{"docs":{},"t":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}},"s":{"docs":{},")":{"docs":{},")":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}},".":{"docs":{},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"m":{"docs":{},"s":{"docs":{},"(":{"docs":{},")":{"docs":{},"]":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}},"[":{"docs":{},"g":{"docs":{},"]":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}}},"推":{"docs":{},"荐":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}},"w":{"docs":{},"o":{"docs":{},"r":{"docs":{},"d":{"docs":{},"s":{"docs":{},"(":{"docs":{},"m":{"docs":{},"r":{"docs":{},"j":{"docs":{},"o":{"docs":{},"b":{"docs":{},")":{"docs":{},":":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}}}},".":{"docs":{},"r":{"docs":{},"u":{"docs":{},"n":{"docs":{},"(":{"docs":{},")":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}}}}}}},"统":{"docs":{},"计":{"docs":{},"（":{"docs":{},"实":{"docs":{},"验":{"docs":{},"）":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}}},"_":{"docs":{},"k":{"docs":{},"_":{"docs":{},"r":{"docs":{},"s":{"docs":{},"_":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"u":{"docs":{},"l":{"docs":{},"t":{"docs":{},"(":{"2":{"0":{"docs":{},")":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}}}},"docs":{}},"docs":{},"k":{"docs":{},")":{"docs":{},":":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}}}}}}}}}}}}}}}}},"n":{"docs":{},"_":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{},"e":{"docs":{},"r":{"docs":{},"(":{"docs":{},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"f":{"docs":{},",":{"docs":{},"_":{"docs":{},",":{"docs":{},"w":{"docs":{},"o":{"docs":{},"r":{"docs":{},"d":{"docs":{},"_":{"docs":{},"c":{"docs":{},"n":{"docs":{},"t":{"docs":{},"s":{"docs":{},")":{"docs":{},":":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"u":{"docs":{},"c":{"docs":{},"h":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625}}}}},"m":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}},",":{"4":{"3":{"0":{"0":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"o":{"docs":{},"k":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}},"r":{"docs":{},"u":{"docs":{},"e":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}},":":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}},")":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0056657223796034},"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.018760195758564437},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.011829492147664695},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.015519299641862315}},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.011538461538461539},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}},"|":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.0045662100456621}}}},"n":{"docs":{},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}},"y":{"docs":{},":":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}},"a":{"docs":{},"i":{"docs":{},"n":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683}}}}}},"d":{"docs":{},"f":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}},"t":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{},"d":{"docs":{},"f":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}},".":{"docs":{},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},"'":{"docs":{},"c":{"docs":{},"l":{"docs":{},"s":{"docs":{},"'":{"docs":{},")":{"docs":{},".":{"docs":{},"s":{"docs":{},"u":{"docs":{},"b":{"docs":{},"t":{"docs":{},"r":{"docs":{},"a":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},"t":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{},"d":{"docs":{},"f":{"docs":{},".":{"docs":{},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},"'":{"docs":{},"c":{"docs":{},"l":{"docs":{},"s":{"docs":{},"'":{"docs":{},")":{"docs":{},")":{"docs":{},".":{"docs":{},"d":{"docs":{},"i":{"docs":{},"s":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},".":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},".":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"(":{"docs":{},"l":{"docs":{},"a":{"docs":{},"m":{"docs":{},"b":{"docs":{},"d":{"docs":{},"a":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"w":{"docs":{},"i":{"docs":{},"t":{"docs":{},"h":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{},"(":{"docs":{},"'":{"docs":{},"n":{"docs":{},"e":{"docs":{},"w":{"docs":{},"_":{"docs":{},"c":{"docs":{},"l":{"docs":{},"s":{"docs":{},"'":{"docs":{},",":{"docs":{},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},"c":{"docs":{},"k":{"docs":{},"(":{"docs":{},"t":{"docs":{},"r":{"docs":{},"a":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"f":{"docs":{},"[":{"docs":{},"'":{"docs":{},"c":{"docs":{},"l":{"docs":{},"s":{"docs":{},"'":{"docs":{},"]":{"docs":{},")":{"docs":{},")":{"docs":{},".":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"(":{"docs":{},"'":{"docs":{},"n":{"docs":{},"e":{"docs":{},"w":{"docs":{},"_":{"docs":{},"c":{"docs":{},"l":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"_":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"2":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}},"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"s":{"docs":{},"_":{"1":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}},".":{"docs":{},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"5":{"docs":{},")":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}}}}}}}},"docs":{}}}}}}}}}},"s":{"docs":{},"a":{"docs":{},"m":{"docs":{},"p":{"docs":{},"l":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}},"d":{"docs":{},"i":{"docs":{},"t":{"docs":{"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html":{"ref":"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html","tf":0.07407407407407407}}}}},"n":{"docs":{},"s":{"docs":{},"f":{"docs":{},"o":{"docs":{},"r":{"docs":{},"m":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.020080321285140562},"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}},"(":{"docs":{},"f":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},",":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}}}},",":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"算":{"docs":{},"子":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}},":":{"docs":{},"延":{"docs":{},"迟":{"docs":{},"性":{"docs":{},"操":{"docs":{},"作":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}},"中":{"docs":{},"提":{"docs":{},"供":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"i":{"docs":{},"d":{"docs":{},"和":{"docs":{},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"i":{"docs":{},"d":{"docs":{},"可":{"docs":{},"以":{"docs":{},"对":{"docs":{},"打":{"docs":{},"分":{"docs":{},"进":{"docs":{},"行":{"docs":{},"预":{"docs":{},"测":{"docs":{},"，":{"docs":{},"利":{"docs":{},"用":{"docs":{},"打":{"docs":{},"分":{"docs":{},"结":{"docs":{},"果":{"docs":{},"排":{"docs":{},"序":{"docs":{},"后":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}},"，":{"docs":{},"同":{"docs":{},"样":{"docs":{},"可":{"docs":{},"以":{"docs":{},"实":{"docs":{},"现":{"docs":{},"t":{"docs":{},"o":{"docs":{},"p":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}},"(":{"docs":{},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"f":{"docs":{},",":{"docs":{},"t":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},")":{"docs":{},":":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}}}}}}}}}}}},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.003430531732418525}},".":{"docs":{},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"t":{"docs":{},"u":{"docs":{},"p":{"docs":{},"l":{"docs":{},"e":{"docs":{},"s":{"docs":{},"(":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{},"=":{"docs":{},"f":{"docs":{},"a":{"docs":{},"l":{"docs":{},"s":{"docs":{},"e":{"docs":{},")":{"docs":{},":":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}}}}}}}}}}}}}}}}}}}}}},"_":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.003430531732418525}}}}}}}}}}},".":{"docs":{},"t":{"docs":{},"x":{"docs":{},"t":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.03125}}}}}},";":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}},"'":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}},"d":{"docs":{},"f":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}},".":{"docs":{},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},"'":{"docs":{},"c":{"docs":{},"l":{"docs":{},"s":{"docs":{},"'":{"docs":{},")":{"docs":{},".":{"docs":{},"s":{"docs":{},"u":{"docs":{},"b":{"docs":{},"t":{"docs":{},"r":{"docs":{},"a":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},"t":{"docs":{},"r":{"docs":{},"a":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"f":{"docs":{},".":{"docs":{},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},"'":{"docs":{},"c":{"docs":{},"l":{"docs":{},"s":{"docs":{},"'":{"docs":{},")":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"_":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"s":{"docs":{},"_":{"1":{"docs":{},".":{"docs":{},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"5":{"docs":{},")":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}}}}}}}},"docs":{}}}}}}}}}}}}},"x":{"docs":{},"t":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.0125}},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"e":{"docs":{},";":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.007407407407407408}}}}}}}}},"r":{"docs":{},"m":{"docs":{},"i":{"docs":{},"n":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.025925925925925925},"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0045351473922902496}}}}}},"m":{"docs":{},"p":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},"o":{"docs":{},"r":{"docs":{},"a":{"docs":{},"r":{"docs":{},"i":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}}}},"a":{"docs":{},"c":{"docs":{},"h":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}}}},"i":{"docs":{},"p":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}},"t":{"docs":{},"l":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}},"e":{"docs":{},",":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}},"m":{"docs":{},"e":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"m":{"docs":{},"p":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}},"=":{"1":{"5":{"5":{"8":{"3":{"2":{"3":{"1":{"3":{"9":{"5":{"7":{"5":{"docs":{},",":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}},"docs":{}},"docs":{}},"6":{"3":{"6":{"docs":{},",":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}},"docs":{}},"7":{"8":{"docs":{},",":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}},"docs":{}},"docs":{}},"7":{"3":{"2":{"docs":{},",":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}},"docs":{}},"docs":{}},"8":{"6":{"6":{"docs":{},",":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.004405286343612335}}}},"docs":{}},"docs":{}},"9":{"0":{"7":{"docs":{},",":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.004405286343612335}}}},"docs":{}},"6":{"3":{"docs":{},",":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.004405286343612335}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"4":{"0":{"0":{"3":{"6":{"docs":{},",":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.004405286343612335}}}},"docs":{}},"docs":{}},"1":{"0":{"7":{"docs":{},",":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.004405286343612335}}}},"docs":{}},"4":{"3":{"docs":{},",":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.004405286343612335}}}},"docs":{}},"8":{"8":{"docs":{},",":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.004405286343612335}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"7":{"5":{"8":{"6":{"9":{"6":{"docs":{},",":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"9":{"0":{"4":{"1":{"3":{"3":{"docs":{},",":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.004405286343612335}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"1":{"8":{"9":{"5":{"3":{"docs":{},",":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.006607929515418502}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},":":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}},"|":{"docs":{},"b":{"docs":{},"t":{"docs":{},"a":{"docs":{},"g":{"docs":{},"|":{"docs":{},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"i":{"docs":{},"d":{"docs":{},"|":{"docs":{},"b":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"i":{"docs":{},"d":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}},"a":{"docs":{},"d":{"docs":{},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"i":{"docs":{},"d":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}}}}}}}}}},"c":{"docs":{},"l":{"docs":{},"k":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}}}}}}}}}},"_":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"m":{"docs":{},"p":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}},"为":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{},"，":{"docs":{},"会":{"docs":{},"有":{"docs":{},"很":{"docs":{},"多":{"docs":{},"重":{"docs":{},"复":{"docs":{},"的":{"docs":{},"记":{"docs":{},"录":{"docs":{},"；":{"docs":{},"这":{"docs":{},"是":{"docs":{},"因":{"docs":{},"为":{"docs":{},"我":{"docs":{},"们":{"docs":{},"的":{"docs":{},"不":{"docs":{},"同":{"docs":{},"的":{"docs":{},"类":{"docs":{},"型":{"docs":{},"的":{"docs":{},"行":{"docs":{},"为":{"docs":{},"数":{"docs":{},"据":{"docs":{},"是":{"docs":{},"不":{"docs":{},"同":{"docs":{},"部":{"docs":{},"门":{"docs":{},"记":{"docs":{},"录":{"docs":{},"的":{"docs":{},"，":{"docs":{},"在":{"docs":{},"打":{"docs":{},"包":{"docs":{},"到":{"docs":{},"一":{"docs":{},"起":{"docs":{},"的":{"docs":{},"时":{"docs":{},"候":{"docs":{},"，":{"docs":{},"实":{"docs":{},"际":{"docs":{},"上":{"docs":{},"会":{"docs":{},"有":{"docs":{},"小":{"docs":{},"的":{"docs":{},"偏":{"docs":{},"差":{"docs":{},"（":{"docs":{},"即":{"docs":{},"两":{"docs":{},"个":{"docs":{},"一":{"docs":{},"样":{"docs":{},"的":{"docs":{},"t":{"docs":{},"i":{"docs":{},"m":{"docs":{},"e":{"docs":{},"_":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"m":{"docs":{},"p":{"docs":{},"实":{"docs":{},"际":{"docs":{},"上":{"docs":{},"是":{"docs":{},"差":{"docs":{},"异":{"docs":{},"比":{"docs":{},"较":{"docs":{},"小":{"docs":{},"的":{"docs":{},"两":{"docs":{},"个":{"docs":{},"时":{"docs":{},"间":{"docs":{},"）":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"：":{"docs":{},"时":{"docs":{},"间":{"docs":{},"戳":{"docs":{},"；":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.010526315789473684}}}}}}},":":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}}}}}}}},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"g":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}},"n":{"docs":{},"y":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}},"a":{"docs":{},"g":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}},",":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}},"s":{"docs":{},".":{"docs":{},"c":{"docs":{},"s":{"docs":{},"v":{"docs":{},"\"":{"docs":{},",":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}},"来":{"docs":{},"自":{"docs":{},"m":{"docs":{},"l":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}},"l":{"docs":{},"o":{"docs":{},"c":{"docs":{},"[":{"docs":{},"l":{"docs":{},"i":{"docs":{},"s":{"docs":{},"t":{"docs":{},"(":{"docs":{},"m":{"docs":{},"o":{"docs":{},"v":{"docs":{},"i":{"docs":{},"e":{"docs":{},"s":{"docs":{},"_":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"x":{"docs":{},")":{"docs":{},"]":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}}}}}}}}}}}}},"r":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428},"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}},"s":{"docs":{},"k":{"docs":{"day03_Hadoop/ha3.2.html":{"ref":"day03_Hadoop/ha3.2.html","tf":0.04081632653061224}},"t":{"docs":{},"r":{"docs":{},"a":{"docs":{},"c":{"docs":{},"k":{"docs":{},"e":{"docs":{},"r":{"docs":{},"：":{"docs":{},"由":{"docs":{},"j":{"docs":{},"o":{"docs":{},"b":{"docs":{},"t":{"docs":{},"r":{"docs":{},"a":{"docs":{},"c":{"docs":{},"k":{"docs":{},"e":{"docs":{},"r":{"docs":{},"指":{"docs":{},"派":{"docs":{},"任":{"docs":{},"务":{"docs":{},"，":{"docs":{},"定":{"docs":{},"期":{"docs":{},"向":{"docs":{},"j":{"docs":{},"o":{"docs":{},"b":{"docs":{},"t":{"docs":{},"r":{"docs":{},"a":{"docs":{},"c":{"docs":{},"k":{"docs":{},"e":{"docs":{},"r":{"docs":{},"汇":{"docs":{},"报":{"docs":{},"状":{"docs":{},"态":{"docs":{},"，":{"docs":{},"在":{"docs":{},"每":{"docs":{},"一":{"docs":{},"个":{"docs":{},"工":{"docs":{},"作":{"docs":{},"节":{"docs":{},"点":{"docs":{},"上":{"docs":{},"永":{"docs":{},"远":{"docs":{},"只":{"docs":{},"会":{"docs":{},"有":{"docs":{},"一":{"docs":{},"个":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"k":{"docs":{},"t":{"docs":{},"r":{"docs":{},"a":{"docs":{},"c":{"docs":{},"k":{"docs":{"day03_Hadoop/ha3.5.html":{"ref":"day03_Hadoop/ha3.5.html","tf":0.030303030303030304}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"s":{"docs":{},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"l":{"docs":{},"e":{"docs":{},"r":{"docs":{},"：":{"docs":{},"实":{"docs":{},"现":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"k":{"docs":{},"分":{"docs":{},"配":{"docs":{},"到":{"docs":{},"e":{"docs":{},"x":{"docs":{},"e":{"docs":{},"c":{"docs":{},"u":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{},"上":{"docs":{},"执":{"docs":{},"行":{"docs":{},"。":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"，":{"docs":{},"一":{"docs":{},"个":{"docs":{},"e":{"docs":{},"x":{"docs":{},"e":{"docs":{},"c":{"docs":{},"u":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{},"可":{"docs":{},"以":{"docs":{},"执":{"docs":{},"行":{"docs":{},"一":{"docs":{},"到":{"docs":{},"多":{"docs":{},"个":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"k":{"docs":{},"。":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}}}}}}}}}}}}}}}}}}}}}}}},"：":{"docs":{},"一":{"docs":{},"个":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"g":{"docs":{},"e":{"docs":{},"包":{"docs":{},"含":{"docs":{},"一":{"docs":{},"到":{"docs":{},"多":{"docs":{},"个":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"k":{"docs":{},"，":{"docs":{},"通":{"docs":{},"过":{"docs":{},"多":{"docs":{},"个":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"k":{"docs":{},"实":{"docs":{},"现":{"docs":{},"并":{"docs":{},"行":{"docs":{},"运":{"docs":{},"行":{"docs":{},"的":{"docs":{},"功":{"docs":{},"能":{"docs":{},"。":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"b":{"docs":{},"l":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196},"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.044444444444444446},"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.020080321285140562},"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.005668934240362812},"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676},"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.028328611898016998}},"e":{"docs":{},"(":{"docs":{},"默":{"docs":{},"认":{"docs":{},")":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}},")":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196},"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.007407407407407408}}},"：":{"docs":{},"在":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}},"数":{"docs":{},"据":{"docs":{},"存":{"docs":{},"放":{"docs":{},"位":{"docs":{},"置":{"docs":{},"可":{"docs":{},"以":{"docs":{},"在":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}}},"_":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},";":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}},"）":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}},")":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}}}},".":{"docs":{},"d":{"docs":{},"e":{"docs":{},"l":{"docs":{},"e":{"docs":{},"t":{"docs":{},"e":{"docs":{},"(":{"docs":{},"'":{"docs":{},"r":{"docs":{},"k":{"docs":{},"_":{"0":{"1":{"docs":{},"'":{"docs":{},",":{"docs":{},"[":{"docs":{},"'":{"docs":{},"c":{"docs":{},"f":{"1":{"docs":{},":":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"'":{"docs":{},"]":{"docs":{},")":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0056657223796034}}}}}}}}}}}}}}},"docs":{}}}}}}}},"docs":{}},"docs":{}}}}},"r":{"docs":{},"o":{"docs":{},"w":{"docs":{},"_":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{},",":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}}}}}}}}}}}}},"p":{"docs":{},"u":{"docs":{},"t":{"docs":{},"(":{"docs":{},"'":{"docs":{},"r":{"docs":{},"k":{"docs":{},"_":{"0":{"1":{"docs":{},"'":{"docs":{},",":{"docs":{},"{":{"docs":{},"'":{"docs":{},"c":{"docs":{},"f":{"1":{"docs":{},":":{"docs":{},"a":{"docs":{},"d":{"docs":{},"d":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"s":{"docs":{},"'":{"docs":{},":":{"docs":{},"'":{"docs":{},"b":{"docs":{},"e":{"docs":{},"i":{"docs":{},"j":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"'":{"docs":{},"}":{"docs":{},")":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0056657223796034}}}}}}}}}}}}}}}}}}}}}}}},"docs":{}}}}}}}},"docs":{}},"docs":{}}}}},"r":{"docs":{},"o":{"docs":{},"w":{"docs":{},"_":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{},",":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}}}}}}}}}},"r":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"docs":{},"'":{"docs":{},"r":{"docs":{},"o":{"docs":{},"w":{"docs":{},"_":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{},"'":{"docs":{},")":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}}}},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{},"_":{"2":{"2":{"docs":{},"'":{"docs":{},",":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{},"s":{"docs":{},"=":{"docs":{},"[":{"docs":{},"'":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"_":{"docs":{},"i":{"docs":{},"n":{"docs":{},"f":{"docs":{},"o":{"docs":{},":":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"'":{"docs":{},"]":{"docs":{},")":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0113314447592068}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{}},"docs":{}}}}}}}}}},"s":{"docs":{},"(":{"docs":{},"[":{"docs":{},"'":{"docs":{},"r":{"docs":{},"o":{"docs":{},"w":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{},"_":{"2":{"2":{"docs":{},"'":{"docs":{},",":{"docs":{},"'":{"docs":{},"r":{"docs":{},"o":{"docs":{},"w":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{},"_":{"1":{"6":{"docs":{},"'":{"docs":{},"]":{"docs":{},",":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{},"s":{"docs":{},"=":{"docs":{},"[":{"docs":{},"'":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"_":{"docs":{},"i":{"docs":{},"n":{"docs":{},"f":{"docs":{},"o":{"docs":{},":":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"'":{"docs":{},"]":{"docs":{},")":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0056657223796034}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{}},"docs":{}}}}}}}}}}}},"docs":{}},"docs":{}}}}}}}}},"r":{"docs":{},"o":{"docs":{},"w":{"docs":{},"_":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{},"s":{"docs":{},"]":{"docs":{},")":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}}}}}}}}}}}}}},"s":{"docs":{},"c":{"docs":{},"a":{"docs":{},"n":{"docs":{},"(":{"docs":{},")":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}},":":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0113314447592068}}}},"r":{"docs":{},"o":{"docs":{},"w":{"docs":{},"_":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{},"=":{"docs":{},"'":{"docs":{},"r":{"docs":{},"o":{"docs":{},"w":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{},"_":{"1":{"0":{"docs":{},"'":{"docs":{},",":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"=":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},")":{"docs":{},":":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0056657223796034}}}}}}}}}}}}}}}}}}}},"docs":{}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}},"类":{"docs":{},"提":{"docs":{},"供":{"docs":{},"了":{"docs":{},"大":{"docs":{},"量":{"docs":{},"a":{"docs":{},"p":{"docs":{},"i":{"docs":{},",":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}}}}}}}}}}},"k":{"docs":{},"e":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}},"(":{"docs":{},"n":{"docs":{},"u":{"docs":{},"m":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}},"f":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.005836575875486381}},"i":{"docs":{},"d":{"docs":{},"f":{"docs":{},"m":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"l":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876},"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}},"(":{"docs":{},"c":{"docs":{},"o":{"docs":{},"r":{"docs":{},"p":{"docs":{},"u":{"docs":{},"s":{"docs":{},")":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876}}}}}}}}}}}}}}}}}},"指":{"docs":{},"的":{"docs":{},"是":{"docs":{},"一":{"docs":{},"个":{"docs":{},"词":{"docs":{},"语":{"docs":{},"在":{"docs":{},"文":{"docs":{},"档":{"docs":{},"中":{"docs":{},"的":{"docs":{},"出":{"docs":{},"现":{"docs":{},"频":{"docs":{},"率":{"docs":{},"。":{"docs":{},"假":{"docs":{},"设":{"docs":{},"文":{"docs":{},"档":{"docs":{},"集":{"docs":{},"包":{"docs":{},"含":{"docs":{},"的":{"docs":{},"文":{"docs":{},"档":{"docs":{},"数":{"docs":{},"为":{"docs":{},"n":{"docs":{},"，":{"docs":{},"文":{"docs":{},"档":{"docs":{},"集":{"docs":{},"中":{"docs":{},"包":{"docs":{},"含":{"docs":{},"关":{"docs":{},"键":{"docs":{},"词":{"docs":{},"k":{"docs":{},"_":{"docs":{},"i":{"docs":{},"的":{"docs":{},"文":{"docs":{},"档":{"docs":{},"数":{"docs":{},"为":{"docs":{},"n":{"docs":{},"_":{"docs":{},"i":{"docs":{},"，":{"docs":{},"f":{"docs":{},"_":{"docs":{},"{":{"docs":{},"i":{"docs":{},"j":{"docs":{},"}":{"docs":{},"表":{"docs":{},"示":{"docs":{},"关":{"docs":{},"键":{"docs":{},"词":{"docs":{},"k":{"docs":{},"_":{"docs":{},"i":{"docs":{},"在":{"docs":{},"文":{"docs":{},"档":{"docs":{},"d":{"docs":{},"_":{"docs":{},"j":{"docs":{},"中":{"docs":{},"出":{"docs":{},"现":{"docs":{},"的":{"docs":{},"次":{"docs":{},"数":{"docs":{},"，":{"docs":{},"f":{"docs":{},"_":{"docs":{},"{":{"docs":{},"d":{"docs":{},"j":{"docs":{},"}":{"docs":{},"表":{"docs":{},"示":{"docs":{},"文":{"docs":{},"档":{"docs":{},"d":{"docs":{},"_":{"docs":{},"j":{"docs":{},"中":{"docs":{},"出":{"docs":{},"现":{"docs":{},"的":{"docs":{},"词":{"docs":{},"语":{"docs":{},"总":{"docs":{},"数":{"docs":{},"，":{"docs":{},"k":{"docs":{},"_":{"docs":{},"i":{"docs":{},"在":{"docs":{},"文":{"docs":{},"档":{"docs":{},"d":{"docs":{},"j":{"docs":{},"中":{"docs":{},"的":{"docs":{},"词":{"docs":{},"频":{"docs":{},"t":{"docs":{},"f":{"docs":{},"_":{"docs":{},"{":{"docs":{},"i":{"docs":{},"j":{"docs":{},"}":{"docs":{},"定":{"docs":{},"义":{"docs":{},"为":{"docs":{},"：":{"docs":{},"t":{"docs":{},"f":{"docs":{},"_":{"docs":{},"{":{"docs":{},"i":{"docs":{},"j":{"docs":{},"}":{"docs":{},"=":{"docs":{},"\\":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"c":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"b":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"进":{"docs":{},"行":{"docs":{},"排":{"docs":{},"序":{"docs":{},"只":{"docs":{},"花":{"docs":{},"了":{"6":{"2":{"docs":{},"秒":{"docs":{},"时":{"docs":{},"间":{"docs":{},"。":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}}}},"docs":{}},"docs":{}}}}}}}}}}}},"x":{"docs":{},"t":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}},"y":{"docs":{},"p":{"docs":{},"e":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044},"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617},"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}},":":{"docs":{},"j":{"docs":{},"p":{"docs":{},"g":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}},"t":{"docs":{},"x":{"docs":{},"t":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}}}},"t":{"docs":{},"l":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}},"m":{"docs":{},"p":{"2":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}},".":{"docs":{},"e":{"docs":{},"x":{"docs":{},"t":{"docs":{},"e":{"docs":{},"n":{"docs":{},"d":{"docs":{},"(":{"docs":{},"[":{"docs":{},"(":{"docs":{},"'":{"docs":{},"w":{"docs":{},"h":{"docs":{},"o":{"docs":{},"s":{"docs":{},"e":{"docs":{},"'":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}}}}}}}}}}}}},"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}},"_":{"docs":{},"t":{"docs":{},"a":{"docs":{},"b":{"docs":{},"l":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}}}}},"c":{"docs":{},"p":{"docs":{},"/":{"docs":{},"i":{"docs":{},"p":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}}}}},"_":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981}}}}}}}}},"{":{"0":{"docs":{},".":{"9":{"7":{"docs":{},"*":{"5":{"docs":{},"+":{"0":{"docs":{},".":{"5":{"8":{"docs":{},"*":{"4":{"docs":{},"}":{"docs":{},"{":{"0":{"docs":{},".":{"9":{"7":{"docs":{},"+":{"0":{"docs":{},".":{"5":{"8":{"docs":{},"}":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}},"docs":{}},"docs":{}}},"docs":{}}},"docs":{}},"docs":{}}},"docs":{}}}},"docs":{}}},"docs":{}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}},"docs":{}},":":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"2":{"docs":{},":":{"2":{"docs":{},",":{"3":{"docs":{},":":{"7":{"docs":{},"}":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}}},"docs":{}},"}":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}}},"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289}},"}":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.005649717514124294},"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876},"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842},"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775},"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}}},"\"":{"1":{"docs":{},"\"":{"docs":{},":":{"docs":{},"n":{"docs":{},"u":{"docs":{},"l":{"docs":{},"l":{"docs":{},",":{"docs":{},"\"":{"docs":{},"k":{"docs":{},"w":{"3":{"docs":{},"\"":{"docs":{},":":{"docs":{},"\"":{"1":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"docs":{},"k":{"docs":{},"w":{"6":{"docs":{},"\"":{"docs":{},":":{"docs":{},"\"":{"1":{"docs":{},"\"":{"docs":{},"}":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0022675736961451248}}}}},"docs":{}}}}},"docs":{}}}}}}},"docs":{}}}}},"docs":{}}}}}}}}}}}},"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},":":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}}}},"k":{"docs":{},"w":{"1":{"docs":{},"\"":{"docs":{},":":{"docs":{},"\"":{"1":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"docs":{},"k":{"docs":{},"w":{"3":{"docs":{},"\"":{"docs":{},":":{"docs":{},"\"":{"1":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"docs":{},"k":{"docs":{},"w":{"4":{"docs":{},"\"":{"docs":{},":":{"docs":{},"\"":{"1":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"docs":{},"k":{"docs":{},"w":{"5":{"docs":{},"\"":{"docs":{},":":{"docs":{},"\"":{"1":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"docs":{},"k":{"docs":{},"w":{"6":{"docs":{},"\"":{"docs":{},":":{"docs":{},"\"":{"1":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"docs":{},"k":{"docs":{},"w":{"7":{"docs":{},"\"":{"docs":{},":":{"docs":{},"\"":{"2":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"docs":{},"k":{"docs":{},"w":{"9":{"docs":{},"\"":{"docs":{},":":{"docs":{},"\"":{"1":{"docs":{},"\"":{"docs":{},"}":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}},"docs":{}}}}},"docs":{}}}}}}},"docs":{}}}}},"docs":{}}}}}}},"docs":{}}}}},"docs":{}}}}}}},"docs":{}}}}},"docs":{}}}}}}},"docs":{}}}}},"6":{"docs":{},"\"":{"docs":{},":":{"docs":{},"\"":{"1":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"docs":{},"k":{"docs":{},"w":{"7":{"docs":{},"\"":{"docs":{},":":{"docs":{},"\"":{"1":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"docs":{},"k":{"docs":{},"w":{"8":{"docs":{},"\"":{"docs":{},":":{"docs":{},"\"":{"1":{"docs":{},"\"":{"docs":{},"}":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}},"docs":{}}}}},"docs":{}}}}}}},"docs":{}}}}},"docs":{}}}}}}},"docs":{}}}}},"docs":{}}}}}}},"docs":{}}}}},"4":{"docs":{},"\"":{"docs":{},":":{"docs":{},"\"":{"1":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"docs":{},"k":{"docs":{},"w":{"5":{"docs":{},"\"":{"docs":{},":":{"docs":{},"\"":{"1":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"docs":{},"k":{"docs":{},"w":{"7":{"docs":{},"\"":{"docs":{},":":{"docs":{},"\"":{"1":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"docs":{},"k":{"docs":{},"w":{"9":{"docs":{},"\"":{"docs":{},":":{"docs":{},"\"":{"1":{"docs":{},"\"":{"docs":{},"}":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}},"docs":{}}}}},"docs":{}}}}}}},"docs":{}}}}},"docs":{}}}}}}},"docs":{}}}}},"docs":{}}}}}}},"docs":{}}}}},"docs":{}}}}}}},"4":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"docs":{},"k":{"docs":{},"w":{"4":{"docs":{},"\"":{"docs":{},":":{"docs":{},"\"":{"1":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"docs":{},"k":{"docs":{},"w":{"5":{"docs":{},"\"":{"docs":{},":":{"docs":{},"\"":{"1":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"docs":{},"k":{"docs":{},"w":{"8":{"docs":{},"\"":{"docs":{},":":{"docs":{},"\"":{"3":{"docs":{},"\"":{"docs":{},",":{"docs":{},"\"":{"docs":{},"k":{"docs":{},"w":{"9":{"docs":{},"\"":{"docs":{},":":{"docs":{},"\"":{"1":{"docs":{},"\"":{"docs":{},"}":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}},"docs":{}}}}},"docs":{}}}}}}},"docs":{}}}}},"docs":{}}}}}}},"docs":{}}}}},"docs":{}}}}}}},"docs":{}}}}},"docs":{}}}}}}},"docs":{}}}}},"docs":{}}}},"\\":{"docs":{},"s":{"docs":{},"u":{"docs":{},"m":{"docs":{},"_":{"docs":{},"{":{"docs":{},"u":{"docs":{},",":{"docs":{},"i":{"docs":{},"\\":{"docs":{},"i":{"docs":{},"n":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}},"k":{"docs":{},"=":{"1":{"docs":{},"}":{"docs":{},"}":{"docs":{},"^":{"docs":{},"k":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.011385199240986717},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.02546296296296296}}}}}}},"docs":{}}}}}}}}},"b":{"docs":{},"_":{"docs":{},"i":{"docs":{},"}":{"docs":{},"^":{"2":{"docs":{},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0017152658662092624}},"​":{"docs":{},"是":{"docs":{},"正":{"docs":{},"则":{"docs":{},"化":{"docs":{},"项":{"docs":{},"，":{"docs":{},"用":{"docs":{},"于":{"docs":{},"避":{"docs":{},"免":{"docs":{},"过":{"docs":{},"拟":{"docs":{},"合":{"docs":{},"现":{"docs":{},"象":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}}}}}}}},"docs":{}}}},"u":{"docs":{},"}":{"docs":{},"^":{"2":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683}}},"docs":{}}}}}},"p":{"docs":{},"_":{"docs":{},"{":{"docs":{},"u":{"docs":{},"k":{"docs":{},"}":{"docs":{},"}":{"docs":{},"\\":{"docs":{},"c":{"docs":{},"d":{"docs":{},"o":{"docs":{},"t":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}}}}}}}},"q":{"docs":{},"_":{"docs":{},"{":{"docs":{},"i":{"docs":{},"k":{"docs":{},"}":{"docs":{},"}":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}},"k":{"docs":{},"i":{"docs":{},"}":{"docs":{},"}":{"docs":{"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}}},"r":{"docs":{},"}":{"docs":{},"_":{"docs":{},"{":{"docs":{},"u":{"docs":{},"i":{"docs":{},"}":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}},"o":{"docs":{},"w":{"docs":{},"p":{"docs":{},"r":{"docs":{},"e":{"docs":{},"f":{"docs":{},"i":{"docs":{},"x":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"=":{"docs":{},">":{"docs":{},"'":{"docs":{},"r":{"docs":{},"o":{"docs":{},"w":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{},"_":{"2":{"2":{"docs":{},"'":{"docs":{},"}":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}},"docs":{}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}},"f":{"docs":{},"_":{"docs":{},"{":{"docs":{},"i":{"docs":{},"j":{"docs":{},"}":{"docs":{},"}":{"docs":{},"{":{"docs":{},"f":{"docs":{},"_":{"docs":{},"{":{"docs":{},"d":{"docs":{},"j":{"docs":{},"}":{"docs":{},"}":{"docs":{},"·":{"docs":{},"l":{"docs":{},"o":{"docs":{},"g":{"docs":{},"\\":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"c":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}},"。":{"docs":{},"并":{"docs":{},"且":{"docs":{},"注":{"docs":{},"意":{"docs":{},"，":{"docs":{},"这":{"docs":{},"个":{"docs":{},"数":{"docs":{},"字":{"docs":{},"通":{"docs":{},"常":{"docs":{},"会":{"docs":{},"被":{"docs":{},"正":{"docs":{},"规":{"docs":{},"化":{"docs":{},"，":{"docs":{},"以":{"docs":{},"防":{"docs":{},"止":{"docs":{},"它":{"docs":{},"偏":{"docs":{},"向":{"docs":{},"长":{"docs":{},"的":{"docs":{},"文":{"docs":{},"件":{"docs":{},"（":{"docs":{},"指":{"docs":{},"同":{"docs":{},"一":{"docs":{},"个":{"docs":{},"词":{"docs":{},"语":{"docs":{},"在":{"docs":{},"长":{"docs":{},"文":{"docs":{},"件":{"docs":{},"里":{"docs":{},"可":{"docs":{},"能":{"docs":{},"会":{"docs":{},"比":{"docs":{},"短":{"docs":{},"文":{"docs":{},"件":{"docs":{},"有":{"docs":{},"更":{"docs":{},"高":{"docs":{},"的":{"docs":{},"词":{"docs":{},"频":{"docs":{},"，":{"docs":{},"而":{"docs":{},"不":{"docs":{},"管":{"docs":{},"该":{"docs":{},"词":{"docs":{},"语":{"docs":{},"重":{"docs":{},"要":{"docs":{},"与":{"docs":{},"否":{"docs":{},"）":{"docs":{},"。":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"n":{"docs":{},"}":{"docs":{},"{":{"docs":{},"n":{"docs":{},"_":{"docs":{},"i":{"docs":{},"}":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876}}}}}}}},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.004405286343612335}}}}}}}},"'":{"docs":{},"c":{"docs":{},"f":{"docs":{},":":{"docs":{},"c":{"docs":{},"q":{"docs":{},"'":{"docs":{},":":{"docs":{},"'":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"'":{"docs":{},"}":{"docs":{},")":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}}}}}}}}}}}}}},"a":{"docs":{},"g":{"docs":{},"e":{"docs":{},"'":{"docs":{},":":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}},"​":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.00423728813559322},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416},"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428},"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.1282051282051282},"day03_Hadoop/ha3.5.html":{"ref":"day03_Hadoop/ha3.5.html","tf":0.030303030303030304},"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624},"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965},"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.02631578947368421}}},"“":{"docs":{},"跟":{"docs":{},"你":{"docs":{},"喜":{"docs":{},"好":{"docs":{},"相":{"docs":{},"似":{"docs":{},"的":{"docs":{},"人":{"docs":{},"喜":{"docs":{},"欢":{"docs":{},"的":{"docs":{},"东":{"docs":{},"西":{"docs":{},"你":{"docs":{},"也":{"docs":{},"很":{"docs":{},"有":{"docs":{},"可":{"docs":{},"能":{"docs":{},"喜":{"docs":{},"欢":{"docs":{},"”":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}}}}}}},"欢":{"docs":{},"的":{"docs":{},"东":{"docs":{},"西":{"docs":{},"相":{"docs":{},"似":{"docs":{},"的":{"docs":{},"东":{"docs":{},"西":{"docs":{},"你":{"docs":{},"也":{"docs":{},"很":{"docs":{},"有":{"docs":{},"可":{"docs":{},"能":{"docs":{},"喜":{"docs":{},"欢":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}}}}}}}},"阿":{"docs":{},"甘":{"docs":{},"正":{"docs":{},"传":{"docs":{},"”":{"docs":{},"比":{"docs":{},"较":{"docs":{},"热":{"docs":{},"门":{"docs":{},"且":{"docs":{},"备":{"docs":{},"受":{"docs":{},"好":{"docs":{},"评":{"docs":{},"，":{"docs":{},"评":{"docs":{},"分":{"docs":{},"普":{"docs":{},"遍":{"docs":{},"比":{"docs":{},"平":{"docs":{},"均":{"docs":{},"评":{"docs":{},"分":{"docs":{},"要":{"docs":{},"高":{"1":{"docs":{},".":{"2":{"docs":{},"分":{"docs":{},"，":{"docs":{},"“":{"docs":{},"阿":{"docs":{},"甘":{"docs":{},"正":{"docs":{},"传":{"docs":{},"”":{"docs":{},"的":{"docs":{},"偏":{"docs":{},"置":{"docs":{},"是":{"docs":{},"+":{"1":{"docs":{},".":{"2":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}},"docs":{}}},"docs":{}}}}}}}}}}}}}}},"docs":{}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}},"海":{"docs":{},"盗":{"docs":{},"”":{"docs":{},"出":{"docs":{},"现":{"docs":{},"的":{"docs":{},"词":{"docs":{},"频":{"docs":{},"为":{"2":{"0":{"docs":{},"/":{"2":{"0":{"0":{"docs":{},"＝":{"0":{"docs":{},".":{"1":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}},"docs":{}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}}}}}}},"的":{"docs":{},"i":{"docs":{},"d":{"docs":{},"f":{"docs":{},"为":{"docs":{},"：":{"docs":{},"l":{"docs":{},"o":{"docs":{},"g":{"docs":{},"(":{"1":{"0":{"0":{"0":{"docs":{},"/":{"1":{"0":{"0":{"0":{"docs":{},")":{"docs":{},"=":{"0":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}},"docs":{}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}}}},"自":{"docs":{},"由":{"docs":{},"”":{"docs":{},"出":{"docs":{},"现":{"docs":{},"的":{"docs":{},"词":{"docs":{},"频":{"docs":{},"为":{"1":{"0":{"docs":{},"/":{"2":{"0":{"0":{"docs":{},"=":{"0":{"docs":{},".":{"0":{"5":{"docs":{},"；":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}},"docs":{}},"docs":{}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}}}}}}},"的":{"docs":{},"i":{"docs":{},"d":{"docs":{},"f":{"docs":{},"为":{"docs":{},"：":{"docs":{},"l":{"docs":{},"o":{"docs":{},"g":{"docs":{},"(":{"1":{"0":{"0":{"0":{"docs":{},"/":{"1":{"0":{"0":{"docs":{},")":{"docs":{},"=":{"1":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}},"docs":{}}}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}}}},"船":{"docs":{},"长":{"docs":{},"”":{"docs":{},"出":{"docs":{},"现":{"docs":{},"的":{"docs":{},"词":{"docs":{},"频":{"docs":{},"为":{"1":{"5":{"docs":{},"/":{"2":{"0":{"0":{"docs":{},"=":{"0":{"docs":{},".":{"0":{"7":{"5":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}}}}}}},"的":{"docs":{},"i":{"docs":{},"d":{"docs":{},"f":{"docs":{},"为":{"docs":{},"：":{"docs":{},"l":{"docs":{},"o":{"docs":{},"g":{"docs":{},"(":{"1":{"0":{"0":{"0":{"docs":{},"/":{"5":{"0":{"0":{"docs":{},")":{"docs":{},"=":{"0":{"docs":{},".":{"3":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}},"docs":{}}},"docs":{}}}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}}}},"查":{"docs":{},"询":{"docs":{},"词":{"docs":{},"(":{"docs":{},"q":{"docs":{},"u":{"docs":{},"e":{"docs":{},"r":{"docs":{},"y":{"docs":{},")":{"docs":{},"”":{"docs":{},"，":{"docs":{},"查":{"docs":{},"询":{"docs":{},"词":{"docs":{},"和":{"docs":{},"广":{"docs":{},"告":{"docs":{},"内":{"docs":{},"容":{"docs":{},"的":{"docs":{},"匹":{"docs":{},"配":{"docs":{},"程":{"docs":{},"度":{"docs":{},"很":{"docs":{},"大":{"docs":{},"程":{"docs":{},"度":{"docs":{},"影":{"docs":{},"响":{"docs":{},"了":{"docs":{},"点":{"docs":{},"击":{"docs":{},"概":{"docs":{},"率":{"docs":{},"，":{"docs":{},"搜":{"docs":{},"索":{"docs":{},"广":{"docs":{},"告":{"docs":{},"的":{"docs":{},"点":{"docs":{},"击":{"docs":{},"率":{"docs":{},"普":{"docs":{},"遍":{"docs":{},"较":{"docs":{},"高":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"n":{"docs":{},"e":{"docs":{},"w":{"docs":{},"_":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"c":{"docs":{},"l":{"docs":{},"a":{"docs":{},"s":{"docs":{},"s":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"”":{"docs":{},"的":{"docs":{},"特":{"docs":{},"征":{"docs":{},"对":{"docs":{},"应":{"docs":{},"关":{"docs":{},"系":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"”":{"docs":{},"：":{"docs":{},"基":{"docs":{},"于":{"docs":{},"物":{"docs":{},"品":{"docs":{},"的":{"docs":{},"协":{"docs":{},"同":{"docs":{},"过":{"docs":{},"滤":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"（":{"docs":{},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"m":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}}}}}},"≈":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}},"一":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}},"个":{"docs":{},"给":{"docs":{},"定":{"docs":{},"的":{"docs":{},"商":{"docs":{},"品":{"docs":{},"，":{"docs":{},"可":{"docs":{},"能":{"docs":{},"被":{"docs":{},"拥":{"docs":{},"有":{"docs":{},"类":{"docs":{},"似":{"docs":{},"品":{"docs":{},"味":{"docs":{},"或":{"docs":{},"需":{"docs":{},"求":{"docs":{},"的":{"docs":{},"用":{"docs":{},"户":{"docs":{},"购":{"docs":{},"买":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}}}}}}}}}}},"或":{"docs":{},"多":{"docs":{},"个":{"docs":{},"序":{"docs":{},"列":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}},"集":{"docs":{},"群":{"docs":{},"中":{"docs":{},"可":{"docs":{},"以":{"docs":{},"包":{"docs":{},"含":{"docs":{},"数":{"docs":{},"以":{"docs":{},"千":{"docs":{},"计":{"docs":{},"的":{"docs":{},"节":{"docs":{},"点":{"docs":{"day03_Hadoop/ha1.3.html":{"ref":"day03_Hadoop/ha1.3.html","tf":0.07692307692307693}}}}}}}}}}}}}}}},"人":{"docs":{},"数":{"docs":{},"所":{"docs":{},"有":{"docs":{},"的":{"docs":{},"钞":{"docs":{},"票":{"docs":{},"，":{"docs":{},"数":{"docs":{},"出":{"docs":{},"各":{"docs":{},"种":{"docs":{},"面":{"docs":{},"值":{"docs":{},"有":{"docs":{},"多":{"docs":{},"少":{"docs":{},"张":{"docs":{"day03_Hadoop/ha3.2.html":{"ref":"day03_Hadoop/ha3.2.html","tf":0.02040816326530612}}}}}}}}}}}}}}}}}}}},"脚":{"docs":{},"本":{"docs":{},"至":{"docs":{},"于":{"docs":{},"是":{"docs":{},"做":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"p":{"docs":{},"e":{"docs":{},"r":{"docs":{},"还":{"docs":{},"是":{"docs":{},"做":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{},"e":{"docs":{},"r":{"docs":{},"，":{"docs":{},"又":{"docs":{},"或":{"docs":{},"者":{"docs":{},"是":{"docs":{},"做":{"docs":{},"u":{"docs":{},"d":{"docs":{},"f":{"docs":{},"还":{"docs":{},"是":{"docs":{},"做":{"docs":{},"u":{"docs":{},"d":{"docs":{},"a":{"docs":{},"f":{"docs":{},"，":{"docs":{},"取":{"docs":{},"决":{"docs":{},"于":{"docs":{},"我":{"docs":{},"们":{"docs":{},"把":{"docs":{},"它":{"docs":{},"放":{"docs":{},"在":{"docs":{},"什":{"docs":{},"么":{"docs":{},"样":{"docs":{},"的":{"docs":{},"h":{"docs":{},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{},"操":{"docs":{},"作":{"docs":{},"符":{"docs":{},"中":{"docs":{},"。":{"docs":{},"放":{"docs":{},"在":{"docs":{},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"中":{"docs":{},"的":{"docs":{},"基":{"docs":{},"本":{"docs":{},"就":{"docs":{},"是":{"docs":{},"u":{"docs":{},"d":{"docs":{},"f":{"docs":{},"，":{"docs":{},"放":{"docs":{},"在":{"docs":{},"d":{"docs":{},"i":{"docs":{},"s":{"docs":{},"t":{"docs":{},"r":{"docs":{},"i":{"docs":{},"b":{"docs":{},"u":{"docs":{},"t":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"r":{"docs":{},"e":{"docs":{},"g":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"中":{"docs":{},"会":{"docs":{},"有":{"docs":{},"个":{"docs":{},"多":{"docs":{},"个":{"docs":{},"s":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{},"e":{"docs":{},"，":{"docs":{},"每":{"docs":{},"个":{"docs":{},"s":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{},"e":{"docs":{},"用":{"docs":{},"来":{"docs":{},"存":{"docs":{},"储":{"docs":{},"一":{"docs":{},"个":{"docs":{},"列":{"docs":{},"簇":{"docs":{},"。":{"docs":{},"如":{"docs":{},"果":{"docs":{},"只":{"docs":{},"有":{"docs":{},"一":{"docs":{},"个":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"表":{"docs":{},"最":{"docs":{},"开":{"docs":{},"始":{"docs":{},"存":{"docs":{},"储":{"docs":{},"的":{"docs":{},"时":{"docs":{},"候":{"docs":{},"，":{"docs":{},"是":{"docs":{},"一":{"docs":{},"个":{"docs":{},"r":{"docs":{},"e":{"docs":{},"g":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"。":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}}}}}}}}}}}}}}}},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"作":{"docs":{},"业":{"docs":{},"运":{"docs":{},"行":{"docs":{},"时":{"docs":{},"包":{"docs":{},"括":{"docs":{},"一":{"docs":{},"个":{"docs":{},"d":{"docs":{},"r":{"docs":{},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{},"r":{"docs":{},"进":{"docs":{},"程":{"docs":{},"，":{"docs":{},"也":{"docs":{},"是":{"docs":{},"作":{"docs":{},"业":{"docs":{},"的":{"docs":{},"主":{"docs":{},"进":{"docs":{},"程":{"docs":{},"，":{"docs":{},"负":{"docs":{},"责":{"docs":{},"作":{"docs":{},"业":{"docs":{},"的":{"docs":{},"解":{"docs":{},"析":{"docs":{},"、":{"docs":{},"生":{"docs":{},"成":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"g":{"docs":{},"e":{"docs":{},"并":{"docs":{},"调":{"docs":{},"度":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"k":{"docs":{},"到":{"docs":{},"e":{"docs":{},"x":{"docs":{},"e":{"docs":{},"c":{"docs":{},"u":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{},"上":{"docs":{},"。":{"docs":{},"包":{"docs":{},"括":{"docs":{},"d":{"docs":{},"a":{"docs":{},"g":{"docs":{},"s":{"docs":{},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"l":{"docs":{},"e":{"docs":{},"r":{"docs":{},"，":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"k":{"docs":{},"s":{"docs":{},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"l":{"docs":{},"e":{"docs":{},"r":{"docs":{},"。":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"t":{"docs":{},"e":{"docs":{},"x":{"docs":{},"t":{"docs":{},"创":{"docs":{},"建":{"docs":{},"一":{"docs":{},"个":{"docs":{},"s":{"docs":{},"t":{"docs":{},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"m":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}}}}}}}}}},"对":{"docs":{},"象":{"docs":{},"可":{"docs":{},"以":{"docs":{},"重":{"docs":{},"复":{"docs":{},"利":{"docs":{},"用":{"docs":{},"去":{"docs":{},"创":{"docs":{},"建":{"docs":{},"多":{"docs":{},"个":{"docs":{},"s":{"docs":{},"t":{"docs":{},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"m":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"数":{"docs":{},"字":{"docs":{},"、":{"docs":{},"字":{"docs":{},"符":{"docs":{},"串":{"docs":{},"，":{"docs":{},"这":{"docs":{},"时":{"docs":{},"整":{"docs":{},"个":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"中":{"docs":{},"所":{"docs":{},"有":{"docs":{},"的":{"docs":{},"缺":{"docs":{},"失":{"docs":{},"值":{"docs":{},"都":{"docs":{},"会":{"docs":{},"被":{"docs":{},"填":{"docs":{},"充":{"docs":{},"为":{"docs":{},"相":{"docs":{},"同":{"docs":{},"的":{"docs":{},"值":{"docs":{},"。":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"些":{"docs":{},"物":{"docs":{},"品":{"docs":{},"的":{"docs":{},"评":{"docs":{},"分":{"docs":{},"普":{"docs":{},"遍":{"docs":{},"高":{"docs":{},"于":{"docs":{},"其":{"docs":{},"他":{"docs":{},"物":{"docs":{},"品":{"docs":{},"，":{"docs":{},"一":{"docs":{},"些":{"docs":{},"物":{"docs":{},"品":{"docs":{},"的":{"docs":{},"评":{"docs":{},"分":{"docs":{},"普":{"docs":{},"遍":{"docs":{},"低":{"docs":{},"于":{"docs":{},"其":{"docs":{},"他":{"docs":{},"物":{"docs":{},"品":{"docs":{},"。":{"docs":{},"比":{"docs":{},"如":{"docs":{},"一":{"docs":{},"些":{"docs":{},"物":{"docs":{},"品":{"docs":{},"一":{"docs":{},"被":{"docs":{},"生":{"docs":{},"产":{"docs":{},"便":{"docs":{},"决":{"docs":{},"定":{"docs":{},"了":{"docs":{},"它":{"docs":{},"的":{"docs":{},"地":{"docs":{},"位":{"docs":{},"，":{"docs":{},"有":{"docs":{},"的":{"docs":{},"比":{"docs":{},"较":{"docs":{},"受":{"docs":{},"人":{"docs":{},"们":{"docs":{},"欢":{"docs":{},"迎":{"docs":{},"，":{"docs":{},"有":{"docs":{},"的":{"docs":{},"则":{"docs":{},"被":{"docs":{},"人":{"docs":{},"嫌":{"docs":{},"弃":{"docs":{},"。":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"致":{"docs":{},"性":{"docs":{},"(":{"docs":{},"所":{"docs":{},"有":{"docs":{},"节":{"docs":{},"点":{"docs":{},"在":{"docs":{},"同":{"docs":{},"一":{"docs":{},"时":{"docs":{},"间":{"docs":{},"具":{"docs":{},"有":{"docs":{},"相":{"docs":{},"同":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},")":{"docs":{"Hive&HBase/06_hbase数据模型.html":{"ref":"Hive&HBase/06_hbase数据模型.html","tf":0.020833333333333332}}}}}}}}}}}}}}}}}}}}}},"旦":{"docs":{},"一":{"docs":{},"个":{"docs":{},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"t":{"docs":{},"e":{"docs":{},"x":{"docs":{},"t":{"docs":{},"已":{"docs":{},"经":{"docs":{},"停":{"docs":{},"止":{"docs":{},",":{"docs":{},"不":{"docs":{},"能":{"docs":{},"重":{"docs":{},"新":{"docs":{},"启":{"docs":{},"动":{"docs":{},"(":{"docs":{},"s":{"docs":{},"t":{"docs":{},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"m":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}}}}}}}}}}}}}}}},"启":{"docs":{},"动":{"docs":{},"(":{"docs":{},"调":{"docs":{},"用":{"docs":{},"了":{"docs":{},"s":{"docs":{},"t":{"docs":{},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"m":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}}}}}}}}}}}}}}}}}}}}}}}},"般":{"docs":{},"超":{"docs":{},"过":{"docs":{},"一":{"docs":{},"天":{"docs":{},"都":{"docs":{},"是":{"docs":{},"用":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"或":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}}}}}}}}}}},"份":{"docs":{},"广":{"docs":{},"告":{"docs":{},"基":{"docs":{},"本":{"docs":{},"信":{"docs":{},"息":{"docs":{},"数":{"docs":{},"据":{"docs":{},"a":{"docs":{},"d":{"docs":{},"_":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"e":{"docs":{},".":{"docs":{},"c":{"docs":{},"s":{"docs":{},"v":{"docs":{},"：":{"docs":{},"体":{"docs":{},"现":{"docs":{},"的":{"docs":{},"是":{"docs":{},"每":{"docs":{},"个":{"docs":{},"广":{"docs":{},"告":{"docs":{},"的":{"docs":{},"类":{"docs":{},"目":{"docs":{},"(":{"docs":{},"i":{"docs":{},"d":{"docs":{},")":{"docs":{},"、":{"docs":{},"品":{"docs":{},"牌":{"docs":{},"(":{"docs":{},"i":{"docs":{},"d":{"docs":{},")":{"docs":{},"、":{"docs":{},"价":{"docs":{},"格":{"docs":{},"特":{"docs":{},"征":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"点":{"docs":{},"击":{"docs":{},"的":{"docs":{},"样":{"docs":{},"本":{"docs":{},"数":{"docs":{},"据":{"docs":{},"r":{"docs":{},"a":{"docs":{},"w":{"docs":{},"_":{"docs":{},"s":{"docs":{},"a":{"docs":{},"m":{"docs":{},"p":{"docs":{},"l":{"docs":{},"e":{"docs":{},".":{"docs":{},"c":{"docs":{},"s":{"docs":{},"v":{"docs":{},"：":{"docs":{},"体":{"docs":{},"现":{"docs":{},"的":{"docs":{},"是":{"docs":{},"用":{"docs":{},"户":{"docs":{},"对":{"docs":{},"不":{"docs":{},"同":{"docs":{},"位":{"docs":{},"置":{"docs":{},"广":{"docs":{},"告":{"docs":{},"点":{"docs":{},"击":{"docs":{},"、":{"docs":{},"没":{"docs":{},"点":{"docs":{},"击":{"docs":{},"的":{"docs":{},"情":{"docs":{},"况":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"用":{"docs":{},"户":{"docs":{},"基":{"docs":{},"本":{"docs":{},"信":{"docs":{},"息":{"docs":{},"数":{"docs":{},"据":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"p":{"docs":{},"r":{"docs":{},"o":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"e":{"docs":{},".":{"docs":{},"c":{"docs":{},"s":{"docs":{},"v":{"docs":{},"：":{"docs":{},"体":{"docs":{},"现":{"docs":{},"的":{"docs":{},"是":{"docs":{},"用":{"docs":{},"户":{"docs":{},"群":{"docs":{},"组":{"docs":{},"、":{"docs":{},"性":{"docs":{},"别":{"docs":{},"、":{"docs":{},"年":{"docs":{},"龄":{"docs":{},"、":{"docs":{},"消":{"docs":{},"费":{"docs":{},"购":{"docs":{},"物":{"docs":{},"档":{"docs":{},"次":{"docs":{},"、":{"docs":{},"所":{"docs":{},"在":{"docs":{},"城":{"docs":{},"市":{"docs":{},"级":{"docs":{},"别":{"docs":{},"等":{"docs":{},"特":{"docs":{},"征":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"行":{"docs":{},"为":{"docs":{},"日":{"docs":{},"志":{"docs":{},"数":{"docs":{},"据":{"docs":{},"b":{"docs":{},"e":{"docs":{},"h":{"docs":{},"a":{"docs":{},"v":{"docs":{},"i":{"docs":{},"o":{"docs":{},"r":{"docs":{},"_":{"docs":{},"l":{"docs":{},"o":{"docs":{},"g":{"docs":{},".":{"docs":{},"c":{"docs":{},"s":{"docs":{},"v":{"docs":{},"：":{"docs":{},"体":{"docs":{},"现":{"docs":{},"用":{"docs":{},"户":{"docs":{},"对":{"docs":{},"商":{"docs":{},"品":{"docs":{},"类":{"docs":{},"目":{"docs":{},"(":{"docs":{},"i":{"docs":{},"d":{"docs":{},")":{"docs":{},"、":{"docs":{},"品":{"docs":{},"牌":{"docs":{},"(":{"docs":{},"i":{"docs":{},"d":{"docs":{},")":{"docs":{},"的":{"docs":{},"浏":{"docs":{},"览":{"docs":{},"、":{"docs":{},"加":{"docs":{},"购":{"docs":{},"物":{"docs":{},"车":{"docs":{},"、":{"docs":{},"收":{"docs":{},"藏":{"docs":{},"、":{"docs":{},"购":{"docs":{},"买":{"docs":{},"等":{"docs":{},"信":{"docs":{},"息":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"一":{"docs":{},"对":{"docs":{},"应":{"docs":{},"的":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}},"与":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208},"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196},"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}},"向":{"docs":{},"量":{"docs":{},"长":{"docs":{},"度":{"docs":{},"无":{"docs":{},"关":{"docs":{},",":{"docs":{},"余":{"docs":{},"弦":{"docs":{},"相":{"docs":{},"似":{"docs":{},"度":{"docs":{},"计":{"docs":{},"算":{"docs":{},"要":{"docs":{},"对":{"docs":{},"向":{"docs":{},"量":{"docs":{},"长":{"docs":{},"度":{"docs":{},"归":{"docs":{},"一":{"docs":{},"化":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}}}}}}}}}}}},"传":{"docs":{},"统":{"docs":{},"数":{"docs":{},"据":{"docs":{},"库":{"docs":{},"对":{"docs":{},"比":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}},"非":{"docs":{},"缺":{"docs":{},"失":{"docs":{},"数":{"docs":{},"据":{"docs":{},"进":{"docs":{},"行":{"docs":{},"拼":{"docs":{},"接":{"docs":{},"，":{"docs":{},"完":{"docs":{},"成":{"docs":{},"p":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"的":{"docs":{},"缺":{"docs":{},"失":{"docs":{},"值":{"docs":{},"预":{"docs":{},"测":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"两":{"docs":{},"个":{"docs":{},"向":{"docs":{},"量":{"docs":{},"只":{"docs":{},"要":{"docs":{},"方":{"docs":{},"向":{"docs":{},"一":{"docs":{},"致":{"docs":{},",":{"docs":{},"无":{"docs":{},"论":{"docs":{},"程":{"docs":{},"度":{"docs":{},"强":{"docs":{},"弱":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}},"的":{"docs":{},"夹":{"docs":{},"角":{"docs":{},"为":{"0":{"docs":{},"是":{"docs":{},",":{"docs":{},"余":{"docs":{},"弦":{"docs":{},"值":{"docs":{},"为":{"1":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}},"docs":{}}}}}}}},"docs":{}}}}}}},"物":{"docs":{},"体":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}},"集":{"docs":{},"合":{"docs":{},"的":{"docs":{},"交":{"docs":{},"集":{"docs":{},"元":{"docs":{},"素":{"docs":{},"个":{"docs":{},"数":{"docs":{},"在":{"docs":{},"并":{"docs":{},"集":{"docs":{},"中":{"docs":{},"所":{"docs":{},"占":{"docs":{},"的":{"docs":{},"比":{"docs":{},"例":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}}}}}}},"特":{"docs":{},"征":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}},"者":{"docs":{},"本":{"docs":{},"质":{"docs":{},"上":{"docs":{},"的":{"docs":{},"区":{"docs":{},"别":{"docs":{},"，":{"docs":{},"词":{"docs":{},"袋":{"docs":{},"是":{"docs":{},"在":{"docs":{},"词":{"docs":{},"集":{"docs":{},"的":{"docs":{},"基":{"docs":{},"础":{"docs":{},"上":{"docs":{},"增":{"docs":{},"加":{"docs":{},"了":{"docs":{},"频":{"docs":{},"率":{"docs":{},"的":{"docs":{},"维":{"docs":{},"度":{"docs":{},"，":{"docs":{},"词":{"docs":{},"集":{"docs":{},"只":{"docs":{},"关":{"docs":{},"注":{"docs":{},"有":{"docs":{},"和":{"docs":{},"没":{"docs":{},"有":{"docs":{},"，":{"docs":{},"词":{"docs":{},"袋":{"docs":{},"还":{"docs":{},"要":{"docs":{},"关":{"docs":{},"注":{"docs":{},"有":{"docs":{},"几":{"docs":{},"个":{"docs":{},"。":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"数":{"docs":{},"相":{"docs":{},"加":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}}}},"产":{"docs":{},"生":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"结":{"docs":{},"果":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"&":{"docs":{},"日":{"docs":{},"志":{"docs":{},"同":{"docs":{},"步":{"docs":{},"到":{"docs":{},"大":{"docs":{},"数":{"docs":{},"据":{"docs":{},"系":{"docs":{},"统":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}}}}}}}}},"品":{"docs":{},"增":{"docs":{},"长":{"docs":{},"性":{"docs":{},"的":{"docs":{},"关":{"docs":{},"键":{"docs":{},"指":{"docs":{},"标":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}}}},"从":{"docs":{},"排":{"docs":{},"序":{"docs":{},"之":{"docs":{},"后":{"docs":{},"的":{"docs":{},"结":{"docs":{},"果":{"docs":{},"中":{"docs":{},"切":{"docs":{},"片":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}},"数":{"docs":{},"据":{"docs":{},"中":{"docs":{},"筛":{"docs":{},"选":{"docs":{},"特":{"docs":{},"征":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}},"i":{"docs":{},"i":{"docs":{},"d":{"docs":{},"物":{"docs":{},"品":{"docs":{},"的":{"docs":{},"近":{"docs":{},"邻":{"docs":{},"相":{"docs":{},"似":{"docs":{},"物":{"docs":{},"品":{"docs":{},"中":{"docs":{},"筛":{"docs":{},"选":{"docs":{},"出":{"docs":{},"u":{"docs":{},"i":{"docs":{},"d":{"docs":{},"用":{"docs":{},"户":{"docs":{},"评":{"docs":{},"分":{"docs":{},"过":{"docs":{},"的":{"docs":{},"物":{"docs":{},"品":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.0016233766233766235}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"u":{"docs":{},"i":{"docs":{},"d":{"docs":{},"用":{"docs":{},"户":{"docs":{},"的":{"docs":{},"近":{"docs":{},"邻":{"docs":{},"相":{"docs":{},"似":{"docs":{},"用":{"docs":{},"户":{"docs":{},"中":{"docs":{},"筛":{"docs":{},"选":{"docs":{},"出":{"docs":{},"对":{"docs":{},"i":{"docs":{},"i":{"docs":{},"d":{"docs":{},"物":{"docs":{},"品":{"docs":{},"有":{"docs":{},"评":{"docs":{},"分":{"docs":{},"记":{"docs":{},"录":{"docs":{},"的":{"docs":{},"近":{"docs":{},"邻":{"docs":{},"用":{"docs":{},"户":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"用":{"docs":{},"户":{"1":{"docs":{},"的":{"docs":{},"近":{"docs":{},"邻":{"docs":{},"相":{"docs":{},"似":{"docs":{},"用":{"docs":{},"户":{"docs":{},"中":{"docs":{},"筛":{"docs":{},"选":{"docs":{},"出":{"docs":{},"对":{"docs":{},"物":{"docs":{},"品":{"1":{"docs":{},"有":{"docs":{},"评":{"docs":{},"分":{"docs":{},"记":{"docs":{},"录":{"docs":{},"的":{"docs":{},"近":{"docs":{},"邻":{"docs":{},"用":{"docs":{},"户":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.0016233766233766235}}}}}}}}}}}}},"docs":{}}}}}}}}}}}}}}}},"docs":{}}},"单":{"docs":{},"个":{"docs":{},"服":{"docs":{},"务":{"docs":{},"器":{"docs":{},"扩":{"docs":{},"展":{"docs":{},"到":{"docs":{},"数":{"docs":{},"千":{"docs":{},"台":{"docs":{},"计":{"docs":{},"算":{"docs":{},"机":{"docs":{},"，":{"docs":{},"每":{"docs":{},"台":{"docs":{},"计":{"docs":{},"算":{"docs":{},"机":{"docs":{},"都":{"docs":{},"提":{"docs":{},"供":{"docs":{},"本":{"docs":{},"地":{"docs":{},"计":{"docs":{},"算":{"docs":{},"和":{"docs":{},"存":{"docs":{},"储":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"本":{"docs":{},"地":{"docs":{},"文":{"docs":{},"件":{"docs":{},"系":{"docs":{},"统":{"docs":{},"中":{"docs":{},"复":{"docs":{},"制":{"docs":{},"单":{"docs":{},"个":{"docs":{},"或":{"docs":{},"多":{"docs":{},"个":{"docs":{},"源":{"docs":{},"路":{"docs":{},"径":{"docs":{},"到":{"docs":{},"目":{"docs":{},"标":{"docs":{},"文":{"docs":{},"件":{"docs":{},"系":{"docs":{},"统":{"docs":{},"。":{"docs":{},"也":{"docs":{},"支":{"docs":{},"持":{"docs":{},"从":{"docs":{},"标":{"docs":{},"准":{"docs":{},"输":{"docs":{},"入":{"docs":{},"中":{"docs":{},"读":{"docs":{},"取":{"docs":{},"输":{"docs":{},"入":{"docs":{},"写":{"docs":{},"入":{"docs":{},"目":{"docs":{},"标":{"docs":{},"文":{"docs":{},"件":{"docs":{},"系":{"docs":{},"统":{"docs":{},"。":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"标":{"docs":{},"准":{"docs":{},"输":{"docs":{},"入":{"docs":{},"中":{"docs":{},"读":{"docs":{},"取":{"docs":{},"输":{"docs":{},"入":{"docs":{},"。":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625}}}}}}}}}}}},"模":{"docs":{},"板":{"docs":{},"文":{"docs":{},"件":{"docs":{},"复":{"docs":{},"制":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428}}}}}}}},"其":{"docs":{},"它":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"n":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"上":{"docs":{},"读":{"docs":{},"取":{"docs":{},"备":{"docs":{},"份":{"docs":{},"数":{"docs":{},"据":{"docs":{"day03_Hadoop/ha4.2.html":{"ref":"day03_Hadoop/ha4.2.html","tf":0.0196078431372549}}}}}}}}},"服":{"docs":{},"务":{"docs":{},"器":{"docs":{},"上":{"docs":{},"复":{"docs":{},"制":{"docs":{},"数":{"docs":{},"据":{"docs":{"day03_Hadoop/ha4.2.html":{"ref":"day03_Hadoop/ha4.2.html","tf":0.0196078431372549}}}}}}}}}}}}}}}}}}}},"执":{"docs":{},"行":{"docs":{},"结":{"docs":{},"果":{"docs":{},"可":{"docs":{},"以":{"docs":{},"看":{"docs":{},"出":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}}}},"表":{"docs":{},"中":{"docs":{},"获":{"docs":{},"取":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"中":{"docs":{},"所":{"docs":{},"有":{"docs":{},"的":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}}}}}}}}}},"通":{"docs":{},"过":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{},"查":{"docs":{},"询":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"中":{"docs":{},"的":{"docs":{},"值":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}}}}}}}}}}}}},"一":{"docs":{},"个":{"docs":{},"已":{"docs":{},"经":{"docs":{},"存":{"docs":{},"在":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{},"创":{"docs":{},"建":{"docs":{},"一":{"docs":{},"个":{"docs":{},"新":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}}}}}}}}}}}}},"c":{"docs":{},"s":{"docs":{},"v":{"docs":{},"中":{"docs":{},"读":{"docs":{},"取":{"docs":{},"数":{"docs":{},"据":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}},"j":{"docs":{},"s":{"docs":{},"o":{"docs":{},"n":{"docs":{},"到":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}}}}}}},"字":{"docs":{},"符":{"docs":{},"串":{"docs":{},"数":{"docs":{},"组":{"docs":{},"得":{"docs":{},"到":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}}}}}},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"有":{"docs":{},"两":{"docs":{},"种":{"docs":{},"方":{"docs":{},"法":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}}}}}}}}}}}}}}}}},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},"中":{"docs":{},"加":{"docs":{},"载":{"docs":{},"c":{"docs":{},"s":{"docs":{},"v":{"docs":{},"文":{"docs":{},"件":{"docs":{},"为":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}},"广":{"docs":{},"告":{"docs":{},"基":{"docs":{},"本":{"docs":{},"信":{"docs":{},"息":{"docs":{},"数":{"docs":{},"据":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}},"，":{"docs":{},"返":{"docs":{},"回":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}}}},"样":{"docs":{},"本":{"docs":{},"数":{"docs":{},"据":{"docs":{},"信":{"docs":{},"息":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}},"加":{"docs":{},"载":{"docs":{},"c":{"docs":{},"s":{"docs":{},"v":{"docs":{},"文":{"docs":{},"件":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}},"为":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}},"之":{"docs":{},"前":{"docs":{},"存":{"docs":{},"储":{"docs":{},"的":{"docs":{},"模":{"docs":{},"型":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}},"数":{"docs":{},"据":{"docs":{},"为":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"，":{"docs":{},"并":{"docs":{},"设":{"docs":{},"置":{"docs":{},"结":{"docs":{},"构":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}}}}}}}}}}}}}}}}}},"模":{"docs":{},"型":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}},"预":{"docs":{},"处":{"docs":{},"理":{"docs":{},"好":{"docs":{},"的":{"docs":{},"品":{"docs":{},"牌":{"docs":{},"的":{"docs":{},"统":{"docs":{},"计":{"docs":{},"数":{"docs":{},"据":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}},"用":{"docs":{},"户":{"docs":{},"基":{"docs":{},"本":{"docs":{},"信":{"docs":{},"息":{"docs":{},"数":{"docs":{},"据":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}},"前":{"2":{"0":{"docs":{},"个":{"docs":{},"分":{"docs":{},"类":{"docs":{},"中":{"docs":{},"选":{"docs":{},"出":{"5":{"0":{"0":{"docs":{},"个":{"docs":{},"进":{"docs":{},"行":{"docs":{},"召":{"docs":{},"回":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}},"docs":{}},"docs":{}},"docs":{}}}}}}}},"docs":{}},"docs":{}}},"以":{"docs":{},"下":{"docs":{},"是":{"docs":{},"一":{"docs":{},"个":{"docs":{},"简":{"docs":{},"单":{"docs":{},"的":{"docs":{},"示":{"docs":{},"例":{"docs":{},"，":{"docs":{},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{},"相":{"docs":{},"当":{"docs":{},"于":{"docs":{},"一":{"docs":{},"个":{"docs":{},"用":{"docs":{},"户":{"docs":{},"对":{"docs":{},"物":{"docs":{},"品":{"docs":{},"的":{"docs":{},"购":{"docs":{},"买":{"docs":{},"记":{"docs":{},"录":{"docs":{},"表":{"docs":{},"：":{"docs":{},"打":{"docs":{},"勾":{"docs":{},"表":{"docs":{},"示":{"docs":{},"用":{"docs":{},"户":{"docs":{},"对":{"docs":{},"物":{"docs":{},"品":{"docs":{},"的":{"docs":{},"有":{"docs":{},"购":{"docs":{},"买":{"docs":{},"记":{"docs":{},"录":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"三":{"docs":{},"项":{"docs":{},"配":{"docs":{},"置":{"docs":{},"，":{"docs":{},"可":{"docs":{},"以":{"docs":{},"控":{"docs":{},"制":{"docs":{},"执":{"docs":{},"行":{"docs":{},"器":{"docs":{},"数":{"docs":{},"量":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}},"上":{"docs":{},"两":{"docs":{},"种":{"docs":{},"最":{"docs":{},"优":{"docs":{},"化":{"docs":{},"函":{"docs":{},"数":{"docs":{},"都":{"docs":{},"可":{"docs":{},"以":{"docs":{},"通":{"docs":{},"过":{"docs":{},"梯":{"docs":{},"度":{"docs":{},"下":{"docs":{},"降":{"docs":{},"或":{"docs":{},"者":{"docs":{},"随":{"docs":{},"机":{"docs":{},"梯":{"docs":{},"度":{"docs":{},"下":{"docs":{},"降":{"docs":{},"法":{"docs":{},"来":{"docs":{},"寻":{"docs":{},"求":{"docs":{},"最":{"docs":{},"优":{"docs":{},"解":{"docs":{},"。":{"docs":{"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html":{"ref":"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html","tf":0.037037037037037035}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"四":{"docs":{},"个":{"docs":{},"特":{"docs":{},"征":{"docs":{},"均":{"docs":{},"属":{"docs":{},"于":{"docs":{},"分":{"docs":{},"类":{"docs":{},"特":{"docs":{},"征":{"docs":{},"，":{"docs":{},"但":{"docs":{},"由":{"docs":{},"于":{"docs":{},"分":{"docs":{},"类":{"docs":{},"值":{"docs":{},"个":{"docs":{},"数":{"docs":{},"均":{"docs":{},"过":{"docs":{},"于":{"docs":{},"庞":{"docs":{},"大":{"docs":{},"，":{"docs":{},"如":{"docs":{},"果":{"docs":{},"去":{"docs":{},"做":{"docs":{},"热":{"docs":{},"独":{"docs":{},"编":{"docs":{},"码":{"docs":{},"处":{"docs":{},"理":{"docs":{},"，":{"docs":{},"会":{"docs":{},"导":{"docs":{},"致":{"docs":{},"数":{"docs":{},"据":{"docs":{},"过":{"docs":{},"于":{"docs":{},"稀":{"docs":{},"疏":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"参":{"docs":{},"数":{"docs":{},"序":{"docs":{},"列":{"docs":{},"中":{"docs":{},"的":{"docs":{},"每":{"docs":{},"一":{"docs":{},"个":{"docs":{},"元":{"docs":{},"素":{"docs":{},"调":{"docs":{},"用":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}},"及":{"docs":{"day03_Hadoop/ha2.1.html":{"ref":"day03_Hadoop/ha2.1.html","tf":0.0136986301369863}},"这":{"docs":{},"些":{"docs":{},"数":{"docs":{},"据":{"docs":{},"在":{"docs":{},"其":{"docs":{},"它":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"n":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"服":{"docs":{},"务":{"docs":{},"器":{"docs":{},"上":{"docs":{},"的":{"docs":{},"存":{"docs":{},"储":{"docs":{},"情":{"docs":{},"况":{"docs":{"day03_Hadoop/ha4.2.html":{"ref":"day03_Hadoop/ha4.2.html","tf":0.0196078431372549}}}}}}}}}}}}}}}}}}}}}}}}}}},"二":{"docs":{},"进":{"docs":{},"制":{"docs":{},"的":{"docs":{},"字":{"docs":{},"节":{"docs":{},"来":{"docs":{},"存":{"docs":{},"储":{"docs":{"Hive&HBase/06_hbase数据模型.html":{"ref":"Hive&HBase/06_hbase数据模型.html","tf":0.020833333333333332}}}}}}}}}}},"元":{"docs":{},"组":{"docs":{},"中":{"docs":{},"的":{"docs":{},"第":{"0":{"docs":{},"个":{"docs":{},"元":{"docs":{},"素":{"docs":{},"作":{"docs":{},"为":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{},"，":{"docs":{},"进":{"docs":{},"行":{"docs":{},"分":{"docs":{},"组":{"docs":{},"，":{"docs":{},"返":{"docs":{},"回":{"docs":{},"一":{"docs":{},"个":{"docs":{},"新":{"docs":{},"的":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{}}}}}},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"为":{"docs":{},"单":{"docs":{},"位":{"docs":{},"处":{"docs":{},"理":{"docs":{},"数":{"docs":{},"据":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}}}}}}}}},"e":{"docs":{},"c":{"docs":{},"o":{"docs":{},"r":{"docs":{},"d":{"docs":{},"为":{"docs":{},"单":{"docs":{},"位":{"docs":{},"处":{"docs":{},"理":{"docs":{},"数":{"docs":{},"据":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}}}}}}}}}}}}}},"余":{"docs":{},"弦":{"docs":{},"相":{"docs":{},"似":{"docs":{},"度":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}},"/":{"docs":{},"皮":{"docs":{},"尔":{"docs":{},"逊":{"docs":{},"相":{"docs":{},"关":{"docs":{},"系":{"docs":{},"数":{"docs":{},"适":{"docs":{},"合":{"docs":{},"用":{"docs":{},"户":{"docs":{},"评":{"docs":{},"分":{"docs":{},"数":{"docs":{},"据":{"docs":{},"(":{"docs":{},"实":{"docs":{},"数":{"docs":{},"值":{"docs":{},")":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}}}}}}}}}},"在":{"docs":{},"度":{"docs":{},"量":{"docs":{},"文":{"docs":{},"本":{"docs":{},"相":{"docs":{},"似":{"docs":{},"度":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}},"的":{"docs":{},"特":{"docs":{},"点":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}},"假":{"docs":{},"如":{"docs":{},"叫":{"docs":{},"做":{"docs":{},"p":{"docs":{},",":{"docs":{},"q":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}},"设":{"docs":{},"有":{"docs":{},"三":{"docs":{},"组":{"docs":{},"特":{"docs":{},"征":{"docs":{},"，":{"docs":{},"分":{"docs":{},"别":{"docs":{},"表":{"docs":{},"示":{"docs":{},"年":{"docs":{},"龄":{"docs":{},"，":{"docs":{},"城":{"docs":{},"市":{"docs":{},"，":{"docs":{},"设":{"docs":{},"备":{"docs":{},"；":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}},"关":{"docs":{},"于":{"docs":{},"协":{"docs":{},"同":{"docs":{},"过":{"docs":{},"滤":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"算":{"docs":{},"法":{"docs":{},"使":{"docs":{},"用":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}},"用":{"docs":{},"户":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}},"相":{"docs":{},"似":{"docs":{},"度":{"docs":{},"计":{"docs":{},"算":{"docs":{},"这":{"docs":{},"里":{"docs":{},"先":{"docs":{},"用":{"docs":{},"一":{"docs":{},"个":{"docs":{},"简":{"docs":{},"单":{"docs":{},"的":{"docs":{},"思":{"docs":{},"想":{"docs":{},"：":{"docs":{},"如":{"docs":{},"有":{"docs":{},"两":{"docs":{},"个":{"docs":{},"同":{"docs":{},"学":{"docs":{},"x":{"docs":{},"和":{"docs":{},"y":{"docs":{},"，":{"docs":{},"x":{"docs":{},"同":{"docs":{},"学":{"docs":{},"爱":{"docs":{},"好":{"docs":{},"[":{"docs":{},"足":{"docs":{},"球":{"docs":{},"、":{"docs":{},"篮":{"docs":{},"球":{"docs":{},"、":{"docs":{},"乒":{"docs":{},"乓":{"docs":{},"球":{"docs":{},"]":{"docs":{},"，":{"docs":{},"y":{"docs":{},"同":{"docs":{},"学":{"docs":{},"爱":{"docs":{},"好":{"docs":{},"[":{"docs":{},"网":{"docs":{},"球":{"docs":{},"、":{"docs":{},"足":{"docs":{},"球":{"docs":{},"、":{"docs":{},"篮":{"docs":{},"球":{"docs":{},"、":{"docs":{},"羽":{"docs":{},"毛":{"docs":{},"球":{"docs":{},"]":{"docs":{},"，":{"docs":{},"可":{"docs":{},"见":{"docs":{},"他":{"docs":{},"们":{"docs":{},"的":{"docs":{},"共":{"docs":{},"同":{"docs":{},"爱":{"docs":{},"好":{"docs":{},"有":{"2":{"docs":{},"个":{"docs":{},"，":{"docs":{},"那":{"docs":{},"么":{"docs":{},"他":{"docs":{},"们":{"docs":{},"的":{"docs":{},"相":{"docs":{},"似":{"docs":{},"度":{"docs":{},"可":{"docs":{},"以":{"docs":{},"用":{"docs":{},"：":{"2":{"docs":{},"/":{"3":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}},"docs":{}}},"docs":{}}}}}}}}}}}}}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"评":{"docs":{},"分":{"docs":{},"预":{"docs":{},"测":{"docs":{},"的":{"docs":{},"方":{"docs":{},"法":{"docs":{},"也":{"docs":{},"有":{"docs":{},"比":{"docs":{},"较":{"docs":{},"多":{"docs":{},"的":{"docs":{},"方":{"docs":{},"案":{"docs":{},"，":{"docs":{},"下":{"docs":{},"面":{"docs":{},"介":{"docs":{},"绍":{"docs":{},"一":{"docs":{},"种":{"docs":{},"效":{"docs":{},"果":{"docs":{},"比":{"docs":{},"较":{"docs":{},"好":{"docs":{},"的":{"docs":{},"方":{"docs":{},"案":{"docs":{},"，":{"docs":{},"该":{"docs":{},"方":{"docs":{},"案":{"docs":{},"考":{"docs":{},"虑":{"docs":{},"了":{"docs":{},"用":{"docs":{},"户":{"docs":{},"本":{"docs":{},"身":{"docs":{},"的":{"docs":{},"评":{"docs":{},"分":{"docs":{},"评":{"docs":{},"分":{"docs":{},"以":{"docs":{},"及":{"docs":{},"近":{"docs":{},"邻":{"docs":{},"用":{"docs":{},"户":{"docs":{},"的":{"docs":{},"加":{"docs":{},"权":{"docs":{},"平":{"docs":{},"均":{"docs":{},"相":{"docs":{},"似":{"docs":{},"度":{"docs":{},"打":{"docs":{},"分":{"docs":{},"来":{"docs":{},"进":{"docs":{},"行":{"docs":{},"预":{"docs":{},"测":{"docs":{},"：":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"z":{"docs":{},"i":{"docs":{},"p":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}},"联":{"docs":{},"分":{"docs":{},"析":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}},",":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}},"推":{"docs":{},"荐":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}},"对":{"docs":{},"应":{"docs":{},"的":{"docs":{},"广":{"docs":{},"告":{"docs":{},"完":{"docs":{},"成":{"docs":{},"召":{"docs":{},"回":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}}}}},"广":{"docs":{},"告":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}},"系":{"docs":{},"型":{"docs":{},"数":{"docs":{},"据":{"docs":{},"库":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196},"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}},"中":{"docs":{},"数":{"docs":{},"据":{"docs":{},"示":{"docs":{},"例":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}},"的":{"docs":{},"\"":{"docs":{},"数":{"docs":{},"据":{"docs":{},"库":{"docs":{},"\"":{"docs":{},"(":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},")":{"docs":{"Hive&HBase/06_hbase数据模型.html":{"ref":"Hive&HBase/06_hbase数据模型.html","tf":0.020833333333333332}}}}}}}}}}}}}}}}}}}}}},"运":{"docs":{},"算":{"docs":{},"符":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}},"闭":{"docs":{},"连":{"docs":{},"接":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0226628895184136}}}},"防":{"docs":{},"火":{"docs":{},"墙":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}}}}},"再":{"docs":{},"求":{"docs":{},"元":{"docs":{},"素":{"docs":{},"和":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}},"计":{"docs":{},"算":{"docs":{},"余":{"docs":{},"弦":{"docs":{},"相":{"docs":{},"似":{"docs":{},"度":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}},"次":{"docs":{},"创":{"docs":{},"建":{"docs":{},"外":{"docs":{},"部":{"docs":{},"表":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}},"回":{"docs":{},"头":{"docs":{},"看":{"docs":{},"看":{"docs":{},"这":{"docs":{},"些":{"docs":{},"异":{"docs":{},"常":{"docs":{},"值":{"docs":{},"的":{"docs":{},"值":{"docs":{},"，":{"docs":{},"重":{"docs":{},"新":{"docs":{},"和":{"docs":{},"原":{"docs":{},"始":{"docs":{},"数":{"docs":{},"据":{"docs":{},"关":{"docs":{},"联":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}}}}}}}}}}}}}}},"准":{"docs":{},"备":{"docs":{},"空":{"docs":{},"白":{"docs":{},"d":{"docs":{},"i":{"docs":{},"c":{"docs":{},"t":{"docs":{},"用":{"docs":{},"来":{"docs":{},"保":{"docs":{},"存":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"结":{"docs":{},"果":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}},"要":{"docs":{},"预":{"docs":{},"测":{"docs":{},"的":{"docs":{},"物":{"docs":{},"品":{"docs":{},"的":{"docs":{},"i":{"docs":{},"d":{"docs":{},"列":{"docs":{},"表":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}}}}}}}}}}}}},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"p":{"docs":{},"e":{"docs":{},"r":{"docs":{},"数":{"docs":{},"据":{"docs":{"day03_Hadoop/ha3.2.html":{"ref":"day03_Hadoop/ha3.2.html","tf":0.02040816326530612}}}}}}},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{},"e":{"docs":{},"的":{"docs":{},"输":{"docs":{},"入":{"docs":{},"数":{"docs":{},"据":{"docs":{"day03_Hadoop/ha3.2.html":{"ref":"day03_Hadoop/ha3.2.html","tf":0.02040816326530612}}}}}}}}}}}}}}}},"案":{"docs":{},"例":{"docs":{},"环":{"docs":{},"境":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}},"确":{"docs":{},"性":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.032}},"指":{"docs":{},"标":{"docs":{},"计":{"docs":{},"算":{"docs":{},"方":{"docs":{},"法":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}},"评":{"docs":{},"估":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}},"实":{"docs":{},"时":{"docs":{"day03_Hadoop/ha4.1.html":{"ref":"day03_Hadoop/ha4.1.html","tf":0.023809523809523808}}}}},"利":{"docs":{},"用":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}},"t":{"docs":{},"o":{"docs":{},"p":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}},"a":{"docs":{},"g":{"docs":{},"s":{"docs":{},".":{"docs":{},"c":{"docs":{},"s":{"docs":{},"v":{"docs":{},"中":{"docs":{},"每":{"docs":{},"部":{"docs":{},"电":{"docs":{},"影":{"docs":{},"的":{"docs":{},"标":{"docs":{},"签":{"docs":{},"作":{"docs":{},"为":{"docs":{},"电":{"docs":{},"影":{"docs":{},"的":{"docs":{},"候":{"docs":{},"选":{"docs":{},"关":{"docs":{},"键":{"docs":{},"词":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876}}}}}}}}}}}}}}}}}}}}}}}}}}},"f":{"docs":{},"·":{"docs":{},"i":{"docs":{},"d":{"docs":{},"f":{"docs":{},"计":{"docs":{},"算":{"docs":{},"每":{"docs":{},"部":{"docs":{},"电":{"docs":{},"影":{"docs":{},"的":{"docs":{},"标":{"docs":{},"签":{"docs":{},"的":{"docs":{},"t":{"docs":{},"f":{"docs":{},"i":{"docs":{},"d":{"docs":{},"f":{"docs":{},"值":{"docs":{},"，":{"docs":{},"选":{"docs":{},"取":{"docs":{},"t":{"docs":{},"o":{"docs":{},"p":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},")":{"docs":{},"：":{"docs":{},"选":{"docs":{},"择":{"docs":{},"现":{"docs":{},"在":{"docs":{},"可":{"docs":{},"能":{"docs":{},"最":{"docs":{},"佳":{"docs":{},"的":{"docs":{},"⽅":{"docs":{},"案":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}}}}}}}}}}},"物":{"docs":{},"品":{"docs":{},"的":{"docs":{},"内":{"docs":{},"容":{"docs":{},"信":{"docs":{},"息":{"docs":{},"，":{"docs":{},"将":{"docs":{},"新":{"docs":{},"物":{"docs":{},"品":{"docs":{},"先":{"docs":{},"投":{"docs":{},"放":{"docs":{},"给":{"docs":{},"曾":{"docs":{},"经":{"docs":{},"喜":{"docs":{},"欢":{"docs":{},"过":{"docs":{},"和":{"docs":{},"它":{"docs":{},"内":{"docs":{},"容":{"docs":{},"相":{"docs":{},"似":{"docs":{},"的":{"docs":{},"其":{"docs":{},"他":{"docs":{},"物":{"docs":{},"品":{"docs":{},"的":{"docs":{},"用":{"docs":{},"户":{"docs":{},"。":{"docs":{"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html":{"ref":"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html","tf":0.024390243902439025}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"画":{"docs":{},"像":{"docs":{},"计":{"docs":{},"算":{"docs":{},"物":{"docs":{},"品":{"docs":{},"间":{"docs":{},"两":{"docs":{},"两":{"docs":{},"相":{"docs":{},"似":{"docs":{},"情":{"docs":{},"况":{"docs":{"day02_推荐算法/07_基于内容的推荐算法.html":{"ref":"day02_推荐算法/07_基于内容的推荐算法.html","tf":0.03125}}}}}}}}}}}}}}}}},"随":{"docs":{},"机":{"docs":{},"梯":{"docs":{},"度":{"docs":{},"下":{"docs":{},"降":{"docs":{},"，":{"docs":{},"优":{"docs":{},"化":{"docs":{},"b":{"docs":{},"u":{"docs":{},"，":{"docs":{},"b":{"docs":{},"i":{"docs":{},"的":{"docs":{},"值":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683}}}}}}}}}}}}}}}},"森":{"docs":{},"林":{"docs":{},"对":{"docs":{},"n":{"docs":{},"e":{"docs":{},"w":{"docs":{},"_":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"c":{"docs":{},"l":{"docs":{},"a":{"docs":{},"s":{"docs":{},"s":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"的":{"docs":{},"缺":{"docs":{},"失":{"docs":{},"值":{"docs":{},"进":{"docs":{},"行":{"docs":{},"预":{"docs":{},"测":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"p":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"的":{"docs":{},"缺":{"docs":{},"失":{"docs":{},"值":{"docs":{},"进":{"docs":{},"行":{"docs":{},"预":{"docs":{},"测":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}},"l":{"docs":{},"f":{"docs":{},"m":{"docs":{},"预":{"docs":{},"测":{"docs":{},"用":{"docs":{},"户":{"docs":{},"对":{"docs":{},"物":{"docs":{},"品":{"docs":{},"的":{"docs":{},"评":{"docs":{},"分":{"docs":{},"，":{"docs":{},"$":{"docs":{},"k":{"docs":{},"​":{"docs":{},"$":{"docs":{},"表":{"docs":{},"示":{"docs":{},"隐":{"docs":{},"含":{"docs":{},"特":{"docs":{},"征":{"docs":{},"数":{"docs":{},"量":{"docs":{},"：":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"矩":{"docs":{},"阵":{"docs":{},"分":{"docs":{},"解":{"docs":{},"技":{"docs":{},"术":{"docs":{},"，":{"docs":{},"将":{"docs":{},"原":{"docs":{},"始":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}}}}}}}}}}},"b":{"docs":{},"i":{"docs":{},"a":{"docs":{},"s":{"docs":{},"s":{"docs":{},"v":{"docs":{},"d":{"docs":{},"预":{"docs":{},"测":{"docs":{},"用":{"docs":{},"户":{"docs":{},"对":{"docs":{},"物":{"docs":{},"品":{"docs":{},"的":{"docs":{},"评":{"docs":{},"分":{"docs":{},"，":{"docs":{},"k":{"docs":{},"表":{"docs":{},"示":{"docs":{},"隐":{"docs":{},"含":{"docs":{},"特":{"docs":{},"征":{"docs":{},"数":{"docs":{},"量":{"docs":{},"：":{"docs":{"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"次":{"docs":{},"数":{"docs":{},"计":{"docs":{},"算":{"docs":{},"权":{"docs":{},"重":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}}}}}},"m":{"docs":{},"r":{"docs":{},"j":{"docs":{},"o":{"docs":{},"b":{"docs":{},"编":{"docs":{},"写":{"docs":{},"和":{"docs":{},"运":{"docs":{},"行":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{},"e":{"docs":{},"代":{"docs":{},"码":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}}}}}}}}}}}}}}}}},"s":{"docs":{},"q":{"docs":{},"l":{"docs":{},"进":{"docs":{},"行":{"docs":{},"数":{"docs":{},"据":{"docs":{},"统":{"docs":{},"计":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099}}}}}},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},"m":{"docs":{},"a":{"docs":{},"从":{"docs":{},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},"加":{"docs":{},"载":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}},"分":{"docs":{},"区":{"docs":{},"表":{"docs":{},"方":{"docs":{},"式":{"docs":{},"减":{"docs":{},"少":{"docs":{},"查":{"docs":{},"询":{"docs":{},"时":{"docs":{},"需":{"docs":{},"要":{"docs":{},"扫":{"docs":{},"描":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"量":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}}}}}}}}}}}}}},"了":{"docs":{},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},"的":{"docs":{},"容":{"docs":{},"错":{"docs":{},"能":{"docs":{},"力":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}}}}}}},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"f":{"docs":{},"i":{"docs":{},"g":{"docs":{},"对":{"docs":{},"象":{"docs":{},"，":{"docs":{},"创":{"docs":{},"建":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}},"打":{"docs":{},"分":{"docs":{},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"训":{"docs":{},"练":{"docs":{},"a":{"docs":{},"l":{"docs":{},"s":{"docs":{},"模":{"docs":{},"型":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}}}}}}}}}}}},"管":{"docs":{},"道":{"docs":{},"对":{"docs":{},"每":{"docs":{},"一":{"docs":{},"个":{"docs":{},"数":{"docs":{},"据":{"docs":{},"进":{"docs":{},"行":{"docs":{},"热":{"docs":{},"独":{"docs":{},"编":{"docs":{},"码":{"docs":{},"处":{"docs":{},"理":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}},"a":{"docs":{},"l":{"docs":{},"s":{"docs":{},"模":{"docs":{},"型":{"docs":{},"进":{"docs":{},"行":{"docs":{},"类":{"docs":{},"别":{"docs":{},"的":{"docs":{},"召":{"docs":{},"回":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}},"模":{"docs":{},"型":{"docs":{},"，":{"docs":{},"传":{"docs":{},"入":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"s":{"docs":{},"(":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"i":{"docs":{},"d":{"docs":{},",":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}}}}}}}}}}},"加":{"docs":{},"购":{"docs":{},"物":{"docs":{},"车":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}},"载":{"docs":{},"r":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"s":{"docs":{},".":{"docs":{},"c":{"docs":{},"s":{"docs":{},"v":{"docs":{},"，":{"docs":{},"转":{"docs":{},"换":{"docs":{},"为":{"docs":{},"用":{"docs":{},"户":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}}}}}}}}}}}}}}}}}}},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"我":{"docs":{},"们":{"docs":{},"只":{"docs":{},"用":{"docs":{},"前":{"docs":{},"三":{"docs":{},"列":{"docs":{},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"分":{"docs":{},"别":{"docs":{},"是":{"docs":{},"用":{"docs":{},"户":{"docs":{},"i":{"docs":{},"d":{"docs":{},"，":{"docs":{},"电":{"docs":{},"影":{"docs":{},"i":{"docs":{},"d":{"docs":{},"，":{"docs":{},"已":{"docs":{},"经":{"docs":{},"用":{"docs":{},"户":{"docs":{},"对":{"docs":{},"电":{"docs":{},"影":{"docs":{},"的":{"docs":{},"对":{"docs":{},"应":{"docs":{},"评":{"docs":{},"分":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"集":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}},"到":{"docs":{},"分":{"docs":{},"区":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}},"基":{"docs":{},"于":{"docs":{},"所":{"docs":{},"有":{"docs":{},"电":{"docs":{},"影":{"docs":{},"的":{"docs":{},"标":{"docs":{},"签":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}},"电":{"docs":{},"影":{"docs":{},"列":{"docs":{},"表":{"docs":{},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}},"文":{"docs":{},"件":{"docs":{},"到":{"docs":{},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}}},"城":{"docs":{},"市":{"docs":{},"i":{"docs":{},"p":{"docs":{},"段":{"docs":{},"信":{"docs":{},"息":{"docs":{},"，":{"docs":{},"获":{"docs":{},"取":{"docs":{},"i":{"docs":{},"p":{"docs":{},"起":{"docs":{},"始":{"docs":{},"数":{"docs":{},"字":{"docs":{},"和":{"docs":{},"结":{"docs":{},"束":{"docs":{},"数":{"docs":{},"字":{"docs":{},"，":{"docs":{},"经":{"docs":{},"度":{"docs":{},"，":{"docs":{},"纬":{"docs":{},"度":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"日":{"docs":{},"志":{"docs":{},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"获":{"docs":{},"取":{"docs":{},"i":{"docs":{},"p":{"docs":{},"信":{"docs":{},"息":{"docs":{},"，":{"docs":{},"然":{"docs":{},"后":{"docs":{},"转":{"docs":{},"换":{"docs":{},"为":{"docs":{},"数":{"docs":{},"字":{"docs":{},"，":{"docs":{},"和":{"docs":{},"i":{"docs":{},"p":{"docs":{},"段":{"docs":{},"比":{"docs":{},"较":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}}}}}}}}}}}}}}}}}}}}}}}},"j":{"docs":{},"s":{"docs":{},"o":{"docs":{},"n":{"docs":{},"数":{"docs":{},"据":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}}}},"a":{"docs":{},"l":{"docs":{},"s":{"docs":{},"模":{"docs":{},"型":{"docs":{},"，":{"docs":{},"注":{"docs":{},"意":{"docs":{},"必":{"docs":{},"须":{"docs":{},"先":{"docs":{},"有":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"上":{"docs":{},"下":{"docs":{},"文":{"docs":{},"管":{"docs":{},"理":{"docs":{},"器":{"docs":{},"，":{"docs":{},"即":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"t":{"docs":{},"e":{"docs":{},"x":{"docs":{},"t":{"docs":{},"，":{"docs":{},"但":{"docs":{},"这":{"docs":{},"里":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"s":{"docs":{},"e":{"docs":{},"s":{"docs":{},"s":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"创":{"docs":{},"建":{"docs":{},"后":{"docs":{},"，":{"docs":{},"自":{"docs":{},"动":{"docs":{},"创":{"docs":{},"建":{"docs":{},"了":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"t":{"docs":{},"e":{"docs":{},"x":{"docs":{},"t":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"权":{"docs":{},"求":{"docs":{},"和":{"docs":{},"得":{"docs":{},"到":{"docs":{},"最":{"docs":{},"终":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"结":{"docs":{},"果":{"docs":{"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html":{"ref":"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html","tf":0.024390243902439025}}}}}}}}}}}}},"入":{"docs":{},"l":{"2":{"docs":{},"正":{"docs":{},"则":{"docs":{},"化":{"docs":{},"：":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}},"docs":{}},"购":{"docs":{},"物":{"docs":{},"车":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}},"历":{"docs":{},"史":{"docs":{},"订":{"docs":{},"单":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}},"兴":{"docs":{},"趣":{"docs":{},"程":{"docs":{},"度":{"docs":{"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}}}}}},"推":{"docs":{},"荐":{"docs":{},"结":{"docs":{},"果":{"docs":{"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}}}}}},"数":{"docs":{},"据":{"docs":{"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}}}},"上":{"docs":{},"所":{"docs":{},"有":{"docs":{},"订":{"docs":{},"单":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}},"样":{"docs":{},"本":{"docs":{},"数":{"docs":{},"据":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}},"取":{"docs":{},"出":{"docs":{},"前":{"docs":{},"两":{"docs":{},"条":{"docs":{},"（":{"docs":{},"相":{"docs":{},"似":{"docs":{},"度":{"docs":{},"最":{"docs":{},"高":{"docs":{},"的":{"docs":{},"两":{"docs":{},"个":{"docs":{},"）":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}},"每":{"docs":{},"一":{"docs":{},"列":{"docs":{},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"并":{"docs":{},"删":{"docs":{},"除":{"docs":{},"自":{"docs":{},"身":{"docs":{},"，":{"docs":{},"然":{"docs":{},"后":{"docs":{},"排":{"docs":{},"序":{"docs":{},"数":{"docs":{},"据":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147}}}}}}}}}}}}}}}}}}},"个":{"docs":{},"用":{"docs":{},"户":{"docs":{},"当":{"docs":{},"前":{"docs":{},"已":{"docs":{},"购":{"docs":{},"物":{"docs":{},"品":{"docs":{},"列":{"docs":{},"表":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}},"出":{"docs":{},"现":{"docs":{},"次":{"docs":{},"数":{"docs":{},"最":{"docs":{},"多":{"docs":{},"的":{"docs":{},"前":{"5":{"0":{"docs":{},"个":{"docs":{},"词":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}}},"docs":{}},"docs":{}},"词":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}}}}}}}}}},"因":{"docs":{},"此":{"docs":{},"在":{"docs":{},"协":{"docs":{},"同":{"docs":{},"过":{"docs":{},"滤":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"算":{"docs":{},"法":{"docs":{},"中":{"docs":{},"其":{"docs":{},"实":{"docs":{},"会":{"docs":{},"更":{"docs":{},"多":{"docs":{},"的":{"docs":{},"利":{"docs":{},"用":{"docs":{},"用":{"docs":{},"户":{"docs":{},"对":{"docs":{},"物":{"docs":{},"品":{"docs":{},"的":{"docs":{},"“":{"docs":{},"评":{"docs":{},"分":{"docs":{},"”":{"docs":{},"数":{"docs":{},"据":{"docs":{},"来":{"docs":{},"进":{"docs":{},"行":{"docs":{},"预":{"docs":{},"测":{"docs":{},"，":{"docs":{},"通":{"docs":{},"过":{"docs":{},"评":{"docs":{},"分":{"docs":{},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{},"，":{"docs":{},"我":{"docs":{},"们":{"docs":{},"可":{"docs":{},"以":{"docs":{},"预":{"docs":{},"测":{"docs":{},"用":{"docs":{},"户":{"docs":{},"对":{"docs":{},"于":{"docs":{},"他":{"docs":{},"没":{"docs":{},"有":{"docs":{},"评":{"docs":{},"分":{"docs":{},"过":{"docs":{},"的":{"docs":{},"物":{"docs":{},"品":{"docs":{},"的":{"docs":{},"评":{"docs":{},"分":{"docs":{},"。":{"docs":{},"其":{"docs":{},"实":{"docs":{},"现":{"docs":{},"原":{"docs":{},"理":{"docs":{},"和":{"docs":{},"思":{"docs":{},"想":{"docs":{},"和":{"docs":{},"都":{"docs":{},"是":{"docs":{},"一":{"docs":{},"样":{"docs":{},"的":{"docs":{},"，":{"docs":{},"只":{"docs":{},"是":{"docs":{},"使":{"docs":{},"用":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{},"是":{"docs":{},"用":{"docs":{},"户":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"就":{"docs":{},"可":{"docs":{},"以":{"docs":{},"预":{"docs":{},"测":{"docs":{},"出":{"docs":{},"用":{"docs":{},"户":{"docs":{},"a":{"docs":{},"对":{"docs":{},"电":{"docs":{},"影":{"docs":{},"“":{"docs":{},"阿":{"docs":{},"甘":{"docs":{},"正":{"docs":{},"传":{"docs":{},"”":{"docs":{},"的":{"docs":{},"评":{"docs":{},"分":{"docs":{},"为":{"docs":{},"：":{"3":{"docs":{},".":{"5":{"docs":{},"+":{"docs":{},"(":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}},"docs":{}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}},"最":{"docs":{},"终":{"docs":{},"，":{"docs":{},"我":{"docs":{},"们":{"docs":{},"的":{"docs":{},"目":{"docs":{},"标":{"docs":{},"也":{"docs":{},"就":{"docs":{},"是":{"docs":{},"要":{"docs":{},"求":{"docs":{},"出":{"docs":{},"p":{"docs":{},"矩":{"docs":{},"阵":{"docs":{},"和":{"docs":{},"q":{"docs":{},"矩":{"docs":{},"阵":{"docs":{},"及":{"docs":{},"其":{"docs":{},"当":{"docs":{},"中":{"docs":{},"的":{"docs":{},"每":{"docs":{},"一":{"docs":{},"个":{"docs":{},"值":{"docs":{},"，":{"docs":{},"然":{"docs":{},"后":{"docs":{},"再":{"docs":{},"对":{"docs":{},"用":{"docs":{},"户":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"这":{"docs":{},"时":{"docs":{},"就":{"docs":{},"需":{"docs":{},"要":{"docs":{},"借":{"docs":{},"助":{"docs":{},"一":{"docs":{},"些":{"docs":{},"自":{"docs":{},"然":{"docs":{},"语":{"docs":{},"言":{"docs":{},"处":{"docs":{},"理":{"docs":{},"、":{"docs":{},"信":{"docs":{},"息":{"docs":{},"检":{"docs":{},"索":{"docs":{},"等":{"docs":{},"技":{"docs":{},"术":{"docs":{},"，":{"docs":{},"将":{"docs":{},"如":{"docs":{},"用":{"docs":{},"户":{"docs":{},"的":{"docs":{},"文":{"docs":{},"本":{"docs":{},"评":{"docs":{},"论":{"docs":{},"或":{"docs":{},"其":{"docs":{},"他":{"docs":{},"文":{"docs":{},"本":{"docs":{},"内":{"docs":{},"容":{"docs":{},"信":{"docs":{},"息":{"docs":{},"的":{"docs":{},"非":{"docs":{},"结":{"docs":{},"构":{"docs":{},"化":{"docs":{},"数":{"docs":{},"据":{"docs":{},"进":{"docs":{},"行":{"docs":{},"量":{"docs":{},"化":{"docs":{},"处":{"docs":{},"理":{"docs":{},"，":{"docs":{},"从":{"docs":{},"而":{"docs":{},"实":{"docs":{},"现":{"docs":{},"更":{"docs":{},"加":{"docs":{},"完":{"docs":{},"善":{"docs":{},"的":{"docs":{},"物":{"docs":{},"品":{"docs":{},"画":{"docs":{},"像":{"docs":{},"/":{"docs":{},"用":{"docs":{},"户":{"docs":{},"画":{"docs":{},"像":{"docs":{},"。":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"里":{"docs":{},"不":{"docs":{},"选":{"docs":{},"取":{"docs":{},"它":{"docs":{},"们":{"docs":{},"作":{"docs":{},"为":{"docs":{},"特":{"docs":{},"征":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}},"经":{"docs":{},"过":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"函":{"docs":{},"数":{"docs":{},"处":{"docs":{},"理":{"docs":{},"，":{"docs":{},"将":{"docs":{},"每":{"docs":{},"一":{"docs":{},"行":{"docs":{},"数":{"docs":{},"据":{"docs":{},"转":{"docs":{},"换":{"docs":{},"为":{"docs":{},"普":{"docs":{},"通":{"docs":{},"的":{"docs":{},"列":{"docs":{},"表":{"docs":{},"数":{"docs":{},"据":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}},"对":{"docs":{},"于":{"docs":{},"p":{"docs":{},"i":{"docs":{},"d":{"docs":{},"，":{"docs":{},"应":{"docs":{},"该":{"docs":{},"是":{"docs":{},"由":{"docs":{},"广":{"docs":{},"告":{"docs":{},"系":{"docs":{},"统":{"docs":{},"发":{"docs":{},"起":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"请":{"docs":{},"求":{"docs":{},"时":{"docs":{},"，":{"docs":{},"向":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"系":{"docs":{},"统":{"docs":{},"明":{"docs":{},"确":{"docs":{},"要":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"的":{"docs":{},"用":{"docs":{},"户":{"docs":{},"是":{"docs":{},"谁":{"docs":{},"，":{"docs":{},"以":{"docs":{},"及":{"docs":{},"对":{"docs":{},"应":{"docs":{},"的":{"docs":{},"资":{"docs":{},"源":{"docs":{},"位":{"docs":{},"，":{"docs":{},"或":{"docs":{},"者":{"docs":{},"说":{"docs":{},"有":{"docs":{},"哪":{"docs":{},"些":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"，":{"docs":{},"t":{"docs":{},"f":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}},"我":{"docs":{},"们":{"docs":{},"需":{"docs":{},"要":{"docs":{},"通":{"docs":{},"过":{"docs":{},"日":{"docs":{},"志":{"docs":{},"信":{"docs":{},"息":{"docs":{},"（":{"docs":{},"运":{"docs":{},"行":{"docs":{},"商":{"docs":{},"或":{"docs":{},"者":{"docs":{},"网":{"docs":{},"站":{"docs":{},"自":{"docs":{},"己":{"docs":{},"生":{"docs":{},"成":{"docs":{},"）":{"docs":{},"和":{"docs":{},"城":{"docs":{},"市":{"docs":{},"i":{"docs":{},"p":{"docs":{},"段":{"docs":{},"信":{"docs":{},"息":{"docs":{},"来":{"docs":{},"判":{"docs":{},"断":{"docs":{},"用":{"docs":{},"户":{"docs":{},"的":{"docs":{},"i":{"docs":{},"p":{"docs":{},"段":{"docs":{},"，":{"docs":{},"统":{"docs":{},"计":{"docs":{},"热":{"docs":{},"点":{"docs":{},"经":{"docs":{},"纬":{"docs":{},"度":{"docs":{},"。":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"直":{"docs":{},"接":{"docs":{},"利":{"docs":{},"用":{"docs":{},"s":{"docs":{},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},"m":{"docs":{},"a":{"docs":{},"就":{"docs":{},"可":{"docs":{},"以":{"docs":{},"加":{"docs":{},"载":{"docs":{},"进":{"docs":{},"该":{"docs":{},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"无":{"docs":{},"需":{"docs":{},"替":{"docs":{},"换":{"docs":{},"n":{"docs":{},"u":{"docs":{},"l":{"docs":{},"l":{"docs":{},"值":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"为":{"docs":{},"不":{"docs":{},"可":{"docs":{},"变":{"docs":{},"类":{"docs":{},"型":{"docs":{},"不":{"docs":{},"能":{"docs":{},"被":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}}}},"所":{"docs":{},"有":{"docs":{},"的":{"docs":{},"结":{"docs":{},"果":{"docs":{},"都":{"docs":{},"会":{"docs":{},"加":{"docs":{},"载":{"docs":{},"到":{"docs":{},"内":{"docs":{},"存":{"docs":{},"中":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}}}}}}},"前":{"docs":{},"面":{"docs":{},"提":{"docs":{},"到":{"docs":{},"广":{"docs":{},"告":{"docs":{},"的":{"docs":{},"点":{"docs":{},"击":{"docs":{},"率":{"docs":{},"一":{"docs":{},"般":{"docs":{},"都":{"docs":{},"比":{"docs":{},"较":{"docs":{},"低":{"docs":{},"，":{"docs":{},"所":{"docs":{},"以":{"docs":{},"预":{"docs":{},"测":{"docs":{},"值":{"docs":{},"通":{"docs":{},"常":{"docs":{},"都":{"docs":{},"是":{"0":{"docs":{},"，":{"docs":{},"因":{"docs":{},"此":{"docs":{},"通":{"docs":{},"常":{"docs":{},"需":{"docs":{},"要":{"docs":{},"反":{"docs":{},"减":{"docs":{},"得":{"docs":{},"出":{"docs":{},"点":{"docs":{},"击":{"docs":{},"的":{"docs":{},"概":{"docs":{},"率":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"在":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.017857142857142856},"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}},"前":{"docs":{},"面":{"docs":{},"的":{"docs":{},"d":{"docs":{},"e":{"docs":{},"m":{"docs":{},"o":{"docs":{},"中":{"docs":{},"，":{"docs":{},"我":{"docs":{},"们":{"docs":{},"只":{"docs":{},"是":{"docs":{},"使":{"docs":{},"用":{"docs":{},"用":{"docs":{},"户":{"docs":{},"对":{"docs":{},"物":{"docs":{},"品":{"docs":{},"的":{"docs":{},"一":{"docs":{},"个":{"docs":{},"购":{"docs":{},"买":{"docs":{},"记":{"docs":{},"录":{"docs":{},"，":{"docs":{},"类":{"docs":{},"似":{"docs":{},"也":{"docs":{},"可":{"docs":{},"以":{"docs":{},"是":{"docs":{},"比":{"docs":{},"如":{"docs":{},"浏":{"docs":{},"览":{"docs":{},"点":{"docs":{},"击":{"docs":{},"记":{"docs":{},"录":{"docs":{},"、":{"docs":{},"收":{"docs":{},"听":{"docs":{},"记":{"docs":{},"录":{"docs":{},"等":{"docs":{},"等":{"docs":{},"。":{"docs":{},"这":{"docs":{},"样":{"docs":{},"数":{"docs":{},"据":{"docs":{},"我":{"docs":{},"们":{"docs":{},"预":{"docs":{},"测":{"docs":{},"的":{"docs":{},"结":{"docs":{},"果":{"docs":{},"其":{"docs":{},"实":{"docs":{},"相":{"docs":{},"当":{"docs":{},"于":{"docs":{},"是":{"docs":{},"在":{"docs":{},"预":{"docs":{},"测":{"docs":{},"用":{"docs":{},"户":{"docs":{},"是":{"docs":{},"否":{"docs":{},"对":{"docs":{},"某":{"docs":{},"物":{"docs":{},"品":{"docs":{},"感":{"docs":{},"兴":{"docs":{},"趣":{"docs":{},"，":{"docs":{},"对":{"docs":{},"于":{"docs":{},"喜":{"docs":{},"好":{"docs":{},"程":{"docs":{},"度":{"docs":{},"不":{"docs":{},"能":{"docs":{},"很":{"docs":{},"好":{"docs":{},"的":{"docs":{},"预":{"docs":{},"测":{"docs":{},"。":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"做":{"docs":{},"两":{"docs":{},"类":{"docs":{},"决":{"docs":{},"策":{"docs":{},"的":{"docs":{},"过":{"docs":{},"程":{"docs":{},"中":{"docs":{},"，":{"docs":{},"不":{"docs":{},"断":{"docs":{},"更":{"docs":{},"新":{"docs":{},"对":{"docs":{},"所":{"docs":{},"有":{"docs":{},"决":{"docs":{},"策":{"docs":{},"的":{"docs":{},"不":{"docs":{},"确":{"docs":{},"定":{"docs":{},"性":{"docs":{},"的":{"docs":{},"认":{"docs":{},"知":{"docs":{},"，":{"docs":{},"优":{"docs":{},"化":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"线":{"docs":{},"评":{"docs":{},"估":{"docs":{},":":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}},"推":{"docs":{},"荐":{"docs":{"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}}}},"处":{"docs":{},"理":{"docs":{},"业":{"docs":{},"务":{"docs":{},"流":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}},"f":{"docs":{},"u":{"docs":{},"n":{"docs":{},"k":{"docs":{},"s":{"docs":{},"v":{"docs":{},"d":{"docs":{},"提":{"docs":{},"出":{"docs":{},"来":{"docs":{},"之":{"docs":{},"后":{"docs":{},"，":{"docs":{},"出":{"docs":{},"现":{"docs":{},"了":{"docs":{},"很":{"docs":{},"多":{"docs":{},"变":{"docs":{},"形":{"docs":{},"版":{"docs":{},"本":{"docs":{},"，":{"docs":{},"其":{"docs":{},"中":{"docs":{},"一":{"docs":{},"个":{"docs":{},"相":{"docs":{},"对":{"docs":{},"成":{"docs":{},"功":{"docs":{},"的":{"docs":{},"方":{"docs":{},"法":{"docs":{},"是":{"docs":{},"b":{"docs":{},"i":{"docs":{},"a":{"docs":{},"s":{"docs":{},"s":{"docs":{},"v":{"docs":{},"d":{"docs":{},"，":{"docs":{},"顾":{"docs":{},"名":{"docs":{},"思":{"docs":{},"义":{"docs":{},"，":{"docs":{},"即":{"docs":{},"带":{"docs":{},"有":{"docs":{},"偏":{"docs":{},"置":{"docs":{},"项":{"docs":{},"的":{"docs":{},"s":{"docs":{},"v":{"docs":{},"d":{"docs":{},"分":{"docs":{},"解":{"docs":{},"：":{"docs":{"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html":{"ref":"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html","tf":0.037037037037037035}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"其":{"docs":{},"他":{"docs":{},"文":{"docs":{},"档":{"docs":{},"出":{"docs":{},"现":{"docs":{},"的":{"docs":{},"频":{"docs":{},"率":{"docs":{},"低":{"docs":{},"。":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}},"本":{"docs":{},"文":{"docs":{},"档":{"docs":{},"出":{"docs":{},"现":{"docs":{},"的":{"docs":{},"频":{"docs":{},"率":{"docs":{},"高":{"docs":{},"；":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}},"地":{"docs":{},"文":{"docs":{},"件":{"docs":{},"系":{"docs":{},"统":{"docs":{},"创":{"docs":{},"建":{"docs":{},"一":{"docs":{},"个":{"docs":{},"如":{"docs":{},"下":{"docs":{},"的":{"docs":{},"文":{"docs":{},"本":{"docs":{},"文":{"docs":{},"件":{"docs":{},"：":{"docs":{},"/":{"docs":{},"h":{"docs":{},"o":{"docs":{},"m":{"docs":{},"e":{"docs":{},"/":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},"/":{"docs":{},"t":{"docs":{},"m":{"docs":{},"p":{"docs":{},"/":{"docs":{},"s":{"docs":{},"t":{"docs":{},"u":{"docs":{},"d":{"docs":{},"e":{"docs":{},"n":{"docs":{},"t":{"docs":{},".":{"docs":{},"t":{"docs":{},"x":{"docs":{},"t":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"c":{"docs":{},"e":{"docs":{},"n":{"docs":{},"t":{"docs":{},"o":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625}},"s":{"docs":{},"中":{"docs":{},"为":{"docs":{},"t":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{},".":{"docs":{},"t":{"docs":{},"x":{"docs":{},"t":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625}}}}}}}}}}}}}}}}}},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},"中":{"docs":{},"创":{"docs":{},"建":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625},"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}}},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}},"中":{"docs":{},"创":{"docs":{},"建":{"docs":{},"临":{"docs":{},"时":{"docs":{},"u":{"docs":{},"d":{"docs":{},"f":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}}}}}}}}},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"r":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428}}}}}},"s":{"docs":{},"b":{"docs":{},"i":{"docs":{},"n":{"docs":{},"中":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428}}}}}},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.01276595744680851},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}},"集":{"docs":{},"群":{"docs":{},"中":{"docs":{},"通":{"docs":{},"过":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"t":{"docs":{},"e":{"docs":{},"x":{"docs":{},"t":{"docs":{},"来":{"docs":{},"创":{"docs":{},"建":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}}}}}}}}}}}}}}}}}}}},"语":{"docs":{},"义":{"docs":{},"中":{"docs":{},"，":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"是":{"docs":{},"一":{"docs":{},"个":{"docs":{},"分":{"docs":{},"布":{"docs":{},"式":{"docs":{},"的":{"docs":{},"行":{"docs":{},"集":{"docs":{},"合":{"docs":{},"，":{"docs":{},"可":{"docs":{},"以":{"docs":{},"想":{"docs":{},"象":{"docs":{},"为":{"docs":{},"一":{"docs":{},"个":{"docs":{},"关":{"docs":{},"系":{"docs":{},"型":{"docs":{},"数":{"docs":{},"据":{"docs":{},"库":{"docs":{},"的":{"docs":{},"表":{"docs":{},"，":{"docs":{},"或":{"docs":{},"者":{"docs":{},"一":{"docs":{},"个":{"docs":{},"带":{"docs":{},"有":{"docs":{},"列":{"docs":{},"名":{"docs":{},"的":{"docs":{},"e":{"docs":{},"x":{"docs":{},"c":{"docs":{},"e":{"docs":{},"l":{"docs":{},"表":{"docs":{},"格":{"docs":{},"。":{"docs":{},"它":{"docs":{},"和":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"一":{"docs":{},"样":{"docs":{},"，":{"docs":{},"有":{"docs":{},"这":{"docs":{},"样":{"docs":{},"一":{"docs":{},"些":{"docs":{},"特":{"docs":{},"点":{"docs":{},"：":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"t":{"docs":{},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"m":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}}}}}},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{},"e":{"docs":{},"中":{"docs":{},"同":{"docs":{},"样":{"docs":{},"要":{"docs":{},"进":{"docs":{},"行":{"docs":{},"m":{"docs":{},"e":{"docs":{},"r":{"docs":{},"g":{"docs":{},"e":{"docs":{},"操":{"docs":{},"作":{"docs":{"day03_Hadoop/ha3.5.html":{"ref":"day03_Hadoop/ha3.5.html","tf":0.030303030303030304}}}}}}}}}}}}}}}}}}}},"d":{"docs":{},"d":{"docs":{},"中":{"docs":{},"无":{"docs":{},"法":{"docs":{},"看":{"docs":{},"出":{"docs":{},"，":{"docs":{},"解":{"docs":{},"释":{"docs":{},"性":{"docs":{},"不":{"docs":{},"强":{"docs":{},"，":{"docs":{},"无":{"docs":{},"法":{"docs":{},"告":{"docs":{},"诉":{"docs":{},"引":{"docs":{},"擎":{"docs":{},"信":{"docs":{},"息":{"docs":{},"，":{"docs":{},"没":{"docs":{},"法":{"docs":{},"详":{"docs":{},"细":{"docs":{},"优":{"docs":{},"化":{"docs":{},"。":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"社":{"docs":{},"区":{"docs":{},"版":{"docs":{},"的":{"docs":{},"基":{"docs":{},"础":{"docs":{},"上":{"docs":{},"做":{"docs":{},"了":{"docs":{},"一":{"docs":{},"些":{"docs":{},"修":{"docs":{},"改":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}}}}}}},"创":{"docs":{},"建":{"docs":{},"表":{"docs":{},"时":{"docs":{},"指":{"docs":{},"定":{"docs":{},"数":{"docs":{},"据":{"docs":{},"中":{"docs":{},"的":{"docs":{},"分":{"docs":{},"隔":{"docs":{},"符":{"docs":{},"，":{"docs":{},"h":{"docs":{},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}}}}}}}}}}},"写":{"docs":{},"入":{"docs":{},"数":{"docs":{},"据":{"docs":{},"时":{"docs":{},"自":{"docs":{},"动":{"docs":{},"创":{"docs":{},"建":{"docs":{},"分":{"docs":{},"区":{"docs":{},"(":{"docs":{},"包":{"docs":{},"括":{"docs":{},"目":{"docs":{},"录":{"docs":{},"结":{"docs":{},"构":{"docs":{},")":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}}}}}}}}}}}}}}},"之":{"docs":{},"前":{"docs":{},"的":{"docs":{},"案":{"docs":{},"例":{"docs":{},"中":{"docs":{},"使":{"docs":{},"用":{"docs":{},"临":{"docs":{},"时":{"docs":{},"自":{"docs":{},"定":{"docs":{},"义":{"docs":{},"函":{"docs":{},"数":{"docs":{},"(":{"docs":{},"函":{"docs":{},"数":{"docs":{},"功":{"docs":{},"能":{"docs":{},":":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}}}}}}}}}}}}}}}}}},"上":{"docs":{},"面":{"docs":{},"的":{"docs":{},"示":{"docs":{},"例":{"docs":{},"中":{"docs":{},"，":{"docs":{},"我":{"docs":{},"们":{"docs":{},"已":{"docs":{},"经":{"docs":{},"使":{"docs":{},"用":{"docs":{},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"n":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},".":{"docs":{},"t":{"docs":{},"a":{"docs":{},"b":{"docs":{},"l":{"docs":{},"e":{"docs":{},"s":{"docs":{},"（":{"docs":{},"）":{"docs":{},"方":{"docs":{},"法":{"docs":{},"查":{"docs":{},"询":{"docs":{},"h":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"中":{"docs":{},"的":{"docs":{},"表":{"docs":{},"。":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"分":{"docs":{},"布":{"docs":{},"式":{"docs":{},"系":{"docs":{},"统":{"docs":{},"环":{"docs":{},"境":{"docs":{},"中":{"docs":{},"，":{"docs":{},"无":{"docs":{},"法":{"docs":{},"避":{"docs":{},"免":{"docs":{},"系":{"docs":{},"统":{"docs":{},"出":{"docs":{},"错":{"docs":{},"或":{"docs":{},"者":{"docs":{},"宕":{"docs":{},"机":{"docs":{},"，":{"docs":{},"一":{"docs":{},"旦":{"docs":{},"h":{"docs":{},"r":{"docs":{},"e":{"docs":{},"g":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"v":{"docs":{},"e":{"docs":{},"r":{"docs":{},"意":{"docs":{},"外":{"docs":{},"退":{"docs":{},"出":{"docs":{},"，":{"docs":{},"m":{"docs":{},"e":{"docs":{},"m":{"docs":{},"s":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{},"e":{"docs":{},"中":{"docs":{},"的":{"docs":{},"内":{"docs":{},"存":{"docs":{},"数":{"docs":{},"据":{"docs":{},"就":{"docs":{},"会":{"docs":{},"丢":{"docs":{},"失":{"docs":{},"，":{"docs":{},"引":{"docs":{},"入":{"docs":{},"h":{"docs":{},"l":{"docs":{},"o":{"docs":{},"g":{"docs":{},"就":{"docs":{},"是":{"docs":{},"防":{"docs":{},"止":{"docs":{},"这":{"docs":{},"种":{"docs":{},"情":{"docs":{},"况":{"docs":{},"。":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"$":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"_":{"docs":{},"h":{"docs":{},"o":{"docs":{},"m":{"docs":{},"e":{"docs":{},"/":{"docs":{},"s":{"docs":{},"b":{"docs":{},"i":{"docs":{},"n":{"docs":{},"目":{"docs":{},"录":{"docs":{},"下":{"docs":{},"执":{"docs":{},"行":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}}}}}}}}}}}}}}}}}}}},"浏":{"docs":{},"览":{"docs":{},"器":{"docs":{},"访":{"docs":{},"问":{"docs":{},"当":{"docs":{},"前":{"docs":{},"c":{"docs":{},"e":{"docs":{},"n":{"docs":{},"t":{"docs":{},"o":{"docs":{},"s":{"docs":{},"的":{"4":{"0":{"4":{"0":{"docs":{},"端":{"docs":{},"口":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}}}}},"通":{"docs":{},"过":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"a":{"docs":{},"l":{"docs":{},"l":{"docs":{},"e":{"docs":{},"l":{"docs":{},"i":{"docs":{},"z":{"docs":{},"e":{"docs":{},"方":{"docs":{},"法":{"docs":{},"创":{"docs":{},"建":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}}}}}}}}}}}}}}}}},"新":{"docs":{},"闻":{"docs":{},"类":{"docs":{},"网":{"docs":{},"站":{"docs":{},"中":{"docs":{},"，":{"docs":{},"经":{"docs":{},"常":{"docs":{},"要":{"docs":{},"衡":{"docs":{},"量":{"docs":{},"一":{"docs":{},"条":{"docs":{},"网":{"docs":{},"络":{"docs":{},"新":{"docs":{},"闻":{"docs":{},"的":{"docs":{},"页":{"docs":{},"面":{"docs":{},"访":{"docs":{},"问":{"docs":{},"量":{"docs":{},"，":{"docs":{},"最":{"docs":{},"常":{"docs":{},"见":{"docs":{},"的":{"docs":{},"就":{"docs":{},"是":{"docs":{},"u":{"docs":{},"v":{"docs":{},"和":{"docs":{},"p":{"docs":{},"v":{"docs":{},"，":{"docs":{},"如":{"docs":{},"果":{"docs":{},"在":{"docs":{},"所":{"docs":{},"有":{"docs":{},"新":{"docs":{},"闻":{"docs":{},"中":{"docs":{},"找":{"docs":{},"到":{"docs":{},"访":{"docs":{},"问":{"docs":{},"最":{"docs":{},"多":{"docs":{},"的":{"docs":{},"前":{"docs":{},"几":{"docs":{},"条":{"docs":{},"新":{"docs":{},"闻":{"docs":{},"，":{"docs":{},"t":{"docs":{},"o":{"docs":{},"p":{"docs":{},"n":{"docs":{},"是":{"docs":{},"最":{"docs":{},"常":{"docs":{},"见":{"docs":{},"的":{"docs":{},"指":{"docs":{},"标":{"docs":{},"。":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"系":{"docs":{},"统":{"docs":{},"上":{"docs":{},"执":{"docs":{},"行":{"docs":{},"指":{"docs":{},"令":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}}}}}},"i":{"docs":{},"p":{"docs":{},"日":{"docs":{},"志":{"docs":{},"信":{"docs":{},"息":{"docs":{},"中":{"docs":{},"，":{"docs":{},"我":{"docs":{},"们":{"docs":{},"只":{"docs":{},"需":{"docs":{},"要":{"docs":{},"关":{"docs":{},"心":{"docs":{},"i":{"docs":{},"p":{"docs":{},"这":{"docs":{},"一":{"docs":{},"个":{"docs":{},"维":{"docs":{},"度":{"docs":{},"就":{"docs":{},"可":{"docs":{},"以":{"docs":{},"了":{"docs":{},"，":{"docs":{},"其":{"docs":{},"他":{"docs":{},"的":{"docs":{},"不":{"docs":{},"做":{"docs":{},"介":{"docs":{},"绍":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"互":{"docs":{},"联":{"docs":{},"网":{"docs":{},"中":{"docs":{},"，":{"docs":{},"我":{"docs":{},"们":{"docs":{},"经":{"docs":{},"常":{"docs":{},"会":{"docs":{},"见":{"docs":{},"到":{"docs":{},"城":{"docs":{},"市":{"docs":{},"热":{"docs":{},"点":{"docs":{},"图":{"docs":{},"这":{"docs":{},"样":{"docs":{},"的":{"docs":{},"报":{"docs":{},"表":{"docs":{},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"例":{"docs":{},"如":{"docs":{},"在":{"docs":{},"百":{"docs":{},"度":{"docs":{},"统":{"docs":{},"计":{"docs":{},"中":{"docs":{},"，":{"docs":{},"会":{"docs":{},"统":{"docs":{},"计":{"docs":{},"今":{"docs":{},"年":{"docs":{},"的":{"docs":{},"热":{"docs":{},"门":{"docs":{},"旅":{"docs":{},"游":{"docs":{},"城":{"docs":{},"市":{"docs":{},"、":{"docs":{},"热":{"docs":{},"门":{"docs":{},"报":{"docs":{},"考":{"docs":{},"学":{"docs":{},"校":{"docs":{},"等":{"docs":{},"，":{"docs":{},"会":{"docs":{},"将":{"docs":{},"这":{"docs":{},"样":{"docs":{},"的":{"docs":{},"信":{"docs":{},"息":{"docs":{},"显":{"docs":{},"示":{"docs":{},"在":{"docs":{},"热":{"docs":{},"点":{"docs":{},"图":{"docs":{},"中":{"docs":{},"。":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"j":{"docs":{},"v":{"docs":{},"m":{"docs":{},"(":{"docs":{},"j":{"docs":{},"a":{"docs":{},"v":{"docs":{},"a":{"docs":{},"虚":{"docs":{},"拟":{"docs":{},"机":{"docs":{},")":{"docs":{},"中":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}}}}}}}}}}}}}},"内":{"docs":{},"部":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}}},"p":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"a":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}},"特":{"docs":{},"征":{"docs":{},"中":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}},"如":{"docs":{"Hive&HBase/06_hbase数据模型.html":{"ref":"Hive&HBase/06_hbase数据模型.html","tf":0.020833333333333332}},"下":{"docs":{},"转":{"docs":{},"化":{"docs":{},"公":{"docs":{},"式":{"docs":{},":":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}},"何":{"docs":{},"选":{"docs":{},"择":{"docs":{},"余":{"docs":{},"弦":{"docs":{},"相":{"docs":{},"似":{"docs":{},"度":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}},"平":{"docs":{},"衡":{"docs":{},"大":{"docs":{},"众":{"docs":{},"口":{"docs":{},"味":{"docs":{},"和":{"docs":{},"小":{"docs":{},"众":{"docs":{},"需":{"docs":{},"求":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}}}}}}},"实":{"docs":{},"时":{"docs":{},"兴":{"docs":{},"趣":{"docs":{},"和":{"docs":{},"长":{"docs":{},"期":{"docs":{},"兴":{"docs":{},"趣":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}}}}}}},"短":{"docs":{},"期":{"docs":{},"产":{"docs":{},"品":{"docs":{},"体":{"docs":{},"验":{"docs":{},"和":{"docs":{},"长":{"docs":{},"期":{"docs":{},"系":{"docs":{},"统":{"docs":{},"生":{"docs":{},"态":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}}}}}}}}}}}}},"存":{"docs":{},"储":{"docs":{},"持":{"docs":{},"续":{"docs":{},"增":{"docs":{},"长":{"docs":{},"的":{"docs":{},"海":{"docs":{},"量":{"docs":{},"网":{"docs":{},"页":{"docs":{},":":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282}}}}}}}}}}}}}},"对":{"docs":{},"持":{"docs":{},"续":{"docs":{},"增":{"docs":{},"长":{"docs":{},"的":{"docs":{},"海":{"docs":{},"量":{"docs":{},"网":{"docs":{},"页":{"docs":{},"进":{"docs":{},"行":{"docs":{},"排":{"docs":{},"序":{"docs":{},":":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282}}}}}}}}}}}}}}}}},"使":{"docs":{},"用":{"docs":{},"h":{"docs":{},"a":{"docs":{},"p":{"docs":{},"p":{"docs":{},"y":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}}}}}}}}},"果":{"docs":{},"各":{"docs":{},"个":{"docs":{},"迭":{"docs":{},"代":{"docs":{},"器":{"docs":{},"的":{"docs":{},"元":{"docs":{},"素":{"docs":{},"个":{"docs":{},"数":{"docs":{},"不":{"docs":{},"一":{"docs":{},"致":{"docs":{},"，":{"docs":{},"则":{"docs":{},"返":{"docs":{},"回":{"docs":{},"列":{"docs":{},"表":{"docs":{},"长":{"docs":{},"度":{"docs":{},"与":{"docs":{},"最":{"docs":{},"短":{"docs":{},"的":{"docs":{},"对":{"docs":{},"象":{"docs":{},"相":{"docs":{},"同":{"docs":{},"，":{"docs":{},"利":{"docs":{},"用":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"我":{"docs":{},"们":{"docs":{},"将":{"docs":{},"评":{"docs":{},"分":{"docs":{},"看":{"docs":{},"作":{"docs":{},"是":{"docs":{},"一":{"docs":{},"个":{"docs":{},"连":{"docs":{},"续":{"docs":{},"的":{"docs":{},"值":{"docs":{},"而":{"docs":{},"不":{"docs":{},"是":{"docs":{},"离":{"docs":{},"散":{"docs":{},"的":{"docs":{},"值":{"docs":{},"，":{"docs":{},"那":{"docs":{},"么":{"docs":{},"就":{"docs":{},"可":{"docs":{},"以":{"docs":{},"借":{"docs":{},"助":{"docs":{},"线":{"docs":{},"性":{"docs":{},"回":{"docs":{},"归":{"docs":{},"思":{"docs":{},"想":{"docs":{},"来":{"docs":{},"预":{"docs":{},"测":{"docs":{},"目":{"docs":{},"标":{"docs":{},"用":{"docs":{},"户":{"docs":{},"对":{"docs":{},"某":{"docs":{},"物":{"docs":{},"品":{"docs":{},"的":{"docs":{},"评":{"docs":{},"分":{"docs":{},"。":{"docs":{},"其":{"docs":{},"中":{"docs":{},"一":{"docs":{},"种":{"docs":{},"实":{"docs":{},"现":{"docs":{},"策":{"docs":{},"略":{"docs":{},"被":{"docs":{},"称":{"docs":{},"为":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"i":{"docs":{},"n":{"docs":{},"e":{"docs":{},"（":{"docs":{},"基":{"docs":{},"准":{"docs":{},"预":{"docs":{},"测":{"docs":{},"）":{"docs":{},"。":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"u":{"docs":{},"i":{"docs":{},"d":{"docs":{},"或":{"docs":{},"i":{"docs":{},"i":{"docs":{},"d":{"docs":{},"不":{"docs":{},"在":{"docs":{},"，":{"docs":{},"我":{"docs":{},"们":{"docs":{},"使":{"docs":{},"用":{"docs":{},"全":{"docs":{},"剧":{"docs":{},"平":{"docs":{},"均":{"docs":{},"分":{"docs":{},"作":{"docs":{},"为":{"docs":{},"预":{"docs":{},"测":{"docs":{},"结":{"docs":{},"果":{"docs":{},"返":{"docs":{},"回":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.003795066413662239}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"取":{"docs":{},"不":{"docs":{},"到":{"docs":{},"就":{"docs":{},"返":{"docs":{},"回":{"docs":{},"[":{"docs":{},"]":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}},"电":{"docs":{},"影":{"docs":{},"没":{"docs":{},"有":{"docs":{},"标":{"docs":{},"签":{"docs":{},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"那":{"docs":{},"么":{"docs":{},"就":{"docs":{},"替":{"docs":{},"换":{"docs":{},"为":{"docs":{},"空":{"docs":{},"列":{"docs":{},"表":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}}}}},"是":{"docs":{},"文":{"docs":{},"件":{"docs":{},"，":{"docs":{},"则":{"docs":{},"按":{"docs":{},"照":{"docs":{},"如":{"docs":{},"下":{"docs":{},"格":{"docs":{},"式":{"docs":{},"返":{"docs":{},"回":{"docs":{},"文":{"docs":{},"件":{"docs":{},"信":{"docs":{},"息":{"docs":{},"：":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625}}}}}}}}}}}}}}}}}}},"目":{"docs":{},"录":{"docs":{},"，":{"docs":{},"则":{"docs":{},"返":{"docs":{},"回":{"docs":{},"它":{"docs":{},"直":{"docs":{},"接":{"docs":{},"子":{"docs":{},"文":{"docs":{},"件":{"docs":{},"的":{"docs":{},"一":{"docs":{},"个":{"docs":{},"列":{"docs":{},"表":{"docs":{},"，":{"docs":{},"就":{"docs":{},"像":{"docs":{},"在":{"docs":{},"u":{"docs":{},"n":{"docs":{},"i":{"docs":{},"x":{"docs":{},"中":{"docs":{},"一":{"docs":{},"样":{"docs":{},"。":{"docs":{},"目":{"docs":{},"录":{"docs":{},"返":{"docs":{},"回":{"docs":{},"列":{"docs":{},"表":{"docs":{},"的":{"docs":{},"信":{"docs":{},"息":{"docs":{},"如":{"docs":{},"下":{"docs":{},"：":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"没":{"docs":{},"有":{"docs":{},"通":{"docs":{},"用":{"docs":{},"资":{"docs":{},"源":{"docs":{},"管":{"docs":{},"理":{"docs":{},"系":{"docs":{},"统":{"docs":{},"，":{"docs":{},"只":{"docs":{},"能":{"docs":{},"为":{"docs":{},"多":{"docs":{},"个":{"docs":{},"集":{"docs":{},"群":{"docs":{},"分":{"docs":{},"别":{"docs":{},"提":{"docs":{},"供":{"docs":{},"数":{"docs":{},"据":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}}}}}}}}}}}}}}}}}}}},"u":{"docs":{},"p":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"b":{"docs":{},"y":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{},"，":{"docs":{},"我":{"docs":{},"们":{"docs":{},"需":{"docs":{},"要":{"docs":{},"将":{"docs":{},"每":{"docs":{},"一":{"docs":{},"秒":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"计":{"docs":{},"算":{"docs":{},"好":{"docs":{},"放":{"docs":{},"入":{"docs":{},"m":{"docs":{},"y":{"docs":{},"s":{"docs":{},"q":{"docs":{},"l":{"docs":{},"中":{"docs":{},"取":{"docs":{},"，":{"docs":{},"再":{"docs":{},"用":{"docs":{},"m":{"docs":{},"y":{"docs":{},"s":{"docs":{},"q":{"docs":{},"l":{"docs":{},"来":{"docs":{},"进":{"docs":{},"行":{"docs":{},"统":{"docs":{},"计":{"docs":{},"计":{"docs":{},"算":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"不":{"docs":{},"想":{"docs":{},"成":{"docs":{},"为":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},"专":{"docs":{},"家":{"docs":{},",":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}}}}}}}},"足":{"5":{"0":{"0":{"docs":{},"个":{"docs":{},"，":{"docs":{},"那":{"docs":{},"么":{"docs":{},"随":{"docs":{},"机":{"docs":{},"选":{"docs":{},"出":{"docs":{},"n":{"docs":{},"e":{"docs":{},"e":{"docs":{},"d":{"docs":{},"个":{"docs":{},"广":{"docs":{},"告":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}}}},"docs":{}},"docs":{}},"docs":{}}},"重":{"docs":{},"复":{"docs":{},"加":{"docs":{},"载":{"docs":{},"同":{"docs":{},"名":{"docs":{},"文":{"docs":{},"件":{"docs":{},"，":{"docs":{},"不":{"docs":{},"会":{"docs":{},"报":{"docs":{},"错":{"docs":{},"，":{"docs":{},"会":{"docs":{},"自":{"docs":{},"动":{"docs":{},"创":{"docs":{},"建":{"docs":{},"一":{"docs":{},"个":{"docs":{},"*":{"docs":{},"_":{"docs":{},"c":{"docs":{},"o":{"docs":{},"p":{"docs":{},"y":{"docs":{},"_":{"1":{"docs":{},".":{"docs":{},"t":{"docs":{},"x":{"docs":{},"t":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"你":{"docs":{},"是":{"docs":{},"使":{"docs":{},"用":{"docs":{},"h":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"自":{"docs":{},"带":{"docs":{},"的":{"docs":{},"z":{"docs":{},"k":{"docs":{},"就":{"docs":{},"是":{"docs":{},"t":{"docs":{},"r":{"docs":{},"u":{"docs":{},"e":{"docs":{},"，":{"docs":{},"如":{"docs":{},"果":{"docs":{},"使":{"docs":{},"用":{"docs":{},"自":{"docs":{},"己":{"docs":{},"的":{"docs":{},"z":{"docs":{},"k":{"docs":{},"就":{"docs":{},"是":{"docs":{},"f":{"docs":{},"a":{"docs":{},"l":{"docs":{},"s":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"还":{"docs":{},"没":{"docs":{},"有":{"docs":{},"任":{"docs":{},"何":{"docs":{},"表":{"docs":{},"，":{"docs":{},"可":{"docs":{},"使":{"docs":{},"用":{"docs":{},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"n":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},".":{"docs":{},"c":{"docs":{},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"_":{"docs":{},"t":{"docs":{},"a":{"docs":{},"b":{"docs":{},"l":{"docs":{},"e":{"docs":{},"（":{"docs":{},"）":{"docs":{},"创":{"docs":{},"建":{"docs":{},"一":{"docs":{},"个":{"docs":{},"新":{"docs":{},"表":{"docs":{},"：":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"只":{"docs":{},"想":{"docs":{},"仅":{"docs":{},"关":{"docs":{},"闭":{"docs":{},"s":{"docs":{},"t":{"docs":{},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"m":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}}}}}}}}}}},"数":{"docs":{},"据":{"docs":{},"量":{"docs":{},"大":{"docs":{},"，":{"docs":{},"应":{"docs":{},"考":{"docs":{},"虑":{"docs":{},"的":{"docs":{},"是":{"docs":{},"增":{"docs":{},"加":{"docs":{},"内":{"docs":{},"存":{"docs":{},"、":{"docs":{},"或":{"docs":{},"限":{"docs":{},"制":{"docs":{},"迭":{"docs":{},"代":{"docs":{},"次":{"docs":{},"数":{"docs":{},"和":{"docs":{},"训":{"docs":{},"练":{"docs":{},"数":{"docs":{},"据":{"docs":{},"量":{"docs":{},"级":{"docs":{},"等":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"透":{"docs":{},"视":{"docs":{},"的":{"docs":{},"字":{"docs":{},"段":{"docs":{},"中":{"docs":{},"的":{"docs":{},"不":{"docs":{},"同":{"docs":{},"属":{"docs":{},"性":{"docs":{},"值":{"docs":{},"超":{"docs":{},"过":{"1":{"0":{"0":{"0":{"0":{"docs":{},"个":{"docs":{},"，":{"docs":{},"则":{"docs":{},"需":{"docs":{},"要":{"docs":{},"设":{"docs":{},"置":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},".":{"docs":{},"s":{"docs":{},"q":{"docs":{},"l":{"docs":{},".":{"docs":{},"p":{"docs":{},"i":{"docs":{},"v":{"docs":{},"o":{"docs":{},"t":{"docs":{},"m":{"docs":{},"a":{"docs":{},"x":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"s":{"docs":{},"，":{"docs":{},"否":{"docs":{},"则":{"docs":{},"计":{"docs":{},"算":{"docs":{},"过":{"docs":{},"程":{"docs":{},"中":{"docs":{},"会":{"docs":{},"出":{"docs":{},"现":{"docs":{},"错":{"docs":{},"误":{"docs":{},"。":{"docs":{},"文":{"docs":{},"档":{"docs":{},"介":{"docs":{},"绍":{"docs":{},"。":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}}}}},"预":{"docs":{},"测":{"docs":{},"值":{"docs":{},"是":{"0":{"docs":{},"，":{"docs":{},"其":{"docs":{},"概":{"docs":{},"率":{"docs":{},"是":{"0":{"docs":{},".":{"9":{"2":{"4":{"8":{"docs":{},"，":{"docs":{},"那":{"docs":{},"么":{"docs":{},"反":{"docs":{},"之":{"docs":{},"可":{"docs":{},"推":{"docs":{},"出":{"1":{"docs":{},"的":{"docs":{},"可":{"docs":{},"能":{"docs":{},"性":{"docs":{},"就":{"docs":{},"是":{"1":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}},"docs":{}}}}}}}},"docs":{}}}}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}}}}},"docs":{}}}}},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"i":{"docs":{},"s":{"docs":{},"所":{"docs":{},"在":{"docs":{},"机":{"docs":{},"器":{"docs":{},"，":{"docs":{},"内":{"docs":{},"存":{"docs":{},"不":{"docs":{},"足":{"docs":{},"，":{"docs":{},"会":{"docs":{},"抛":{"docs":{},"出":{"docs":{},"异":{"docs":{},"常":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}}}}}}}},"达":{"docs":{},"到":{"5":{"0":{"0":{"docs":{},"个":{"docs":{},"则":{"docs":{},"退":{"docs":{},"出":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}},"docs":{}},"docs":{}},"docs":{}}}},"求":{"docs":{},"b":{"docs":{},"_":{"docs":{},"u":{"docs":{},"时":{"docs":{},"，":{"docs":{},"将":{"docs":{},"b":{"docs":{},"_":{"docs":{},"i":{"docs":{},"看":{"docs":{},"作":{"docs":{},"是":{"docs":{},"已":{"docs":{},"知":{"docs":{},"；":{"docs":{},"求":{"docs":{},"b":{"docs":{},"_":{"docs":{},"i":{"docs":{},"时":{"docs":{},"，":{"docs":{},"将":{"docs":{},"b":{"docs":{},"_":{"docs":{},"u":{"docs":{},"​":{"docs":{},"看":{"docs":{},"作":{"docs":{},"是":{"docs":{},"已":{"docs":{},"知":{"docs":{},"；":{"docs":{},"如":{"docs":{},"此":{"docs":{},"反":{"docs":{},"复":{"docs":{},"交":{"docs":{},"替":{"docs":{},"，":{"docs":{},"不":{"docs":{},"断":{"docs":{},"更":{"docs":{},"新":{"docs":{},"二":{"docs":{},"者":{"docs":{},"的":{"docs":{},"值":{"docs":{},"，":{"docs":{},"求":{"docs":{},"得":{"docs":{},"最":{"docs":{},"终":{"docs":{},"的":{"docs":{},"结":{"docs":{},"果":{"docs":{},"。":{"docs":{},"这":{"docs":{},"就":{"docs":{},"是":{"docs":{},"交":{"docs":{},"替":{"docs":{},"最":{"docs":{},"小":{"docs":{},"二":{"docs":{},"乘":{"docs":{},"法":{"docs":{},"（":{"docs":{},"a":{"docs":{},"l":{"docs":{},"s":{"docs":{},"）":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"将":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}},"所":{"docs":{},"有":{"docs":{},"用":{"docs":{},"户":{"docs":{},"行":{"docs":{},"为":{"docs":{},"合":{"docs":{},"并":{"docs":{},"在":{"docs":{},"一":{"docs":{},"起":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}},"每":{"docs":{},"个":{"docs":{},"用":{"docs":{},"户":{"docs":{},"的":{"docs":{},"x":{"docs":{},"比":{"docs":{},"例":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"作":{"docs":{},"为":{"docs":{},"训":{"docs":{},"练":{"docs":{},"集":{"docs":{},"，":{"docs":{},"剩":{"docs":{},"余":{"docs":{},"的":{"docs":{},"作":{"docs":{},"为":{"docs":{},"测":{"docs":{},"试":{"docs":{},"集":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}}}}}}}}}}}}}}}}}}}}},"影":{"docs":{},"评":{"docs":{},"中":{"docs":{},"出":{"docs":{},"现":{"docs":{},"的":{"docs":{},"停":{"docs":{},"用":{"docs":{},"词":{"docs":{},"过":{"docs":{},"滤":{"docs":{},"掉":{"docs":{},"，":{"docs":{},"计":{"docs":{},"算":{"docs":{},"其":{"docs":{},"他":{"docs":{},"词":{"docs":{},"语":{"docs":{},"的":{"docs":{},"词":{"docs":{},"频":{"docs":{},"。":{"docs":{},"以":{"docs":{},"出":{"docs":{},"现":{"docs":{},"最":{"docs":{},"多":{"docs":{},"的":{"docs":{},"三":{"docs":{},"个":{"docs":{},"词":{"docs":{},"为":{"docs":{},"例":{"docs":{},"进":{"docs":{},"行":{"docs":{},"计":{"docs":{},"算":{"docs":{},"如":{"docs":{},"下":{"docs":{},"：":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"总":{"docs":{},"的":{"docs":{},"影":{"docs":{},"评":{"docs":{},"集":{"docs":{},"中":{"docs":{},"所":{"docs":{},"有":{"docs":{},"的":{"docs":{},"影":{"docs":{},"评":{"docs":{},"向":{"docs":{},"量":{"docs":{},"与":{"docs":{},"特":{"docs":{},"定":{"docs":{},"的":{"docs":{},"系":{"docs":{},"数":{"docs":{},"相":{"docs":{},"乘":{"docs":{},"求":{"docs":{},"和":{"docs":{},"，":{"docs":{},"得":{"docs":{},"到":{"docs":{},"这":{"docs":{},"部":{"docs":{},"电":{"docs":{},"影":{"docs":{},"的":{"docs":{},"综":{"docs":{},"合":{"docs":{},"影":{"docs":{},"评":{"docs":{},"向":{"docs":{},"量":{"docs":{},"，":{"docs":{},"与":{"docs":{},"电":{"docs":{},"影":{"docs":{},"的":{"docs":{},"基":{"docs":{},"本":{"docs":{},"属":{"docs":{},"性":{"docs":{},"结":{"docs":{},"合":{"docs":{},"构":{"docs":{},"建":{"docs":{},"视":{"docs":{},"频":{"docs":{},"的":{"docs":{},"物":{"docs":{},"品":{"docs":{},"画":{"docs":{},"像":{"docs":{},"，":{"docs":{},"同":{"docs":{},"理":{"docs":{},"构":{"docs":{},"建":{"docs":{},"用":{"docs":{},"户":{"docs":{},"画":{"docs":{},"像":{"docs":{},"，":{"docs":{},"可":{"docs":{},"采":{"docs":{},"用":{"docs":{},"多":{"docs":{},"种":{"docs":{},"方":{"docs":{},"法":{"docs":{},"计":{"docs":{},"算":{"docs":{},"物":{"docs":{},"品":{"docs":{},"画":{"docs":{},"像":{"docs":{},"和":{"docs":{},"用":{"docs":{},"户":{"docs":{},"画":{"docs":{},"像":{"docs":{},"之":{"docs":{},"间":{"docs":{},"的":{"docs":{},"相":{"docs":{},"似":{"docs":{},"度":{"docs":{},"，":{"docs":{},"为":{"docs":{},"用":{"docs":{},"户":{"docs":{},"做":{"docs":{},"出":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"。":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"电":{"docs":{},"影":{"docs":{},"的":{"docs":{},"分":{"docs":{},"类":{"docs":{},"词":{"docs":{},"直":{"docs":{},"接":{"docs":{},"作":{"docs":{},"为":{"docs":{},"每":{"docs":{},"部":{"docs":{},"电":{"docs":{},"影":{"docs":{},"的":{"docs":{},"画":{"docs":{},"像":{"docs":{},"标":{"docs":{},"签":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}}}}}},"类":{"docs":{},"别":{"docs":{},"词":{"docs":{},"分":{"docs":{},"开":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}},"的":{"docs":{},"添":{"docs":{},"加":{"docs":{},"进":{"docs":{},"去":{"docs":{},"，":{"docs":{},"并":{"docs":{},"设":{"docs":{},"置":{"docs":{},"权":{"docs":{},"重":{"docs":{},"值":{"docs":{},"为":{"1":{"docs":{},".":{"0":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}},"docs":{}}},"docs":{}}}}}}}}}}}}}}}}},"文":{"docs":{},"件":{"docs":{},"切":{"docs":{},"分":{"docs":{},"成":{"docs":{},"指":{"docs":{},"定":{"docs":{},"大":{"docs":{},"小":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"块":{"docs":{},",":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282}}}}}}}}}}}}}},"从":{"docs":{},"源":{"docs":{},"路":{"docs":{},"径":{"docs":{},"移":{"docs":{},"动":{"docs":{},"到":{"docs":{},"目":{"docs":{},"标":{"docs":{},"路":{"docs":{},"径":{"docs":{},"。":{"docs":{},"这":{"docs":{},"个":{"docs":{},"命":{"docs":{},"令":{"docs":{},"允":{"docs":{},"许":{"docs":{},"有":{"docs":{},"多":{"docs":{},"个":{"docs":{},"源":{"docs":{},"路":{"docs":{},"径":{"docs":{},"，":{"docs":{},"此":{"docs":{},"时":{"docs":{},"目":{"docs":{},"标":{"docs":{},"路":{"docs":{},"径":{"docs":{},"必":{"docs":{},"须":{"docs":{},"是":{"docs":{},"一":{"docs":{},"个":{"docs":{},"目":{"docs":{},"录":{"docs":{},"。":{"docs":{},"不":{"docs":{},"允":{"docs":{},"许":{"docs":{},"在":{"docs":{},"不":{"docs":{},"同":{"docs":{},"的":{"docs":{},"文":{"docs":{},"件":{"docs":{},"系":{"docs":{},"统":{"docs":{},"间":{"docs":{},"移":{"docs":{},"动":{"docs":{},"文":{"docs":{},"件":{"docs":{},"。":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},"中":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625}}}}}}},"源":{"docs":{},"文":{"docs":{},"件":{"docs":{},"输":{"docs":{},"出":{"docs":{},"为":{"docs":{},"文":{"docs":{},"本":{"docs":{},"格":{"docs":{},"式":{"docs":{},"。":{"docs":{},"允":{"docs":{},"许":{"docs":{},"的":{"docs":{},"格":{"docs":{},"式":{"docs":{},"是":{"docs":{},"z":{"docs":{},"i":{"docs":{},"p":{"docs":{},"和":{"docs":{},"t":{"docs":{},"e":{"docs":{},"x":{"docs":{},"t":{"docs":{},"r":{"docs":{},"e":{"docs":{},"c":{"docs":{},"o":{"docs":{},"r":{"docs":{},"d":{"docs":{},"i":{"docs":{},"n":{"docs":{},"p":{"docs":{},"u":{"docs":{},"t":{"docs":{},"s":{"docs":{},"t":{"docs":{},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"m":{"docs":{},"。":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"作":{"docs":{},"业":{"docs":{},"拆":{"docs":{},"分":{"docs":{},"成":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"阶":{"docs":{},"段":{"docs":{},"和":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{},"e":{"docs":{},"阶":{"docs":{},"段":{"docs":{"day03_Hadoop/ha3.2.html":{"ref":"day03_Hadoop/ha3.2.html","tf":0.02040816326530612}}}}}}}}}}}}}}}}}}}}},"数":{"docs":{},"据":{"docs":{},"复":{"docs":{},"制":{"docs":{},"到":{"docs":{},"其":{"docs":{},"他":{"docs":{},"服":{"docs":{},"务":{"docs":{},"器":{"docs":{},"上":{"docs":{"day03_Hadoop/ha4.2.html":{"ref":"day03_Hadoop/ha4.2.html","tf":0.0196078431372549}}}}}}}}}}},"l":{"docs":{},"o":{"docs":{},"a":{"docs":{},"d":{"docs":{},"到":{"docs":{},"表":{"docs":{},"中":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}}}}},"该":{"docs":{},"块":{"docs":{},"磁":{"docs":{},"盘":{"docs":{},"上":{"docs":{},"存":{"docs":{},"储":{"docs":{},"的":{"docs":{},"所":{"docs":{},"有":{"docs":{"day03_Hadoop/ha4.2.html":{"ref":"day03_Hadoop/ha4.2.html","tf":0.0196078431372549}}}}}}}}}}}},"上":{"docs":{},"面":{"docs":{},"三":{"docs":{},"个":{"docs":{},"部":{"docs":{},"分":{"docs":{},"整":{"docs":{},"合":{"docs":{},"起":{"docs":{},"来":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}},"聚":{"docs":{},"合":{"docs":{},"结":{"docs":{},"果":{"docs":{},"转":{"docs":{},"换":{"docs":{},"成":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}}}}}}}}}},"应":{"docs":{},"用":{"docs":{},"产":{"docs":{},"生":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"导":{"docs":{},"入":{"docs":{},"到":{"docs":{},"大":{"docs":{},"数":{"docs":{},"据":{"docs":{},"系":{"docs":{},"统":{"docs":{},",":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}}}}}}}}}},"问":{"docs":{},"题":{"docs":{},"交":{"docs":{},"给":{"docs":{},"数":{"docs":{},"据":{"docs":{},"分":{"docs":{},"析":{"docs":{},"团":{"docs":{},"队":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}}}},"元":{"docs":{},"数":{"docs":{},"据":{"docs":{},"存":{"docs":{},"储":{"docs":{},"在":{"docs":{},"数":{"docs":{},"据":{"docs":{},"库":{"docs":{},"中":{"docs":{},"。":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}}}},"a":{"docs":{},"r":{"docs":{},"r":{"docs":{},"a":{"docs":{},"y":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}}},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}}},"用":{"docs":{},"户":{"docs":{},"查":{"docs":{},"看":{"docs":{},"的":{"docs":{},"关":{"docs":{},"键":{"docs":{},"字":{"docs":{},"和":{"docs":{},"频":{"docs":{},"率":{"docs":{},"合":{"docs":{},"并":{"docs":{},"成":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0022675736961451248}}}}}}}}}}}}}},"的":{"docs":{},"阅":{"docs":{},"读":{"docs":{},"偏":{"docs":{},"好":{"docs":{},"结":{"docs":{},"果":{"docs":{},"保":{"docs":{},"存":{"docs":{},"到":{"docs":{},"表":{"docs":{},"中":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}}}}}}}}}}}},"f":{"docs":{},"u":{"docs":{},"n":{"docs":{},"c":{"docs":{},"函":{"docs":{},"数":{"docs":{},"作":{"docs":{},"用":{"docs":{},"到":{"docs":{},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{},"的":{"docs":{},"每":{"docs":{},"一":{"docs":{},"个":{"docs":{},"元":{"docs":{},"素":{"docs":{},"上":{"docs":{},"，":{"docs":{},"生":{"docs":{},"成":{"docs":{},"一":{"docs":{},"个":{"docs":{},"新":{"docs":{},"的":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"返":{"docs":{},"回":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{},"相":{"docs":{},"同":{"docs":{},"的":{"docs":{},"键":{"docs":{},"值":{"docs":{},"对":{"docs":{},"，":{"docs":{},"按":{"docs":{},"照":{"docs":{},"f":{"docs":{},"u":{"docs":{},"n":{"docs":{},"c":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"进":{"docs":{},"行":{"docs":{},"计":{"docs":{},"算":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}}}}}}}}}}}}}}}}}},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"目":{"docs":{},"录":{"docs":{},"下":{"docs":{},"的":{"docs":{},"p":{"docs":{},"y":{"docs":{},"t":{"docs":{},"h":{"docs":{},"o":{"docs":{},"n":{"docs":{},"目":{"docs":{},"录":{"docs":{},"下":{"docs":{},"的":{"docs":{},"p":{"docs":{},"y":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"整":{"docs":{},"体":{"docs":{},"拷":{"docs":{},"贝":{"docs":{},"到":{"docs":{},"p":{"docs":{},"y":{"docs":{},"c":{"docs":{},"h":{"docs":{},"a":{"docs":{},"r":{"docs":{},"m":{"docs":{},"使":{"docs":{},"用":{"docs":{},"的":{"docs":{},"p":{"docs":{},"y":{"docs":{},"t":{"docs":{},"h":{"docs":{},"o":{"docs":{},"n":{"docs":{},"环":{"docs":{},"境":{"docs":{},"下":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"下":{"docs":{},"图":{"docs":{},"中":{"docs":{},"的":{"docs":{},"p":{"docs":{},"y":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}}}}}}}}}},"代":{"docs":{},"码":{"docs":{},"上":{"docs":{},"传":{"docs":{},"到":{"docs":{},"远":{"docs":{},"程":{"docs":{},"c":{"docs":{},"e":{"docs":{},"n":{"docs":{},"t":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}}}}}}}}}},"模":{"docs":{},"型":{"docs":{},"进":{"docs":{},"行":{"docs":{},"存":{"docs":{},"储":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}}}}}},"p":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"中":{"docs":{},"的":{"docs":{},"空":{"docs":{},"值":{"docs":{},"所":{"docs":{},"在":{"docs":{},"行":{"docs":{},"数":{"docs":{},"据":{"docs":{},"剔":{"docs":{},"除":{"docs":{},"后":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"作":{"docs":{},"为":{"docs":{},"训":{"docs":{},"练":{"docs":{},"样":{"docs":{},"本":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"应":{"docs":{},"用":{"docs":{},"杰":{"docs":{},"卡":{"docs":{},"德":{"docs":{},"相":{"docs":{},"似":{"docs":{},"度":{"docs":{},"实":{"docs":{},"现":{"docs":{},"简":{"docs":{},"单":{"docs":{},"协":{"docs":{},"同":{"docs":{},"过":{"docs":{},"滤":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"案":{"docs":{},"例":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}}}}}},"基":{"docs":{},"于":{"docs":{},"物":{"docs":{},"品":{"docs":{},"的":{"docs":{},"协":{"docs":{},"同":{"docs":{},"过":{"docs":{},"滤":{"docs":{},"实":{"docs":{},"现":{"docs":{},"电":{"docs":{},"影":{"docs":{},"评":{"docs":{},"分":{"docs":{},"预":{"docs":{},"测":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.0016233766233766235}}}}}}}}}}}}}}}}},"用":{"docs":{},"户":{"docs":{},"的":{"docs":{},"协":{"docs":{},"同":{"docs":{},"过":{"docs":{},"滤":{"docs":{},"实":{"docs":{},"现":{"docs":{},"电":{"docs":{},"影":{"docs":{},"评":{"docs":{},"分":{"docs":{},"预":{"docs":{},"测":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.0016233766233766235}}}}}}}}}}}}}}}}}}},"采":{"docs":{},"集":{"docs":{},"数":{"docs":{},"据":{"docs":{},",":{"docs":{},"数":{"docs":{},"据":{"docs":{},"库":{"docs":{},"数":{"docs":{},"据":{"docs":{},"放":{"docs":{},"到":{"docs":{},"一":{"docs":{},"起":{"docs":{},"分":{"docs":{},"析":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}}}}}}}}}}}}}}}}},"度":{"docs":{},"量":{"docs":{},"两":{"docs":{},"个":{"docs":{},"变":{"docs":{},"量":{"docs":{},"是":{"docs":{},"不":{"docs":{},"是":{"docs":{},"同":{"docs":{},"增":{"docs":{},"同":{"docs":{},"减":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}},"的":{"docs":{},"是":{"docs":{},"两":{"docs":{},"个":{"docs":{},"向":{"docs":{},"量":{"docs":{},"之":{"docs":{},"间":{"docs":{},"的":{"docs":{},"夹":{"docs":{},"角":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}},"当":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}},"夹":{"docs":{},"角":{"docs":{},"为":{"9":{"0":{"docs":{},"度":{"docs":{},"是":{"docs":{},"余":{"docs":{},"弦":{"docs":{},"值":{"docs":{},"为":{"0":{"docs":{},",":{"docs":{},"为":{"1":{"8":{"0":{"docs":{},"度":{"docs":{},"是":{"docs":{},"余":{"docs":{},"弦":{"docs":{},"值":{"docs":{},"为":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}},"docs":{}},"docs":{}},"docs":{}}}},"docs":{}}}}}}}},"docs":{}},"docs":{}}}},"多":{"docs":{},"个":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{},"e":{"docs":{},"任":{"docs":{},"务":{"docs":{},"要":{"docs":{},"用":{"docs":{},"到":{"docs":{},"相":{"docs":{},"同":{"docs":{},"的":{"docs":{},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}}}}}}}}}}}}}}}}}}}}}}}},"期":{"docs":{},"新":{"docs":{},"增":{"docs":{},"用":{"docs":{},"户":{"docs":{},"数":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}},"连":{"docs":{},"接":{"docs":{},"建":{"docs":{},"立":{"docs":{},"时":{"docs":{},",":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}}}},"存":{"docs":{},"在":{"docs":{},"多":{"docs":{},"个":{"docs":{},"版":{"docs":{},"本":{"docs":{},"时":{"docs":{},"，":{"docs":{},"不":{"docs":{},"指":{"docs":{},"定":{"docs":{},"很":{"docs":{},"可":{"docs":{},"能":{"docs":{},"会":{"docs":{},"导":{"docs":{},"致":{"docs":{},"出":{"docs":{},"错":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.007633587786259542},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}},"调":{"docs":{},"用":{"docs":{},"d":{"docs":{},"f":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},"时":{"docs":{},"才":{"docs":{},"开":{"docs":{},"始":{"docs":{},"进":{"docs":{},"行":{"docs":{},"计":{"docs":{},"算":{"docs":{},"，":{"docs":{},"这":{"docs":{},"里":{"docs":{},"的":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"计":{"docs":{},"算":{"docs":{},"的":{"docs":{},"是":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"的":{"docs":{},"条":{"docs":{},"目":{"docs":{},"数":{"docs":{},"，":{"docs":{},"也":{"docs":{},"就":{"docs":{},"是":{"docs":{},"共":{"docs":{},"有":{"docs":{},"多":{"docs":{},"少":{"docs":{},"个":{"docs":{},"分":{"docs":{},"组":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"需":{"docs":{},"要":{"docs":{},"p":{"docs":{},"i":{"docs":{},"v":{"docs":{},"o":{"docs":{},"t":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}},"前":{"docs":{},"用":{"docs":{},"户":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}},"得":{"docs":{},"到":{"docs":{},"的":{"docs":{},"就":{"docs":{},"是":{"docs":{},"交":{"docs":{},"集":{"docs":{},"元":{"docs":{},"素":{"docs":{},"的":{"docs":{},"个":{"docs":{},"数":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}},"需":{"docs":{},"要":{"docs":{},"的":{"docs":{},"运":{"docs":{},"营":{"docs":{},"数":{"docs":{},"据":{"docs":{},"报":{"docs":{},"告":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}}}}},"我":{"docs":{"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}},"们":{"docs":{},"要":{"docs":{},"预":{"docs":{},"测":{"docs":{},"用":{"docs":{},"户":{"1":{"docs":{},"对":{"docs":{},"物":{"docs":{},"品":{"docs":{},"e":{"docs":{},"的":{"docs":{},"评":{"docs":{},"分":{"docs":{},"，":{"docs":{},"那":{"docs":{},"么":{"docs":{},"可":{"docs":{},"以":{"docs":{},"根":{"docs":{},"据":{"docs":{},"与":{"docs":{},"物":{"docs":{},"品":{"docs":{},"e":{"docs":{},"最":{"docs":{},"近":{"docs":{},"邻":{"docs":{},"的":{"docs":{},"物":{"docs":{},"品":{"docs":{},"a":{"docs":{},"和":{"docs":{},"物":{"docs":{},"品":{"docs":{},"d":{"docs":{},"进":{"docs":{},"行":{"docs":{},"预":{"docs":{},"测":{"docs":{},"，":{"docs":{},"计":{"docs":{},"算":{"docs":{},"如":{"docs":{},"下":{"docs":{},"：":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}}}}}}}}}}}},"用":{"docs":{},"户":{"1":{"docs":{},"最":{"docs":{},"近":{"docs":{},"邻":{"docs":{},"的":{"docs":{},"用":{"docs":{},"户":{"2":{"docs":{},"和":{"docs":{},"用":{"docs":{},"户":{"3":{"docs":{},"进":{"docs":{},"行":{"docs":{},"预":{"docs":{},"测":{"docs":{},"，":{"docs":{},"计":{"docs":{},"算":{"docs":{},"如":{"docs":{},"下":{"docs":{},"：":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}},"docs":{}}}}},"docs":{}}}}}}}},"docs":{}}}}}}}}}}}}}}}}}}},"docs":{}}}}}},"可":{"docs":{},"以":{"docs":{},"使":{"docs":{},"用":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}},"的":{"docs":{},"目":{"docs":{},"标":{"docs":{},"也":{"docs":{},"就":{"docs":{},"转":{"docs":{},"化":{"docs":{},"为":{"docs":{},"寻":{"docs":{},"找":{"docs":{},"最":{"docs":{},"优":{"docs":{},"的":{"docs":{},"b":{"docs":{},"_":{"docs":{},"u":{"docs":{},"和":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}}}}}}},"只":{"docs":{},"能":{"docs":{},"对":{"docs":{},"最":{"docs":{},"重":{"docs":{},"要":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"进":{"docs":{},"行":{"docs":{},"统":{"docs":{},"计":{"docs":{},"和":{"docs":{},"分":{"docs":{},"析":{"docs":{},"(":{"docs":{},"决":{"docs":{},"策":{"docs":{},"数":{"docs":{},"据":{"docs":{},",":{"docs":{},"财":{"docs":{},"务":{"docs":{},"相":{"docs":{},"关":{"docs":{},")":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"是":{"docs":{},"在":{"docs":{},"对":{"docs":{},"非":{"docs":{},"搜":{"docs":{},"索":{"docs":{},"类":{"docs":{},"型":{"docs":{},"的":{"docs":{},"广":{"docs":{},"告":{"docs":{},"进":{"docs":{},"行":{"docs":{},"点":{"docs":{},"击":{"docs":{},"率":{"docs":{},"预":{"docs":{},"测":{"docs":{},"和":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"(":{"docs":{},"没":{"docs":{},"有":{"docs":{},"搜":{"docs":{},"索":{"docs":{},"词":{"docs":{},"、":{"docs":{},"没":{"docs":{},"有":{"docs":{},"广":{"docs":{},"告":{"docs":{},"的":{"docs":{},"内":{"docs":{},"容":{"docs":{},"特":{"docs":{},"征":{"docs":{},"信":{"docs":{},"息":{"docs":{},")":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"使":{"docs":{},"用":{"docs":{},"c":{"docs":{},"m":{"docs":{},"s":{"docs":{},"_":{"docs":{},"s":{"docs":{},"e":{"docs":{},"g":{"docs":{},"i":{"docs":{},"d":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}},"接":{"docs":{},"下":{"docs":{},"来":{"docs":{},"采":{"docs":{},"用":{"docs":{},"将":{"docs":{},"变":{"docs":{},"量":{"docs":{},"映":{"docs":{},"射":{"docs":{},"到":{"docs":{},"高":{"docs":{},"维":{"docs":{},"空":{"docs":{},"间":{"docs":{},"的":{"docs":{},"方":{"docs":{},"法":{"docs":{},"来":{"docs":{},"处":{"docs":{},"理":{"docs":{},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"即":{"docs":{},"将":{"docs":{},"缺":{"docs":{},"失":{"docs":{},"项":{"docs":{},"也":{"docs":{},"当":{"docs":{},"做":{"docs":{},"一":{"docs":{},"个":{"docs":{},"单":{"docs":{},"独":{"docs":{},"的":{"docs":{},"特":{"docs":{},"征":{"docs":{},"来":{"docs":{},"对":{"docs":{},"待":{"docs":{},"，":{"docs":{},"保":{"docs":{},"证":{"docs":{},"数":{"docs":{},"据":{"docs":{},"的":{"docs":{},"原":{"docs":{},"始":{"docs":{},"性":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"按":{"docs":{},"照":{"docs":{},"相":{"docs":{},"似":{"docs":{},"度":{"docs":{},"降":{"docs":{},"序":{"docs":{},"排":{"docs":{},"列":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}},"t":{"docs":{},"f":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}},"p":{"docs":{},"r":{"docs":{},"o":{"docs":{},"b":{"docs":{},"a":{"docs":{},"b":{"docs":{},"i":{"docs":{},"l":{"docs":{},"i":{"docs":{},"t":{"docs":{},"y":{"docs":{},"升":{"docs":{},"序":{"docs":{},"排":{"docs":{},"列":{"docs":{},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"p":{"docs":{},"r":{"docs":{},"o":{"docs":{},"b":{"docs":{},"a":{"docs":{},"b":{"docs":{},"i":{"docs":{},"l":{"docs":{},"i":{"docs":{},"t":{"docs":{},"y":{"docs":{},"表":{"docs":{},"示":{"docs":{},"预":{"docs":{},"测":{"docs":{},"结":{"docs":{},"果":{"docs":{},"的":{"docs":{},"概":{"docs":{},"率":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"是":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}},"一":{"docs":{},"个":{"docs":{},"欧":{"docs":{},"式":{"docs":{},"空":{"docs":{},"间":{"docs":{},"下":{"docs":{},"度":{"docs":{},"量":{"docs":{},"距":{"docs":{},"离":{"docs":{},"的":{"docs":{},"方":{"docs":{},"法":{"docs":{},".":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}},"开":{"docs":{},"源":{"docs":{},"的":{"docs":{},",":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}}}}},"否":{"docs":{},"收":{"docs":{},"藏":{"docs":{},",":{"docs":{},"是":{"docs":{},"否":{"docs":{},"点":{"docs":{},"击":{"docs":{},",":{"docs":{},"是":{"docs":{},"否":{"docs":{},"加":{"docs":{},"购":{"docs":{},"物":{"docs":{},"车":{"docs":{},")":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}}},"喜":{"docs":{},"欢":{"docs":{},"这":{"docs":{},"个":{"docs":{},"推":{"docs":{},"荐":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}}}},"随":{"docs":{},"机":{"docs":{},"切":{"docs":{},"分":{"docs":{},"，":{"docs":{},"默":{"docs":{},"认":{"docs":{},"f":{"docs":{},"a":{"docs":{},"l":{"docs":{},"s":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}}}}}}},"有":{"docs":{},"负":{"docs":{},"面":{"docs":{},"报":{"docs":{},"道":{"docs":{},"被":{"docs":{},"扩":{"docs":{},"散":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}},"某":{"docs":{},"类":{"docs":{},"商":{"docs":{},"品":{"docs":{},"缺":{"docs":{},"货":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}},"竞":{"docs":{},"争":{"docs":{},"对":{"docs":{},"手":{"docs":{},"在":{"docs":{},"做":{"docs":{},"活":{"docs":{},"动":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}}},"通":{"docs":{},"过":{"docs":{},"浏":{"docs":{},"览":{"docs":{},"器":{"docs":{},"访":{"docs":{},"问":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}},"f":{"docs":{},"a":{"docs":{},"c":{"docs":{},"e":{"docs":{},"b":{"docs":{},"o":{"docs":{},"o":{"docs":{},"k":{"docs":{},"员":{"docs":{},"工":{"docs":{},"开":{"docs":{},"发":{"docs":{},"的":{"docs":{},"操":{"docs":{},"作":{"docs":{},"h":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"的":{"docs":{},"p":{"docs":{},"y":{"docs":{},"t":{"docs":{},"h":{"docs":{},"o":{"docs":{},"n":{"docs":{},"库":{"docs":{},",":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"为":{"docs":{},"基":{"docs":{},"础":{"docs":{},"的":{"docs":{},"分":{"docs":{},"布":{"docs":{},"式":{"docs":{},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{},"，":{"docs":{},"类":{"docs":{},"似":{"docs":{},"于":{"docs":{},"传":{"docs":{},"统":{"docs":{},"关":{"docs":{},"系":{"docs":{},"型":{"docs":{},"数":{"docs":{},"据":{"docs":{},"库":{"docs":{},"的":{"docs":{},"二":{"docs":{},"维":{"docs":{},"表":{"docs":{},"，":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"记":{"docs":{},"录":{"docs":{},"了":{"docs":{},"对":{"docs":{},"应":{"docs":{},"列":{"docs":{},"的":{"docs":{},"名":{"docs":{},"称":{"docs":{},"和":{"docs":{},"类":{"docs":{},"型":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"显":{"docs":{},"性":{"docs":{},"数":{"docs":{},"据":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}},"式":{"docs":{},"反":{"docs":{},"馈":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}},"示":{"docs":{},"反":{"docs":{},"馈":{"docs":{},"指":{"docs":{},"的":{"docs":{},"用":{"docs":{},"户":{"docs":{},"的":{"docs":{},"评":{"docs":{},"分":{"docs":{},"这":{"docs":{},"样":{"docs":{},"的":{"docs":{},"行":{"docs":{},"为":{"docs":{},"，":{"docs":{},"隐":{"docs":{},"式":{"docs":{},"反":{"docs":{},"馈":{"docs":{},"指":{"docs":{},"用":{"docs":{},"户":{"docs":{},"的":{"docs":{},"浏":{"docs":{},"览":{"docs":{},"记":{"docs":{},"录":{"docs":{},"、":{"docs":{},"购":{"docs":{},"买":{"docs":{},"记":{"docs":{},"录":{"docs":{},"、":{"docs":{},"收":{"docs":{},"听":{"docs":{},"记":{"docs":{},"录":{"docs":{},"等":{"docs":{},"。":{"docs":{"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html":{"ref":"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html","tf":0.037037037037037035}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"所":{"docs":{},"有":{"docs":{},"数":{"docs":{},"据":{"docs":{},"库":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}},"函":{"docs":{},"数":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}},"表":{"docs":{},"信":{"docs":{},"息":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}},"某":{"docs":{},"个":{"docs":{},"名":{"docs":{},"称":{"docs":{},"空":{"docs":{},"间":{"docs":{},"下":{"docs":{},"有":{"docs":{},"哪":{"docs":{},"些":{"docs":{},"表":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}}}},"效":{"docs":{},"果":{"docs":{},":":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}},"结":{"docs":{},"果":{"docs":{},":":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.00734094616639478},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.004487048745665919},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.003581376840429765},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.010452961672473868},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.006342494714587738}}}}},"内":{"docs":{},"容":{"docs":{},":":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}},"特":{"docs":{},"征":{"docs":{},"情":{"docs":{},"况":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}},"最":{"docs":{},"大":{"docs":{},"值":{"docs":{},"正":{"docs":{},"无":{"docs":{},"穷":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}},"迭":{"docs":{},"代":{"docs":{},"次":{"docs":{},"数":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}},"终":{"docs":{},"预":{"docs":{},"测":{"docs":{},"出":{"docs":{},"用":{"docs":{},"户":{"1":{"docs":{},"对":{"docs":{},"物":{"docs":{},"品":{"5":{"docs":{},"的":{"docs":{},"评":{"docs":{},"分":{"docs":{},"为":{"3":{"docs":{},".":{"9":{"1":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}},"docs":{}},"docs":{}}},"docs":{}}}}}},"docs":{}}}}},"docs":{}}}}}}},"经":{"docs":{},"典":{"docs":{},"的":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"算":{"docs":{},"法":{"docs":{},"：":{"docs":{},"协":{"docs":{},"同":{"docs":{},"过":{"docs":{},"滤":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"算":{"docs":{},"法":{"docs":{},"（":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"l":{"docs":{},"a":{"docs":{},"b":{"docs":{},"o":{"docs":{},"r":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}}}}}}}}}}}}},"小":{"docs":{},"二":{"docs":{},"乘":{"docs":{},"法":{"docs":{},"和":{"docs":{},"梯":{"docs":{},"度":{"docs":{},"下":{"docs":{},"降":{"docs":{},"法":{"docs":{},"一":{"docs":{},"样":{"docs":{},"，":{"docs":{},"可":{"docs":{},"以":{"docs":{},"用":{"docs":{},"于":{"docs":{},"求":{"docs":{},"极":{"docs":{},"值":{"docs":{},"。":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}}}}}}},"思":{"docs":{},"想":{"docs":{},"：":{"docs":{},"对":{"docs":{},"损":{"docs":{},"失":{"docs":{},"函":{"docs":{},"数":{"docs":{},"求":{"docs":{},"偏":{"docs":{},"导":{"docs":{},"，":{"docs":{},"然":{"docs":{},"后":{"docs":{},"再":{"docs":{},"使":{"docs":{},"偏":{"docs":{},"导":{"docs":{},"为":{"0":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}},"新":{"docs":{},"的":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},"版":{"docs":{},"本":{"docs":{},"都":{"docs":{},"是":{"docs":{},"从":{"docs":{},"a":{"docs":{},"p":{"docs":{},"a":{"docs":{},"c":{"docs":{},"h":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}}}}}}}}}}}},"感":{"docs":{},"兴":{"docs":{},"趣":{"docs":{},"的":{"docs":{},"类":{"docs":{},"别":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}},"来":{"docs":{},"表":{"docs":{},"示":{"docs":{},"。":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}},"到":{"docs":{},"$":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},"_":{"docs":{},"h":{"docs":{},"o":{"docs":{},"m":{"docs":{},"e":{"docs":{},"/":{"docs":{},"s":{"docs":{},"b":{"docs":{},"i":{"docs":{},"n":{"docs":{},"目":{"docs":{},"录":{"docs":{},"下":{"docs":{"day03_Hadoop/ha2.1.html":{"ref":"day03_Hadoop/ha2.1.html","tf":0.0136986301369863}}}}}}}}}}}}}}}}}}}}}},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},"的":{"docs":{},"b":{"docs":{},"i":{"docs":{},"n":{"docs":{},"目":{"docs":{},"录":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428}}}}}}}}}}}}}}},"进":{"docs":{},"行":{"docs":{},"元":{"docs":{},"数":{"docs":{},"据":{"docs":{},"的":{"docs":{},"存":{"docs":{},"储":{"docs":{},"。":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}},"共":{"docs":{},"享":{"docs":{},"这":{"docs":{},"个":{"docs":{},"数":{"docs":{},"据":{"docs":{},",":{"docs":{},"避":{"docs":{},"免":{"docs":{},"数":{"docs":{},"据":{"docs":{},"的":{"docs":{},"多":{"docs":{},"次":{"docs":{},"复":{"docs":{},"制":{"docs":{},",":{"docs":{},"可":{"docs":{},"以":{"docs":{},"大":{"docs":{},"大":{"docs":{},"降":{"docs":{},"低":{"docs":{},"内":{"docs":{},"存":{"docs":{},"的":{"docs":{},"开":{"docs":{},"销":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"自":{"docs":{},"广":{"docs":{},"告":{"docs":{},"基":{"docs":{},"本":{"docs":{},"信":{"docs":{},"息":{"docs":{},"中":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}},"杰":{"docs":{},"卡":{"docs":{},"德":{"docs":{},"相":{"docs":{},"似":{"docs":{},"度":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}},"适":{"docs":{},"用":{"docs":{},"于":{"docs":{},"隐":{"docs":{},"式":{"docs":{},"反":{"docs":{},"馈":{"docs":{},"数":{"docs":{},"据":{"docs":{},"(":{"0":{"docs":{},",":{"1":{"docs":{},"布":{"docs":{},"尔":{"docs":{},"值":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}},"docs":{}}},"docs":{}}}}}}}}}}}}}},"距":{"docs":{},"离":{"docs":{},"=":{"docs":{},"杰":{"docs":{},"卡":{"docs":{},"德":{"docs":{},"相":{"docs":{},"似":{"docs":{},"度":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}},"构":{"docs":{},"建":{"docs":{},"初":{"docs":{},"始":{"docs":{},"的":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"结":{"docs":{},"果":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}},"推":{"docs":{},"荐":{"docs":{},"结":{"docs":{},"果":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}},"：":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}},"注":{"docs":{},"意":{"docs":{},"这":{"docs":{},"里":{"docs":{},"构":{"docs":{},"建":{"docs":{},"评":{"docs":{},"分":{"docs":{},"数":{"docs":{},"据":{"docs":{},"时":{"docs":{},"，":{"docs":{},"对":{"docs":{},"于":{"docs":{},"缺":{"docs":{},"失":{"docs":{},"的":{"docs":{},"部":{"docs":{},"分":{"docs":{},"我":{"docs":{},"们":{"docs":{},"需":{"docs":{},"要":{"docs":{},"保":{"docs":{},"留":{"docs":{},"为":{"docs":{},"n":{"docs":{},"o":{"docs":{},"n":{"docs":{},"e":{"docs":{},"，":{"docs":{},"如":{"docs":{},"果":{"docs":{},"设":{"docs":{},"置":{"docs":{},"为":{"0":{"docs":{},"那":{"docs":{},"么":{"docs":{},"会":{"docs":{},"被":{"docs":{},"当":{"docs":{},"作":{"docs":{},"评":{"docs":{},"分":{"docs":{},"值":{"docs":{},"为":{"0":{"docs":{},"去":{"docs":{},"对":{"docs":{},"待":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}},"docs":{}}}}}}}}}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"电":{"docs":{},"影":{"docs":{},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{},"，":{"docs":{},"包":{"docs":{},"含":{"docs":{},"电":{"docs":{},"影":{"docs":{},"i":{"docs":{},"d":{"docs":{},"、":{"docs":{},"电":{"docs":{},"影":{"docs":{},"名":{"docs":{},"称":{"docs":{},"、":{"docs":{},"类":{"docs":{},"别":{"docs":{},"、":{"docs":{},"标":{"docs":{},"签":{"docs":{},"四":{"docs":{},"个":{"docs":{},"字":{"docs":{},"段":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"结":{"docs":{},"构":{"docs":{},"对":{"docs":{},"象":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}}}},"表":{"docs":{},"结":{"docs":{},"构":{"docs":{},"s":{"docs":{},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},"m":{"docs":{},"a":{"docs":{},"对":{"docs":{},"象":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}},"根":{"docs":{},"据":{"docs":{},"每":{"docs":{},"个":{"docs":{},"物":{"docs":{},"品":{"docs":{},"找":{"docs":{},"出":{"docs":{},"最":{"docs":{},"相":{"docs":{},"似":{"docs":{},"的":{"docs":{},"t":{"docs":{},"o":{"docs":{},"p":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}},"条":{"docs":{},"数":{"docs":{},"据":{"docs":{},"返":{"docs":{},"回":{"docs":{},"，":{"docs":{},"向":{"docs":{},"量":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}},"相":{"docs":{},"似":{"docs":{},"的":{"docs":{},"人":{"docs":{},"或":{"docs":{},"物":{"docs":{},"品":{"docs":{},"产":{"docs":{},"生":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"结":{"docs":{},"果":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}},"评":{"docs":{},"分":{"docs":{},"为":{"docs":{},"指":{"docs":{},"定":{"docs":{},"用":{"docs":{},"户":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"t":{"docs":{},"o":{"docs":{},"p":{"docs":{},"n":{"docs":{},"个":{"docs":{},"电":{"docs":{},"影":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}}}}}}}}}}}}}}}}}},"用":{"docs":{},"户":{"docs":{},"i":{"docs":{},"d":{"docs":{},"分":{"docs":{},"组":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}},"画":{"docs":{},"像":{"docs":{},"从":{"docs":{},"物":{"docs":{},"品":{"docs":{},"中":{"docs":{},"寻":{"docs":{},"找":{"docs":{},"最":{"docs":{},"匹":{"docs":{},"配":{"docs":{},"的":{"docs":{},"t":{"docs":{},"o":{"docs":{},"p":{"docs":{"day02_推荐算法/07_基于内容的推荐算法.html":{"ref":"day02_推荐算法/07_基于内容的推荐算法.html","tf":0.03125}}}}}}}}}}}}}}}}},"行":{"docs":{},"为":{"docs":{},"记":{"docs":{},"录":{"docs":{},"生":{"docs":{},"成":{"docs":{},"用":{"docs":{},"户":{"docs":{},"画":{"docs":{},"像":{"docs":{"day02_推荐算法/07_基于内容的推荐算法.html":{"ref":"day02_推荐算法/07_基于内容的推荐算法.html","tf":0.03125}}}}}}}}}},"以":{"docs":{},"及":{"docs":{},"文":{"docs":{},"章":{"docs":{},"标":{"docs":{},"签":{"docs":{},"筛":{"docs":{},"选":{"docs":{},"出":{"docs":{},"用":{"docs":{},"户":{"docs":{},"最":{"docs":{},"感":{"docs":{},"兴":{"docs":{},"趣":{"docs":{},"(":{"docs":{},"阅":{"docs":{},"读":{"docs":{},"最":{"docs":{},"多":{"docs":{},")":{"docs":{},"的":{"docs":{},"标":{"docs":{},"签":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}}}}}}}}}}}}}}}}}}}}}},"数":{"docs":{},"据":{"docs":{},"创":{"docs":{},"建":{"docs":{},"a":{"docs":{},"l":{"docs":{},"s":{"docs":{},"模":{"docs":{},"型":{"docs":{},"并":{"docs":{},"召":{"docs":{},"回":{"docs":{},"商":{"docs":{},"品":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}},"的":{"docs":{},"评":{"docs":{},"分":{"docs":{},"历":{"docs":{},"史":{"docs":{},"，":{"docs":{},"结":{"docs":{},"合":{"docs":{},"物":{"docs":{},"品":{"docs":{},"画":{"docs":{},"像":{"docs":{},"，":{"docs":{},"将":{"docs":{},"有":{"docs":{},"观":{"docs":{},"影":{"docs":{},"记":{"docs":{},"录":{"docs":{},"的":{"docs":{},"电":{"docs":{},"影":{"docs":{},"的":{"docs":{},"画":{"docs":{},"像":{"docs":{},"标":{"docs":{},"签":{"docs":{},"作":{"docs":{},"为":{"docs":{},"初":{"docs":{},"始":{"docs":{},"标":{"docs":{},"签":{"docs":{},"反":{"docs":{},"打":{"docs":{},"到":{"docs":{},"用":{"docs":{},"户":{"docs":{},"身":{"docs":{},"上":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"对":{"docs":{},"品":{"docs":{},"牌":{"docs":{},"偏":{"docs":{},"好":{"docs":{},"打":{"docs":{},"分":{"docs":{},"训":{"docs":{},"练":{"docs":{},"a":{"docs":{},"l":{"docs":{},"s":{"docs":{},"模":{"docs":{},"型":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}},"类":{"docs":{},"目":{"docs":{},"偏":{"docs":{},"好":{"docs":{},"打":{"docs":{},"分":{"docs":{},"训":{"docs":{},"练":{"docs":{},"a":{"docs":{},"l":{"docs":{},"s":{"docs":{},"模":{"docs":{},"型":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}},"p":{"docs":{},"g":{"docs":{},"c":{"docs":{},"/":{"docs":{},"u":{"docs":{},"g":{"docs":{},"c":{"docs":{},"内":{"docs":{},"容":{"docs":{},"构":{"docs":{},"建":{"docs":{},"物":{"docs":{},"品":{"docs":{},"画":{"docs":{},"像":{"docs":{"day02_推荐算法/07_基于内容的推荐算法.html":{"ref":"day02_推荐算法/07_基于内容的推荐算法.html","tf":0.03125}}}}}}}}}}}}}},"内":{"docs":{},"容":{"docs":{},"构":{"docs":{},"建":{"docs":{},"物":{"docs":{},"品":{"docs":{},"画":{"docs":{},"像":{"docs":{"day02_推荐算法/07_基于内容的推荐算法.html":{"ref":"day02_推荐算法/07_基于内容的推荐算法.html","tf":0.03125}}}}}},"的":{"docs":{},"物":{"docs":{},"品":{"docs":{},"画":{"docs":{},"像":{"docs":{},"的":{"docs":{},"可":{"docs":{},"以":{"docs":{},"解":{"docs":{},"决":{"docs":{},"物":{"docs":{},"品":{"docs":{},"的":{"docs":{},"冷":{"docs":{},"启":{"docs":{},"动":{"docs":{},"问":{"docs":{},"题":{"docs":{"day02_推荐算法/07_基于内容的推荐算法.html":{"ref":"day02_推荐算法/07_基于内容的推荐算法.html","tf":0.03125}}}}}}}}}}}}}}}}}}}}}}}}}}},"关":{"docs":{},"键":{"docs":{},"词":{"docs":{},"提":{"docs":{},"取":{"docs":{},"对":{"docs":{},"应":{"docs":{},"的":{"docs":{},"名":{"docs":{},"称":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}},"将":{"docs":{},"每":{"docs":{},"条":{"docs":{},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"返":{"docs":{},"回":{"docs":{},"对":{"docs":{},"应":{"docs":{},"的":{"docs":{},"词":{"docs":{},"索":{"docs":{},"引":{"docs":{},"和":{"docs":{},"词":{"docs":{},"频":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876}}}}}}}}}}}}}}}}}}},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{},"建":{"docs":{},"立":{"docs":{},"词":{"docs":{},"袋":{"docs":{},"，":{"docs":{},"并":{"docs":{},"统":{"docs":{},"计":{"docs":{},"词":{"docs":{},"频":{"docs":{},"，":{"docs":{},"将":{"docs":{},"所":{"docs":{},"有":{"docs":{},"词":{"docs":{},"放":{"docs":{},"入":{"docs":{},"一":{"docs":{},"个":{"docs":{},"词":{"docs":{},"典":{"docs":{},"，":{"docs":{},"使":{"docs":{},"用":{"docs":{},"索":{"docs":{},"引":{"docs":{},"进":{"docs":{},"行":{"docs":{},"获":{"docs":{},"取":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"观":{"docs":{},"看":{"docs":{},"列":{"docs":{},"表":{"docs":{},"和":{"docs":{},"物":{"docs":{},"品":{"docs":{},"画":{"docs":{},"像":{"docs":{},"为":{"docs":{},"用":{"docs":{},"户":{"docs":{},"匹":{"docs":{},"配":{"docs":{},"关":{"docs":{},"键":{"docs":{},"词":{"docs":{},"，":{"docs":{},"并":{"docs":{},"统":{"docs":{},"计":{"docs":{},"词":{"docs":{},"频":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}}}}}}}}}}}}}}}}}}}}}}},"词":{"docs":{},"频":{"docs":{},"排":{"docs":{},"序":{"docs":{},"，":{"docs":{},"最":{"docs":{},"多":{"docs":{},"保":{"docs":{},"留":{"docs":{},"t":{"docs":{},"o":{"docs":{},"p":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}}}}}}}}}}}},"元":{"docs":{},"数":{"docs":{},"据":{"docs":{},"存":{"docs":{},"储":{"docs":{},"的":{"docs":{},"介":{"docs":{},"质":{"docs":{},"不":{"docs":{},"同":{"docs":{},"，":{"docs":{},"分":{"docs":{},"为":{"docs":{},"下":{"docs":{},"面":{"docs":{},"两":{"docs":{},"个":{"docs":{},"版":{"docs":{},"本":{"docs":{},"，":{"docs":{},"其":{"docs":{},"中":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}}}}}}}}}}}}}}},"文":{"docs":{},"章":{"docs":{},"i":{"docs":{},"d":{"docs":{},"找":{"docs":{},"到":{"docs":{},"用":{"docs":{},"户":{"docs":{},"查":{"docs":{},"看":{"docs":{},"文":{"docs":{},"章":{"docs":{},"的":{"docs":{},"关":{"docs":{},"键":{"docs":{},"字":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}},"并":{"docs":{},"统":{"docs":{},"计":{"docs":{},"频":{"docs":{},"率":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}}}}}}}}}}}}}}}}}}},"您":{"docs":{},"统":{"docs":{},"计":{"docs":{},"的":{"docs":{},"次":{"docs":{},"数":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}},"经":{"docs":{},"验":{"docs":{},"，":{"docs":{},"以":{"docs":{},"上":{"docs":{},"几":{"docs":{},"个":{"docs":{},"分":{"docs":{},"类":{"docs":{},"特":{"docs":{},"征":{"docs":{},"都":{"docs":{},"一":{"docs":{},"定":{"docs":{},"程":{"docs":{},"度":{"docs":{},"能":{"docs":{},"体":{"docs":{},"现":{"docs":{},"用":{"docs":{},"户":{"docs":{},"在":{"docs":{},"购":{"docs":{},"物":{"docs":{},"方":{"docs":{},"面":{"docs":{},"的":{"docs":{},"特":{"docs":{},"征":{"docs":{},"，":{"docs":{},"且":{"docs":{},"类":{"docs":{},"别":{"docs":{},"都":{"docs":{},"较":{"docs":{},"少":{"docs":{},"，":{"docs":{},"都":{"docs":{},"可":{"docs":{},"以":{"docs":{},"用":{"docs":{},"来":{"docs":{},"作":{"docs":{},"为":{"docs":{},"用":{"docs":{},"户":{"docs":{},"特":{"docs":{},"征":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"该":{"docs":{},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{},"中":{"docs":{},"，":{"docs":{},"只":{"docs":{},"有":{"docs":{},"广":{"docs":{},"告":{"docs":{},"展":{"docs":{},"示":{"docs":{},"位":{"docs":{},"p":{"docs":{},"i":{"docs":{},"d":{"docs":{},"对":{"docs":{},"比":{"docs":{},"较":{"docs":{},"重":{"docs":{},"要":{"docs":{},"，":{"docs":{},"且":{"docs":{},"数":{"docs":{},"据":{"docs":{},"不":{"docs":{},"同":{"docs":{},"数":{"docs":{},"据":{"docs":{},"之":{"docs":{},"间":{"docs":{},"的":{"docs":{},"占":{"docs":{},"比":{"docs":{},"约":{"docs":{},"为":{"6":{"docs":{},":":{"4":{"docs":{},"，":{"docs":{},"因":{"docs":{},"此":{"docs":{},"p":{"docs":{},"i":{"docs":{},"d":{"docs":{},"可":{"docs":{},"以":{"docs":{},"作":{"docs":{},"为":{"docs":{},"一":{"docs":{},"个":{"docs":{},"关":{"docs":{},"键":{"docs":{},"特":{"docs":{},"征":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}},"docs":{}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"测":{"docs":{},"试":{"docs":{},"数":{"docs":{},"据":{"docs":{},"进":{"docs":{},"行":{"docs":{},"预":{"docs":{},"测":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}},"特":{"docs":{},"征":{"docs":{},"字":{"docs":{},"段":{"docs":{},"计":{"docs":{},"算":{"docs":{},"出":{"docs":{},"特":{"docs":{},"征":{"docs":{},"向":{"docs":{},"量":{"docs":{},"，":{"docs":{},"并":{"docs":{},"划":{"docs":{},"分":{"docs":{},"出":{"docs":{},"训":{"docs":{},"练":{"docs":{},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{},"和":{"docs":{},"测":{"docs":{},"试":{"docs":{},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}}},"特":{"docs":{},"征":{"docs":{},"向":{"docs":{},"量":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}},"指":{"docs":{},"定":{"docs":{},"的":{"docs":{},"类":{"docs":{},"别":{"docs":{},"找":{"docs":{},"到":{"docs":{},"对":{"docs":{},"应":{"docs":{},"的":{"docs":{},"广":{"docs":{},"告":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}},"欧":{"docs":{},"氏":{"docs":{},"距":{"docs":{},"离":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}},"不":{"docs":{},"适":{"docs":{},"用":{"docs":{},"于":{"docs":{},"布":{"docs":{},"尔":{"docs":{},"向":{"docs":{},"量":{"docs":{},"之":{"docs":{},"间":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}},"的":{"docs":{},"值":{"docs":{},"是":{"docs":{},"一":{"docs":{},"个":{"docs":{},"非":{"docs":{},"负":{"docs":{},"数":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}},"注":{"docs":{},"意":{"docs":{},"：":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}},"我":{"docs":{},"们":{"docs":{},"在":{"docs":{},"预":{"docs":{},"测":{"docs":{},"评":{"docs":{},"分":{"docs":{},"时":{"docs":{},"，":{"docs":{},"往":{"docs":{},"往":{"docs":{},"是":{"docs":{},"通":{"docs":{},"过":{"docs":{},"与":{"docs":{},"其":{"docs":{},"有":{"docs":{},"正":{"docs":{},"相":{"docs":{},"关":{"docs":{},"的":{"docs":{},"用":{"docs":{},"户":{"docs":{},"或":{"docs":{},"物":{"docs":{},"品":{"docs":{},"进":{"docs":{},"行":{"docs":{},"预":{"docs":{},"测":{"docs":{},"，":{"docs":{},"如":{"docs":{},"果":{"docs":{},"不":{"docs":{},"存":{"docs":{},"在":{"docs":{},"正":{"docs":{},"相":{"docs":{},"关":{"docs":{},"的":{"docs":{},"情":{"docs":{},"况":{"docs":{},"，":{"docs":{},"那":{"docs":{},"么":{"docs":{},"将":{"docs":{},"无":{"docs":{},"法":{"docs":{},"做":{"docs":{},"出":{"docs":{},"预":{"docs":{},"测":{"docs":{},"。":{"docs":{},"这":{"docs":{},"一":{"docs":{},"点":{"docs":{},"尤":{"docs":{},"其":{"docs":{},"是":{"docs":{},"在":{"docs":{},"稀":{"docs":{},"疏":{"docs":{},"评":{"docs":{},"分":{"docs":{},"矩":{"docs":{},"阵":{"docs":{},"中":{"docs":{},"尤":{"docs":{},"为":{"docs":{},"常":{"docs":{},"见":{"docs":{},"，":{"docs":{},"因":{"docs":{},"为":{"docs":{},"稀":{"docs":{},"疏":{"docs":{},"评":{"docs":{},"分":{"docs":{},"矩":{"docs":{},"阵":{"docs":{},"中":{"docs":{},"很":{"docs":{},"难":{"docs":{},"得":{"docs":{},"出":{"docs":{},"正":{"docs":{},"相":{"docs":{},"关":{"docs":{},"系":{"docs":{},"数":{"docs":{},"。":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"文":{"docs":{},"档":{"docs":{},"中":{"docs":{},"存":{"docs":{},"在":{"docs":{},"的":{"docs":{},"停":{"docs":{},"用":{"docs":{},"词":{"docs":{},"（":{"docs":{},"s":{"docs":{},"t":{"docs":{},"o":{"docs":{},"p":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}},"r":{"docs":{},"e":{"docs":{},"c":{"docs":{},"o":{"docs":{},"m":{"docs":{},"m":{"docs":{},"e":{"docs":{},"n":{"docs":{},"d":{"docs":{},"f":{"docs":{},"o":{"docs":{},"r":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"s":{"docs":{},"u":{"docs":{},"b":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}},"t":{"docs":{},"o":{"docs":{},"d":{"docs":{},"f":{"docs":{},"不":{"docs":{},"是":{"docs":{},"每":{"docs":{},"个":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"都":{"docs":{},"有":{"docs":{},"的":{"docs":{},"方":{"docs":{},"法":{"docs":{},"，":{"docs":{},"仅":{"docs":{},"局":{"docs":{},"限":{"docs":{},"于":{"docs":{},"此":{"docs":{},"处":{"docs":{},"的":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"由":{"docs":{},"于":{"docs":{},"数":{"docs":{},"据":{"docs":{},"量":{"docs":{},"巨":{"docs":{},"大":{"docs":{},"，":{"docs":{},"因":{"docs":{},"此":{"docs":{},"这":{"docs":{},"里":{"docs":{},"不":{"docs":{},"考":{"docs":{},"虑":{"docs":{},"基":{"docs":{},"于":{"docs":{},"内":{"docs":{},"存":{"docs":{},"的":{"docs":{},"c":{"docs":{},"f":{"docs":{},"算":{"docs":{},"法":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}},"也":{"docs":{},"不":{"docs":{},"考":{"docs":{},"虑":{"docs":{},"基":{"docs":{},"于":{"docs":{},"内":{"docs":{},"存":{"docs":{},"的":{"docs":{},"c":{"docs":{},"f":{"docs":{},"算":{"docs":{},"法":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}},"本":{"docs":{},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{},"中":{"docs":{},"存":{"docs":{},"在":{"docs":{},"n":{"docs":{},"u":{"docs":{},"l":{"docs":{},"l":{"docs":{},"字":{"docs":{},"样":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"无":{"docs":{},"法":{"docs":{},"直":{"docs":{},"接":{"docs":{},"设":{"docs":{},"置":{"docs":{},"s":{"docs":{},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},"m":{"docs":{},"a":{"docs":{},"，":{"docs":{},"只":{"docs":{},"能":{"docs":{},"先":{"docs":{},"将":{"docs":{},"n":{"docs":{},"u":{"docs":{},"l":{"docs":{},"l":{"docs":{},"类":{"docs":{},"型":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"处":{"docs":{},"理":{"docs":{},"掉":{"docs":{},"，":{"docs":{},"然":{"docs":{},"后":{"docs":{},"进":{"docs":{},"行":{"docs":{},"类":{"docs":{},"型":{"docs":{},"转":{"docs":{},"换":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"这":{"docs":{},"里":{"docs":{},"这":{"docs":{},"是":{"docs":{},"召":{"docs":{},"回":{"docs":{},"的":{"docs":{},"是":{"docs":{},"用":{"docs":{},"户":{"docs":{},"最":{"docs":{},"感":{"docs":{},"兴":{"docs":{},"趣":{"docs":{},"的":{"docs":{},"n":{"docs":{},"个":{"docs":{},"类":{"docs":{},"别":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}},"的":{"docs":{},"n":{"docs":{},"u":{"docs":{},"l":{"docs":{},"l":{"docs":{},"会":{"docs":{},"直":{"docs":{},"接":{"docs":{},"被":{"docs":{},"p":{"docs":{},"y":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"识":{"docs":{},"别":{"docs":{},"为":{"docs":{},"n":{"docs":{},"o":{"docs":{},"n":{"docs":{},"e":{"docs":{},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"也":{"docs":{},"就":{"docs":{},"是":{"docs":{},"n":{"docs":{},"a":{"docs":{},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"所":{"docs":{},"以":{"docs":{},"这":{"docs":{},"里":{"docs":{},"可":{"docs":{},"以":{"docs":{},"直":{"docs":{},"接":{"docs":{},"利":{"docs":{},"用":{"docs":{},"s":{"docs":{},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},"m":{"docs":{},"a":{"docs":{},"导":{"docs":{},"入":{"docs":{},"数":{"docs":{},"据":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"u":{"docs":{},"n":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"a":{"docs":{},"l":{"docs":{},"l":{"docs":{},"的":{"docs":{},"使":{"docs":{},"用":{"docs":{},"，":{"docs":{},"两":{"docs":{},"个":{"docs":{},"d":{"docs":{},"f":{"docs":{},"的":{"docs":{},"表":{"docs":{},"结":{"docs":{},"构":{"docs":{},"必":{"docs":{},"须":{"docs":{},"完":{"docs":{},"全":{"docs":{},"一":{"docs":{},"样":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}},"热":{"docs":{},"编":{"docs":{},"码":{"docs":{},"只":{"docs":{},"能":{"docs":{},"对":{"docs":{},"字":{"docs":{},"符":{"docs":{},"串":{"docs":{},"类":{"docs":{},"型":{"docs":{},"的":{"docs":{},"列":{"docs":{},"数":{"docs":{},"据":{"docs":{},"进":{"docs":{},"行":{"docs":{},"处":{"docs":{},"理":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}},"还":{"docs":{},"需":{"docs":{},"要":{"docs":{},"加":{"docs":{},"入":{"docs":{},"广":{"docs":{},"告":{"docs":{},"基":{"docs":{},"本":{"docs":{},"特":{"docs":{},"征":{"docs":{},"和":{"docs":{},"用":{"docs":{},"户":{"docs":{},"基":{"docs":{},"本":{"docs":{},"特":{"docs":{},"征":{"docs":{},"才":{"docs":{},"能":{"docs":{},"做":{"docs":{},"程":{"docs":{},"一":{"docs":{},"份":{"docs":{},"完":{"docs":{},"整":{"docs":{},"的":{"docs":{},"样":{"docs":{},"本":{"docs":{},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"+":{"1":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}},"docs":{}},"用":{"docs":{},"法":{"docs":{},"：":{"docs":{},"h":{"docs":{},"t":{"docs":{},"t":{"docs":{},"p":{"docs":{},"s":{"docs":{},":":{"docs":{},"/":{"docs":{},"/":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},".":{"docs":{},"a":{"docs":{},"p":{"docs":{},"a":{"docs":{},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},".":{"docs":{},"o":{"docs":{},"r":{"docs":{},"g":{"docs":{},"/":{"docs":{},"d":{"docs":{},"o":{"docs":{},"c":{"docs":{},"s":{"docs":{},"/":{"docs":{},"l":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{},"/":{"docs":{},"a":{"docs":{},"p":{"docs":{},"i":{"docs":{},"/":{"docs":{},"p":{"docs":{},"y":{"docs":{},"t":{"docs":{},"h":{"docs":{},"o":{"docs":{},"n":{"docs":{},"/":{"docs":{},"p":{"docs":{},"y":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},".":{"docs":{},"m":{"docs":{},"l":{"docs":{},"l":{"docs":{},"i":{"docs":{},"b":{"docs":{},".":{"docs":{},"h":{"docs":{},"t":{"docs":{},"m":{"docs":{},"l":{"docs":{},"?":{"docs":{},"h":{"docs":{},"i":{"docs":{},"g":{"docs":{},"h":{"docs":{},"l":{"docs":{},"i":{"docs":{},"g":{"docs":{},"h":{"docs":{},"t":{"docs":{},"=":{"docs":{},"t":{"docs":{},"r":{"docs":{},"e":{"docs":{},"e":{"docs":{},"%":{"2":{"0":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"o":{"docs":{},"m":{"docs":{},"#":{"docs":{},"p":{"docs":{},"y":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},".":{"docs":{},"m":{"docs":{},"l":{"docs":{},"l":{"docs":{},"i":{"docs":{},"b":{"docs":{},".":{"docs":{},"t":{"docs":{},"r":{"docs":{},"e":{"docs":{},"e":{"docs":{},".":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"o":{"docs":{},"m":{"docs":{},"f":{"docs":{},"o":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{},"m":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"l":{"docs":{},".":{"docs":{},"p":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"i":{"docs":{},"c":{"docs":{},"t":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"随":{"docs":{},"机":{"docs":{},"森":{"docs":{},"林":{"docs":{},"输":{"docs":{},"入":{"docs":{},"数":{"docs":{},"据":{"docs":{},"时":{"docs":{},"，":{"docs":{},"由":{"docs":{},"于":{"docs":{},"l":{"docs":{},"a":{"docs":{},"b":{"docs":{},"e":{"docs":{},"l":{"docs":{},"的":{"docs":{},"分":{"docs":{},"类":{"docs":{},"数":{"docs":{},"是":{"docs":{},"从":{"0":{"docs":{},"开":{"docs":{},"始":{"docs":{},"的":{"docs":{},"，":{"docs":{},"但":{"docs":{},"p":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"的":{"docs":{},"目":{"docs":{},"前":{"docs":{},"只":{"docs":{},"分":{"docs":{},"别":{"docs":{},"是":{"1":{"docs":{},"，":{"2":{"docs":{},"，":{"3":{"docs":{},"，":{"docs":{},"所":{"docs":{},"以":{"docs":{},"需":{"docs":{},"要":{"docs":{},"对":{"docs":{},"应":{"docs":{},"分":{"docs":{},"别":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}},"docs":{}}},"docs":{}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}},"，":{"docs":{},"一":{"docs":{},"般":{"docs":{},"情":{"docs":{},"况":{"docs":{},"下":{"docs":{},"：":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}},"册":{"docs":{},"到":{"docs":{},"z":{"docs":{},"o":{"docs":{},"o":{"docs":{},"k":{"docs":{},"e":{"docs":{},"e":{"docs":{},"p":{"docs":{},"e":{"docs":{},"r":{"docs":{},",":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}}}}}}}}},"添":{"docs":{},"加":{"docs":{},"到":{"docs":{},"结":{"docs":{},"果":{"docs":{},"中":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}},"l":{"docs":{},"i":{"docs":{},"s":{"docs":{},"t":{"docs":{},"中":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}},"t":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{},"方":{"docs":{},"法":{"docs":{},"，":{"docs":{},"然":{"docs":{},"后":{"docs":{},"使":{"docs":{},"用":{"docs":{},"之":{"docs":{},"前":{"docs":{},"实":{"docs":{},"现":{"docs":{},"a":{"docs":{},"c":{"docs":{},"c":{"docs":{},"u":{"docs":{},"a":{"docs":{},"r":{"docs":{},"y":{"docs":{},"方":{"docs":{},"法":{"docs":{},"计":{"docs":{},"算":{"docs":{},"准":{"docs":{},"确":{"docs":{},"性":{"docs":{},"指":{"docs":{},"标":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"文":{"docs":{},"本":{"docs":{},"内":{"docs":{},"容":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625}}}}}},"分":{"docs":{},"区":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}},"自":{"docs":{},"增":{"docs":{},"长":{"docs":{},"的":{"docs":{},"行":{"docs":{},"号":{"docs":{},")":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}}}},"记":{"docs":{},"录":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}},"过":{"docs":{},"滤":{"docs":{},"器":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0056657223796034}}}}}}},"点":{"docs":{},"击":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}},"商":{"docs":{},"品":{"docs":{},"访":{"docs":{},"问":{"docs":{},"详":{"docs":{},"情":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}},"s":{"docs":{},"t":{"docs":{},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"m":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"，":{"docs":{},"查":{"docs":{},"看":{"docs":{},"效":{"docs":{},"果":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099}}}}}}}}}}}}}}}},"率":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}},"排":{"docs":{},"序":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}},"预":{"docs":{},"测":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}},"(":{"docs":{},"c":{"docs":{},"t":{"docs":{},"r":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}},"使":{"docs":{},"用":{"docs":{},"的":{"docs":{},"算":{"docs":{},"法":{"docs":{},"通":{"docs":{},"常":{"docs":{},"是":{"docs":{},"如":{"docs":{},"逻":{"docs":{},"辑":{"docs":{},"回":{"docs":{},"归":{"docs":{},"(":{"docs":{},"l":{"docs":{},"o":{"docs":{},"g":{"docs":{},"i":{"docs":{},"c":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}}}}}}}}}}}}}}},"是":{"docs":{},"对":{"docs":{},"每":{"docs":{},"次":{"docs":{},"广":{"docs":{},"告":{"docs":{},"的":{"docs":{},"点":{"docs":{},"击":{"docs":{},"情":{"docs":{},"况":{"docs":{},"做":{"docs":{},"出":{"docs":{},"预":{"docs":{},"测":{"docs":{},"，":{"docs":{},"可":{"docs":{},"以":{"docs":{},"判":{"docs":{},"定":{"docs":{},"这":{"docs":{},"次":{"docs":{},"为":{"docs":{},"点":{"docs":{},"击":{"docs":{},"或":{"docs":{},"不":{"docs":{},"点":{"docs":{},"击":{"docs":{},"，":{"docs":{},"也":{"docs":{},"可":{"docs":{},"以":{"docs":{},"给":{"docs":{},"出":{"docs":{},"点":{"docs":{},"击":{"docs":{},"或":{"docs":{},"不":{"docs":{},"点":{"docs":{},"击":{"docs":{},"的":{"docs":{},"概":{"docs":{},"率":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"需":{"docs":{},"要":{"docs":{},"给":{"docs":{},"出":{"docs":{},"精":{"docs":{},"准":{"docs":{},"的":{"docs":{},"点":{"docs":{},"击":{"docs":{},"概":{"docs":{},"率":{"docs":{},"，":{"docs":{},"比":{"docs":{},"如":{"docs":{},"广":{"docs":{},"告":{"docs":{},"a":{"docs":{},"点":{"docs":{},"击":{"docs":{},"率":{"0":{"docs":{},".":{"5":{"docs":{},"%":{"docs":{},"、":{"docs":{},"广":{"docs":{},"告":{"docs":{},"b":{"docs":{},"的":{"docs":{},"点":{"docs":{},"击":{"docs":{},"率":{"0":{"docs":{},".":{"1":{"2":{"docs":{},"%":{"docs":{},"等":{"docs":{},"；":{"docs":{},"而":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"算":{"docs":{},"法":{"docs":{},"很":{"docs":{},"多":{"docs":{},"时":{"docs":{},"候":{"docs":{},"只":{"docs":{},"需":{"docs":{},"要":{"docs":{},"得":{"docs":{},"出":{"docs":{},"一":{"docs":{},"个":{"docs":{},"最":{"docs":{},"优":{"docs":{},"的":{"docs":{},"次":{"docs":{},"序":{"docs":{},"a":{"docs":{},">":{"docs":{},"b":{"docs":{},">":{"docs":{},"c":{"docs":{},"即":{"docs":{},"可":{"docs":{},"。":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{}},"docs":{}}},"docs":{}}}}}}}}}}},"docs":{}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}},"和":{"docs":{},"不":{"docs":{},"点":{"docs":{},"比":{"docs":{},"率":{"docs":{},"约":{"docs":{},"：":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}},"物":{"docs":{},"品":{"docs":{},"之":{"docs":{},"间":{"docs":{},"的":{"docs":{},"两":{"docs":{},"两":{"docs":{},"相":{"docs":{},"似":{"docs":{},"度":{"docs":{},"：":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}},"的":{"docs":{},"评":{"docs":{},"分":{"docs":{},"数":{"docs":{},"据":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}},"。":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}},"矩":{"docs":{},"阵":{"docs":{},"，":{"docs":{},"根":{"docs":{},"据":{"docs":{},"评":{"docs":{},"分":{"docs":{},"矩":{"docs":{},"阵":{"docs":{},"的":{"docs":{},"稀":{"docs":{},"疏":{"docs":{},"程":{"docs":{},"度":{"docs":{},"会":{"docs":{},"有":{"docs":{},"不":{"docs":{},"同":{"docs":{},"的":{"docs":{},"解":{"docs":{},"决":{"docs":{},"方":{"docs":{},"案":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}}}}}}}}}}},"进":{"docs":{},"行":{"docs":{},"预":{"docs":{},"测":{"docs":{},"。":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}}}},"倒":{"docs":{},"排":{"docs":{},"索":{"docs":{},"引":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}},"相":{"docs":{},"似":{"docs":{},"度":{"docs":{},"的":{"docs":{},"时":{"docs":{},"候":{"docs":{},"较":{"docs":{},"为":{"docs":{},"常":{"docs":{},"用":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}},"评":{"docs":{},"分":{"docs":{},"矩":{"docs":{},"阵":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734},"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}},"数":{"docs":{},"据":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002858776443682104},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}},"i":{"docs":{},"d":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}}}},"两":{"docs":{},"两":{"docs":{},"间":{"docs":{},"的":{"docs":{},"相":{"docs":{},"似":{"docs":{},"度":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.0016233766233766235}}}}}}}}},"打":{"docs":{},"分":{"docs":{},"矩":{"docs":{},"阵":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}}}}}},"冷":{"docs":{},"启":{"docs":{},"动":{"docs":{"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html":{"ref":"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html","tf":0.024390243902439025}},"：":{"docs":{},"如":{"docs":{},"何":{"docs":{},"将":{"docs":{},"新":{"docs":{},"物":{"docs":{},"品":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"给":{"docs":{},"⽤":{"docs":{},"户":{"docs":{},"（":{"docs":{},"协":{"docs":{},"同":{"docs":{},"过":{"docs":{},"滤":{"docs":{},"）":{"docs":{"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html":{"ref":"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html","tf":0.024390243902439025}}}}}}}}}}}}}}}}}}}},"处":{"docs":{},"理":{"docs":{},"：":{"docs":{"day02_推荐算法/07_基于内容的推荐算法.html":{"ref":"day02_推荐算法/07_基于内容的推荐算法.html","tf":0.03125}}}}}}}},"画":{"docs":{},"像":{"docs":{"day02_推荐算法/07_基于内容的推荐算法.html":{"ref":"day02_推荐算法/07_基于内容的推荐算法.html","tf":0.03125}},"：":{"docs":{},"例":{"docs":{},"如":{"docs":{},"给":{"docs":{},"电":{"docs":{},"影":{"docs":{},"《":{"docs":{},"战":{"docs":{},"狼":{"2":{"docs":{},"》":{"docs":{},"贴":{"docs":{},"标":{"docs":{},"签":{"docs":{},"，":{"docs":{},"可":{"docs":{},"以":{"docs":{},"有":{"docs":{},"哪":{"docs":{},"些":{"docs":{},"？":{"docs":{"day02_推荐算法/07_基于内容的推荐算法.html":{"ref":"day02_推荐算法/07_基于内容的推荐算法.html","tf":0.03125}}}}}}}}}}}}}},"docs":{}}}}}}}}}},"构":{"docs":{},"建":{"docs":{},"步":{"docs":{},"骤":{"docs":{},"：":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}},"自":{"docs":{},"带":{"docs":{},"的":{"docs":{},"属":{"docs":{},"性":{"docs":{},"（":{"docs":{},"物":{"docs":{},"品":{"docs":{},"一":{"docs":{},"产":{"docs":{},"生":{"docs":{},"就":{"docs":{},"具":{"docs":{},"备":{"docs":{},"的":{"docs":{},"）":{"docs":{},"：":{"docs":{},"如":{"docs":{},"电":{"docs":{},"影":{"docs":{},"的":{"docs":{},"标":{"docs":{},"题":{"docs":{},"、":{"docs":{},"导":{"docs":{},"演":{"docs":{},"、":{"docs":{},"演":{"docs":{},"员":{"docs":{},"、":{"docs":{},"类":{"docs":{},"型":{"docs":{},"等":{"docs":{},"等":{"docs":{"day02_推荐算法/07_基于内容的推荐算法.html":{"ref":"day02_推荐算法/07_基于内容的推荐算法.html","tf":0.03125}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"特":{"docs":{},"征":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}},"工":{"docs":{},"程":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}},"中":{"docs":{},"是":{"docs":{},"否":{"docs":{},"包":{"docs":{},"含":{"docs":{},"分":{"docs":{},"类":{"docs":{},"的":{"docs":{},"特":{"docs":{},"征":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}},"处":{"docs":{},"理":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},"，":{"docs":{},"如":{"1":{"docs":{},"维":{"docs":{},"转":{"docs":{},"多":{"docs":{},"维":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}},"docs":{}}}}},"选":{"docs":{},"取":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},"（":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}},"择":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},"就":{"docs":{},"是":{"docs":{},"选":{"docs":{},"择":{"docs":{},"那":{"docs":{},"些":{"docs":{},"靠":{"docs":{},"谱":{"docs":{},"的":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"e":{"docs":{},"，":{"docs":{},"去":{"docs":{},"掉":{"docs":{},"冗":{"docs":{},"余":{"docs":{},"的":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"e":{"docs":{},"，":{"docs":{},"对":{"docs":{},"于":{"docs":{},"搜":{"docs":{},"索":{"docs":{},"广":{"docs":{},"告":{"docs":{},"，":{"docs":{},"q":{"docs":{},"u":{"docs":{},"e":{"docs":{},"r":{"docs":{},"y":{"docs":{},"关":{"docs":{},"键":{"docs":{},"词":{"docs":{},"和":{"docs":{},"广":{"docs":{},"告":{"docs":{},"的":{"docs":{},"匹":{"docs":{},"配":{"docs":{},"程":{"docs":{},"度":{"docs":{},"很":{"docs":{},"重":{"docs":{},"要":{"docs":{},"；":{"docs":{},"但":{"docs":{},"对":{"docs":{},"于":{"docs":{},"展":{"docs":{},"示":{"docs":{},"广":{"docs":{},"告":{"docs":{},"，":{"docs":{},"广":{"docs":{},"告":{"docs":{},"本":{"docs":{},"身":{"docs":{},"的":{"docs":{},"历":{"docs":{},"史":{"docs":{},"表":{"docs":{},"现":{"docs":{},"，":{"docs":{},"往":{"docs":{},"往":{"docs":{},"是":{"docs":{},"最":{"docs":{},"重":{"docs":{},"要":{"docs":{},"的":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"e":{"docs":{},"。":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"值":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}},"字":{"docs":{},"段":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}},"获":{"docs":{},"取":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}},"点":{"docs":{},"是":{"docs":{},"调":{"docs":{},"试":{"docs":{},"方":{"docs":{},"便":{"docs":{},"，":{"docs":{},"启":{"docs":{},"动":{"docs":{},"单":{"docs":{},"一":{"docs":{},"进":{"docs":{},"程":{"docs":{},"模":{"docs":{},"拟":{"docs":{},"任":{"docs":{},"务":{"docs":{},"执":{"docs":{},"行":{"docs":{},"状":{"docs":{},"态":{"docs":{},"和":{"docs":{},"结":{"docs":{},"果":{"docs":{},"，":{"docs":{},"默":{"docs":{},"认":{"docs":{},"(":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"理":{"docs":{},"解":{"docs":{},"协":{"docs":{},"同":{"docs":{},"过":{"docs":{},"滤":{"docs":{},"原":{"docs":{},"理":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}},"皮":{"docs":{},"尔":{"docs":{},"逊":{"docs":{},"相":{"docs":{},"似":{"docs":{},"度":{"docs":{},"计":{"docs":{},"算":{"docs":{},"结":{"docs":{},"果":{"docs":{},"在":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}},"关":{"docs":{},"系":{"docs":{},"数":{"docs":{},"p":{"docs":{},"e":{"docs":{},"a":{"docs":{},"r":{"docs":{},"s":{"docs":{},"o":{"docs":{},"n":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}},"度":{"docs":{},"量":{"docs":{},"的":{"docs":{},"是":{"docs":{},"两":{"docs":{},"个":{"docs":{},"变":{"docs":{},"量":{"docs":{},"的":{"docs":{},"变":{"docs":{},"化":{"docs":{},"趋":{"docs":{},"势":{"docs":{},"是":{"docs":{},"否":{"docs":{},"一":{"docs":{},"致":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}}}}}}}}}}}}},"直":{"docs":{},"接":{"docs":{},"计":{"docs":{},"算":{"docs":{},"某":{"docs":{},"两":{"docs":{},"项":{"docs":{},"的":{"docs":{},"杰":{"docs":{},"卡":{"docs":{},"德":{"docs":{},"相":{"docs":{},"似":{"docs":{},"系":{"docs":{},"数":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}},"皮":{"docs":{},"尔":{"docs":{},"逊":{"docs":{},"相":{"docs":{},"关":{"docs":{},"系":{"docs":{},"数":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}},"使":{"docs":{},"用":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}},"删":{"docs":{},"除":{"docs":{},"元":{"docs":{},"数":{"docs":{},"据":{"docs":{},"（":{"docs":{},"m":{"docs":{},"e":{"docs":{},"t":{"docs":{},"a":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"）":{"docs":{},"及":{"docs":{},"存":{"docs":{},"储":{"docs":{},"数":{"docs":{},"据":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}}}}}}}}}}}}}}}},"编":{"docs":{},"写":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"也":{"docs":{},"可":{"docs":{},"以":{"docs":{},"自":{"docs":{},"实":{"docs":{},"现":{"docs":{},"优":{"docs":{},"化":{"docs":{},"代":{"docs":{},"码":{"docs":{},"，":{"docs":{},"但":{"docs":{},"是":{"docs":{},"远":{"docs":{},"不":{"docs":{},"及":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"s":{"docs":{},"q":{"docs":{},"l":{"docs":{},"前":{"docs":{},"面":{"docs":{},"的":{"docs":{},"优":{"docs":{},"化":{"docs":{},"操":{"docs":{},"作":{"docs":{},"后":{"docs":{},"转":{"docs":{},"换":{"docs":{},"的":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"效":{"docs":{},"率":{"docs":{},"高":{"docs":{},"，":{"docs":{},"快":{"1":{"docs":{},"倍":{"docs":{},"左":{"docs":{},"右":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}}}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"从":{"docs":{},"文":{"docs":{},"件":{"docs":{},"生":{"docs":{},"成":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.007692307692307693}}}}}}}}}}}}}}},"利":{"docs":{},"用":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},".":{"docs":{},"c":{"docs":{},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{},"，":{"docs":{},"见":{"docs":{},"后":{"docs":{},"面":{"docs":{},"例":{"docs":{},"子":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"相":{"docs":{},"似":{"docs":{},"度":{"docs":{},"的":{"docs":{},"计":{"docs":{},"算":{"docs":{},"方":{"docs":{},"法":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}},"计":{"docs":{},"算":{"docs":{},"(":{"docs":{},"s":{"docs":{},"i":{"docs":{},"m":{"docs":{},"i":{"docs":{},"l":{"docs":{},"a":{"docs":{},"r":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}},"物":{"docs":{},"品":{"docs":{},"筛":{"docs":{},"选":{"docs":{},"规":{"docs":{},"则":{"docs":{},"：":{"docs":{},"正":{"docs":{},"相":{"docs":{},"关":{"docs":{},"的":{"docs":{},"物":{"docs":{},"品":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.0016233766233766235}}}}}}}}}}}}}}},"用":{"docs":{},"户":{"docs":{},"筛":{"docs":{},"选":{"docs":{},"规":{"docs":{},"则":{"docs":{},"：":{"docs":{},"正":{"docs":{},"相":{"docs":{},"关":{"docs":{},"的":{"docs":{},"用":{"docs":{},"户":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.00487012987012987}}}}}}}}}}}}}}},"话":{"docs":{},"题":{"docs":{},",":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}},"反":{"docs":{},"，":{"docs":{},"z":{"docs":{},"i":{"docs":{},"p":{"docs":{},"(":{"docs":{},"*":{"docs":{},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}},"关":{"docs":{},"数":{"docs":{},"据":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}},"函":{"docs":{},"数":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}},"当":{"docs":{},"大":{"docs":{},"的":{"docs":{},"数":{"docs":{},"量":{"docs":{},"或":{"docs":{},"部":{"docs":{},"分":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}},"矩":{"docs":{},"阵":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}},"分":{"docs":{},"解":{"docs":{},"发":{"docs":{},"展":{"docs":{},"史":{"docs":{"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html":{"ref":"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html","tf":0.037037037037037035}}}}}}},"值":{"docs":{},"p":{"docs":{},"_":{"docs":{},"{":{"1":{"1":{"docs":{},"}":{"docs":{},"​":{"docs":{},"表":{"docs":{},"示":{"docs":{},"用":{"docs":{},"户":{"1":{"docs":{},"对":{"docs":{},"隐":{"docs":{},"含":{"docs":{},"特":{"docs":{},"征":{"1":{"docs":{},"的":{"docs":{},"权":{"docs":{},"重":{"docs":{},"值":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}},"docs":{}}}}}}},"docs":{}}}}}}}},"docs":{}},"docs":{}}}},"q":{"docs":{},"_":{"docs":{},"{":{"1":{"1":{"docs":{},"}":{"docs":{},"​":{"docs":{},"表":{"docs":{},"示":{"docs":{},"隐":{"docs":{},"含":{"docs":{},"特":{"docs":{},"征":{"1":{"docs":{},"在":{"docs":{},"物":{"docs":{},"品":{"1":{"docs":{},"上":{"docs":{},"的":{"docs":{},"权":{"docs":{},"重":{"docs":{},"值":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}}},"docs":{}}}}},"docs":{}}}}}}}}}},"docs":{}},"docs":{}}}},"r":{"docs":{},"_":{"docs":{},"{":{"1":{"1":{"docs":{},"}":{"docs":{},"就":{"docs":{},"表":{"docs":{},"示":{"docs":{},"预":{"docs":{},"测":{"docs":{},"的":{"docs":{},"用":{"docs":{},"户":{"1":{"docs":{},"对":{"docs":{},"物":{"docs":{},"品":{"1":{"docs":{},"的":{"docs":{},"评":{"docs":{},"分":{"docs":{},"，":{"docs":{},"且":{"docs":{},"r":{"docs":{},"_":{"docs":{},"{":{"1":{"1":{"docs":{},"}":{"docs":{},"=":{"docs":{},"\\":{"docs":{},"v":{"docs":{},"e":{"docs":{},"c":{"docs":{},"{":{"docs":{},"p":{"docs":{},"_":{"docs":{},"{":{"1":{"docs":{},",":{"docs":{},"k":{"docs":{},"}":{"docs":{},"}":{"docs":{},"\\":{"docs":{},"c":{"docs":{},"d":{"docs":{},"o":{"docs":{},"t":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}}}}}}},"docs":{}}}}}}}}}}}},"docs":{}},"docs":{}}}}}}}}}},"docs":{}}}}},"docs":{}}}}}}}}}}},"docs":{}},"docs":{}}}}}}},"稀":{"docs":{},"疏":{"docs":{},"评":{"docs":{},"分":{"docs":{},"矩":{"docs":{},"阵":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}},"稠":{"docs":{},"密":{"docs":{},"评":{"docs":{},"分":{"docs":{},"矩":{"docs":{},"阵":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}},"计":{"docs":{},"算":{"docs":{},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"m":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}},"所":{"docs":{},"有":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"两":{"docs":{},"两":{"docs":{},"的":{"docs":{},"杰":{"docs":{},"卡":{"docs":{},"德":{"docs":{},"相":{"docs":{},"似":{"docs":{},"系":{"docs":{},"数":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.00423728813559322}}}}}}}}}}}}}}},"电":{"docs":{},"影":{"docs":{},"的":{"docs":{},"平":{"docs":{},"均":{"docs":{},"评":{"docs":{},"分":{"docs":{},"\\":{"docs":{},"m":{"docs":{},"u":{"docs":{},"​":{"docs":{},"（":{"docs":{},"即":{"docs":{},"全":{"docs":{},"局":{"docs":{},"平":{"docs":{},"均":{"docs":{},"评":{"docs":{},"分":{"docs":{},"）":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}}}}}}}}}}}},"时":{"docs":{},"我":{"docs":{},"们":{"docs":{},"数":{"docs":{},"据":{"docs":{},"通":{"docs":{},"常":{"docs":{},"都":{"docs":{},"需":{"docs":{},"要":{"docs":{},"对":{"docs":{},"数":{"docs":{},"据":{"docs":{},"进":{"docs":{},"行":{"docs":{},"处":{"docs":{},"理":{"docs":{},"，":{"docs":{},"或":{"docs":{},"者":{"docs":{},"编":{"docs":{},"码":{"docs":{},"，":{"docs":{},"目":{"docs":{},"的":{"docs":{},"是":{"docs":{},"为":{"docs":{},"了":{"docs":{},"便":{"docs":{},"于":{"docs":{},"我":{"docs":{},"们":{"docs":{},"对":{"docs":{},"数":{"docs":{},"据":{"docs":{},"进":{"docs":{},"行":{"docs":{},"运":{"docs":{},"算":{"docs":{},"处":{"docs":{},"理":{"docs":{},"，":{"docs":{},"比":{"docs":{},"如":{"docs":{},"这":{"docs":{},"里":{"docs":{},"是":{"docs":{},"比":{"docs":{},"较":{"docs":{},"简":{"docs":{},"单":{"docs":{},"的":{"docs":{},"情":{"docs":{},"形":{"docs":{},"，":{"docs":{},"我":{"docs":{},"们":{"docs":{},"用":{"1":{"docs":{},"、":{"0":{"docs":{},"分":{"docs":{},"别":{"docs":{},"来":{"docs":{},"表":{"docs":{},"示":{"docs":{},"用":{"docs":{},"户":{"docs":{},"的":{"docs":{},"是":{"docs":{},"否":{"docs":{},"购":{"docs":{},"买":{"docs":{},"过":{"docs":{},"该":{"docs":{},"物":{"docs":{},"品":{"docs":{},"，":{"docs":{},"则":{"docs":{},"我":{"docs":{},"们":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{},"其":{"docs":{},"实":{"docs":{},"应":{"docs":{},"该":{"docs":{},"是":{"docs":{},"这":{"docs":{},"样":{"docs":{},"的":{"docs":{},"：":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"物":{"docs":{},"品":{"docs":{},"间":{"docs":{},"相":{"docs":{},"似":{"docs":{},"度":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147}}}}}}}},"用":{"docs":{},"户":{"docs":{},"间":{"docs":{},"相":{"docs":{},"似":{"docs":{},"度":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147}}}}}}}},"相":{"docs":{},"似":{"docs":{},"度":{"docs":{},"：":{"docs":{},"对":{"docs":{},"于":{"docs":{},"评":{"docs":{},"分":{"docs":{},"数":{"docs":{},"据":{"docs":{},"这":{"docs":{},"里":{"docs":{},"我":{"docs":{},"们":{"docs":{},"采":{"docs":{},"用":{"docs":{},"皮":{"docs":{},"尔":{"docs":{},"逊":{"docs":{},"相":{"docs":{},"关":{"docs":{},"系":{"docs":{},"数":{"docs":{},"[":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}}}}}}}}}}}},"分":{"docs":{},"子":{"docs":{},"的":{"docs":{},"值":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.006493506493506494}}}}},"母":{"docs":{},"的":{"docs":{},"值":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.006493506493506494}}}}}},"预":{"docs":{},"测":{"docs":{},"的":{"docs":{},"评":{"docs":{},"分":{"docs":{},"值":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.0016233766233766235}},"并":{"docs":{},"返":{"docs":{},"回":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.00487012987012987}}}}}}}}}}},"全":{"docs":{},"局":{"docs":{},"平":{"docs":{},"均":{"docs":{},"分":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002858776443682104},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}},"其":{"docs":{},"中":{"docs":{},"一":{"docs":{},"项":{"docs":{},"，":{"docs":{},"先":{"docs":{},"固":{"docs":{},"定":{"docs":{},"其":{"docs":{},"他":{"docs":{},"未":{"docs":{},"知":{"docs":{},"参":{"docs":{},"数":{"docs":{},"，":{"docs":{},"即":{"docs":{},"看":{"docs":{},"作":{"docs":{},"其":{"docs":{},"他":{"docs":{},"未":{"docs":{},"知":{"docs":{},"参":{"docs":{},"数":{"docs":{},"为":{"docs":{},"已":{"docs":{},"知":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"每":{"docs":{},"个":{"docs":{},"用":{"docs":{},"户":{"docs":{},"评":{"docs":{},"分":{"docs":{},"与":{"docs":{},"平":{"docs":{},"均":{"docs":{},"评":{"docs":{},"分":{"docs":{},"\\":{"docs":{},"m":{"docs":{},"u":{"docs":{},"的":{"docs":{},"偏":{"docs":{},"置":{"docs":{},"值":{"docs":{},"b":{"docs":{},"_":{"docs":{},"u":{"docs":{},"​":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}}}}}}}}}}},"部":{"docs":{},"电":{"docs":{},"影":{"docs":{},"所":{"docs":{},"接":{"docs":{},"受":{"docs":{},"的":{"docs":{},"评":{"docs":{},"分":{"docs":{},"与":{"docs":{},"平":{"docs":{},"均":{"docs":{},"评":{"docs":{},"分":{"docs":{},"\\":{"docs":{},"m":{"docs":{},"u":{"docs":{},"的":{"docs":{},"偏":{"docs":{},"置":{"docs":{},"值":{"docs":{},"b":{"docs":{},"_":{"docs":{},"i":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}}}}}}}}}}}}}}},"列":{"docs":{},"表":{"docs":{},"各":{"docs":{},"个":{"docs":{},"元":{"docs":{},"素":{"docs":{},"的":{"docs":{},"平":{"docs":{},"方":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}},"和":{"docs":{},"：":{"1":{"docs":{},"+":{"2":{"docs":{},"+":{"3":{"docs":{},"+":{"4":{"docs":{},"+":{"5":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}}}},"平":{"docs":{},"方":{"docs":{},"数":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}},"词":{"docs":{},"语":{"docs":{},"的":{"docs":{},"逆":{"docs":{},"文":{"docs":{},"档":{"docs":{},"频":{"docs":{},"率":{"docs":{},"如":{"docs":{},"下":{"docs":{},"：":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}},"框":{"docs":{},"架":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}},"都":{"docs":{},"要":{"docs":{},"用":{"docs":{},"到":{"docs":{},"服":{"docs":{},"务":{"docs":{},"器":{"docs":{},"集":{"docs":{},"群":{"docs":{},"资":{"docs":{},"源":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}}}}}}}}}}}}},"过":{"docs":{},"滤":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}},"掉":{"docs":{},"已":{"docs":{},"经":{"docs":{},"购":{"docs":{},"买":{"docs":{},"过":{"docs":{},"的":{"docs":{},"物":{"docs":{},"品":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}},"用":{"docs":{},"户":{"docs":{},"已":{"docs":{},"购":{"docs":{},"的":{"docs":{},"物":{"docs":{},"品":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}},"运":{"docs":{},"行":{"docs":{},"结":{"docs":{},"果":{"docs":{},"：":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}},"m":{"docs":{},"r":{"docs":{},"j":{"docs":{},"o":{"docs":{},"b":{"docs":{},"的":{"docs":{},"不":{"docs":{},"同":{"docs":{},"方":{"docs":{},"式":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}}}}}},"w":{"docs":{},"o":{"docs":{},"r":{"docs":{},"d":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"代":{"docs":{},"码":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}}}}}}},"时":{"docs":{},"间":{"docs":{},"长":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}},"过":{"docs":{},"程":{"docs":{},"是":{"docs":{},"先":{"docs":{},"将":{"docs":{},"p":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"转":{"docs":{},"换":{"docs":{},"为":{"docs":{},"一":{"docs":{},"列":{"docs":{},"新":{"docs":{},"的":{"docs":{},"特":{"docs":{},"征":{"docs":{},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"然":{"docs":{},"后":{"docs":{},"对":{"docs":{},"该":{"docs":{},"特":{"docs":{},"征":{"docs":{},"数":{"docs":{},"据":{"docs":{},"求":{"docs":{},"出":{"docs":{},"的":{"docs":{},"热":{"docs":{},"编":{"docs":{},"码":{"docs":{},"值":{"docs":{},"，":{"docs":{},"存":{"docs":{},"在":{"docs":{},"了":{"docs":{},"新":{"docs":{},"的":{"docs":{},"一":{"docs":{},"列":{"docs":{},"数":{"docs":{},"据":{"docs":{},"中":{"docs":{},"，":{"docs":{},"类":{"docs":{},"型":{"docs":{},"为":{"docs":{},"一":{"docs":{},"个":{"docs":{},"稀":{"docs":{},"疏":{"docs":{},"矩":{"docs":{},"阵":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"维":{"docs":{},"成":{"docs":{},"本":{"docs":{},"高":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}},"营":{"docs":{},"人":{"docs":{},"员":{"docs":{},"发":{"docs":{},"现":{"docs":{},"从":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}},"常":{"docs":{},"用":{"docs":{},"数":{"docs":{},"据":{"docs":{},"指":{"docs":{},"标":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}},"数":{"docs":{},"据":{"docs":{},"是":{"docs":{},"公":{"docs":{},"司":{"docs":{},"管":{"docs":{},"理":{"docs":{},"的":{"docs":{},"基":{"docs":{},"础":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}},"的":{"docs":{},"获":{"docs":{},"取":{"docs":{},"需":{"docs":{},"要":{"docs":{},"大":{"docs":{},"数":{"docs":{},"据":{"docs":{},"平":{"docs":{},"台":{"docs":{},"的":{"docs":{},"支":{"docs":{},"持":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}}}}}}}}}}},"这":{"docs":{},"里":{"docs":{},"先":{"docs":{},"介":{"docs":{},"绍":{"docs":{},"稠":{"docs":{},"密":{"docs":{},"评":{"docs":{},"分":{"docs":{},"矩":{"docs":{},"阵":{"docs":{},"的":{"docs":{},"处":{"docs":{},"理":{"docs":{},"，":{"docs":{},"稀":{"docs":{},"疏":{"docs":{},"矩":{"docs":{},"阵":{"docs":{},"的":{"docs":{},"处":{"docs":{},"理":{"docs":{},"相":{"docs":{},"对":{"docs":{},"会":{"docs":{},"复":{"docs":{},"杂":{"docs":{},"一":{"docs":{},"些":{"docs":{},"，":{"docs":{},"我":{"docs":{},"们":{"docs":{},"到":{"docs":{},"后":{"docs":{},"面":{"docs":{},"再":{"docs":{},"来":{"docs":{},"介":{"docs":{},"绍":{"docs":{},"。":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"利":{"docs":{},"用":{"docs":{},"物":{"docs":{},"品":{"docs":{},"相":{"docs":{},"似":{"docs":{},"度":{"docs":{},"预":{"docs":{},"测":{"docs":{},"的":{"docs":{},"计":{"docs":{},"算":{"docs":{},"同":{"docs":{},"上":{"docs":{},"，":{"docs":{},"同":{"docs":{},"样":{"docs":{},"考":{"docs":{},"虑":{"docs":{},"了":{"docs":{},"用":{"docs":{},"户":{"docs":{},"自":{"docs":{},"身":{"docs":{},"的":{"docs":{},"平":{"docs":{},"均":{"docs":{},"打":{"docs":{},"分":{"docs":{},"因":{"docs":{},"素":{"docs":{},"，":{"docs":{},"结":{"docs":{},"合":{"docs":{},"预":{"docs":{},"测":{"docs":{},"物":{"docs":{},"品":{"docs":{},"与":{"docs":{},"相":{"docs":{},"似":{"docs":{},"物":{"docs":{},"品":{"docs":{},"的":{"docs":{},"加":{"docs":{},"权":{"docs":{},"平":{"docs":{},"均":{"docs":{},"相":{"docs":{},"似":{"docs":{},"度":{"docs":{},"打":{"docs":{},"分":{"docs":{},"进":{"docs":{},"行":{"docs":{},"来":{"docs":{},"进":{"docs":{},"行":{"docs":{},"预":{"docs":{},"测":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"为":{"docs":{},"了":{"docs":{},"保":{"docs":{},"证":{"docs":{},"用":{"docs":{},"户":{"docs":{},"数":{"docs":{},"量":{"docs":{},"保":{"docs":{},"持":{"docs":{},"不":{"docs":{},"变":{"docs":{},"，":{"docs":{},"将":{"docs":{},"每":{"docs":{},"个":{"docs":{},"用":{"docs":{},"户":{"docs":{},"的":{"docs":{},"评":{"docs":{},"分":{"docs":{},"数":{"docs":{},"据":{"docs":{},"按":{"docs":{},"比":{"docs":{},"例":{"docs":{},"进":{"docs":{},"行":{"docs":{},"拆":{"docs":{},"分":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"以":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}},"由":{"docs":{},"于":{"docs":{},"类":{"docs":{},"型":{"docs":{},"只":{"docs":{},"有":{"docs":{},"四":{"docs":{},"个":{"docs":{},"，":{"docs":{},"所":{"docs":{},"以":{"docs":{},"直":{"docs":{},"接":{"docs":{},"使":{"docs":{},"用":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"，":{"docs":{},"把":{"docs":{},"数":{"docs":{},"据":{"docs":{},"全":{"docs":{},"部":{"docs":{},"加":{"docs":{},"载":{"docs":{},"出":{"docs":{},"来":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"数":{"docs":{},"据":{"docs":{},"量":{"docs":{},"比":{"docs":{},"较":{"docs":{},"小":{"docs":{},"，":{"docs":{},"直":{"docs":{},"接":{"docs":{},"转":{"docs":{},"换":{"docs":{},"为":{"docs":{},"p":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"a":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}},"注":{"docs":{},"意":{"docs":{},"p":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"i":{"docs":{},"c":{"docs":{},"t":{"docs":{},"参":{"docs":{},"数":{"docs":{},"，":{"docs":{},"如":{"docs":{},"果":{"docs":{},"是":{"docs":{},"预":{"docs":{},"测":{"docs":{},"多":{"docs":{},"个":{"docs":{},"，":{"docs":{},"那":{"docs":{},"么":{"docs":{},"参":{"docs":{},"数":{"docs":{},"必":{"docs":{},"须":{"docs":{},"是":{"docs":{},"直":{"docs":{},"接":{"docs":{},"有":{"docs":{},"列":{"docs":{},"表":{"docs":{},"构":{"docs":{},"成":{"docs":{},"的":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"参":{"docs":{},"数":{"docs":{},"，":{"docs":{},"而":{"docs":{},"不":{"docs":{},"能":{"docs":{},"是":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},".":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"类":{"docs":{},"型":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"主":{"docs":{},"要":{"docs":{},"是":{"docs":{},"利":{"docs":{},"用":{"docs":{},"我":{"docs":{},"们":{"docs":{},"前":{"docs":{},"面":{"docs":{},"训":{"docs":{},"练":{"docs":{},"的":{"docs":{},"a":{"docs":{},"l":{"docs":{},"s":{"docs":{},"模":{"docs":{},"型":{"docs":{},"进":{"docs":{},"行":{"docs":{},"协":{"docs":{},"同":{"docs":{},"过":{"docs":{},"滤":{"docs":{},"召":{"docs":{},"回":{"docs":{},"，":{"docs":{},"但":{"docs":{},"是":{"docs":{},"注":{"docs":{},"意":{"docs":{},"，":{"docs":{},"我":{"docs":{},"们":{"docs":{},"a":{"docs":{},"l":{"docs":{},"s":{"docs":{},"模":{"docs":{},"型":{"docs":{},"召":{"docs":{},"回":{"docs":{},"的":{"docs":{},"是":{"docs":{},"用":{"docs":{},"户":{"docs":{},"最":{"docs":{},"感":{"docs":{},"兴":{"docs":{},"趣":{"docs":{},"的":{"docs":{},"类":{"docs":{},"别":{"docs":{},"，":{"docs":{},"而":{"docs":{},"我":{"docs":{},"们":{"docs":{},"需":{"docs":{},"要":{"docs":{},"的":{"docs":{},"是":{"docs":{},"用":{"docs":{},"户":{"docs":{},"可":{"docs":{},"能":{"docs":{},"感":{"docs":{},"兴":{"docs":{},"趣":{"docs":{},"的":{"docs":{},"广":{"docs":{},"告":{"docs":{},"的":{"docs":{},"集":{"docs":{},"合":{"docs":{},"，":{"docs":{},"因":{"docs":{},"此":{"docs":{},"我":{"docs":{},"们":{"docs":{},"还":{"docs":{},"需":{"docs":{},"要":{"docs":{},"根":{"docs":{},"据":{"docs":{},"召":{"docs":{},"回":{"docs":{},"的":{"docs":{},"类":{"docs":{},"别":{"docs":{},"匹":{"docs":{},"配":{"docs":{},"出":{"docs":{},"对":{"docs":{},"应":{"docs":{},"的":{"docs":{},"广":{"docs":{},"告":{"docs":{},"。":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"我":{"docs":{},"们":{"docs":{},"只":{"docs":{},"需":{"docs":{},"要":{"docs":{},"a":{"docs":{},"d":{"docs":{},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"i":{"docs":{},"d":{"docs":{},"、":{"docs":{},"和":{"docs":{},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"i":{"docs":{},"d":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}}}}}}}}}}},"个":{"docs":{},"用":{"docs":{},"户":{"docs":{},"或":{"docs":{},"物":{"docs":{},"品":{"docs":{},"普":{"docs":{},"遍":{"docs":{},"高":{"docs":{},"于":{"docs":{},"或":{"docs":{},"低":{"docs":{},"于":{"docs":{},"平":{"docs":{},"均":{"docs":{},"值":{"docs":{},"的":{"docs":{},"差":{"docs":{},"值":{"docs":{},"，":{"docs":{},"我":{"docs":{},"们":{"docs":{},"称":{"docs":{},"为":{"docs":{},"偏":{"docs":{},"置":{"docs":{},"(":{"docs":{},"b":{"docs":{},"i":{"docs":{},"a":{"docs":{},"s":{"docs":{},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"问":{"docs":{},"题":{"docs":{},"不":{"docs":{},"明":{"docs":{},"显":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}}},"命":{"docs":{},"令":{"docs":{},"将":{"docs":{},"s":{"docs":{},"t":{"docs":{},"u":{"docs":{},"d":{"docs":{},"e":{"docs":{},"n":{"docs":{},"t":{"docs":{},".":{"docs":{},"t":{"docs":{},"x":{"docs":{},"t":{"docs":{},"文":{"docs":{},"件":{"docs":{},"复":{"docs":{},"制":{"docs":{},"到":{"docs":{},"h":{"docs":{},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{},"的":{"docs":{},"w":{"docs":{},"a":{"docs":{},"r":{"docs":{},"e":{"docs":{},"h":{"docs":{},"o":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"目":{"docs":{},"录":{"docs":{},"中":{"docs":{},"，":{"docs":{},"这":{"docs":{},"个":{"docs":{},"目":{"docs":{},"录":{"docs":{},"由":{"docs":{},"h":{"docs":{},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{},".":{"docs":{},"m":{"docs":{},"e":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{},"e":{"docs":{},".":{"docs":{},"w":{"docs":{},"a":{"docs":{},"r":{"docs":{},"e":{"docs":{},"h":{"docs":{},"o":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},".":{"docs":{},"d":{"docs":{},"i":{"docs":{},"r":{"docs":{},"配":{"docs":{},"置":{"docs":{},"项":{"docs":{},"设":{"docs":{},"置":{"docs":{},"，":{"docs":{},"默":{"docs":{},"认":{"docs":{},"值":{"docs":{},"为":{"docs":{},"/":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"/":{"docs":{},"h":{"docs":{},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{},"/":{"docs":{},"w":{"docs":{},"a":{"docs":{},"r":{"docs":{},"e":{"docs":{},"h":{"docs":{},"o":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"。":{"docs":{},"o":{"docs":{},"v":{"docs":{},"e":{"docs":{},"r":{"docs":{},"w":{"docs":{},"r":{"docs":{},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"选":{"docs":{},"项":{"docs":{},"将":{"docs":{},"导":{"docs":{},"致":{"docs":{},"h":{"docs":{},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{},"事":{"docs":{},"先":{"docs":{},"删":{"docs":{},"除":{"docs":{},"s":{"docs":{},"t":{"docs":{},"u":{"docs":{},"d":{"docs":{},"e":{"docs":{},"n":{"docs":{},"t":{"docs":{},"目":{"docs":{},"录":{"docs":{},"下":{"docs":{},"所":{"docs":{},"有":{"docs":{},"的":{"docs":{},"文":{"docs":{},"件":{"docs":{},",":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"类":{"docs":{},"提":{"docs":{},"供":{"docs":{},"了":{"docs":{},"一":{"docs":{},"个":{"docs":{},"与":{"docs":{},"h":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"交":{"docs":{},"互":{"docs":{},"的":{"docs":{},"入":{"docs":{},"口":{"docs":{},",":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}}}}}}}}}}}}}}}}},"些":{"docs":{},"a":{"docs":{},"p":{"docs":{},"i":{"docs":{},"用":{"docs":{},"于":{"docs":{},"检":{"docs":{},"索":{"docs":{},"和":{"docs":{},"操":{"docs":{},"作":{"docs":{},"h":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"中":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"。":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}}}}}}}}}}}}}}}}}},"内":{"docs":{},"存":{"docs":{},"直":{"docs":{},"接":{"docs":{},"受":{"docs":{},"操":{"docs":{},"作":{"docs":{},"系":{"docs":{},"统":{"docs":{},"管":{"docs":{},"理":{"docs":{},"（":{"docs":{},"而":{"docs":{},"不":{"docs":{},"是":{"docs":{},"j":{"docs":{},"v":{"docs":{},"m":{"docs":{},"）":{"docs":{},"。":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}}}}}}},"种":{"docs":{},"设":{"docs":{},"计":{"docs":{},"使":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"运":{"docs":{},"行":{"docs":{},"效":{"docs":{},"率":{"docs":{},"更":{"docs":{},"高":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}}}}}}}}},"样":{"docs":{},"做":{"docs":{},"保":{"docs":{},"留":{"docs":{},"了":{"docs":{},"特":{"docs":{},"征":{"docs":{},"的":{"docs":{},"多":{"docs":{},"样":{"docs":{},"性":{"docs":{},"，":{"docs":{},"但":{"docs":{},"是":{"docs":{},"也":{"docs":{},"要":{"docs":{},"注":{"docs":{},"意":{"docs":{},"如":{"docs":{},"果":{"docs":{},"数":{"docs":{},"据":{"docs":{},"过":{"docs":{},"于":{"docs":{},"稀":{"docs":{},"疏":{"docs":{},"(":{"docs":{},"样":{"docs":{},"本":{"docs":{},"较":{"docs":{},"少":{"docs":{},"、":{"docs":{},"维":{"docs":{},"度":{"docs":{},"过":{"docs":{},"高":{"docs":{},")":{"docs":{},"，":{"docs":{},"其":{"docs":{},"效":{"docs":{},"果":{"docs":{},"反":{"docs":{},"而":{"docs":{},"会":{"docs":{},"变":{"docs":{},"差":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"如":{"docs":{},"果":{"docs":{},"有":{"docs":{},"多":{"docs":{},"个":{"docs":{},"资":{"docs":{},"源":{"docs":{},"位":{"docs":{},"，":{"docs":{},"那":{"docs":{},"么":{"docs":{},"每":{"docs":{},"个":{"docs":{},"资":{"docs":{},"源":{"docs":{},"位":{"docs":{},"都":{"docs":{},"会":{"docs":{},"对":{"docs":{},"应":{"docs":{},"相":{"docs":{},"应":{"docs":{},"的":{"docs":{},"一":{"docs":{},"个":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"列":{"docs":{},"表":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"选":{"docs":{},"择":{"docs":{},"合":{"docs":{},"适":{"docs":{},"的":{"docs":{},"算":{"docs":{},"法":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}},"出":{"docs":{},"所":{"docs":{},"有":{"docs":{},"f":{"docs":{},"u":{"docs":{},"n":{"docs":{},"c":{"docs":{},"返":{"docs":{},"回":{"docs":{},"值":{"docs":{},"为":{"docs":{},"t":{"docs":{},"r":{"docs":{},"u":{"docs":{},"e":{"docs":{},"的":{"docs":{},"元":{"docs":{},"素":{"docs":{},"，":{"docs":{},"生":{"docs":{},"成":{"docs":{},"一":{"docs":{},"个":{"docs":{},"新":{"docs":{},"的":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"返":{"docs":{},"回":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"n":{"docs":{},"e":{"docs":{},"w":{"docs":{},"_":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"c":{"docs":{},"l":{"docs":{},"a":{"docs":{},"s":{"docs":{},"s":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"全":{"docs":{},"部":{"docs":{},"的":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}},"遍":{"docs":{},"历":{"docs":{},"所":{"docs":{},"有":{"docs":{},"用":{"docs":{},"户":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}},"每":{"docs":{},"一":{"docs":{},"行":{"docs":{},"数":{"docs":{},"据":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.002824858757062147}}}}}}},"a":{"docs":{},"l":{"docs":{},"s":{"docs":{},"_":{"docs":{},"m":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"l":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}},"召":{"docs":{},"回":{"docs":{},"集":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}},"那":{"docs":{},"么":{"docs":{},"欧":{"docs":{},"式":{"docs":{},"距":{"docs":{},"离":{"docs":{},"就":{"docs":{},"是":{"docs":{},"衡":{"docs":{},"量":{"docs":{},"这":{"docs":{},"两":{"docs":{},"个":{"docs":{},"点":{"docs":{},"之":{"docs":{},"间":{"docs":{},"的":{"docs":{},"距":{"docs":{},"离":{"docs":{},".":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}}}}}}}},"都":{"docs":{},"可":{"docs":{},"以":{"docs":{},"视":{"docs":{},"为":{"docs":{},"'":{"docs":{},"相":{"docs":{},"似":{"docs":{},"'":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}},"在":{"docs":{},"同":{"docs":{},"一":{"docs":{},"个":{"docs":{},"空":{"docs":{},"间":{"docs":{},"下":{"docs":{},"表":{"docs":{},"示":{"docs":{},"为":{"docs":{},"两":{"docs":{},"个":{"docs":{},"点":{"docs":{},",":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}},"需":{"docs":{},"要":{"docs":{},"这":{"docs":{},"一":{"docs":{},"个":{"docs":{},"i":{"docs":{},"p":{"docs":{},"表":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}}}}}}}},"隐":{"docs":{},"形":{"docs":{},"数":{"docs":{},"据":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}},"式":{"docs":{},"反":{"docs":{},"馈":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}},"类":{"docs":{},"别":{"docs":{},"数":{"docs":{},"量":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}},"含":{"docs":{},"特":{"docs":{},"征":{"docs":{},"的":{"docs":{},"矩":{"docs":{},"阵":{"docs":{},"，":{"docs":{},"f":{"docs":{},"u":{"docs":{},"n":{"docs":{},"k":{"docs":{"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html":{"ref":"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html","tf":0.037037037037037035}}}}}}}}}},"，":{"docs":{},"项":{"docs":{},"目":{"docs":{"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html":{"ref":"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html","tf":0.037037037037037035}}}}}}},"因":{"docs":{},"子":{"docs":{},"个":{"docs":{},"数":{"docs":{},"是":{"1":{"0":{"docs":{},"个":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}},"docs":{}},"docs":{}}}}}}}},"非":{"docs":{},"常":{"docs":{},"适":{"docs":{},"用":{"docs":{},"于":{"docs":{},"布":{"docs":{},"尔":{"docs":{},"向":{"docs":{},"量":{"docs":{},"表":{"docs":{},"示":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}},"结":{"docs":{},"构":{"docs":{},"化":{"docs":{},"数":{"docs":{},"据":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}},"是":{"docs":{},"数":{"docs":{},"据":{"docs":{},"结":{"docs":{},"构":{"docs":{},"不":{"docs":{},"规":{"docs":{},"则":{"docs":{},"或":{"docs":{},"不":{"docs":{},"完":{"docs":{},"整":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}}}}}}}}}}}}}},"搜":{"docs":{},"索":{"docs":{},"广":{"docs":{},"告":{"docs":{},"（":{"docs":{},"例":{"docs":{},"如":{"docs":{},"展":{"docs":{},"示":{"docs":{},"广":{"docs":{},"告":{"docs":{},"，":{"docs":{},"信":{"docs":{},"息":{"docs":{},"流":{"docs":{},"广":{"docs":{},"告":{"docs":{},"）":{"docs":{},"的":{"docs":{},"点":{"docs":{},"击":{"docs":{},"率":{"docs":{},"的":{"docs":{},"计":{"docs":{},"算":{"docs":{},"很":{"docs":{},"多":{"docs":{},"就":{"docs":{},"来":{"docs":{},"源":{"docs":{},"于":{"docs":{},"用":{"docs":{},"户":{"docs":{},"的":{"docs":{},"兴":{"docs":{},"趣":{"docs":{},"和":{"docs":{},"广":{"docs":{},"告":{"docs":{},"自":{"docs":{},"身":{"docs":{},"的":{"docs":{},"特":{"docs":{},"征":{"docs":{},"，":{"docs":{},"以":{"docs":{},"及":{"docs":{},"上":{"docs":{},"下":{"docs":{},"文":{"docs":{},"环":{"docs":{},"境":{"docs":{},"。":{"docs":{},"通":{"docs":{},"常":{"docs":{},"好":{"docs":{},"位":{"docs":{},"置":{"docs":{},"能":{"docs":{},"达":{"docs":{},"到":{"docs":{},"百":{"docs":{},"分":{"docs":{},"之":{"docs":{},"几":{"docs":{},"的":{"docs":{},"点":{"docs":{},"击":{"docs":{},"率":{"docs":{},"。":{"docs":{},"对":{"docs":{},"于":{"docs":{},"很":{"docs":{},"多":{"docs":{},"底":{"docs":{},"部":{"docs":{},"的":{"docs":{},"广":{"docs":{},"告":{"docs":{},"，":{"docs":{},"点":{"docs":{},"击":{"docs":{},"率":{"docs":{},"非":{"docs":{},"常":{"docs":{},"低":{"docs":{},"，":{"docs":{},"常":{"docs":{},"常":{"docs":{},"是":{"docs":{},"千":{"docs":{},"分":{"docs":{},"之":{"docs":{},"几":{"docs":{},"，":{"docs":{},"甚":{"docs":{},"至":{"docs":{},"更":{"docs":{},"低":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"页":{"docs":{},"面":{"docs":{},"浏":{"docs":{},"览":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}},"跳":{"docs":{},"转":{"docs":{},"都":{"docs":{},"记":{"docs":{},"一":{"docs":{},"次":{"docs":{},"p":{"docs":{},"v":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}}}},"默":{"docs":{},"认":{"docs":{},"是":{"docs":{},"按":{"docs":{},"列":{"docs":{},"进":{"docs":{},"行":{"docs":{},"计":{"docs":{},"算":{"docs":{},"，":{"docs":{},"因":{"docs":{},"此":{"docs":{},"如":{"docs":{},"果":{"docs":{},"计":{"docs":{},"算":{"docs":{},"用":{"docs":{},"户":{"docs":{},"间":{"docs":{},"的":{"docs":{},"相":{"docs":{},"似":{"docs":{},"度":{"docs":{},"，":{"docs":{},"当":{"docs":{},"前":{"docs":{},"需":{"docs":{},"要":{"docs":{},"进":{"docs":{},"行":{"docs":{},"转":{"docs":{},"置":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"没":{"docs":{},"有":{"docs":{},"这":{"docs":{},"个":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428}}}}}},"情":{"docs":{},"况":{"docs":{},"下":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}},"为":{"docs":{},"批":{"docs":{},"处":{"docs":{},"理":{"docs":{},"的":{"docs":{},"滑":{"docs":{},"动":{"docs":{},"间":{"docs":{},"隔":{"docs":{},"来":{"docs":{},"确":{"docs":{},"定":{"docs":{},"计":{"docs":{},"算":{"docs":{},"结":{"docs":{},"果":{"docs":{},"的":{"docs":{},"频":{"docs":{},"率":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}}}}}}}}}}}}}}},"，":{"1":{"docs":{},":":{"docs":{},"是":{"docs":{},",":{"0":{"docs":{},":":{"docs":{},"否":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}},"docs":{}}}}},"docs":{},"形":{"docs":{},"成":{"docs":{},"一":{"docs":{},"个":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}},"则":{"docs":{},"返":{"docs":{},"回":{"docs":{},"值":{"docs":{},"为":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}}},"是":{"docs":{},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},"的":{"docs":{},"特":{"docs":{},"殊":{"docs":{},"形":{"docs":{},"式":{"docs":{},"。":{"docs":{},"第":{"docs":{},"一":{"docs":{},"个":{"docs":{},"参":{"docs":{},"数":{"docs":{},"是":{"docs":{},"其":{"docs":{},"它":{"docs":{},"参":{"docs":{},"数":{"docs":{},"的":{"docs":{},"分":{"docs":{},"隔":{"docs":{},"符":{"docs":{},"。":{"docs":{},"分":{"docs":{},"隔":{"docs":{},"符":{"docs":{},"的":{"docs":{},"位":{"docs":{},"置":{"docs":{},"放":{"docs":{},"在":{"docs":{},"要":{"docs":{},"连":{"docs":{},"接":{"docs":{},"的":{"docs":{},"两":{"docs":{},"个":{"docs":{},"字":{"docs":{},"符":{"docs":{},"串":{"docs":{},"之":{"docs":{},"间":{"docs":{},"。":{"docs":{},"分":{"docs":{},"隔":{"docs":{},"符":{"docs":{},"可":{"docs":{},"以":{"docs":{},"是":{"docs":{},"一":{"docs":{},"个":{"docs":{},"字":{"docs":{},"符":{"docs":{},"串":{"docs":{},"，":{"docs":{},"也":{"docs":{},"可":{"docs":{},"以":{"docs":{},"是":{"docs":{},"其":{"docs":{},"它":{"docs":{},"参":{"docs":{},"数":{"docs":{},"。":{"docs":{},"如":{"docs":{},"果":{"docs":{},"分":{"docs":{},"隔":{"docs":{},"符":{"docs":{},"为":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"：":{"docs":{},"基":{"docs":{},"于":{"docs":{},"用":{"docs":{},"户":{"docs":{},"的":{"docs":{},"协":{"docs":{},"同":{"docs":{},"过":{"docs":{},"滤":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"（":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{"day01_推荐系统介绍/03_推荐算法.html":{"ref":"day01_推荐系统介绍/03_推荐算法.html","tf":0.0014124293785310734}}}}}}}}}}}}}}}}}}},"%":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.00974025974025974},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.003430531732418525},"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017},"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}},"s":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}},"i":{"docs":{},"\"":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}},"'":{"1":{"docs":{},"'":{"docs":{},",":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}},"6":{"5":{"5":{"3":{"6":{"docs":{},"'":{"docs":{},"}":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"9":{"9":{"9":{"9":{"9":{"docs":{},"'":{"docs":{},")":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{},"'":{"docs":{},"'":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.012987012987012988},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.02058319039451115},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.015180265654648957},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.018518518518518517},"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.01556420233463035},"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.010526315789473684},"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.0136986301369863},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.004228329809725159}},"评":{"docs":{},"分":{"docs":{},"预":{"docs":{},"测":{"docs":{},"'":{"docs":{},"'":{"docs":{},"'":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}}},"预":{"docs":{},"测":{"docs":{},"测":{"docs":{},"试":{"docs":{},"集":{"docs":{},"数":{"docs":{},"据":{"docs":{},"'":{"docs":{},"'":{"docs":{},"'":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}}}}}}},"特":{"docs":{},"征":{"docs":{},"处":{"docs":{},"理":{"docs":{},"'":{"docs":{},"'":{"docs":{},"'":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}},"p":{"docs":{},"i":{"docs":{},"d":{"docs":{},"和":{"docs":{},"特":{"docs":{},"征":{"docs":{},"的":{"docs":{},"对":{"docs":{},"应":{"docs":{},"关":{"docs":{},"系":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}},"从":{"docs":{},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},"中":{"docs":{},"加":{"docs":{},"载":{"docs":{},"样":{"docs":{},"本":{"docs":{},"数":{"docs":{},"据":{"docs":{},"信":{"docs":{},"息":{"docs":{},"'":{"docs":{},"'":{"docs":{},"'":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}},"对":{"docs":{},"缺":{"docs":{},"失":{"docs":{},"数":{"docs":{},"据":{"docs":{},"进":{"docs":{},"行":{"docs":{},"特":{"docs":{},"征":{"docs":{},"热":{"docs":{},"编":{"docs":{},"码":{"docs":{},"'":{"docs":{},"'":{"docs":{},"'":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}},"_":{"docs":{},"_":{"docs":{},"m":{"docs":{},"a":{"docs":{},"i":{"docs":{},"n":{"docs":{},"_":{"docs":{},"_":{"docs":{},"'":{"docs":{},":":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.006493506493506494},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147},"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269},"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577},"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}}}}}}},"/":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}},"m":{"docs":{},"i":{"docs":{},"s":{"docs":{},"s":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"'":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}},"o":{"docs":{},"'":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}},",":{"docs":{},"'":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.014814814814814815},"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0022675736961451248}},";":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}},"/":{"docs":{},"h":{"docs":{},"o":{"docs":{},"m":{"docs":{},"e":{"docs":{},"/":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},"/":{"docs":{},"t":{"docs":{},"m":{"docs":{},"p":{"docs":{},"/":{"docs":{},"s":{"docs":{},"t":{"docs":{},"u":{"docs":{},"d":{"docs":{},"e":{"docs":{},"n":{"docs":{},"t":{"docs":{},".":{"docs":{},"t":{"docs":{},"x":{"docs":{},"t":{"docs":{},"'":{"docs":{},"o":{"docs":{},"v":{"docs":{},"e":{"docs":{},"r":{"docs":{},"w":{"docs":{},"r":{"docs":{},"i":{"docs":{},"t":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"r":{"docs":{},"o":{"docs":{},"o":{"docs":{},"t":{"docs":{},"/":{"docs":{},"t":{"docs":{},"m":{"docs":{},"p":{"docs":{},"/":{"docs":{},"e":{"docs":{},"m":{"docs":{},"p":{"docs":{},"l":{"docs":{},"o":{"docs":{},"y":{"docs":{},"e":{"docs":{},"e":{"docs":{},".":{"docs":{},"t":{"docs":{},"x":{"docs":{},"t":{"docs":{},"'":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}}}}}}}}},"s":{"docs":{},"t":{"docs":{},"u":{"docs":{},"d":{"docs":{},"e":{"docs":{},"n":{"docs":{},"t":{"docs":{},".":{"docs":{},"t":{"docs":{},"x":{"docs":{},"t":{"docs":{},"'":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}}}}}}}}}}}}}}}}},"t":{"docs":{},"m":{"docs":{},"p":{"docs":{},"/":{"docs":{},"s":{"docs":{},"t":{"docs":{},"u":{"docs":{},"d":{"docs":{},"e":{"docs":{},"n":{"docs":{},"t":{"docs":{},"'":{"docs":{},";":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}}}}},"d":{"docs":{},"e":{"docs":{},"m":{"docs":{},"o":{"docs":{},"/":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{},"i":{"docs":{},"c":{"docs":{},"l":{"docs":{},"e":{"docs":{},"_":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{},"w":{"docs":{},"o":{"docs":{},"r":{"docs":{},"d":{"docs":{},"s":{"docs":{},"'":{"docs":{},";":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}}}}}}}}}}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"a":{"docs":{},"c":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"'":{"docs":{},";":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}}}}}}}}}}}}}}}}}}}}},"\\":{"docs":{},"n":{"docs":{},"'":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.007407407407407408}}}},"t":{"docs":{},"'":{"docs":{},".":{"docs":{},"j":{"docs":{},"o":{"docs":{},"i":{"docs":{},"n":{"docs":{},"(":{"docs":{},"[":{"docs":{},"f":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},",":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}}}}}}}}}}}}},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},":":{"docs":{},"/":{"docs":{},"/":{"docs":{},"/":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"/":{"docs":{},"h":{"docs":{},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{},"/":{"docs":{},"l":{"docs":{},"i":{"docs":{},"b":{"docs":{},"/":{"docs":{},"h":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}}}}}}}}}}}}}}}}}}},"'":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}},"e":{"docs":{},"i":{"docs":{},"g":{"docs":{},"h":{"docs":{},"t":{"docs":{},"'":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.0091324200913242}}},":":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}},"o":{"docs":{},"r":{"docs":{},"g":{"docs":{},".":{"docs":{},"a":{"docs":{},"p":{"docs":{},"a":{"docs":{},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},".":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},".":{"docs":{},"h":{"docs":{},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"t":{"docs":{},"r":{"docs":{},"i":{"docs":{},"b":{"docs":{},".":{"docs":{},"u":{"docs":{},"d":{"docs":{},"f":{"docs":{},".":{"docs":{},"u":{"docs":{},"d":{"docs":{},"f":{"docs":{},"r":{"docs":{},"o":{"docs":{},"w":{"docs":{},"s":{"docs":{},"e":{"docs":{},"q":{"docs":{},"u":{"docs":{},"e":{"docs":{},"n":{"docs":{},"c":{"docs":{},"e":{"docs":{},"'":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.008032128514056224}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"u":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"'":{"docs":{},")":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}},"p":{"docs":{},"y":{"docs":{},"t":{"docs":{},"h":{"docs":{},"o":{"docs":{},"n":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}},"i":{"docs":{},"d":{"docs":{},"和":{"docs":{},"特":{"docs":{},"征":{"docs":{},"的":{"docs":{},"对":{"docs":{},"应":{"docs":{},"关":{"docs":{},"系":{"docs":{},"\\":{"docs":{},"n":{"4":{"3":{"0":{"5":{"4":{"8":{"docs":{},"_":{"1":{"0":{"0":{"7":{"docs":{},"：":{"0":{"docs":{},"\\":{"docs":{},"n":{"4":{"3":{"0":{"5":{"4":{"9":{"docs":{},"_":{"1":{"0":{"0":{"7":{"docs":{},"：":{"1":{"docs":{},"\\":{"docs":{},"n":{"docs":{},"'":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}}}},"|":{"docs":{},"'":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0022675736961451248}}}},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"_":{"docs":{},"i":{"docs":{},"n":{"docs":{},"f":{"docs":{},"o":{"docs":{},"'":{"docs":{},",":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.004405286343612335}}}},":":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"'":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}}}}}}}}}}},"'":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.004545454545454545}}}}},"d":{"docs":{},"e":{"docs":{},"l":{"docs":{},"e":{"docs":{},"t":{"docs":{},"e":{"docs":{},"'":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}},"'":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}},"f":{"2":{"docs":{},"'":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.004405286343612335}}}},"docs":{},"a":{"docs":{},"l":{"docs":{},"s":{"docs":{},"e":{"docs":{},"'":{"docs":{},",":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.011013215859030838}}}}}}}},"o":{"docs":{},"r":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"r":{"docs":{},"'":{"docs":{},",":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}},"'":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726},"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.0045662100456621}}},"]":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}},")":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00684931506849315}}}}}},"n":{"docs":{},"o":{"docs":{},"n":{"docs":{},"e":{"docs":{},"'":{"docs":{},",":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.006607929515418502}}}}}}}},"r":{"docs":{},"o":{"docs":{},"w":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{},"_":{"1":{"6":{"docs":{},"'":{"docs":{},",":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.004405286343612335}}}}},"docs":{}},"docs":{}}}}}}}},"t":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{},"'":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.004405286343612335}}},":":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"'":{"docs":{},",":{"docs":{},"'":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"_":{"docs":{},"i":{"docs":{},"n":{"docs":{},"f":{"docs":{},"o":{"docs":{},"'":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}}}}}}}}}}}}}},"i":{"docs":{},"m":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"m":{"docs":{},"p":{"docs":{},"s":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"t":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.004405286343612335}}}}}}}}}}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"'":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.015418502202643172}},",":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.011013215859030838}},"'":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"_":{"docs":{},"i":{"docs":{},"n":{"docs":{},"f":{"docs":{},"o":{"docs":{},"'":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}}},"r":{"docs":{},"o":{"docs":{},"w":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{},"_":{"1":{"0":{"docs":{},"'":{"docs":{},",":{"docs":{},"'":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"_":{"docs":{},"i":{"docs":{},"n":{"docs":{},"f":{"docs":{},"o":{"docs":{},":":{"docs":{},"a":{"docs":{},"d":{"docs":{},"d":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"s":{"docs":{},"'":{"docs":{},",":{"docs":{},"'":{"docs":{},"t":{"docs":{},"o":{"docs":{},"k":{"docs":{},"y":{"docs":{},"o":{"docs":{},"'":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}}}}}}}}},"b":{"docs":{},"i":{"docs":{},"r":{"docs":{},"t":{"docs":{},"h":{"docs":{},"d":{"docs":{},"a":{"docs":{},"y":{"docs":{},"'":{"docs":{},",":{"docs":{},"'":{"2":{"0":{"1":{"4":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}},"s":{"docs":{},"e":{"docs":{},"x":{"docs":{},"'":{"docs":{},",":{"docs":{},"'":{"1":{"docs":{},"'":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}},"docs":{}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"'":{"docs":{},",":{"docs":{},"'":{"docs":{},"t":{"docs":{},"o":{"docs":{},"m":{"docs":{},"'":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.004405286343612335}}}}}}}}}}}}}}}}}}}}}}}}}}}},"{":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{},"=":{"docs":{},">":{"docs":{},"'":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"_":{"docs":{},"i":{"docs":{},"n":{"docs":{},"f":{"docs":{},"o":{"docs":{},":":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"'":{"docs":{},",":{"docs":{},"t":{"docs":{},"i":{"docs":{},"m":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"m":{"docs":{},"p":{"docs":{},"=":{"docs":{},">":{"1":{"5":{"5":{"8":{"3":{"2":{"3":{"9":{"0":{"4":{"1":{"3":{"3":{"docs":{},"}":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"1":{"8":{"9":{"5":{"3":{"docs":{},"}":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}},"v":{"docs":{},"e":{"docs":{},"r":{"docs":{},"s":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"s":{"docs":{},"=":{"docs":{},">":{"1":{"0":{"docs":{},"}":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}},"docs":{}},"2":{"docs":{},",":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"t":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}},"t":{"docs":{},"i":{"docs":{},"m":{"docs":{},"e":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"g":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}},"}":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"6":{"docs":{},"'":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}},",":{"docs":{},"'":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"_":{"docs":{},"i":{"docs":{},"n":{"docs":{},"f":{"docs":{},"o":{"docs":{},"'":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}},":":{"docs":{},"a":{"docs":{},"d":{"docs":{},"d":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"s":{"docs":{},"'":{"docs":{},",":{"docs":{},"'":{"docs":{},"b":{"docs":{},"e":{"docs":{},"i":{"docs":{},"j":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"'":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}}}}}}}}}}},"b":{"docs":{},"i":{"docs":{},"r":{"docs":{},"t":{"docs":{},"h":{"docs":{},"d":{"docs":{},"a":{"docs":{},"y":{"docs":{},"'":{"docs":{},",":{"docs":{},"'":{"2":{"0":{"1":{"4":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}},"s":{"docs":{},"e":{"docs":{},"x":{"docs":{},"'":{"docs":{},",":{"docs":{},"'":{"1":{"docs":{},"'":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}},"docs":{}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"'":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}},",":{"docs":{},"'":{"docs":{},"m":{"docs":{},"i":{"docs":{},"k":{"docs":{},"e":{"docs":{},"'":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{}},"2":{"2":{"docs":{},"'":{"docs":{},",":{"docs":{},"'":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"_":{"docs":{},"i":{"docs":{},"n":{"docs":{},"f":{"docs":{},"o":{"docs":{},":":{"docs":{},"a":{"docs":{},"d":{"docs":{},"d":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"s":{"docs":{},"'":{"docs":{},",":{"docs":{},"'":{"docs":{},"n":{"docs":{},"e":{"docs":{},"w":{"docs":{},"y":{"docs":{},"o":{"docs":{},"r":{"docs":{},"k":{"docs":{},"'":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}}}}}}}}}}},"b":{"docs":{},"i":{"docs":{},"r":{"docs":{},"t":{"docs":{},"h":{"docs":{},"d":{"docs":{},"a":{"docs":{},"y":{"docs":{},"'":{"docs":{},",":{"docs":{},"'":{"2":{"0":{"1":{"4":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}},"s":{"docs":{},"e":{"docs":{},"x":{"docs":{},"'":{"docs":{},",":{"docs":{},"'":{"1":{"docs":{},"'":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}},"docs":{}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"'":{"docs":{},",":{"docs":{},"'":{"docs":{},"j":{"docs":{},"e":{"docs":{},"r":{"docs":{},"r":{"docs":{},"y":{"docs":{},"'":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"4":{"docs":{},"'":{"docs":{},",":{"docs":{},"'":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"_":{"docs":{},"i":{"docs":{},"n":{"docs":{},"f":{"docs":{},"o":{"docs":{},":":{"docs":{},"a":{"docs":{},"d":{"docs":{},"d":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"s":{"docs":{},"'":{"docs":{},",":{"docs":{},"'":{"docs":{},"s":{"docs":{},"h":{"docs":{},"a":{"docs":{},"n":{"docs":{},"g":{"docs":{},"h":{"docs":{},"a":{"docs":{},"i":{"docs":{},"'":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}}}}}}}}}}}},"b":{"docs":{},"i":{"docs":{},"r":{"docs":{},"t":{"docs":{},"h":{"docs":{},"d":{"docs":{},"a":{"docs":{},"y":{"docs":{},"'":{"docs":{},",":{"docs":{},"'":{"2":{"0":{"1":{"4":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}},"s":{"docs":{},"e":{"docs":{},"x":{"docs":{},"'":{"docs":{},",":{"docs":{},"'":{"1":{"docs":{},"'":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}},"docs":{}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"'":{"docs":{},",":{"docs":{},"'":{"docs":{},"n":{"docs":{},"i":{"docs":{},"c":{"docs":{},"o":{"docs":{},"'":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"5":{"docs":{},"'":{"docs":{},",":{"docs":{},"'":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"_":{"docs":{},"i":{"docs":{},"n":{"docs":{},"f":{"docs":{},"o":{"docs":{},":":{"docs":{},"a":{"docs":{},"d":{"docs":{},"d":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"s":{"docs":{},"'":{"docs":{},",":{"docs":{},"'":{"docs":{},"s":{"docs":{},"o":{"docs":{},"u":{"docs":{},"l":{"docs":{},"'":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}}}}}}}},"b":{"docs":{},"i":{"docs":{},"r":{"docs":{},"t":{"docs":{},"h":{"docs":{},"d":{"docs":{},"a":{"docs":{},"y":{"docs":{},"'":{"docs":{},",":{"docs":{},"'":{"2":{"0":{"1":{"4":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}},"s":{"docs":{},"e":{"docs":{},"x":{"docs":{},"'":{"docs":{},",":{"docs":{},"'":{"1":{"docs":{},"'":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}},"docs":{}}}}}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"'":{"docs":{},",":{"docs":{},"'":{"docs":{},"r":{"docs":{},"o":{"docs":{},"s":{"docs":{},"e":{"docs":{},"'":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{}},"docs":{}}}}}}}}},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"=":{"docs":{},">":{"docs":{},"'":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"_":{"docs":{},"i":{"docs":{},"n":{"docs":{},"f":{"docs":{},"o":{"docs":{},"'":{"docs":{},",":{"docs":{},"v":{"docs":{},"e":{"docs":{},"r":{"docs":{},"s":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"s":{"docs":{},"=":{"docs":{},">":{"1":{"0":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}},"docs":{}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"{":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"t":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}},"a":{"docs":{},"'":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}},":":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}},"n":{"docs":{},"k":{"docs":{},"n":{"docs":{},"o":{"docs":{},"w":{"docs":{},"n":{"docs":{},"'":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}},"列":{"docs":{},"族":{"docs":{},"名":{"1":{"docs":{},"'":{"docs":{},",":{"docs":{},"'":{"docs":{},"列":{"docs":{},"族":{"docs":{},"名":{"2":{"docs":{},"'":{"docs":{},",":{"docs":{},"'":{"docs":{},"列":{"docs":{},"族":{"docs":{},"名":{"docs":{},"n":{"docs":{},"'":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}},"docs":{}}}}}}}},"2":{"docs":{},"'":{"docs":{},"]":{"docs":{},",":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}},"docs":{}}}},"名":{"docs":{},"称":{"docs":{},"空":{"docs":{},"间":{"docs":{},":":{"docs":{},"表":{"docs":{},"名":{"docs":{},"'":{"docs":{},",":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}},"行":{"docs":{},"名":{"docs":{},"'":{"docs":{},",":{"docs":{},"'":{"docs":{},"列":{"docs":{},"名":{"docs":{},"'":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}},"表":{"docs":{},"名":{"docs":{},"'":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.00881057268722467}},",":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.004405286343612335}},"'":{"docs":{},"行":{"docs":{},"名":{"docs":{},"'":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}},",":{"docs":{},"'":{"docs":{},"列":{"docs":{},"名":{"docs":{},":":{"docs":{},"'":{"docs":{},",":{"docs":{},"'":{"docs":{},"值":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}}}}}}}}}},"起":{"docs":{},"始":{"docs":{},"的":{"docs":{},"r":{"docs":{},"o":{"docs":{},"w":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{},"'":{"docs":{},"}":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}}}},"a":{"docs":{},"b":{"docs":{},"c":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}},"g":{"docs":{},"e":{"docs":{},"'":{"docs":{},")":{"docs":{},".":{"docs":{},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.0045662100456621}}},"]":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}},"u":{"docs":{},"s":{"docs":{},"'":{"docs":{},":":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}},"t":{"docs":{},"r":{"docs":{},"a":{"docs":{},"l":{"docs":{},"i":{"docs":{},"a":{"docs":{},"'":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}}}},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"'":{"docs":{},".":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}}}},"c":{"docs":{},"'":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}},"]":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}},"e":{"docs":{},"'":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.004545454545454545}}}}},"i":{"docs":{},"'":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.004545454545454545}}}},"n":{"docs":{},"c":{"docs":{},"o":{"docs":{},"m":{"docs":{},"e":{"docs":{},"'":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}},"]":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}},"d":{"docs":{},"'":{"docs":{},":":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}},"i":{"docs":{},"a":{"docs":{},"'":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}},"j":{"docs":{},"'":{"docs":{},"]":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}},"]":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}},"g":{"docs":{},"e":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"r":{"docs":{},"'":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}},"]":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}},".":{"docs":{},"t":{"docs":{},"o":{"docs":{},"p":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"a":{"docs":{},"s":{"docs":{},"(":{"docs":{},")":{"docs":{},".":{"docs":{},"t":{"docs":{},"o":{"docs":{},"_":{"docs":{},"d":{"docs":{},"i":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},"'":{"docs":{},"r":{"docs":{},"e":{"docs":{},"c":{"docs":{},"o":{"docs":{},"r":{"docs":{},"d":{"docs":{},"s":{"docs":{},"'":{"docs":{},")":{"docs":{},"[":{"0":{"docs":{},"]":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"m":{"docs":{},"'":{"docs":{},")":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.0091324200913242}}}},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.0045662100456621}}}},"i":{"docs":{},"s":{"docs":{},"s":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"'":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}},"w":{"docs":{},"e":{"docs":{},"i":{"docs":{},"g":{"docs":{},"h":{"docs":{},"t":{"docs":{},"'":{"docs":{},")":{"docs":{},".":{"docs":{},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00684931506849315}}},":":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}},"+":{"0":{"0":{"0":{"0":{"docs":{},"]":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.030201342281879196}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.024013722126929673},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0056925996204933585},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.034722222222222224},"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938},"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842},"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102},"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617},"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577},"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391},"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965},"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.08447488584474885},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.007633587786259542},"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.02631578947368421},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.057096247960848286},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.10972873750764837},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.10385992837246319},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.020905923344947737},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.08879492600422834}},"=":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.012987012987012988},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.017152658662092625},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.011385199240986717},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.009259259259259259},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}},":":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938},"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842},"day03_Hadoop/ha4.1.html":{"ref":"day03_Hadoop/ha4.1.html","tf":0.023809523809523808},"Hive&HBase/06_hbase数据模型.html":{"ref":"Hive&HBase/06_hbase数据模型.html","tf":0.020833333333333332},"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017},"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726},"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965},"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.06153846153846154}},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.022727272727272728},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.011435105774728416},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147},"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876}}}}}}},"r":{"docs":{},"e":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"n":{"docs":{},":":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.006493506493506494},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.010291595197255575},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0056925996204933585},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.006944444444444444},"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876}}}}}}}}},"=":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}},"x":{"docs":{},"[":{"0":{"docs":{},"]":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}},"docs":{}}}},"x":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.005836575875486381},"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842},"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726},"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.013245033112582781},"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.01048951048951049},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.007633587786259542}},":":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416},"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.011673151750972763},"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775},"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102},"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.004545454545454545},"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577},"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.019867549668874173},"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}},"(":{"docs":{},"d":{"docs":{},"c":{"docs":{},"t":{"docs":{},"[":{"docs":{},"x":{"docs":{},"[":{"0":{"docs":{},"]":{"docs":{},"]":{"docs":{},",":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}},"docs":{}}}}}}},"\"":{"docs":{},"p":{"docs":{},"v":{"docs":{},"\"":{"docs":{},",":{"1":{"docs":{},")":{"docs":{},")":{"docs":{},".":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{},"e":{"docs":{},"b":{"docs":{},"y":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{},"(":{"docs":{},"l":{"docs":{},"a":{"docs":{},"m":{"docs":{},"b":{"docs":{},"d":{"docs":{},"a":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}}}}}}}}}}}}}}}}}}}}},"docs":{}}}}},"u":{"docs":{},"v":{"docs":{},"\"":{"docs":{},",":{"1":{"docs":{},")":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}},"docs":{}}}}}},"x":{"docs":{},"[":{"1":{"0":{"docs":{},"]":{"docs":{},",":{"1":{"docs":{},")":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}},"docs":{}}}},"docs":{}},"docs":{}}}},"x":{"docs":{},"[":{"0":{"docs":{},"]":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}},"1":{"docs":{},"]":{"docs":{},",":{"docs":{"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}},"a":{"docs":{},"s":{"docs":{},"c":{"docs":{},"e":{"docs":{},"n":{"docs":{},"d":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"=":{"docs":{},"f":{"docs":{},"a":{"docs":{},"l":{"docs":{},"s":{"docs":{},"e":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}}}}}}}}}}}}}}}}}},"docs":{}},"*":{"2":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}},"docs":{}},".":{"docs":{},"s":{"docs":{},"p":{"docs":{},"l":{"docs":{},"i":{"docs":{},"t":{"docs":{},"(":{"docs":{},"\"":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.004545454545454545},"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.006711409395973154}},"|":{"docs":{},"\"":{"docs":{},")":{"docs":{},")":{"docs":{},".":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"(":{"docs":{},"l":{"docs":{},"a":{"docs":{},"m":{"docs":{},"b":{"docs":{},"d":{"docs":{},"a":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}}}}}}}}}}}}}}}}}}}}},">":{"4":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}},"docs":{}}},"l":{"docs":{},"e":{"docs":{},"n":{"docs":{},"(":{"docs":{},"x":{"docs":{},")":{"docs":{},">":{"1":{"0":{"docs":{},")":{"docs":{},".":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"(":{"docs":{},"l":{"docs":{},"a":{"docs":{},"m":{"docs":{},"b":{"docs":{},"d":{"docs":{},"a":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}}}}}}}}}}}},"docs":{}},"docs":{}}}}}}}},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},"x":{"docs":{},")":{"docs":{},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}},"[":{"1":{"3":{"docs":{},"]":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}},"4":{"docs":{},"]":{"docs":{},")":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}},"docs":{},"]":{"docs":{},")":{"docs":{},",":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876}}}},",":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.007782101167315175}}}}},"2":{"docs":{},"]":{"docs":{},",":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247},"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876}}},"+":{"docs":{},"x":{"docs":{},"[":{"3":{"docs":{},"]":{"docs":{},")":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}},"docs":{}}}}}},"3":{"docs":{},"]":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}},",":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}},"docs":{}},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683}}},"=":{"0":{"docs":{},".":{"8":{"docs":{},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}},"docs":{}}},"docs":{}},",":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938},"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.010526315789473684},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.007633587786259542}},"i":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}},"y":{"docs":{},":":{"docs":{},"x":{"docs":{},"+":{"docs":{},"y":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099}},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}}}}}}}}}}},".":{"docs":{},"s":{"docs":{},"p":{"docs":{},"l":{"docs":{},"i":{"docs":{},"t":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"|":{"docs":{},"\"":{"docs":{},")":{"docs":{},")":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}},"[":{"1":{"docs":{},"]":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}},"docs":{}}}}}}}}}}}}},"+":{"1":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}},")":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}},"docs":{},"y":{"docs":{},",":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}},")":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}},"]":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}},"x":{"docs":{},"_":{"docs":{},"t":{"docs":{},"a":{"docs":{},"b":{"docs":{},"l":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}},"下":{"docs":{},"载":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}},"地":{"docs":{},"址":{"docs":{},"：":{"docs":{},"m":{"docs":{},"o":{"docs":{},"v":{"docs":{},"i":{"docs":{},"e":{"docs":{},"l":{"docs":{},"e":{"docs":{},"n":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.0016233766233766235}}}}}}}}}}}}},"j":{"docs":{},"d":{"docs":{},"k":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428}}}}},"h":{"docs":{},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{},"的":{"docs":{},"安":{"docs":{},"装":{"docs":{},"包":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}},"安":{"docs":{},"装":{"docs":{},"包":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}},"面":{"docs":{},"这":{"docs":{},"张":{"docs":{},"图":{"docs":{},"是":{"docs":{},"数":{"docs":{},"据":{"docs":{},"块":{"docs":{},"多":{"docs":{},"份":{"docs":{},"复":{"docs":{},"制":{"docs":{},"存":{"docs":{},"储":{"docs":{},"的":{"docs":{},"示":{"docs":{},"意":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282}}}}}}}}}}}}}}}}}},"我":{"docs":{},"们":{"docs":{},"来":{"docs":{},"讲":{"docs":{},"解":{"docs":{},"如":{"docs":{},"何":{"docs":{},"进":{"docs":{},"行":{"docs":{},"程":{"docs":{},"序":{"docs":{},"指":{"docs":{},"定":{"docs":{},"s":{"docs":{},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},"m":{"docs":{},"a":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}}}}}}}}}}}}}}}}}}},"为":{"docs":{},"某":{"docs":{},"一":{"docs":{},"用":{"docs":{},"户":{"docs":{},"预":{"docs":{},"测":{"docs":{},"所":{"docs":{},"有":{"docs":{},"电":{"docs":{},"影":{"docs":{},"评":{"docs":{},"分":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}}}}}}}}}}}}}},"了":{"docs":{},"保":{"docs":{},"证":{"docs":{},"每":{"docs":{},"个":{"docs":{},"用":{"docs":{},"户":{"docs":{},"在":{"docs":{},"测":{"docs":{},"试":{"docs":{},"集":{"docs":{},"和":{"docs":{},"训":{"docs":{},"练":{"docs":{},"集":{"docs":{},"都":{"docs":{},"有":{"docs":{},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"因":{"docs":{},"此":{"docs":{},"按":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"i":{"docs":{},"d":{"docs":{},"聚":{"docs":{},"合":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"根":{"docs":{},"据":{"docs":{},"指":{"docs":{},"定":{"docs":{},"关":{"docs":{},"键":{"docs":{},"词":{"docs":{},"迅":{"docs":{},"速":{"docs":{},"匹":{"docs":{},"配":{"docs":{},"到":{"docs":{},"对":{"docs":{},"应":{"docs":{},"的":{"docs":{},"电":{"docs":{},"影":{"docs":{},"，":{"docs":{},"因":{"docs":{},"此":{"docs":{},"需":{"docs":{},"要":{"docs":{},"对":{"docs":{},"物":{"docs":{},"品":{"docs":{},"画":{"docs":{},"像":{"docs":{},"的":{"docs":{},"标":{"docs":{},"签":{"docs":{},"词":{"docs":{},"，":{"docs":{},"建":{"docs":{},"立":{"docs":{},"倒":{"docs":{},"排":{"docs":{},"索":{"docs":{},"引":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"正":{"docs":{},"则":{"docs":{},"化":{"docs":{},"系":{"docs":{},"数":{"docs":{},"）":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}},"每":{"docs":{},"个":{"docs":{},"物":{"docs":{},"品":{"docs":{},"产":{"docs":{},"生":{"docs":{},"t":{"docs":{},"o":{"docs":{},"p":{"docs":{"day02_推荐算法/07_基于内容的推荐算法.html":{"ref":"day02_推荐算法/07_基于内容的推荐算法.html","tf":0.03125}}}}}}}}}},"部":{"docs":{},"电":{"docs":{},"影":{"docs":{},"匹":{"docs":{},"配":{"docs":{},"对":{"docs":{},"应":{"docs":{},"的":{"docs":{},"标":{"docs":{},"签":{"docs":{},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"如":{"docs":{},"果":{"docs":{},"没":{"docs":{},"有":{"docs":{},"将":{"docs":{},"会":{"docs":{},"是":{"docs":{},"n":{"docs":{},"a":{"docs":{},"n":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}}}}}}}}}}},"机":{"docs":{},"器":{"docs":{},"学":{"docs":{},"习":{"docs":{},"提":{"docs":{},"供":{"docs":{},"燃":{"docs":{},"料":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}}}}}}},"上":{"docs":{},"层":{"docs":{},"应":{"docs":{},"用":{"docs":{},"提":{"docs":{},"供":{"docs":{},"统":{"docs":{},"一":{"docs":{},"的":{"docs":{},"资":{"docs":{},"源":{"docs":{},"管":{"docs":{},"理":{"docs":{},"和":{"docs":{},"调":{"docs":{},"度":{"docs":{},"，":{"docs":{},"为":{"docs":{},"集":{"docs":{},"群":{"docs":{},"在":{"docs":{},"利":{"docs":{},"用":{"docs":{},"率":{"docs":{},"、":{"docs":{},"资":{"docs":{},"源":{"docs":{},"统":{"docs":{},"一":{"docs":{},"管":{"docs":{},"理":{"docs":{},"和":{"docs":{},"数":{"docs":{},"据":{"docs":{},"共":{"docs":{},"享":{"docs":{},"等":{"docs":{},"方":{"docs":{},"面":{"docs":{},"带":{"docs":{},"来":{"docs":{},"了":{"docs":{},"巨":{"docs":{},"大":{"docs":{},"好":{"docs":{},"处":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"应":{"docs":{},"用":{"docs":{},"程":{"docs":{},"序":{"docs":{},"向":{"docs":{},"r":{"docs":{},"m":{"docs":{},"申":{"docs":{},"请":{"docs":{},"资":{"docs":{},"源":{"docs":{},"（":{"docs":{},"c":{"docs":{},"o":{"docs":{},"r":{"docs":{},"e":{"docs":{},"、":{"docs":{},"m":{"docs":{},"e":{"docs":{},"m":{"docs":{},"o":{"docs":{},"r":{"docs":{},"y":{"docs":{},"）":{"docs":{},"，":{"docs":{},"分":{"docs":{},"配":{"docs":{},"给":{"docs":{},"内":{"docs":{},"部":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"k":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"什":{"docs":{},"么":{"docs":{},"使":{"docs":{},"用":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}},"要":{"docs":{},"学":{"docs":{},"习":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"s":{"docs":{},"q":{"docs":{},"l":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}}}}}}}}}}}}}}},"异":{"docs":{},"常":{"docs":{},"值":{"docs":{},"字":{"docs":{},"段":{"docs":{},"打":{"docs":{},"标":{"docs":{},"志":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}},"封":{"docs":{},"装":{"docs":{},"成":{"docs":{},"方":{"docs":{},"法":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}}}}},"了":{"docs":{},"c":{"docs":{},"p":{"docs":{},"u":{"docs":{},"、":{"docs":{},"m":{"docs":{},"e":{"docs":{},"m":{"docs":{},"o":{"docs":{},"r":{"docs":{},"y":{"docs":{},"等":{"docs":{},"资":{"docs":{},"源":{"docs":{},"的":{"docs":{},"一":{"docs":{},"个":{"docs":{},"容":{"docs":{},"器":{"docs":{},",":{"docs":{},"是":{"docs":{},"一":{"docs":{},"个":{"docs":{},"任":{"docs":{},"务":{"docs":{},"运":{"docs":{},"行":{"docs":{},"环":{"docs":{},"境":{"docs":{},"的":{"docs":{},"抽":{"docs":{},"象":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"建":{"docs":{},"议":{"docs":{},"下":{"docs":{},"载":{"docs":{},"m":{"docs":{},"l":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.0016233766233766235}}}}}}},"立":{"docs":{},"t":{"docs":{},"a":{"docs":{},"g":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}},"连":{"docs":{},"接":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"i":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}},"s":{"docs":{},"客":{"docs":{},"户":{"docs":{},"端":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}},"案":{"docs":{},"例":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.0016233766233766235},"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}},"：":{"docs":{},"u":{"docs":{},"p":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"b":{"docs":{},"y":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}}}}}}}}}}}}},"生":{"docs":{},"成":{"docs":{},"器":{"docs":{},"，":{"docs":{},"逐":{"docs":{},"个":{"docs":{},"返":{"docs":{},"回":{"docs":{},"预":{"docs":{},"测":{"docs":{},"评":{"docs":{},"分":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}}}}}}}}}}}}}},"电":{"docs":{},"影":{"docs":{},"评":{"docs":{},"分":{"docs":{},"矩":{"docs":{},"阵":{"docs":{},"并":{"docs":{},"计":{"docs":{},"算":{"docs":{},"用":{"docs":{},"户":{"docs":{},"之":{"docs":{},"间":{"docs":{},"相":{"docs":{},"似":{"docs":{},"度":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}}}}}}}}}}}}}}}},"/":{"docs":{},"书":{"docs":{},"籍":{"docs":{},"评":{"docs":{},"分":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}}},"i":{"docs":{},"d":{"docs":{},":":{"docs":{},"[":{"0":{"docs":{},".":{"2":{"docs":{},",":{"0":{"docs":{},".":{"5":{"docs":{},",":{"0":{"docs":{},".":{"7":{"docs":{},"]":{"docs":{"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}},"docs":{}}}}}},"商":{"docs":{},"网":{"docs":{},"站":{"docs":{},"统":{"docs":{},"计":{"docs":{},"营":{"docs":{},"业":{"docs":{},"额":{"docs":{},",":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}},"广":{"docs":{},"告":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"通":{"docs":{},"常":{"docs":{},"使":{"docs":{},"用":{"docs":{},"广":{"docs":{},"告":{"docs":{},"点":{"docs":{},"击":{"docs":{},"率":{"docs":{},"(":{"docs":{},"c":{"docs":{},"t":{"docs":{},"r":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}}}}}}}}}}}}}}},"近":{"docs":{},"邻":{"docs":{},"物":{"docs":{},"品":{"docs":{},"的":{"docs":{},"评":{"docs":{},"分":{"docs":{},"数":{"docs":{},"据":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.0016233766233766235}}}}}}}}},"用":{"docs":{},"户":{"docs":{},"对":{"docs":{},"i":{"docs":{},"i":{"docs":{},"d":{"docs":{},"物":{"docs":{},"品":{"docs":{},"的":{"docs":{},"评":{"docs":{},"分":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.00487012987012987}}}}}}}}}}},"的":{"docs":{},"评":{"docs":{},"分":{"docs":{},"数":{"docs":{},"据":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.00487012987012987}}}}}}}}}},"线":{"docs":{},"：":{"docs":{},"最":{"docs":{},"近":{"1":{"docs":{},"天":{"docs":{},"、":{"3":{"docs":{},"天":{"docs":{},"、":{"7":{"docs":{},"天":{"docs":{"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}}}},"docs":{}}}},"docs":{}}}},"docs":{}}}}}},"透":{"docs":{},"视":{"docs":{},"表":{"docs":{},"，":{"docs":{},"将":{"docs":{},"电":{"docs":{},"影":{"docs":{},"i":{"docs":{},"d":{"docs":{},"转":{"docs":{},"换":{"docs":{},"为":{"docs":{},"列":{"docs":{},"名":{"docs":{},"称":{"docs":{},"，":{"docs":{},"转":{"docs":{},"换":{"docs":{},"成":{"docs":{},"为":{"docs":{},"一":{"docs":{},"个":{"docs":{},"u":{"docs":{},"s":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}}}}}}}}}}}}}}}}}}}}}}}}}},"逐":{"docs":{},"个":{"docs":{},"预":{"docs":{},"测":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}}}}}},"预":{"docs":{},"测":{"docs":{},"任":{"docs":{},"意":{"docs":{},"用":{"docs":{},"户":{"docs":{},"对":{"docs":{},"任":{"docs":{},"意":{"docs":{},"电":{"docs":{},"影":{"docs":{},"的":{"docs":{},"评":{"docs":{},"分":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}}}}}}}}}}}}}},"全":{"docs":{},"部":{"docs":{},"评":{"docs":{},"分":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}}}},"的":{"docs":{},"p":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"值":{"docs":{},":":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}},"用":{"docs":{},"户":{"docs":{},"对":{"docs":{},"物":{"docs":{},"品":{"docs":{},"的":{"docs":{},"评":{"docs":{},"分":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}}}}}}},"电":{"docs":{},"影":{"docs":{},"的":{"docs":{},"评":{"docs":{},"分":{"docs":{},"：":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}},"电":{"docs":{},"影":{"docs":{},"评":{"docs":{},"分":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}}}}}},"的":{"docs":{},"评":{"docs":{},"分":{"docs":{},"值":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}}}}}},"给":{"docs":{},"定":{"docs":{},"用":{"docs":{},"户":{"docs":{},"对":{"docs":{},"给":{"docs":{},"定":{"docs":{},"物":{"docs":{},"品":{"docs":{},"的":{"docs":{},"评":{"docs":{},"分":{"docs":{},"值":{"docs":{"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"ref":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","tf":0.003246753246753247}}}}}}}}}}}}}}},"结":{"docs":{},"果":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}},"，":{"docs":{},"类":{"docs":{},"型":{"docs":{},"为":{"docs":{},"容":{"docs":{},"器":{"docs":{},"，":{"docs":{},"每":{"docs":{},"个":{"docs":{},"元":{"docs":{},"素":{"docs":{},"是":{"docs":{},"一":{"docs":{},"个":{"docs":{},"包":{"docs":{},"含":{"docs":{},"u":{"docs":{},"i":{"docs":{},"d":{"docs":{},",":{"docs":{},"i":{"docs":{},"i":{"docs":{},"d":{"docs":{},",":{"docs":{},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"l":{"docs":{},"_":{"docs":{},"r":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},",":{"docs":{},"p":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"_":{"docs":{},"r":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"的":{"docs":{},"序":{"docs":{},"列":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"评":{"docs":{},"分":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}},"值":{"docs":{},"实":{"docs":{},"际":{"docs":{},"应":{"docs":{},"该":{"docs":{},"为":{"2":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}},"docs":{}}}}}},"总":{"docs":{},"数":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}},"单":{"docs":{},"个":{"docs":{},"数":{"docs":{},"据":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}},"处":{"docs":{},"理":{"docs":{},"b":{"docs":{},"e":{"docs":{},"h":{"docs":{},"a":{"docs":{},"v":{"docs":{},"i":{"docs":{},"o":{"docs":{},"r":{"docs":{},"_":{"docs":{},"l":{"docs":{},"o":{"docs":{},"g":{"docs":{},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}},"•":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.096}}},"⽤":{"docs":{},"户":{"docs":{},"留":{"docs":{},"存":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}},"冷":{"docs":{},"启":{"docs":{},"动":{"docs":{},"：":{"docs":{},"如":{"docs":{},"何":{"docs":{},"为":{"docs":{},"新":{"docs":{},"⽤":{"docs":{},"户":{"docs":{},"做":{"docs":{},"个":{"docs":{},"性":{"docs":{},"化":{"docs":{},"推":{"docs":{},"荐":{"docs":{"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html":{"ref":"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html","tf":0.024390243902439025}}}}}}}}}}}}}}}}}},"注":{"docs":{},"册":{"docs":{},"信":{"docs":{},"息":{"docs":{},"：":{"docs":{},"性":{"docs":{},"别":{"docs":{},"、":{"docs":{},"年":{"docs":{},"龄":{"docs":{},"、":{"docs":{},"地":{"docs":{},"域":{"docs":{"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html":{"ref":"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html","tf":0.024390243902439025}}}}}}}}}}}}}}}}},"人":{"docs":{},"群":{"docs":{},"算":{"docs":{},"法":{"docs":{},":":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}},"们":{"docs":{},"后":{"docs":{},"来":{"docs":{},"又":{"docs":{},"提":{"docs":{},"出":{"docs":{},"了":{"docs":{},"改":{"docs":{},"进":{"docs":{},"的":{"docs":{},"b":{"docs":{},"i":{"docs":{},"a":{"docs":{},"s":{"docs":{},"s":{"docs":{},"v":{"docs":{},"d":{"docs":{},"，":{"docs":{},"被":{"docs":{},"称":{"docs":{},"为":{"docs":{},"s":{"docs":{},"v":{"docs":{},"d":{"docs":{},"+":{"docs":{},"+":{"docs":{},"，":{"docs":{},"该":{"docs":{},"算":{"docs":{},"法":{"docs":{},"是":{"docs":{},"在":{"docs":{},"b":{"docs":{},"i":{"docs":{},"a":{"docs":{},"s":{"docs":{},"s":{"docs":{},"v":{"docs":{},"d":{"docs":{},"的":{"docs":{},"基":{"docs":{},"础":{"docs":{},"上":{"docs":{},"添":{"docs":{},"加":{"docs":{},"了":{"docs":{},"用":{"docs":{},"户":{"docs":{},"的":{"docs":{},"隐":{"docs":{},"式":{"docs":{},"反":{"docs":{},"馈":{"docs":{},"信":{"docs":{},"息":{"docs":{},"：":{"docs":{"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html":{"ref":"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html","tf":0.037037037037037035}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"员":{"docs":{},"学":{"docs":{},"习":{"docs":{},"成":{"docs":{},"本":{"docs":{},"太":{"docs":{},"高":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}},"低":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.016},"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.006172839506172839}},"延":{"docs":{},"迟":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"访":{"docs":{},"问":{"docs":{"day03_Hadoop/ha2.4.html":{"ref":"day03_Hadoop/ha2.4.html","tf":0.03333333333333333}}}}}}}}},"维":{"docs":{},"转":{"docs":{},"高":{"docs":{},"维":{"docs":{},"方":{"docs":{},"式":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}},"例":{"docs":{},"子":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}},"如":{"docs":{},"腾":{"docs":{},"讯":{"docs":{},"视":{"docs":{},"频":{"docs":{},"&":{"docs":{},"q":{"docs":{},"q":{"docs":{},"音":{"docs":{},"乐":{"docs":{"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html":{"ref":"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html","tf":0.024390243902439025}}}}}}}}}}},"，":{"docs":{},"假":{"docs":{},"设":{"docs":{},"已":{"docs":{},"知":{"docs":{},"电":{"docs":{},"影":{"docs":{},"a":{"docs":{},"是":{"docs":{},"一":{"docs":{},"部":{"docs":{},"喜":{"docs":{},"剧":{"docs":{},"，":{"docs":{},"而":{"docs":{},"恰":{"docs":{},"巧":{"docs":{},"我":{"docs":{},"们":{"docs":{},"得":{"docs":{},"知":{"docs":{},"某":{"docs":{},"个":{"docs":{},"用":{"docs":{},"户":{"docs":{},"喜":{"docs":{},"欢":{"docs":{},"看":{"docs":{},"喜":{"docs":{},"剧":{"docs":{},"电":{"docs":{},"影":{"docs":{},"，":{"docs":{},"那":{"docs":{},"么":{"docs":{},"我":{"docs":{},"们":{"docs":{},"基":{"docs":{},"于":{"docs":{},"这":{"docs":{},"样":{"docs":{},"的":{"docs":{},"已":{"docs":{},"知":{"docs":{},"信":{"docs":{},"息":{"docs":{},"，":{"docs":{},"就":{"docs":{},"可":{"docs":{},"以":{"docs":{},"将":{"docs":{},"电":{"docs":{},"影":{"docs":{},"a":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"给":{"docs":{},"该":{"docs":{},"用":{"docs":{},"户":{"docs":{},"。":{"docs":{"day02_推荐算法/07_基于内容的推荐算法.html":{"ref":"day02_推荐算法/07_基于内容的推荐算法.html","tf":0.03125}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}},"在":{"docs":{},"热":{"docs":{},"词":{"docs":{},"时":{"docs":{},"，":{"docs":{},"在":{"docs":{},"上":{"docs":{},"一":{"docs":{},"个":{"docs":{},"窗":{"docs":{},"口":{"docs":{},"中":{"docs":{},"可":{"docs":{},"能":{"docs":{},"是":{"docs":{},"热":{"docs":{},"词":{"docs":{},"，":{"docs":{},"这":{"docs":{},"个":{"docs":{},"一":{"docs":{},"个":{"docs":{},"窗":{"docs":{},"口":{"docs":{},"中":{"docs":{},"可":{"docs":{},"能":{"docs":{},"不":{"docs":{},"是":{"docs":{},"热":{"docs":{},"词":{"docs":{},"，":{"docs":{},"就":{"docs":{},"需":{"docs":{},"要":{"docs":{},"在":{"docs":{},"这":{"docs":{},"个":{"docs":{},"窗":{"docs":{},"口":{"docs":{},"中":{"docs":{},"把":{"docs":{},"该":{"docs":{},"次":{"docs":{},"剔":{"docs":{},"除":{"docs":{},"掉":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"全":{"docs":{},"量":{"docs":{},"上":{"docs":{},"线":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}},"面":{"docs":{},"的":{"docs":{},"a":{"docs":{},"c":{"docs":{},"i":{"docs":{},"d":{"docs":{},"支":{"docs":{},"持":{"docs":{},",":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}}}}}}},"兴":{"docs":{},"趣":{"docs":{},"扩":{"docs":{},"展":{"docs":{},":":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}}},"内":{"docs":{},"容":{"docs":{},"提":{"docs":{},"供":{"docs":{},"方":{"docs":{},"的":{"docs":{},"共":{"docs":{},"赢":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}}}},"推":{"docs":{},"荐":{"docs":{},"数":{"docs":{},"据":{"docs":{},"处":{"docs":{},"理":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}}}}},"置":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}},"函":{"docs":{},"数":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}},"运":{"docs":{},"算":{"docs":{},"符":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}},"部":{"docs":{},"表":{"docs":{},"(":{"docs":{},"m":{"docs":{},"a":{"docs":{},"n":{"docs":{},"a":{"docs":{},"g":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}}}},"存":{"docs":{},"计":{"docs":{},"算":{"docs":{},"引":{"docs":{},"擎":{"docs":{},"，":{"docs":{},"提":{"docs":{},"供":{"docs":{},"c":{"docs":{},"a":{"docs":{},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},"机":{"docs":{},"制":{"docs":{},"来":{"docs":{},"支":{"docs":{},"持":{"docs":{},"需":{"docs":{},"要":{"docs":{},"反":{"docs":{},"复":{"docs":{},"迭":{"docs":{},"代":{"docs":{},"计":{"docs":{},"算":{"docs":{},"或":{"docs":{},"者":{"docs":{},"多":{"docs":{},"次":{"docs":{},"数":{"docs":{},"据":{"docs":{},"共":{"docs":{},"享":{"docs":{},"，":{"docs":{},"减":{"docs":{},"少":{"docs":{},"数":{"docs":{},"据":{"docs":{},"读":{"docs":{},"取":{"docs":{},"的":{"docs":{},"i":{"docs":{},"o":{"docs":{},"开":{"docs":{},"销":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"只":{"docs":{},"能":{"docs":{},"在":{"docs":{},"用":{"docs":{},"户":{"docs":{},"看":{"docs":{},"到":{"docs":{},"过":{"docs":{},"的":{"docs":{},"候":{"docs":{},"选":{"docs":{},"集":{"docs":{},"上":{"docs":{},"做":{"docs":{},"评":{"docs":{},"估":{"docs":{},",":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}}}}}}}}}}}}},"评":{"docs":{},"估":{"docs":{},"少":{"docs":{},"数":{"docs":{},"指":{"docs":{},"标":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}}}},"用":{"docs":{},"在":{"docs":{},"f":{"docs":{},"r":{"docs":{},"o":{"docs":{},"m":{"docs":{},"子":{"docs":{},"句":{"docs":{},"中":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}}},"考":{"docs":{},"虑":{"docs":{},"兴":{"docs":{},"趣":{"docs":{},"词":{"docs":{},"与":{"docs":{},"电":{"docs":{},"影":{"docs":{},"的":{"docs":{},"关":{"docs":{},"联":{"docs":{},"程":{"docs":{},"度":{"docs":{"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}}}}}}}}}}}}},"用":{"docs":{},"户":{"docs":{},"的":{"docs":{},"兴":{"docs":{},"趣":{"docs":{},"程":{"docs":{},"度":{"docs":{"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}}}}}}}}}}},"支":{"docs":{},"持":{"docs":{},"r":{"docs":{},"o":{"docs":{},"w":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}},"有":{"docs":{},"当":{"docs":{},"数":{"docs":{},"据":{"docs":{},"量":{"docs":{},"较":{"docs":{},"小":{"docs":{},"的":{"docs":{},"时":{"docs":{},"候":{"docs":{},"使":{"docs":{},"用":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}}}}}}}}}}}},"调":{"docs":{},"用":{"docs":{},"a":{"docs":{},"c":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"一":{"docs":{},"类":{"docs":{},"的":{"docs":{},"操":{"docs":{},"作":{"docs":{},"之":{"docs":{},"后":{"docs":{},"才":{"docs":{},"会":{"docs":{},"计":{"docs":{},"算":{"docs":{},"所":{"docs":{},"有":{"docs":{},"t":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"s":{"docs":{},"f":{"docs":{},"o":{"docs":{},"r":{"docs":{},"m":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"a":{"docs":{},"c":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"才":{"docs":{},"会":{"docs":{},"触":{"docs":{},"发":{"docs":{},"t":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"s":{"docs":{},"f":{"docs":{},"o":{"docs":{},"r":{"docs":{},"m":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"的":{"docs":{},"执":{"docs":{},"行":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"四":{"docs":{},"种":{"docs":{},"类":{"docs":{},"型":{"docs":{},"数":{"docs":{},"据":{"docs":{},"：":{"docs":{},"p":{"docs":{},"v":{"docs":{},"、":{"docs":{},"f":{"docs":{},"a":{"docs":{},"v":{"docs":{},"、":{"docs":{},"c":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{},"、":{"docs":{},"b":{"docs":{},"u":{"docs":{},"y":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}},"两":{"docs":{},"种":{"docs":{},"广":{"docs":{},"告":{"docs":{},"展":{"docs":{},"示":{"docs":{},"位":{"docs":{},"，":{"docs":{},"占":{"docs":{},"比":{"docs":{},"约":{"docs":{},"为":{"docs":{},"六":{"docs":{},"比":{"docs":{},"四":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}},"记":{"docs":{},"下":{"docs":{},"应":{"docs":{},"用":{"docs":{},"于":{"docs":{},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{},"的":{"docs":{},"t":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"s":{"docs":{},"f":{"docs":{},"o":{"docs":{},"r":{"docs":{},"m":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"操":{"docs":{},"作":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}}}}}}}}}}}}}}}}}}},"给":{"docs":{},"部":{"docs":{},"分":{"docs":{},"用":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"，":{"docs":{},"运":{"docs":{},"算":{"docs":{},"时":{"docs":{},"间":{"docs":{},"短":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}},"商":{"docs":{},"业":{"docs":{},"⽬":{"docs":{},"标":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}},"智":{"docs":{},"能":{"docs":{},"(":{"docs":{},"b":{"docs":{},"u":{"docs":{},"s":{"docs":{},"i":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}}}},"通":{"docs":{},"常":{"docs":{},"被":{"docs":{},"理":{"docs":{},"解":{"docs":{},"为":{"docs":{},"将":{"docs":{},"企":{"docs":{},"业":{"docs":{},"中":{"docs":{},"现":{"docs":{},"有":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"(":{"docs":{},"订":{"docs":{},"单":{"docs":{},"、":{"docs":{},"库":{"docs":{},"存":{"docs":{},"、":{"docs":{},"交":{"docs":{},"易":{"docs":{},"账":{"docs":{},"目":{"docs":{},"、":{"docs":{},"客":{"docs":{},"户":{"docs":{},"和":{"docs":{},"供":{"docs":{},"应":{"docs":{},"商":{"docs":{},"等":{"docs":{},"数":{"docs":{},"据":{"docs":{},")":{"docs":{},"转":{"docs":{},"化":{"docs":{},"为":{"docs":{},"知":{"docs":{},"识":{"docs":{},"，":{"docs":{},"帮":{"docs":{},"助":{"docs":{},"企":{"docs":{},"业":{"docs":{},"做":{"docs":{},"出":{"docs":{},"明":{"docs":{},"智":{"docs":{},"的":{"docs":{},"业":{"docs":{},"务":{"docs":{},"经":{"docs":{},"营":{"docs":{},"决":{"docs":{},"策":{"docs":{},"的":{"docs":{},"工":{"docs":{},"具":{"docs":{},"。":{"docs":{},"从":{"docs":{},"技":{"docs":{},"术":{"docs":{},"层":{"docs":{},"面":{"docs":{},"上":{"docs":{},"讲":{"docs":{},"，":{"docs":{},"是":{"docs":{},"数":{"docs":{},"据":{"docs":{},"仓":{"docs":{},"库":{"docs":{},"、":{"docs":{},"数":{"docs":{},"据":{"docs":{},"挖":{"docs":{},"掘":{"docs":{},"等":{"docs":{},"技":{"docs":{},"术":{"docs":{},"的":{"docs":{},"综":{"docs":{},"合":{"docs":{},"运":{"docs":{},"用":{"docs":{},"。":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"多":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}},"样":{"docs":{},"性":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}},"&":{"docs":{},"新":{"docs":{},"颖":{"docs":{},"性":{"docs":{},"&":{"docs":{},"惊":{"docs":{},"喜":{"docs":{},"性":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}}}}}},"：":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"列":{"docs":{},"表":{"docs":{},"中":{"docs":{},"两":{"docs":{},"两":{"docs":{},"物":{"docs":{},"品":{"docs":{},"的":{"docs":{},"不":{"docs":{},"相":{"docs":{},"似":{"docs":{},"性":{"docs":{},"。":{"docs":{},"（":{"docs":{},"相":{"docs":{},"似":{"docs":{},"性":{"docs":{},"如":{"docs":{},"何":{"docs":{},"度":{"docs":{},"量":{"docs":{},"？":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"级":{"docs":{},"子":{"docs":{},"目":{"docs":{},"录":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}},"索":{"docs":{},"引":{"docs":{},",":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}},"好":{"docs":{},"的":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"系":{"docs":{},"统":{"docs":{},"可":{"docs":{},"以":{"docs":{},"实":{"docs":{},"现":{"docs":{},"用":{"docs":{},"户":{"docs":{},",":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}}}}}}}}}}},"定":{"docs":{},"期":{"docs":{},"做":{"docs":{},"问":{"docs":{},"卷":{"docs":{},"调":{"docs":{},"查":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}}}},"时":{"docs":{},"向":{"docs":{},"r":{"docs":{},"m":{"docs":{},"汇":{"docs":{},"报":{"docs":{},"本":{"docs":{},"节":{"docs":{},"点":{"docs":{},"的":{"docs":{},"资":{"docs":{},"源":{"docs":{},"使":{"docs":{},"用":{"docs":{},"情":{"docs":{},"况":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}}}}}}}}}}}}}},"义":{"docs":{},"了":{"docs":{},"执":{"docs":{},"行":{"docs":{},"的":{"docs":{},"顺":{"docs":{},"序":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}}},"理":{"docs":{},"是":{"docs":{},"这":{"docs":{},"方":{"docs":{},"面":{"docs":{},"的":{"docs":{},"基":{"docs":{},"本":{"docs":{},"定":{"docs":{},"理":{"docs":{},"，":{"docs":{},"也":{"docs":{},"是":{"docs":{},"理":{"docs":{},"解":{"docs":{},"分":{"docs":{},"布":{"docs":{},"式":{"docs":{},"系":{"docs":{},"统":{"docs":{},"的":{"docs":{},"起":{"docs":{},"点":{"docs":{},"。":{"docs":{"Hive&HBase/06_hbase数据模型.html":{"ref":"Hive&HBase/06_hbase数据模型.html","tf":0.020833333333333332}}}}}}}}}}}}}}}}}}}}}}}}}}},"少":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}},"平":{"docs":{},"衡":{"docs":{},"个":{"docs":{},"性":{"docs":{},"化":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"和":{"docs":{},"热":{"docs":{},"门":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"比":{"docs":{},"例":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}}}}}}}}}}}},"往":{"docs":{},"往":{"docs":{},"需":{"docs":{},"要":{"docs":{},"牺":{"docs":{},"牲":{"docs":{},"准":{"docs":{},"确":{"docs":{},"性":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}}}}}}},"惊":{"docs":{},"喜":{"docs":{},"度":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}},"性":{"docs":{},"：":{"docs":{},"历":{"docs":{},"史":{"docs":{},"不":{"docs":{},"相":{"docs":{},"似":{"docs":{},"（":{"docs":{},"惊":{"docs":{},"）":{"docs":{},"但":{"docs":{},"很":{"docs":{},"满":{"docs":{},"意":{"docs":{},"（":{"docs":{},"喜":{"docs":{},"）":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}}}}}}}}}}}}}}}}},"成":{"docs":{},"本":{"docs":{},"高":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}},"功":{"docs":{},"返":{"docs":{},"回":{"0":{"docs":{},"，":{"docs":{},"失":{"docs":{},"败":{"docs":{},"返":{"docs":{},"回":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.025}}}}}}}},"docs":{}}}},"熟":{"docs":{},"的":{"docs":{},"生":{"docs":{},"态":{"docs":{},"圈":{"docs":{"day03_Hadoop/ha4.1.html":{"ref":"day03_Hadoop/ha4.1.html","tf":0.023809523809523808}}}}}}},"交":{"docs":{},"总":{"docs":{},"金":{"docs":{},"额":{"docs":{},"(":{"docs":{},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"s":{"docs":{},"s":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}}}}},"探":{"docs":{},"索":{"docs":{},"与":{"docs":{},"利":{"docs":{},"用":{"docs":{},"问":{"docs":{},"题":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}}},"伤":{"docs":{},"害":{"docs":{},"用":{"docs":{},"户":{"docs":{},"体":{"docs":{},"验":{"docs":{},",":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}}}}},"带":{"docs":{},"来":{"docs":{},"的":{"docs":{},"长":{"docs":{},"期":{"docs":{},"收":{"docs":{},"益":{"docs":{},"(":{"docs":{},"留":{"docs":{},"存":{"docs":{},"率":{"docs":{},")":{"docs":{},"评":{"docs":{},"估":{"docs":{},"周":{"docs":{},"期":{"docs":{},"长":{"docs":{},",":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}}}}}}}}}}}}}}}}}},"搭":{"docs":{},"配":{"docs":{},"推":{"docs":{},"荐":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}},"建":{"docs":{},"大":{"docs":{},"型":{"docs":{},"数":{"docs":{},"据":{"docs":{},"仓":{"docs":{},"库":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}}}}}}},"播":{"docs":{},"放":{"docs":{},"/":{"docs":{},"点":{"docs":{},"击":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}}},"新":{"docs":{},"颖":{"docs":{},"性":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}},"：":{"docs":{},"未":{"docs":{},"曾":{"docs":{},"关":{"docs":{},"注":{"docs":{},"的":{"docs":{},"类":{"docs":{},"别":{"docs":{},"、":{"docs":{},"作":{"docs":{},"者":{"docs":{},"；":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"结":{"docs":{},"果":{"docs":{},"的":{"docs":{},"平":{"docs":{},"均":{"docs":{},"流":{"docs":{},"⾏":{"docs":{},"度":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}}}}}}}}}}}}}}}}}}}}}},"⽤":{"docs":{},"户":{"docs":{},"在":{"docs":{},"冷":{"docs":{},"启":{"docs":{},"动":{"docs":{},"阶":{"docs":{},"段":{"docs":{},"更":{"docs":{},"倾":{"docs":{},"向":{"docs":{},"于":{"docs":{},"热":{"docs":{},"门":{"docs":{},"排":{"docs":{},"⾏":{"docs":{},"榜":{"docs":{},"，":{"docs":{},"⽼":{"docs":{},"⽤":{"docs":{},"户":{"docs":{},"会":{"docs":{},"更":{"docs":{},"加":{"docs":{},"需":{"docs":{},"要":{"docs":{},"长":{"docs":{},"尾":{"docs":{},"推":{"docs":{},"荐":{"docs":{"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html":{"ref":"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html","tf":0.024390243902439025}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"老":{"docs":{},"用":{"docs":{},"户":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"策":{"docs":{},"略":{"docs":{},"的":{"docs":{},"差":{"docs":{},"异":{"docs":{"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html":{"ref":"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html","tf":0.024390243902439025}}}}}}}}}}}},"增":{"docs":{},"用":{"docs":{},"户":{"docs":{},"出":{"docs":{},"现":{"docs":{},"问":{"docs":{},"题":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}},"数":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}},"访":{"docs":{},"问":{"docs":{},"网":{"docs":{},"站":{"docs":{},"(":{"docs":{},"新":{"docs":{},"下":{"docs":{},"载":{"docs":{},"a":{"docs":{},"p":{"docs":{},"p":{"docs":{},")":{"docs":{},"的":{"docs":{},"用":{"docs":{},"户":{"docs":{},"数":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}}}}}}}}}}}},"满":{"docs":{},"意":{"docs":{},"度":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}},"灰":{"docs":{},"度":{"docs":{},"发":{"docs":{},"布":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}},"离":{"docs":{},"线":{"docs":{},"评":{"docs":{},"估":{"docs":{},":":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}},"和":{"docs":{},"在":{"docs":{},"线":{"docs":{},"评":{"docs":{},"估":{"docs":{},"结":{"docs":{},"合":{"docs":{},",":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}}}}}}}},"推":{"docs":{},"荐":{"docs":{"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}},"数":{"docs":{},"据":{"docs":{},"缓":{"docs":{},"存":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}},"计":{"docs":{},"算":{"docs":{"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}},",":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}},"特":{"docs":{},"点":{"docs":{},":":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}},"通":{"docs":{},"常":{"docs":{},"针":{"docs":{},"对":{"docs":{},"(":{"docs":{},"某":{"docs":{},"一":{"docs":{},"类":{"docs":{},"别":{"docs":{},")":{"docs":{},"全":{"docs":{},"体":{"docs":{},"数":{"docs":{},"据":{"docs":{},",":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}}}}}}}}}}},"存":{"docs":{},"储":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}},"处":{"docs":{},"理":{"docs":{},"业":{"docs":{},"务":{"docs":{},"流":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}},"数":{"docs":{},"据":{"docs":{},"缓":{"docs":{},"存":{"docs":{},"之":{"docs":{},"离":{"docs":{},"线":{"docs":{},"特":{"docs":{},"征":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}},"精":{"docs":{},"准":{"docs":{},"率":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}},"美":{"docs":{},"国":{"docs":{},"录":{"docs":{},"像":{"docs":{},"带":{"docs":{},"租":{"docs":{},"赁":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}}}}},"获":{"docs":{},"取":{"docs":{},"成":{"docs":{},"本":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}},"最":{"docs":{},"近":{"docs":{},"多":{"docs":{},"个":{"docs":{},"版":{"docs":{},"本":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}},"对":{"docs":{},"数":{"docs":{},"据":{"docs":{},"进":{"docs":{},"行":{"docs":{},"运":{"docs":{},"算":{"docs":{},"操":{"docs":{},"作":{"docs":{},"之":{"docs":{},"后":{"docs":{},"的":{"docs":{},"结":{"docs":{},"果":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}}}}}}}},"用":{"docs":{},"户":{"docs":{},"召":{"docs":{},"回":{"docs":{},"集":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}},"特":{"docs":{},"征":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}},"该":{"docs":{},"广":{"docs":{},"告":{"docs":{},"的":{"docs":{},"特":{"docs":{},"征":{"docs":{},"值":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}}}},"得":{"docs":{},"t":{"docs":{},"a":{"docs":{},"b":{"docs":{},"l":{"docs":{},"e":{"docs":{},"对":{"docs":{},"象":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0226628895184136}}}}}}}}}}},"覆":{"docs":{},"盖":{"docs":{},"度":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}},"率":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.016}}}}},"购":{"docs":{},"买":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008},"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}},"速":{"docs":{},"度":{"docs":{},"快":{"docs":{},",":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}},"问":{"docs":{},"卷":{"docs":{},"调":{"docs":{},"查":{"docs":{},":":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}},"题":{"docs":{},"：":{"docs":{},"物":{"docs":{},"品":{"docs":{},"的":{"docs":{},"标":{"docs":{},"签":{"docs":{},"来":{"docs":{},"自":{"docs":{},"哪":{"docs":{},"儿":{"docs":{},"？":{"docs":{"day02_推荐算法/07_基于内容的推荐算法.html":{"ref":"day02_推荐算法/07_基于内容的推荐算法.html","tf":0.03125}}}}}}}}}}}}}}},"随":{"docs":{},"机":{"docs":{},"丢":{"docs":{},"弃":{"docs":{},"用":{"docs":{},"户":{"docs":{},"行":{"docs":{},"为":{"docs":{},"历":{"docs":{},"史":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}}}}}},"扰":{"docs":{},"动":{"docs":{},"模":{"docs":{},"型":{"docs":{},"参":{"docs":{},"数":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}}}}},"梯":{"docs":{},"度":{"docs":{},"下":{"docs":{},"降":{"docs":{},"法":{"docs":{},"优":{"docs":{},"化":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}},"：":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}},"森":{"docs":{},"林":{"docs":{},"中":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}},"模":{"docs":{},"型":{"docs":{},"：":{"docs":{},"p":{"docs":{},"y":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},".":{"docs":{},"m":{"docs":{},"l":{"docs":{},"l":{"docs":{},"i":{"docs":{},"b":{"docs":{},".":{"docs":{},"t":{"docs":{},"r":{"docs":{},"e":{"docs":{},"e":{"docs":{},".":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"o":{"docs":{},"m":{"docs":{},"f":{"docs":{},"o":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{},"m":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"l":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"：":{"docs":{},"p":{"docs":{},"y":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},".":{"docs":{},"m":{"docs":{},"l":{"docs":{},"l":{"docs":{},"i":{"docs":{},"b":{"docs":{},".":{"docs":{},"t":{"docs":{},"r":{"docs":{},"e":{"docs":{},"e":{"docs":{},".":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"o":{"docs":{},"m":{"docs":{},"f":{"docs":{},"o":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"着":{"docs":{},"机":{"docs":{},"器":{"docs":{},"学":{"docs":{},"习":{"docs":{},"技":{"docs":{},"术":{"docs":{},"的":{"docs":{},"逐":{"docs":{},"渐":{"docs":{},"发":{"docs":{},"展":{"docs":{},"与":{"docs":{},"完":{"docs":{},"善":{"docs":{},"，":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"系":{"docs":{},"统":{"docs":{},"也":{"docs":{},"逐":{"docs":{},"渐":{"docs":{},"运":{"docs":{},"用":{"docs":{},"机":{"docs":{},"器":{"docs":{},"学":{"docs":{},"习":{"docs":{},"的":{"docs":{},"思":{"docs":{},"想":{"docs":{},"来":{"docs":{},"进":{"docs":{},"行":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"。":{"docs":{},"将":{"docs":{},"机":{"docs":{},"器":{"docs":{},"学":{"docs":{},"习":{"docs":{},"应":{"docs":{},"用":{"docs":{},"到":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"系":{"docs":{},"统":{"docs":{},"中":{"docs":{},"的":{"docs":{},"方":{"docs":{},"案":{"docs":{},"真":{"docs":{},"是":{"docs":{},"不":{"docs":{},"胜":{"docs":{},"枚":{"docs":{},"举":{"docs":{},"。":{"docs":{},"以":{"docs":{},"下":{"docs":{},"对":{"docs":{},"m":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"l":{"docs":{"day02_推荐算法/01_基于模型的协同过滤推荐.html":{"ref":"day02_推荐算法/01_基于模型的协同过滤推荐.html","tf":0.07692307692307693}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"大":{"docs":{},"数":{"docs":{},"据":{"docs":{},"技":{"docs":{},"术":{"docs":{},"的":{"docs":{},"发":{"docs":{},"展":{"docs":{},"，":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}}}}}}}}}}}},"表":{"docs":{},"的":{"docs":{},"不":{"docs":{},"断":{"docs":{},"增":{"docs":{},"大":{"docs":{},"，":{"docs":{},"对":{"docs":{},"于":{"docs":{},"新":{"docs":{},"纪":{"docs":{},"录":{"docs":{},"的":{"docs":{},"增":{"docs":{},"加":{"docs":{},"，":{"docs":{},"查":{"docs":{},"找":{"docs":{},"，":{"docs":{},"删":{"docs":{},"除":{"docs":{},"等":{"docs":{},"(":{"docs":{},"d":{"docs":{},"m":{"docs":{},"l":{"docs":{},")":{"docs":{},"的":{"docs":{},"维":{"docs":{},"护":{"docs":{},"也":{"docs":{},"更":{"docs":{},"加":{"docs":{},"困":{"docs":{},"难":{"docs":{},"。":{"docs":{},"对":{"docs":{},"于":{"docs":{},"数":{"docs":{},"据":{"docs":{},"库":{"docs":{},"中":{"docs":{},"的":{"docs":{},"超":{"docs":{},"大":{"docs":{},"型":{"docs":{},"表":{"docs":{},"，":{"docs":{},"可":{"docs":{},"以":{"docs":{},"通":{"docs":{},"过":{"docs":{},"把":{"docs":{},"它":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"分":{"docs":{},"成":{"docs":{},"若":{"docs":{},"干":{"docs":{},"个":{"docs":{},"小":{"docs":{},"表":{"docs":{},"，":{"docs":{},"从":{"docs":{},"而":{"docs":{},"简":{"docs":{},"化":{"docs":{},"数":{"docs":{},"据":{"docs":{},"库":{"docs":{},"的":{"docs":{},"管":{"docs":{},"理":{"docs":{},"活":{"docs":{},"动":{"docs":{},"，":{"docs":{},"对":{"docs":{},"于":{"docs":{},"每":{"docs":{},"一":{"docs":{},"个":{"docs":{},"简":{"docs":{},"化":{"docs":{},"后":{"docs":{},"的":{"docs":{},"小":{"docs":{},"表":{"docs":{},"，":{"docs":{},"我":{"docs":{},"们":{"docs":{},"称":{"docs":{},"为":{"docs":{},"一":{"docs":{},"个":{"docs":{},"单":{"docs":{},"个":{"docs":{},"的":{"docs":{},"分":{"docs":{},"区":{"docs":{},"。":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"鲁":{"docs":{},"棒":{"docs":{},"性":{"docs":{"day01_推荐系统介绍/07_ 推荐系统评估.html":{"ref":"day01_推荐系统介绍/07_ 推荐系统评估.html","tf":0.008}}}}},"举":{"docs":{},"例":{"docs":{"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html":{"ref":"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html","tf":0.024390243902439025}},"：":{"docs":{},"通":{"docs":{},"过":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"i":{"docs":{},"n":{"docs":{},"e":{"docs":{},"来":{"docs":{},"预":{"docs":{},"测":{"docs":{},"用":{"docs":{},"户":{"docs":{},"a":{"docs":{},"对":{"docs":{},"电":{"docs":{},"影":{"docs":{},"“":{"docs":{},"阿":{"docs":{},"甘":{"docs":{},"正":{"docs":{},"传":{"docs":{},"”":{"docs":{},"的":{"docs":{},"评":{"docs":{},"分":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"词":{"docs":{},"统":{"docs":{},"计":{"docs":{},"。":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}},"今":{"docs":{},"日":{"docs":{},"头":{"docs":{},"条":{"docs":{},"&":{"docs":{},"抖":{"docs":{},"音":{"docs":{"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html":{"ref":"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html","tf":0.024390243902439025}}}}}}}}},"引":{"docs":{},"导":{"docs":{},"用":{"docs":{},"户":{"docs":{},"填":{"docs":{},"写":{"docs":{},"兴":{"docs":{},"趣":{"docs":{"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html":{"ref":"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html","tf":0.024390243902439025}}}}}}}}}},"性":{"docs":{},"别":{"docs":{},"与":{"docs":{},"电":{"docs":{},"视":{"docs":{},"剧":{"docs":{},"的":{"docs":{},"关":{"docs":{},"系":{"docs":{"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html":{"ref":"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html","tf":0.024390243902439025}}}}}}}}}}},"本":{"docs":{},"质":{"docs":{},"是":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"系":{"docs":{},"统":{"docs":{},"依":{"docs":{},"赖":{"docs":{},"历":{"docs":{},"史":{"docs":{},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"没":{"docs":{},"有":{"docs":{},"历":{"docs":{},"史":{"docs":{},"数":{"docs":{},"据":{"docs":{},"⽆":{"docs":{},"法":{"docs":{},"预":{"docs":{},"测":{"docs":{},"⽤":{"docs":{},"户":{"docs":{},"偏":{"docs":{},"好":{"docs":{"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html":{"ref":"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html","tf":0.024390243902439025}}}}}}}}}}}}}}}}}}}}}}}}}}}},":":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{},"涵":{"docs":{},"盖":{"docs":{},"了":{"docs":{},"r":{"docs":{},"a":{"docs":{},"w":{"docs":{},"_":{"docs":{},"s":{"docs":{},"a":{"docs":{},"m":{"docs":{},"p":{"docs":{},"l":{"docs":{},"e":{"docs":{},"中":{"docs":{},"全":{"docs":{},"部":{"docs":{},"广":{"docs":{},"告":{"docs":{},"的":{"docs":{},"基":{"docs":{},"本":{"docs":{},"信":{"docs":{},"息":{"docs":{},"(":{"docs":{},"约":{"8":{"0":{"docs":{},"万":{"docs":{},"条":{"docs":{},"目":{"docs":{},")":{"docs":{},"。":{"docs":{},"字":{"docs":{},"段":{"docs":{},"说":{"docs":{},"明":{"docs":{},"如":{"docs":{},"下":{"docs":{},"：":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}}}}}}}}},"docs":{}},"docs":{}}}}}}}}}},"用":{"docs":{},"户":{"2":{"2":{"docs":{},"天":{"docs":{},"内":{"docs":{},"的":{"docs":{},"购":{"docs":{},"物":{"docs":{},"行":{"docs":{},"为":{"docs":{},"(":{"docs":{},"共":{"docs":{},"七":{"docs":{},"亿":{"docs":{},"条":{"docs":{},"记":{"docs":{},"录":{"docs":{},")":{"docs":{},"。":{"docs":{},"字":{"docs":{},"段":{"docs":{},"说":{"docs":{},"明":{"docs":{},"如":{"docs":{},"下":{"docs":{},"：":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{}},"docs":{},"的":{"docs":{},"基":{"docs":{},"本":{"docs":{},"信":{"docs":{},"息":{"docs":{},"(":{"docs":{},"约":{"1":{"0":{"0":{"docs":{},"多":{"docs":{},"万":{"docs":{},"用":{"docs":{},"户":{"docs":{},")":{"docs":{},"。":{"docs":{},"字":{"docs":{},"段":{"docs":{},"说":{"docs":{},"明":{"docs":{},"如":{"docs":{},"下":{"docs":{},"：":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}}}}}}}}}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}},"无":{"docs":{},"空":{"docs":{},"值":{"docs":{},"条":{"docs":{},"目":{"docs":{},"，":{"docs":{},"可":{"docs":{},"放":{"docs":{},"心":{"docs":{},"处":{"docs":{},"理":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}},"样":{"docs":{},"本":{"docs":{},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{},"共":{"docs":{},"计":{"8":{"docs":{},"天":{"docs":{},"数":{"docs":{},"据":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}},"docs":{}}}}}}}},"小":{"docs":{},"节":{"docs":{},"主":{"docs":{},"要":{"docs":{},"根":{"docs":{},"据":{"docs":{},"广":{"docs":{},"告":{"docs":{},"点":{"docs":{},"击":{"docs":{},"样":{"docs":{},"本":{"docs":{},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{},"(":{"docs":{},"r":{"docs":{},"a":{"docs":{},"w":{"docs":{},"_":{"docs":{},"s":{"docs":{},"a":{"docs":{},"m":{"docs":{},"p":{"docs":{},"l":{"docs":{},"e":{"docs":{},")":{"docs":{},"、":{"docs":{},"广":{"docs":{},"告":{"docs":{},"基":{"docs":{},"本":{"docs":{},"特":{"docs":{},"征":{"docs":{},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{},"(":{"docs":{},"a":{"docs":{},"d":{"docs":{},"_":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"e":{"docs":{},")":{"docs":{},"、":{"docs":{},"用":{"docs":{},"户":{"docs":{},"基":{"docs":{},"本":{"docs":{},"信":{"docs":{},"息":{"docs":{},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{},"(":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"p":{"docs":{},"r":{"docs":{},"o":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"e":{"docs":{},")":{"docs":{},"构":{"docs":{},"建":{"docs":{},"出":{"docs":{},"了":{"docs":{},"一":{"docs":{},"个":{"docs":{},"完":{"docs":{},"整":{"docs":{},"的":{"docs":{},"样":{"docs":{},"本":{"docs":{},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{},"，":{"docs":{},"并":{"docs":{},"按":{"docs":{},"日":{"docs":{},"期":{"docs":{},"划":{"docs":{},"分":{"docs":{},"为":{"docs":{},"了":{"docs":{},"训":{"docs":{},"练":{"docs":{},"集":{"docs":{},"(":{"docs":{},"前":{"docs":{},"七":{"docs":{},"天":{"docs":{},")":{"docs":{},"和":{"docs":{},"测":{"docs":{},"试":{"docs":{},"集":{"docs":{},"(":{"docs":{},"最":{"docs":{},"后":{"docs":{},"一":{"docs":{},"天":{"docs":{},")":{"docs":{},"，":{"docs":{},"利":{"docs":{},"用":{"docs":{},"逻":{"docs":{},"辑":{"docs":{},"回":{"docs":{},"归":{"docs":{},"进":{"docs":{},"行":{"docs":{},"训":{"docs":{},"练":{"docs":{},"。":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"给":{"docs":{},"物":{"docs":{},"品":{"docs":{},"打":{"docs":{},"标":{"docs":{},"签":{"docs":{"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html":{"ref":"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html","tf":0.024390243902439025}}}}}}},"运":{"docs":{},"营":{"docs":{},"和":{"docs":{},"决":{"docs":{},"策":{"docs":{},"层":{"docs":{},"提":{"docs":{},"供":{"docs":{},"各":{"docs":{},"种":{"docs":{},"统":{"docs":{},"计":{"docs":{},"报":{"docs":{},"告":{"docs":{},",":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}}}}}}}}},"所":{"docs":{},"有":{"docs":{},"用":{"docs":{},"户":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"t":{"docs":{},"o":{"docs":{},"p":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}},"用":{"docs":{},"户":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"t":{"docs":{},"o":{"docs":{},"p":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0024469820554649264}}}}}}}}},"部":{"docs":{},"分":{"docs":{},"用":{"docs":{},"户":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"t":{"docs":{},"o":{"docs":{},"p":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}},"设":{"docs":{},"备":{"docs":{},"信":{"docs":{},"息":{"docs":{},"：":{"docs":{},"定":{"docs":{},"位":{"docs":{},"、":{"docs":{},"⼿":{"docs":{},"机":{"docs":{},"型":{"docs":{},"号":{"docs":{},"、":{"docs":{},"a":{"docs":{},"p":{"docs":{},"p":{"docs":{},"列":{"docs":{},"表":{"docs":{"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html":{"ref":"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html","tf":0.024390243902439025}}}}}}}}}}}}}}}}}}},"置":{"docs":{},"要":{"docs":{},"加":{"docs":{},"载":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"字":{"docs":{},"段":{"docs":{},"的":{"docs":{},"类":{"docs":{},"型":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}}}}}}},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},"c":{"docs":{},"k":{"docs":{},"p":{"docs":{},"o":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"的":{"docs":{},"话":{"docs":{},"，":{"docs":{},"会":{"docs":{},"把":{"docs":{},"所":{"docs":{},"有":{"docs":{},"数":{"docs":{},"据":{"docs":{},"落":{"docs":{},"盘":{"docs":{},"，":{"docs":{},"这":{"docs":{},"样":{"docs":{},"如":{"docs":{},"果":{"docs":{},"异":{"docs":{},"常":{"docs":{},"退":{"docs":{},"出":{"docs":{},"，":{"docs":{},"下":{"docs":{},"次":{"docs":{},"重":{"docs":{},"启":{"docs":{},"后":{"docs":{},"，":{"docs":{},"可":{"docs":{},"以":{"docs":{},"接":{"docs":{},"着":{"docs":{},"上":{"docs":{},"次":{"docs":{},"的":{"docs":{},"训":{"docs":{},"练":{"docs":{},"节":{"docs":{},"点":{"docs":{},"继":{"docs":{},"续":{"docs":{},"运":{"docs":{},"行":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}},"启":{"docs":{},"动":{"docs":{},"的":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"的":{"docs":{},"a":{"docs":{},"p":{"docs":{},"p":{"docs":{},"名":{"docs":{},"称":{"docs":{},"，":{"docs":{},"没":{"docs":{},"有":{"docs":{},"提":{"docs":{},"供":{"docs":{},"，":{"docs":{},"将":{"docs":{},"随":{"docs":{},"机":{"docs":{},"产":{"docs":{},"生":{"docs":{},"一":{"docs":{},"个":{"docs":{},"名":{"docs":{},"称":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"该":{"docs":{},"a":{"docs":{},"p":{"docs":{},"p":{"docs":{},"启":{"docs":{},"动":{"docs":{},"时":{"docs":{},"占":{"docs":{},"用":{"docs":{},"的":{"docs":{},"内":{"docs":{},"存":{"docs":{},"用":{"docs":{},"量":{"docs":{},"，":{"docs":{},"默":{"docs":{},"认":{"1":{"docs":{},"g":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}},"docs":{}}}}}}}}}}}}}}}}}},"目":{"docs":{},"标":{"docs":{},"字":{"docs":{},"段":{"docs":{},"、":{"docs":{},"特":{"docs":{},"征":{"docs":{},"值":{"docs":{},"字":{"docs":{},"段":{"docs":{},"并":{"docs":{},"训":{"docs":{},"练":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}},"定":{"docs":{},"范":{"docs":{},"围":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}},"接":{"docs":{},"下":{"docs":{},"来":{"docs":{},"我":{"docs":{},"们":{"docs":{},"重":{"docs":{},"点":{"docs":{},"学":{"docs":{},"习":{"docs":{},"以":{"docs":{},"下":{"docs":{},"几":{"docs":{},"种":{"docs":{},"应":{"docs":{},"用":{"docs":{},"较":{"docs":{},"多":{"docs":{},"的":{"docs":{},"方":{"docs":{},"案":{"docs":{},"：":{"docs":{"day02_推荐算法/01_基于模型的协同过滤推荐.html":{"ref":"day02_推荐算法/01_基于模型的协同过滤推荐.html","tf":0.07692307692307693}}}}}}}}}}}}}}}}}}}}}},"收":{"docs":{},"并":{"docs":{},"处":{"docs":{},"理":{"docs":{},"来":{"docs":{},"自":{"docs":{},"r":{"docs":{},"m":{"docs":{},"的":{"docs":{},"各":{"docs":{},"种":{"docs":{},"命":{"docs":{},"令":{"docs":{},"：":{"docs":{},"启":{"docs":{},"动":{"docs":{},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"t":{"docs":{},"a":{"docs":{},"i":{"docs":{},"n":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}}}}}}}}}}}}}}}}}}}}}},".":{"docs":{},".":{"docs":{},".":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876},"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.010526315789473684},"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625},"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548},"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.004545454545454545},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.010452961672473868}},"]":{"docs":{},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}},")":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}},"。":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}},".":{"docs":{},".":{"docs":{},".":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.005836575875486381},"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842},"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}}}}}}},"/":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{"day03_Hadoop/ha2.1.html":{"ref":"day03_Hadoop/ha2.1.html","tf":0.0136986301369863},"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428},"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.021505376344086023}}}}}}},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428}}}}}}}},"p":{"docs":{},"y":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}}}}}}},"f":{"docs":{},"l":{"docs":{},"a":{"docs":{},"t":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"(":{"docs":{},"l":{"docs":{},"a":{"docs":{},"m":{"docs":{},"b":{"docs":{},"d":{"docs":{},"a":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102},"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}}}}}}}}}}}}},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"(":{"docs":{},"l":{"docs":{},"a":{"docs":{},"m":{"docs":{},"b":{"docs":{},"d":{"docs":{},"a":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102},"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}}}},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{},"e":{"docs":{},"b":{"docs":{},"y":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{},"(":{"docs":{},"l":{"docs":{},"a":{"docs":{},"m":{"docs":{},"b":{"docs":{},"d":{"docs":{},"a":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102},"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}}}}}},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"w":{"docs":{},"i":{"docs":{},"n":{"docs":{},"d":{"docs":{},"o":{"docs":{},"w":{"docs":{},"(":{"docs":{},"a":{"docs":{},"d":{"docs":{},"d":{"docs":{},"f":{"docs":{},"u":{"docs":{},"n":{"docs":{},"c":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"_":{"docs":{},"_":{"docs":{},"/":{"docs":{},"\\":{"docs":{},"_":{"docs":{},",":{"docs":{},"_":{"docs":{},"/":{"docs":{},"_":{"docs":{},"/":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}}}}}}},"l":{"docs":{},"o":{"docs":{},"a":{"docs":{},"d":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"i":{"docs":{},"r":{"docs":{},"i":{"docs":{},"s":{"docs":{},".":{"docs":{},"c":{"docs":{},"s":{"docs":{},"v":{"docs":{},"\"":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}},"a":{"docs":{},"d":{"docs":{},"d":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"c":{"docs":{},"i":{"docs":{},"t":{"docs":{},"y":{"docs":{},"\"":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.007692307692307693}}}}}}}},"i":{"docs":{},"d":{"docs":{},"\"":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.007692307692307693}}}}}},"p":{"docs":{},"o":{"docs":{},"p":{"docs":{},"\"":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.007692307692307693}}}}}},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"\"":{"docs":{},",":{"docs":{},"s":{"docs":{},"t":{"docs":{},"r":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"t":{"docs":{},"y":{"docs":{},"p":{"docs":{},"e":{"docs":{},"(":{"docs":{},")":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.007692307692307693}}}}}}}}}}}}}}}}}}}}}}}}}}},"u":{"docs":{},"p":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"b":{"docs":{},"y":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{},"(":{"docs":{},"u":{"docs":{},"p":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"f":{"docs":{},"u":{"docs":{},"n":{"docs":{},"c":{"docs":{},"=":{"docs":{},"u":{"docs":{},"p":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"f":{"docs":{},"u":{"docs":{},"n":{"docs":{},"c":{"docs":{},")":{"docs":{},"#":{"docs":{},"应":{"docs":{},"用":{"docs":{},"u":{"docs":{},"p":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"b":{"docs":{},"y":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{},"函":{"docs":{},"数":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"w":{"docs":{},"i":{"docs":{},"t":{"docs":{},"h":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{},"(":{"docs":{},"\"":{"docs":{},"n":{"docs":{},"e":{"docs":{},"w":{"docs":{},"_":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"c":{"docs":{},"l":{"docs":{},"a":{"docs":{},"s":{"docs":{},"s":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"\"":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"/":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.008004574042309892},"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.01680672268907563},"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00851063829787234},"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.013422818791946308},"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"s":{"docs":{},"/":{"docs":{},"s":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"e":{"docs":{},"r":{"docs":{},"p":{"docs":{},"/":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"/":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282}}}}}}}}}}}}}}}}}}}}},"/":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},"/":{"docs":{},"e":{"docs":{},"m":{"docs":{},"p":{"docs":{},"t":{"docs":{},"y":{"docs":{},"d":{"docs":{},"i":{"docs":{},"r":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625}}}}}}}}}},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{},"e":{"1":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.0125}}},"2":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.0125}}},"docs":{}}}}},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},"d":{"docs":{},"i":{"docs":{},"r":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625}}}}},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625}}}}}}}}}}}}}}}}},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{},"/":{"docs":{},"w":{"docs":{},"a":{"docs":{},"r":{"docs":{},"e":{"docs":{},"h":{"docs":{},"o":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"/":{"docs":{},"e":{"docs":{},"m":{"docs":{},"p":{"docs":{},"l":{"docs":{},"o":{"docs":{},"y":{"docs":{},"e":{"docs":{},"e":{"docs":{},"/":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"1":{"docs":{},"=":{"2":{"0":{"1":{"8":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}}}}}}}}}}}},"t":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{},".":{"docs":{},"d":{"docs":{},"b":{"docs":{},"/":{"docs":{},"e":{"docs":{},"m":{"docs":{},"p":{"docs":{},"l":{"docs":{},"o":{"docs":{},"y":{"docs":{},"e":{"docs":{},"e":{"docs":{},"/":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"1":{"docs":{},"=":{"2":{"0":{"1":{"8":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"l":{"docs":{},"i":{"docs":{},"b":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}},"/":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.008032128514056224}}},"目":{"docs":{},"录":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}}}}}}}}}}}},"h":{"docs":{},"o":{"docs":{},"m":{"docs":{},"e":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}},"/":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},"/":{"docs":{},"a":{"docs":{},"p":{"docs":{},"p":{"docs":{},"/":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{"day03_Hadoop/ha2.1.html":{"ref":"day03_Hadoop/ha2.1.html","tf":0.0410958904109589}}}}}}}}}}}}}}}}}}},"p":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"0":{"0":{"1":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625}},"/":{"docs":{},"t":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625}},"/":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625}},"t":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{},".":{"docs":{},"t":{"docs":{},"x":{"docs":{},"t":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.0125}}}}}}}}}}}}}}}}},"docs":{}},"docs":{}},"docs":{}}}}}},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"/":{"docs":{},"b":{"docs":{},"i":{"docs":{},"n":{"docs":{},"/":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}}}}}}}},"n":{"docs":{},"o":{"docs":{},"n":{"docs":{},"e":{"docs":{},"x":{"docs":{},"i":{"docs":{},"s":{"docs":{},"t":{"docs":{},"e":{"docs":{},"n":{"docs":{},"t":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625}}}}}}}}}}}}}}}},"r":{"docs":{},"o":{"docs":{},"o":{"docs":{},"t":{"docs":{},"/":{"docs":{},"b":{"docs":{},"i":{"docs":{},"g":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"/":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},"/":{"docs":{},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},"/":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428}}}}}},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428}}}}}}}}}}}}}}}}},"z":{"docs":{},"o":{"docs":{},"o":{"docs":{},"k":{"docs":{},"e":{"docs":{},"e":{"docs":{},"p":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}}}}}}}},"t":{"docs":{},"m":{"docs":{},"p":{"docs":{},"/":{"docs":{},"u":{"docs":{},"d":{"docs":{},"f":{"1":{"docs":{},".":{"docs":{},"p":{"docs":{},"y":{"docs":{},";":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}},"docs":{}}}}}}}}}}}}},"t":{"docs":{},"m":{"docs":{},"p":{"docs":{},"/":{"docs":{},"e":{"docs":{},"m":{"docs":{},"p":{"docs":{},"l":{"docs":{},"o":{"docs":{},"y":{"docs":{},"e":{"docs":{},"e":{"docs":{},".":{"docs":{},"t":{"docs":{},"x":{"docs":{},"t":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}}}}}}}},"d":{"docs":{},"e":{"docs":{},"m":{"docs":{},"o":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}},"/":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"a":{"docs":{},"c":{"docs":{},"t":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}}}}}}}}}}}}}}},"*":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}},"_":{"docs":{},"/":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}},"\\":{"docs":{},"_":{"docs":{},"\\":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}},"_":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00851063829787234}}}},"i":{"docs":{},"m":{"docs":{},"a":{"docs":{},"g":{"docs":{},"e":{"docs":{},"s":{"docs":{},"/":{"docs":{},"m":{"docs":{},"y":{"docs":{},".":{"docs":{},"j":{"docs":{},"p":{"docs":{},"g":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}}}}}}}}}}}},"w":{"docs":{},"p":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.006711409395973154}}}}},"g":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}},"l":{"docs":{},"o":{"docs":{},"b":{"docs":{},"a":{"docs":{},"l":{"docs":{},"_":{"docs":{},"m":{"docs":{},"e":{"docs":{},"a":{"docs":{},"n":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}}}}}}},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704},"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112},"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.012471655328798186}},"b":{"docs":{},"i":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}},"y":{"docs":{},"(":{"docs":{},"'":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"i":{"docs":{},"d":{"docs":{},"'":{"docs":{},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}}}}}},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},")":{"docs":{},".":{"docs":{},"a":{"docs":{},"g":{"docs":{},"g":{"docs":{},"(":{"docs":{},"{":{"docs":{},"'":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"'":{"docs":{},":":{"docs":{},"'":{"docs":{},"f":{"docs":{},"u":{"docs":{},"n":{"docs":{},"'":{"docs":{},",":{"docs":{},"'":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"2":{"docs":{},"'":{"docs":{},":":{"docs":{},"'":{"docs":{},"f":{"docs":{},"u":{"docs":{},"n":{"2":{"docs":{},"'":{"docs":{},"}":{"docs":{},")":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}},"docs":{}}}}}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},")":{"docs":{},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{},"：":{"docs":{},"可":{"docs":{},"以":{"docs":{},"看":{"docs":{},"到":{"docs":{},"数":{"docs":{},"据":{"docs":{},"的":{"docs":{},"重":{"docs":{},"复":{"docs":{},"情":{"docs":{},"况":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}}}}}}}}}}}}}}},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}},"之":{"docs":{},"后":{"docs":{},"的":{"docs":{},"结":{"docs":{},"果":{"docs":{},"中":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}}}}}}},"w":{"docs":{},"t":{"docs":{},"h":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}},"e":{"docs":{},"n":{"docs":{},"r":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}},"e":{"docs":{},"s":{"docs":{},":":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}},"s":{"docs":{},"i":{"docs":{},"m":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"r":{"docs":{},"p":{"docs":{},"o":{"docs":{},"r":{"docs":{},"a":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876}}}}}}}}},"m":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"l":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876},"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}}}}}},"介":{"docs":{},"绍":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}},"基":{"docs":{},"本":{"docs":{},"概":{"docs":{},"念":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}},"e":{"docs":{},"r":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"最":{"docs":{},"终":{"docs":{},"生":{"docs":{},"成":{"docs":{},"为":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}}}}}}}}}}}}}}}}},"：":{"docs":{},"代":{"docs":{},"码":{"docs":{},"生":{"docs":{},"成":{"docs":{},"器":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}}}}}}}}},"t":{"docs":{},"_":{"docs":{},"m":{"docs":{},"o":{"docs":{},"v":{"docs":{},"i":{"docs":{},"e":{"docs":{},"_":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.005836575875486381},"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}},":":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}}},"p":{"docs":{},"o":{"docs":{},"s":{"docs":{},"(":{"docs":{},"x":{"docs":{},")":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}},":":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}}}}},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"u":{"docs":{},"l":{"docs":{},"t":{"docs":{},"(":{"docs":{},"i":{"docs":{},"p":{"docs":{},")":{"docs":{},":":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}}}}}}}}},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"r":{"docs":{},"y":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"(":{"docs":{},"l":{"docs":{},"i":{"docs":{},"n":{"docs":{},"e":{"docs":{},")":{"docs":{},":":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}}}}}}}}}}}}},"q":{"docs":{},"u":{"docs":{},"e":{"docs":{},"r":{"docs":{},"y":{"docs":{},"(":{"docs":{},")":{"docs":{},":":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0056657223796034}}}}}}}}}}},"c":{"docs":{},"k":{"docs":{},"o":{"docs":{},")":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.006711409395973154}}}}}}},"b":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}},"每":{"docs":{},"个":{"docs":{},"节":{"docs":{},"点":{"docs":{},")":{"docs":{},"在":{"1":{"8":{"8":{"docs":{},"个":{"docs":{},"节":{"docs":{},"点":{"docs":{},"上":{"docs":{},"运":{"docs":{},"行":{"4":{"7":{"docs":{},".":{"9":{"docs":{},"个":{"docs":{},"小":{"docs":{},"时":{"docs":{},"。":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}}}},"docs":{}}},"docs":{}},"docs":{}}}}}}}},"docs":{}},"docs":{}},"docs":{}}}}}}}},"f":{"docs":{},"s":{"docs":{},"：":{"docs":{},"g":{"docs":{},"o":{"docs":{},"o":{"docs":{},"g":{"docs":{},"l":{"docs":{},"e":{"docs":{},"的":{"docs":{},"分":{"docs":{},"布":{"docs":{},"式":{"docs":{},"文":{"docs":{},"件":{"docs":{},"系":{"docs":{},"统":{"docs":{},"g":{"docs":{},"o":{"docs":{},"o":{"docs":{},"g":{"docs":{},"l":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}}}}}}}}}}}}}}}}}}}}},"o":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}},"o":{"docs":{},"g":{"docs":{},"l":{"docs":{},"e":{"docs":{},"发":{"docs":{},"表":{"docs":{},"了":{"docs":{},"三":{"docs":{},"篇":{"docs":{},"论":{"docs":{},"文":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}}}}}}}}}}},"m":{"docs":{},"v":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}},"相":{"docs":{},"关":{"docs":{},"的":{"docs":{},"指":{"docs":{},"标":{"docs":{},":":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"w":{"docs":{},"a":{"docs":{},"y":{"docs":{},",":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}}}}},"i":{"docs":{},"t":{"3":{"docs":{},".":{"docs":{},"p":{"docs":{},"n":{"docs":{},"g":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}}}},"docs":{}}},"c":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}},".":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"l":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"(":{"docs":{},")":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}},"z":{"docs":{},"i":{"docs":{},"p":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0017152658662092624}},"(":{"docs":{},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}},"*":{"docs":{},"z":{"docs":{},"i":{"docs":{},"p":{"docs":{},"(":{"docs":{},"a":{"docs":{},",":{"docs":{},"b":{"docs":{},")":{"docs":{},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}},"[":{"docs":{},"i":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"a":{"docs":{},"b":{"docs":{},"l":{"docs":{},"e":{"docs":{},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}},"a":{"docs":{},",":{"docs":{},"b":{"docs":{},")":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}},"i":{"docs":{},"i":{"docs":{},"d":{"docs":{},"s":{"docs":{},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0017152658662092624}}}}}}},"u":{"docs":{},"i":{"docs":{},"d":{"docs":{},"s":{"docs":{},",":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0017152658662092624}}}}}}}}}},"x":{"docs":{},"v":{"docs":{},"f":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428},"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}},"o":{"docs":{},"o":{"docs":{},"k":{"docs":{},"e":{"docs":{},"e":{"docs":{},"p":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}},"e":{"docs":{},"r":{"docs":{},":":{"docs":{},"用":{"docs":{},"户":{"docs":{},"无":{"docs":{},"感":{"docs":{},"知":{"docs":{},"，":{"docs":{},"主":{"docs":{},"节":{"docs":{},"点":{"docs":{},"挂":{"docs":{},"掉":{"docs":{},"选":{"docs":{},"择":{"docs":{},"从":{"docs":{},"节":{"docs":{},"点":{"docs":{},"作":{"docs":{},"为":{"docs":{},"主":{"docs":{},"的":{"docs":{"day03_Hadoop/ha4.1.html":{"ref":"day03_Hadoop/ha4.1.html","tf":0.023809523809523808}}}}}}}}}}}}}}}}}}}}}}},"配":{"docs":{},"合":{"docs":{"day03_Hadoop/ha4.2.html":{"ref":"day03_Hadoop/ha4.2.html","tf":0.0196078431372549}}}}}}}}}}}},"k":{"docs":{},"返":{"docs":{},"回":{"docs":{},"r":{"docs":{},"e":{"docs":{},"g":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"v":{"docs":{},"e":{"docs":{},"r":{"docs":{},"地":{"docs":{},"址":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}}}}}}}}}}}}},"e":{"docs":{},"r":{"docs":{},"o":{"docs":{},":":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}},"|":{"1":{"1":{"7":{"8":{"4":{"0":{"docs":{},"|":{"1":{"4":{"9":{"4":{"0":{"3":{"6":{"7":{"4":{"3":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"4":{"9":{"4":{"2":{"6":{"1":{"9":{"3":{"8":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"4":{"3":{"6":{"7":{"8":{"4":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"5":{"5":{"3":{"9":{"1":{"3":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"6":{"7":{"7":{"2":{"9":{"2":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.001193792280143255}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"8":{"4":{"0":{"0":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"7":{"7":{"0":{"0":{"2":{"docs":{},"|":{"1":{"4":{"9":{"4":{"6":{"9":{"1":{"1":{"8":{"6":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"2":{"4":{"3":{"6":{"7":{"1":{"docs":{},"|":{"1":{"4":{"9":{"4":{"6":{"9":{"1":{"1":{"8":{"6":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"8":{"6":{"6":{"3":{"0":{"docs":{},"|":{"1":{"4":{"9":{"4":{"2":{"1":{"8":{"5":{"7":{"9":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"8":{"9":{"2":{"4":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"9":{"8":{"1":{"3":{"9":{"docs":{},"|":{"1":{"4":{"9":{"4":{"4":{"6":{"2":{"5":{"9":{"3":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"3":{"2":{"2":{"2":{"4":{"4":{"docs":{},"|":{"1":{"4":{"9":{"4":{"6":{"9":{"1":{"1":{"7":{"9":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"3":{"2":{"6":{"3":{"4":{"docs":{},"|":{"1":{"4":{"9":{"3":{"8":{"0":{"9":{"8":{"9":{"5":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"9":{"9":{"9":{"0":{"7":{"docs":{},"|":{"1":{"4":{"9":{"4":{"3":{"0":{"2":{"9":{"5":{"8":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"4":{"2":{"1":{"5":{"9":{"0":{"docs":{},"|":{"1":{"4":{"9":{"4":{"0":{"3":{"4":{"1":{"4":{"4":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"4":{"9":{"8":{"1":{"8":{"docs":{},"|":{"1":{"4":{"9":{"4":{"6":{"3":{"8":{"7":{"7":{"8":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"6":{"7":{"0":{"4":{"2":{"docs":{},"|":{"1":{"4":{"9":{"3":{"7":{"7":{"2":{"6":{"4":{"1":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}},"4":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"8":{"8":{"5":{"2":{"7":{"docs":{},"|":{"1":{"4":{"9":{"4":{"6":{"9":{"1":{"1":{"8":{"4":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"5":{"3":{"0":{"4":{"5":{"4":{"docs":{},"|":{"1":{"4":{"9":{"4":{"2":{"9":{"3":{"7":{"4":{"6":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"5":{"5":{"2":{"6":{"6":{"docs":{},"|":{"1":{"4":{"9":{"4":{"3":{"0":{"7":{"1":{"3":{"6":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"8":{"1":{"5":{"7":{"docs":{},"|":{"1":{"4":{"9":{"3":{"7":{"4":{"1":{"6":{"2":{"5":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}},"6":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}},"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"8":{"1":{"7":{"3":{"8":{"docs":{},"|":{"1":{"4":{"9":{"4":{"1":{"3":{"7":{"6":{"4":{"4":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"6":{"1":{"9":{"3":{"8":{"1":{"docs":{},"|":{"1":{"4":{"9":{"3":{"7":{"7":{"4":{"6":{"3":{"8":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"2":{"3":{"9":{"1":{"1":{"docs":{},"|":{"1":{"4":{"9":{"4":{"4":{"5":{"1":{"6":{"0":{"8":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"6":{"2":{"5":{"3":{"0":{"1":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"7":{"2":{"0":{"0":{"docs":{},"|":{"1":{"4":{"9":{"4":{"6":{"9":{"1":{"1":{"7":{"9":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"8":{"1":{"3":{"7":{"docs":{},"|":{"1":{"4":{"9":{"4":{"5":{"2":{"4":{"9":{"3":{"5":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"9":{"9":{"8":{"docs":{},"|":{"1":{"4":{"9":{"4":{"6":{"9":{"1":{"1":{"8":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"7":{"4":{"4":{"4":{"4":{"docs":{},"|":{"1":{"4":{"9":{"4":{"6":{"9":{"1":{"1":{"7":{"9":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"7":{"0":{"4":{"2":{"2":{"3":{"docs":{},"|":{"1":{"4":{"9":{"4":{"6":{"9":{"1":{"1":{"8":{"3":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"7":{"1":{"2":{"0":{"docs":{},"|":{"1":{"4":{"9":{"4":{"2":{"2":{"0":{"8":{"1":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"2":{"8":{"6":{"9":{"0":{"docs":{},"|":{"1":{"4":{"9":{"3":{"7":{"7":{"6":{"9":{"9":{"8":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"3":{"8":{"3":{"3":{"5":{"docs":{},"|":{"1":{"4":{"9":{"4":{"6":{"9":{"1":{"1":{"7":{"9":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"9":{"8":{"1":{"5":{"docs":{},"|":{"1":{"4":{"9":{"4":{"1":{"1":{"5":{"3":{"8":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"7":{"1":{"4":{"3":{"1":{"docs":{},"|":{"1":{"4":{"9":{"4":{"1":{"5":{"3":{"8":{"6":{"7":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"5":{"4":{"7":{"5":{"docs":{},"|":{"1":{"4":{"9":{"4":{"5":{"6":{"1":{"0":{"3":{"6":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"8":{"3":{"9":{"4":{"9":{"3":{"docs":{},"|":{"1":{"4":{"9":{"4":{"6":{"9":{"1":{"1":{"8":{"3":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"5":{"7":{"2":{"3":{"7":{"docs":{},"|":{"1":{"4":{"9":{"3":{"8":{"1":{"6":{"9":{"4":{"5":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"9":{"1":{"4":{"8":{"3":{"6":{"docs":{},"|":{"1":{"4":{"9":{"4":{"6":{"5":{"0":{"8":{"7":{"9":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}},"docs":{}},"docs":{}},"docs":{}},"1":{"0":{"2":{"9":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"7":{"6":{"3":{"5":{"8":{"docs":{},"|":{"1":{"4":{"9":{"4":{"1":{"5":{"6":{"9":{"4":{"9":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"9":{"1":{"5":{"2":{"8":{"docs":{},"|":{"1":{"4":{"9":{"3":{"7":{"8":{"0":{"6":{"3":{"3":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}},"docs":{}},"docs":{}},"7":{"1":{"0":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}},"2":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.004893964110929853}}}},"4":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0032626427406199023}}}},"docs":{}},"6":{"4":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0032626427406199023}}}},"5":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0032626427406199023}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391},"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.0273972602739726},"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.02631578947368421},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.05464926590538336},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.06791760146848869},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.11977715877437325},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.03484320557491289},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.08456659619450317}},"r":{"docs":{},"(":{"docs":{},"i":{"docs":{},")":{"docs":{},"|":{"docs":{},"}":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}},"u":{"docs":{},")":{"docs":{},"|":{"docs":{},"}":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}},"[":{"docs":{},"[":{"1":{"0":{"4":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}},"docs":{}},"6":{"1":{"0":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.009787928221859706}}}},"docs":{}},"docs":{}},"docs":{}},"3":{"3":{"4":{"7":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}},"docs":{}},"docs":{}},"docs":{}},"5":{"6":{"0":{"7":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}},"docs":{}},"3":{"1":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}},"docs":{}},"docs":{}},"7":{"2":{"0":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}},"docs":{}},"3":{"1":{"docs":{},",":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"i":{"docs":{},"d":{"docs":{},"|":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0032626427406199023},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}},"c":{"docs":{},"m":{"docs":{},"s":{"docs":{},"_":{"docs":{},"s":{"docs":{},"e":{"docs":{},"g":{"docs":{},"i":{"docs":{},"d":{"docs":{},"|":{"docs":{},"c":{"docs":{},"m":{"docs":{},"s":{"docs":{},"_":{"docs":{},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},"|":{"docs":{},"f":{"docs":{},"i":{"docs":{},"n":{"docs":{},"a":{"docs":{},"l":{"docs":{},"_":{"docs":{},"g":{"docs":{},"e":{"docs":{},"n":{"docs":{},"d":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"c":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"|":{"docs":{},"a":{"docs":{},"g":{"docs":{},"e":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"|":{"docs":{},"p":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"|":{"docs":{},"s":{"docs":{},"h":{"docs":{},"o":{"docs":{},"p":{"docs":{},"p":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"|":{"docs":{},"o":{"docs":{},"c":{"docs":{},"c":{"docs":{},"u":{"docs":{},"p":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"|":{"docs":{},"n":{"docs":{},"e":{"docs":{},"w":{"docs":{},"_":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"c":{"docs":{},"l":{"docs":{},"a":{"docs":{},"s":{"docs":{},"s":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0012237405669997961}},"p":{"docs":{},"l":{"docs":{},"_":{"docs":{},"o":{"docs":{},"n":{"docs":{},"e":{"docs":{},"h":{"docs":{},"o":{"docs":{},"t":{"docs":{},"_":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"e":{"docs":{},"|":{"docs":{},"p":{"docs":{},"l":{"docs":{},"_":{"docs":{},"o":{"docs":{},"n":{"docs":{},"e":{"docs":{},"h":{"docs":{},"o":{"docs":{},"t":{"docs":{},"_":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},"n":{"docs":{},"u":{"docs":{},"c":{"docs":{},"l":{"docs":{},"_":{"docs":{},"o":{"docs":{},"n":{"docs":{},"e":{"docs":{},"h":{"docs":{},"o":{"docs":{},"t":{"docs":{},"_":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"e":{"docs":{},"|":{"docs":{},"n":{"docs":{},"u":{"docs":{},"c":{"docs":{},"l":{"docs":{},"_":{"docs":{},"o":{"docs":{},"n":{"docs":{},"e":{"docs":{},"h":{"docs":{},"o":{"docs":{},"t":{"docs":{},"_":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"i":{"docs":{},"d":{"docs":{},"|":{"docs":{},"p":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"i":{"docs":{},"c":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"|":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}}}}}},"a":{"docs":{},"d":{"docs":{},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"i":{"docs":{},"d":{"docs":{},"|":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.004228329809725159}}}}}}}}}}}}}}}}}}},"(":{"1":{"0":{"docs":{},",":{"docs":{},"[":{"0":{"docs":{},",":{"1":{"docs":{},",":{"5":{"docs":{},"]":{"docs":{},",":{"docs":{},"[":{"4":{"docs":{},".":{"0":{"docs":{},",":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0006118702834998981}}}}}}}},"docs":{}}},"docs":{}}}}},"6":{"docs":{},"]":{"docs":{},",":{"docs":{},"[":{"3":{"docs":{},".":{"0":{"docs":{},",":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}},"docs":{}}},"docs":{}}}}},"7":{"docs":{},"]":{"docs":{},",":{"docs":{},"[":{"5":{"docs":{},".":{"0":{"docs":{},",":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}},"docs":{}}},"docs":{}}}}},"8":{"docs":{},"]":{"docs":{},",":{"docs":{},"[":{"5":{"docs":{},".":{"0":{"docs":{},",":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}}}}}}},"docs":{}}},"docs":{}}}}},"docs":{}}},"2":{"docs":{},",":{"5":{"docs":{},"]":{"docs":{},",":{"docs":{},"[":{"2":{"docs":{},".":{"0":{"docs":{},",":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}},"docs":{}}},"4":{"docs":{},".":{"0":{"docs":{},",":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}}}}}}},"docs":{}}},"docs":{}}}}},"6":{"docs":{},"]":{"docs":{},",":{"docs":{},"[":{"2":{"docs":{},".":{"0":{"docs":{},",":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}}}}}}},"docs":{}}},"4":{"docs":{},".":{"0":{"docs":{},",":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}},"docs":{}}},"5":{"docs":{},".":{"0":{"docs":{},",":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}},"docs":{}}},"6":{"docs":{},".":{"0":{"docs":{},",":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}},"docs":{}}},"docs":{}}}}},"9":{"docs":{},"]":{"docs":{},",":{"docs":{},"[":{"5":{"docs":{},".":{"0":{"docs":{},",":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}},"docs":{}}},"docs":{}}}}},"docs":{}}},"3":{"docs":{},",":{"6":{"docs":{},"]":{"docs":{},",":{"docs":{},"[":{"2":{"docs":{},".":{"0":{"docs":{},",":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}},"docs":{}}},"docs":{}}}}},"7":{"docs":{},"]":{"docs":{},",":{"docs":{},"[":{"2":{"docs":{},".":{"0":{"docs":{},",":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}},"docs":{}}},"docs":{}}}}},"8":{"docs":{},"]":{"docs":{},",":{"docs":{},"[":{"1":{"docs":{},".":{"0":{"docs":{},",":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}},"docs":{}}},"4":{"docs":{},".":{"0":{"docs":{},",":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}},"docs":{}}},"docs":{}}}}},"docs":{}}},"docs":{}}},"docs":{}}}},"docs":{}},"docs":{}},"a":{"docs":{},"d":{"docs":{},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},"|":{"docs":{},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},"|":{"docs":{},"c":{"docs":{},"a":{"docs":{},"m":{"docs":{},"p":{"docs":{},"a":{"docs":{},"i":{"docs":{},"g":{"docs":{},"n":{"docs":{},"_":{"docs":{},"i":{"docs":{},"d":{"docs":{},"|":{"docs":{},"c":{"docs":{},"u":{"docs":{},"s":{"docs":{},"t":{"docs":{},"o":{"docs":{},"m":{"docs":{},"e":{"docs":{},"r":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"i":{"docs":{},"d":{"docs":{},"|":{"docs":{},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"i":{"docs":{},"d":{"docs":{},"|":{"docs":{},"c":{"docs":{},"a":{"docs":{},"m":{"docs":{},"p":{"docs":{},"a":{"docs":{},"i":{"docs":{},"g":{"docs":{},"n":{"docs":{},"i":{"docs":{},"d":{"docs":{},"|":{"docs":{},"c":{"docs":{},"u":{"docs":{},"s":{"docs":{},"t":{"docs":{},"o":{"docs":{},"m":{"docs":{},"e":{"docs":{},"r":{"docs":{},"i":{"docs":{},"d":{"docs":{},"|":{"docs":{},"b":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"i":{"docs":{},"d":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},"p":{"docs":{},"r":{"docs":{},"i":{"docs":{},"c":{"docs":{},"e":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"n":{"docs":{},"e":{"docs":{},"w":{"docs":{},"_":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"c":{"docs":{},"l":{"docs":{},"a":{"docs":{},"s":{"docs":{},"s":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},"m":{"docs":{},"i":{"docs":{},"n":{"docs":{},"(":{"docs":{},"n":{"docs":{},"u":{"docs":{},"c":{"docs":{},"l":{"docs":{},"_":{"docs":{},"o":{"docs":{},"n":{"docs":{},"e":{"docs":{},"h":{"docs":{},"o":{"docs":{},"t":{"docs":{},"_":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"e":{"docs":{},")":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}}}}}}},"n":{"docs":{},"u":{"docs":{},"c":{"docs":{},"l":{"docs":{},"_":{"docs":{},"o":{"docs":{},"n":{"docs":{},"e":{"docs":{},"h":{"docs":{},"o":{"docs":{},"t":{"docs":{},"_":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"p":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"|":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},"m":{"docs":{},"i":{"docs":{},"n":{"docs":{},"(":{"docs":{},"p":{"docs":{},"l":{"docs":{},"_":{"docs":{},"o":{"docs":{},"n":{"docs":{},"e":{"docs":{},"h":{"docs":{},"o":{"docs":{},"t":{"docs":{},"_":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"e":{"docs":{},")":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}}}}},"p":{"docs":{},"l":{"docs":{},"_":{"docs":{},"o":{"docs":{},"n":{"docs":{},"e":{"docs":{},"h":{"docs":{},"o":{"docs":{},"t":{"docs":{},"_":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"c":{"docs":{},"l":{"docs":{},"k":{"docs":{},"|":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034}}}}}}},"}":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289}},"{":{"docs":{},"\\":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{},"i":{"docs":{},"a":{"docs":{},"l":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}},"\"":{"docs":{},"\"":{"docs":{},"\"":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.007692307692307693}},",":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.007692307692307693}}}}}}},"λ":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}},"正":{"docs":{},"则":{"docs":{},"化":{"docs":{},"系":{"docs":{},"数":{"docs":{},"）":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}}}},"交":{"docs":{},"替":{"docs":{},"最":{"docs":{},"小":{"docs":{},"二":{"docs":{},"乘":{"docs":{},"法":{"docs":{},"应":{"docs":{},"用":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}},"推":{"docs":{},"导":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}},"互":{"docs":{},"式":{"docs":{},"计":{"docs":{},"算":{"docs":{},"：":{"docs":{},"i":{"docs":{},"m":{"docs":{},"p":{"docs":{},"a":{"docs":{},"l":{"docs":{},"a":{"docs":{},"、":{"docs":{},"p":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{},"o":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}}}}}}}}}}}}}}}}},"叉":{"docs":{},"表":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}},"元":{"docs":{},"素":{"docs":{},"个":{"docs":{},"数":{"docs":{},"与":{"docs":{},"最":{"docs":{},"短":{"docs":{},"的":{"docs":{},"列":{"docs":{},"表":{"docs":{},"一":{"docs":{},"致":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}},"数":{"docs":{},"据":{"docs":{"day03_Hadoop/ha2.4.html":{"ref":"day03_Hadoop/ha2.4.html","tf":0.03333333333333333}},"存":{"docs":{},"储":{"docs":{},"：":{"docs":{},"通":{"docs":{},"常":{"docs":{},"是":{"docs":{},"存":{"docs":{},"储":{"docs":{},"在":{"docs":{},"关":{"docs":{},"系":{"docs":{},"数":{"docs":{},"据":{"docs":{},"库":{"docs":{},"如":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}}}}}}}},"库":{"docs":{},"信":{"docs":{},"息":{"docs":{},"(":{"docs":{},"m":{"docs":{},"y":{"docs":{},"s":{"docs":{},"q":{"docs":{},"l":{"docs":{},"安":{"docs":{},"装":{"docs":{},"见":{"docs":{},"文":{"docs":{},"档":{"docs":{},")":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}}}}}}}}}}},"公":{"docs":{},"式":{"docs":{},"第":{"docs":{},"一":{"docs":{},"部":{"docs":{},"分":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}},"二":{"docs":{},"部":{"docs":{},"分":{"docs":{},"\\":{"docs":{},"l":{"docs":{},"a":{"docs":{},"m":{"docs":{},"b":{"docs":{},"d":{"docs":{},"a":{"docs":{},"*":{"docs":{},"(":{"docs":{},"\\":{"docs":{},"s":{"docs":{},"u":{"docs":{},"m":{"docs":{},"_":{"docs":{},"u":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}}}}}}}}},"解":{"docs":{},"析":{"docs":{},"：":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}},"其":{"docs":{},"中":{"docs":{},"|":{"docs":{},"r":{"docs":{},"(":{"docs":{},"i":{"docs":{},")":{"docs":{},"|":{"docs":{},"表":{"docs":{},"示":{"docs":{},"物":{"docs":{},"品":{"docs":{},"i":{"docs":{},"​":{"docs":{},"收":{"docs":{},"到":{"docs":{},"的":{"docs":{},"评":{"docs":{},"分":{"docs":{},"数":{"docs":{},"量":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}}}}}},"u":{"docs":{},")":{"docs":{},"|":{"docs":{},"表":{"docs":{},"示":{"docs":{},"用":{"docs":{},"户":{"docs":{},"u":{"docs":{},"的":{"docs":{},"有":{"docs":{},"过":{"docs":{},"评":{"docs":{},"分":{"docs":{},"数":{"docs":{},"量":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}}}}}}}},"一":{"docs":{},"个":{"docs":{},"广":{"docs":{},"告":{"docs":{},"i":{"docs":{},"d":{"docs":{},"对":{"docs":{},"应":{"docs":{},"一":{"docs":{},"个":{"docs":{},"商":{"docs":{},"品":{"docs":{},"（":{"docs":{},"宝":{"docs":{},"贝":{"docs":{},"）":{"docs":{},"，":{"docs":{},"一":{"docs":{},"个":{"docs":{},"宝":{"docs":{},"贝":{"docs":{},"属":{"docs":{},"于":{"docs":{},"一":{"docs":{},"个":{"docs":{},"类":{"docs":{},"目":{"docs":{},"，":{"docs":{},"一":{"docs":{},"个":{"docs":{},"宝":{"docs":{},"贝":{"docs":{},"属":{"docs":{},"于":{"docs":{},"一":{"docs":{},"个":{"docs":{},"品":{"docs":{},"牌":{"docs":{},"。":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"他":{"docs":{},"渠":{"docs":{},"道":{"docs":{},"：":{"docs":{},"如":{"docs":{},"爬":{"docs":{},"虫":{"docs":{"day02_推荐算法/07_基于内容的推荐算法.html":{"ref":"day02_推荐算法/07_基于内容的推荐算法.html","tf":0.03125}}}}}}}}},"基":{"docs":{},"于":{"docs":{},"p":{"docs":{},"y":{"docs":{},"t":{"docs":{},"h":{"docs":{},"o":{"docs":{},"n":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}}}}}},"实":{"docs":{},"是":{"docs":{},"先":{"docs":{},"建":{"docs":{},"一":{"docs":{},"个":{"docs":{},"d":{"docs":{},"f":{"docs":{},"，":{"docs":{},"不":{"docs":{},"要":{"docs":{},"缺":{"docs":{},"失":{"docs":{},"值":{"docs":{},"的":{"docs":{},"列":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}}}}}}}}},"次":{"docs":{},"，":{"docs":{},"要":{"docs":{},"定":{"docs":{},"义":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"更":{"docs":{},"新":{"docs":{},"函":{"docs":{},"数":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}}}}}}}}},"函":{"docs":{},"数":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938},"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}},"求":{"docs":{},"导":{"docs":{},"：":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}},"用":{"docs":{},"于":{"docs":{},"将":{"docs":{},"可":{"docs":{},"迭":{"docs":{},"代":{"docs":{},"的":{"docs":{},"对":{"docs":{},"象":{"docs":{},"作":{"docs":{},"为":{"docs":{},"参":{"docs":{},"数":{"docs":{},"，":{"docs":{},"将":{"docs":{},"对":{"docs":{},"象":{"docs":{},"中":{"docs":{},"对":{"docs":{},"应":{"docs":{},"的":{"docs":{},"元":{"docs":{},"素":{"docs":{},"打":{"docs":{},"包":{"docs":{},"成":{"docs":{},"一":{"docs":{},"个":{"docs":{},"个":{"docs":{},"元":{"docs":{},"组":{"docs":{},"，":{"docs":{},"然":{"docs":{},"后":{"docs":{},"返":{"docs":{},"回":{"docs":{},"由":{"docs":{},"这":{"docs":{},"些":{"docs":{},"元":{"docs":{},"组":{"docs":{},"组":{"docs":{},"成":{"docs":{},"的":{"docs":{},"对":{"docs":{},"象":{"docs":{},"，":{"docs":{},"这":{"docs":{},"样":{"docs":{},"做":{"docs":{},"的":{"docs":{},"好":{"docs":{},"处":{"docs":{},"是":{"docs":{},"节":{"docs":{},"约":{"docs":{},"了":{"docs":{},"不":{"docs":{},"少":{"docs":{},"的":{"docs":{},"内":{"docs":{},"存":{"docs":{},"。":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"语":{"docs":{},"法":{"docs":{},"：":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938},"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}}},"返":{"docs":{},"回":{"docs":{},"值":{"docs":{},"的":{"docs":{},"新":{"docs":{},"列":{"docs":{},"表":{"docs":{},"。":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}},"，":{"docs":{},"返":{"docs":{},"回":{"docs":{},"包":{"docs":{},"含":{"docs":{},"每":{"docs":{},"次":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}},"有":{"docs":{},"两":{"docs":{},"个":{"docs":{},"参":{"docs":{},"数":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}}}}}},"会":{"docs":{},"对":{"docs":{},"参":{"docs":{},"数":{"docs":{},"序":{"docs":{},"列":{"docs":{},"中":{"docs":{},"元":{"docs":{},"素":{"docs":{},"进":{"docs":{},"行":{"docs":{},"累":{"docs":{},"积":{"docs":{},"。":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}}}}}}}}}}}}}},"将":{"docs":{},"一":{"docs":{},"个":{"docs":{},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{},"合":{"docs":{},"（":{"docs":{},"链":{"docs":{},"表":{"docs":{},"，":{"docs":{},"元":{"docs":{},"组":{"docs":{},"等":{"docs":{},"）":{"docs":{},"中":{"docs":{},"的":{"docs":{},"所":{"docs":{},"有":{"docs":{},"数":{"docs":{},"据":{"docs":{},"进":{"docs":{},"行":{"docs":{},"下":{"docs":{},"列":{"docs":{},"操":{"docs":{},"作":{"docs":{},"：":{"docs":{},"用":{"docs":{},"传":{"docs":{},"给":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"运":{"docs":{},"算":{"docs":{},"，":{"docs":{},"最":{"docs":{},"后":{"docs":{},"得":{"docs":{},"到":{"docs":{},"一":{"docs":{},"个":{"docs":{},"结":{"docs":{},"果":{"docs":{},"。":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}}}}}}}}}}}},"名":{"docs":{},";":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.008032128514056224}}}}}},"切":{"docs":{},"分":{"docs":{},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{},"，":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}},"初":{"docs":{},"始":{"docs":{},"化":{"docs":{},"b":{"docs":{},"u":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}},"、":{"docs":{},"b":{"docs":{},"i":{"docs":{},"的":{"docs":{},"值":{"docs":{},"，":{"docs":{},"全":{"docs":{},"部":{"docs":{},"设":{"docs":{},"为":{"0":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}},"docs":{}}}}}}}}}}}}},"p":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}},"和":{"docs":{},"q":{"docs":{},"矩":{"docs":{},"阵":{"docs":{},"，":{"docs":{},"同":{"docs":{},"时":{"docs":{},"为":{"docs":{},"设":{"docs":{},"置":{"0":{"docs":{},"，":{"1":{"docs":{},"之":{"docs":{},"间":{"docs":{},"的":{"docs":{},"随":{"docs":{},"机":{"docs":{},"值":{"docs":{},"作":{"docs":{},"为":{"docs":{},"初":{"docs":{},"始":{"docs":{},"值":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}}}}}}}},"docs":{}}},"docs":{}}}}}}}}}}}}}}},"单":{"docs":{},"样":{"docs":{},"本":{"docs":{},"损":{"docs":{},"失":{"docs":{},"值":{"docs":{},"：":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}},"节":{"docs":{},"点":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282}}}},"点":{"docs":{},"策":{"docs":{},"略":{"docs":{"day03_Hadoop/ha3.2.html":{"ref":"day03_Hadoop/ha3.2.html","tf":0.02040816326530612}}}}},"机":{"docs":{},"程":{"docs":{},"序":{"docs":{},"计":{"docs":{},"算":{"docs":{},"流":{"docs":{},"程":{"docs":{"day03_Hadoop/ha3.5.html":{"ref":"day03_Hadoop/ha3.5.html","tf":0.030303030303030304}}}}}}}}}},"参":{"docs":{},"数":{"1":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}},"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938},"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}},"更":{"docs":{},"新":{"docs":{},"：":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}},"：":{"docs":{},"维":{"docs":{},"度":{"docs":{},"、":{"docs":{},"索":{"docs":{},"引":{"docs":{},"列":{"docs":{},"表":{"docs":{},"、":{"docs":{},"值":{"docs":{},"列":{"docs":{},"表":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}},"考":{"docs":{},"：":{"docs":{},"为":{"docs":{},"什":{"docs":{},"么":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"中":{"docs":{},"只":{"docs":{},"有":{"docs":{},"a":{"docs":{},"l":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}}}}}}}}}}}}}}}},"号":{"docs":{},"操":{"docs":{},"作":{"docs":{},"符":{"docs":{},"，":{"docs":{},"可":{"docs":{},"以":{"docs":{},"将":{"docs":{},"元":{"docs":{},"组":{"docs":{},"解":{"docs":{},"压":{"docs":{},"为":{"docs":{},"列":{"docs":{},"表":{"docs":{},"。":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}}}}},"早":{"docs":{},"晨":{"docs":{},"发":{"docs":{},"现":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}},"的":{"docs":{},"订":{"docs":{},"单":{"docs":{},"量":{"docs":{},"没":{"docs":{},"有":{"docs":{},"恢":{"docs":{},"复":{"docs":{},"正":{"docs":{},"常":{"docs":{},"，":{"docs":{},"运":{"docs":{},"营":{"docs":{},"人":{"docs":{},"员":{"docs":{},"开":{"docs":{},"始":{"docs":{},"尝":{"docs":{},"试":{"docs":{},"寻":{"docs":{},"找":{"docs":{},"原":{"docs":{},"因":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}}}}}}}}}}}}}}}}}},"同":{"docs":{},"样":{"docs":{},"，":{"docs":{},"损":{"docs":{},"失":{"docs":{},"函":{"docs":{},"数":{"docs":{},"：":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}},"对":{"docs":{},"于":{"docs":{},"评":{"docs":{},"分":{"docs":{},"预":{"docs":{},"测":{"docs":{},"我":{"docs":{},"们":{"docs":{},"利":{"docs":{},"用":{"docs":{},"平":{"docs":{},"方":{"docs":{},"差":{"docs":{},"来":{"docs":{},"构":{"docs":{},"建":{"docs":{},"损":{"docs":{},"失":{"docs":{},"函":{"docs":{},"数":{"docs":{},"：":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}}}}}}}}}}}}}}}}},"数":{"docs":{},"据":{"docs":{},"保":{"docs":{},"存":{"docs":{},"到":{"docs":{},"列":{"docs":{},"式":{"docs":{},"数":{"docs":{},"据":{"docs":{},"库":{"docs":{},"中":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}}}}}}}}},"理":{"docs":{},"可":{"docs":{},"得":{"docs":{},"，":{"docs":{},"梯":{"docs":{},"度":{"docs":{},"下":{"docs":{},"降":{"docs":{},"更":{"docs":{},"新":{"docs":{},"b":{"docs":{},"_":{"docs":{},"i":{"docs":{},"​":{"docs":{},":":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}},"：":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}},"：":{"docs":{"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}},"一":{"docs":{},"时":{"docs":{},"间":{"docs":{},"只":{"docs":{},"能":{"docs":{},"有":{"docs":{},"一":{"docs":{},"个":{"docs":{},"s":{"docs":{},"t":{"docs":{},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"m":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}}}}}}}}}}}}}},"s":{"docs":{},"v":{"docs":{},"d":{"docs":{},"，":{"docs":{},"它":{"docs":{},"也":{"docs":{},"是":{"docs":{},"一":{"docs":{},"种":{"docs":{},"矩":{"docs":{},"阵":{"docs":{},"分":{"docs":{},"解":{"docs":{},"技":{"docs":{},"术":{"docs":{},"，":{"docs":{},"但":{"docs":{},"理":{"docs":{},"论":{"docs":{},"上":{"docs":{},"，":{"docs":{},"a":{"docs":{},"l":{"docs":{},"s":{"docs":{},"在":{"docs":{},"海":{"docs":{},"量":{"docs":{},"数":{"docs":{},"据":{"docs":{},"的":{"docs":{},"处":{"docs":{},"理":{"docs":{},"上":{"docs":{},"要":{"docs":{},"优":{"docs":{},"于":{"docs":{},"s":{"docs":{},"v":{"docs":{},"d":{"docs":{},"。":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}},"对":{"docs":{},"数":{"docs":{},"据":{"docs":{},"进":{"docs":{},"行":{"docs":{},"降":{"docs":{},"维":{"docs":{},"处":{"docs":{},"理":{"docs":{},"。":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}}}},"上":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}},"指":{"docs":{},"标":{"docs":{},"方":{"docs":{},"法":{"docs":{},"，":{"docs":{},"类":{"docs":{},"型":{"docs":{},"为":{"docs":{},"字":{"docs":{},"符":{"docs":{},"串":{"docs":{},"，":{"docs":{},"r":{"docs":{},"m":{"docs":{},"s":{"docs":{},"e":{"docs":{},"或":{"docs":{},"m":{"docs":{},"a":{"docs":{},"e":{"docs":{},"，":{"docs":{},"否":{"docs":{},"则":{"docs":{},"返":{"docs":{},"回":{"docs":{},"两":{"docs":{},"者":{"docs":{},"r":{"docs":{},"m":{"docs":{},"s":{"docs":{},"e":{"docs":{},"和":{"docs":{},"m":{"docs":{},"a":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"定":{"docs":{},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},"的":{"docs":{},"访":{"docs":{},"问":{"docs":{},"方":{"docs":{},"式":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428}}}}}}}}}}},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"n":{"docs":{},"o":{"docs":{},"d":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428}}}}}}}}},"了":{"docs":{},"字":{"docs":{},"段":{"docs":{},"的":{"docs":{},"分":{"docs":{},"隔":{"docs":{},"符":{"docs":{},"为":{"docs":{},"逗":{"docs":{},"号":{"docs":{},"，":{"docs":{},"所":{"docs":{},"以":{"docs":{},"l":{"docs":{},"o":{"docs":{},"a":{"docs":{},"d":{"docs":{},"数":{"docs":{},"据":{"docs":{},"的":{"docs":{},"时":{"docs":{},"候":{"docs":{},"，":{"docs":{},"l":{"docs":{},"o":{"docs":{},"a":{"docs":{},"d":{"docs":{},"的":{"docs":{},"文":{"docs":{},"本":{"docs":{},"也":{"docs":{},"要":{"docs":{},"为":{"docs":{},"逗":{"docs":{},"号":{"docs":{},"，":{"docs":{},"否":{"docs":{},"则":{"docs":{},"加":{"docs":{},"载":{"docs":{},"后":{"docs":{},"为":{"docs":{},"n":{"docs":{},"u":{"docs":{},"l":{"docs":{},"l":{"docs":{},"。":{"docs":{},"h":{"docs":{},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{},"只":{"docs":{},"支":{"docs":{},"持":{"docs":{},"单":{"docs":{},"个":{"docs":{},"字":{"docs":{},"符":{"docs":{},"的":{"docs":{},"分":{"docs":{},"隔":{"docs":{},"符":{"docs":{},"，":{"docs":{},"h":{"docs":{},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{},"默":{"docs":{},"认":{"docs":{},"的":{"docs":{},"分":{"docs":{},"隔":{"docs":{},"符":{"docs":{},"是":{"docs":{},"\\":{"0":{"0":{"1":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"具":{"docs":{},"体":{"docs":{},"时":{"docs":{},"间":{"docs":{},"戳":{"docs":{},"的":{"docs":{},"值":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}},"时":{"docs":{},"间":{"docs":{},"范":{"docs":{},"围":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}},"显":{"docs":{},"示":{"docs":{},"多":{"docs":{},"个":{"docs":{},"版":{"docs":{},"本":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}},"起":{"docs":{},"始":{"docs":{},"r":{"docs":{},"o":{"docs":{},"w":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0056657223796034}}}}}}}}}},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"的":{"docs":{},"s":{"docs":{},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},"m":{"docs":{},"a":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.007692307692307693}}}}}}}}}}}}}}}}}},"一":{"docs":{},"个":{"docs":{},"函":{"docs":{},"数":{"docs":{},"如":{"docs":{},"何":{"docs":{},"使":{"docs":{},"用":{"docs":{},"之":{"docs":{},"前":{"docs":{},"的":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"和":{"docs":{},"新":{"docs":{},"值":{"docs":{},"来":{"docs":{},"更":{"docs":{},"新":{"docs":{},"s":{"docs":{},"t":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}}}}}}}}}}}}}}}}}}}},"损":{"docs":{},"失":{"docs":{},"函":{"docs":{},"数":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}},"偏":{"docs":{},"导":{"docs":{},"推":{"docs":{},"导":{"docs":{},"：":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}},"：":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}},"整":{"docs":{},"体":{"docs":{},"封":{"docs":{},"装":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}},"个":{"docs":{},"集":{"docs":{},"群":{"docs":{},"中":{"docs":{},"有":{"docs":{},"多":{"docs":{},"个":{"docs":{},"，":{"docs":{},"负":{"docs":{},"责":{"docs":{},"自":{"docs":{},"己":{"docs":{},"本":{"docs":{},"身":{"docs":{},"节":{"docs":{},"点":{"docs":{},"资":{"docs":{},"源":{"docs":{},"管":{"docs":{},"理":{"docs":{},"和":{"docs":{},"使":{"docs":{},"用":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}}}}}}}}}}}}}}}}}},"同":{"docs":{},"一":{"docs":{},"时":{"docs":{},"间":{"docs":{},"提":{"docs":{},"供":{"docs":{},"服":{"docs":{},"务":{"docs":{},"的":{"docs":{},"r":{"docs":{},"m":{"docs":{},"只":{"docs":{},"有":{"docs":{},"一":{"docs":{},"个":{"docs":{},"，":{"docs":{},"负":{"docs":{},"责":{"docs":{},"集":{"docs":{},"群":{"docs":{},"资":{"docs":{},"源":{"docs":{},"的":{"docs":{},"统":{"docs":{},"一":{"docs":{},"管":{"docs":{},"理":{"docs":{},"和":{"docs":{},"调":{"docs":{},"度":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"分":{"docs":{},"为":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}}}}},"合":{"docs":{},"网":{"docs":{},"站":{"docs":{},"应":{"docs":{},"用":{"docs":{},"和":{"docs":{},"大":{"docs":{},"数":{"docs":{},"据":{"docs":{},"系":{"docs":{},"统":{"docs":{},"之":{"docs":{},"间":{"docs":{},"的":{"docs":{},"差":{"docs":{},"异":{"docs":{},",":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}}}}}}}}}}}},"方":{"docs":{},"法":{"docs":{},"一":{"docs":{},"：":{"docs":{},"随":{"docs":{},"机":{"docs":{},"梯":{"docs":{},"度":{"docs":{},"下":{"docs":{},"降":{"docs":{},"法":{"docs":{},"优":{"docs":{},"化":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}},"二":{"docs":{},"：":{"docs":{},"交":{"docs":{},"替":{"docs":{},"最":{"docs":{},"小":{"docs":{},"二":{"docs":{},"乘":{"docs":{},"法":{"docs":{},"优":{"docs":{},"化":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}},"并":{"docs":{},"且":{"docs":{},"传":{"docs":{},"入":{"docs":{},"已":{"docs":{},"有":{"docs":{},"的":{"docs":{},"可":{"docs":{},"迭":{"docs":{},"代":{"docs":{},"对":{"docs":{},"象":{"docs":{},"或":{"docs":{},"者":{"docs":{},"集":{"docs":{},"合":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}}}}}}}}}}}}}},"便":{"docs":{},"练":{"docs":{},"习":{"docs":{},"可":{"docs":{},"以":{"docs":{},"对":{"docs":{},"数":{"docs":{},"据":{"docs":{},"做":{"docs":{},"拆":{"docs":{},"分":{"docs":{},"处":{"docs":{},"理":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}},"更":{"docs":{},"多":{"docs":{},"关":{"docs":{},"于":{"docs":{},"g":{"docs":{},"r":{"docs":{},"o":{"docs":{},"u":{"docs":{},"p":{"docs":{},"b":{"docs":{},"y":{"docs":{},"的":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}}}}}}},"了":{"docs":{},"解":{"docs":{},"：":{"docs":{},"p":{"docs":{},"y":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},".":{"docs":{},"m":{"docs":{},"l":{"docs":{},".":{"docs":{},"r":{"docs":{},"e":{"docs":{},"c":{"docs":{},"o":{"docs":{},"m":{"docs":{},"m":{"docs":{},"e":{"docs":{},"n":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},".":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"新":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}},"b":{"docs":{},"u":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}},"记":{"docs":{},"录":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}},"改":{"docs":{},"d":{"docs":{},"f":{"docs":{},"表":{"docs":{},"结":{"docs":{},"构":{"docs":{},"：":{"docs":{},"更":{"docs":{},"改":{"docs":{},"列":{"docs":{},"类":{"docs":{},"型":{"docs":{},"和":{"docs":{},"列":{"docs":{},"名":{"docs":{},"称":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289}}}}}}}}}}}}}}}}},"表":{"docs":{},"结":{"docs":{},"构":{"docs":{},"，":{"docs":{},"转":{"docs":{},"换":{"docs":{},"为":{"docs":{},"对":{"docs":{},"应":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"类":{"docs":{},"型":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0007958615200955034},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}}}},"梯":{"docs":{},"度":{"docs":{},"下":{"docs":{},"降":{"docs":{},"参":{"docs":{},"数":{"docs":{},"更":{"docs":{},"新":{"docs":{},"原":{"docs":{},"始":{"docs":{},"公":{"docs":{},"式":{"docs":{},"：":{"docs":{},"（":{"docs":{},"公":{"docs":{},"式":{"docs":{},"中":{"docs":{},"α":{"docs":{},"为":{"docs":{},"学":{"docs":{},"习":{"docs":{},"率":{"docs":{},"）":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}}}}}}}}},"更":{"docs":{},"新":{"docs":{},"b":{"docs":{},"_":{"docs":{},"u":{"docs":{},":":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}},"参":{"docs":{},"数":{"docs":{},"p":{"docs":{},"_":{"docs":{},"{":{"docs":{},"u":{"docs":{},"k":{"docs":{},"}":{"docs":{},"和":{"docs":{},"q":{"docs":{},"_":{"docs":{},"{":{"docs":{},"i":{"docs":{},"k":{"docs":{},"}":{"docs":{},"​":{"docs":{},"：":{"docs":{},"（":{"docs":{},"α":{"docs":{},"学":{"docs":{},"习":{"docs":{},"率":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}}}}}}}}}}},"：":{"docs":{"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}}}}}}},"最":{"docs":{},"高":{"docs":{},"迭":{"docs":{},"代":{"docs":{},"次":{"docs":{},"数":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683}}}}}}}},"优":{"docs":{},"化":{"docs":{},"损":{"docs":{},"失":{"docs":{},"函":{"docs":{},"数":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}}}}}}},"正":{"docs":{},"则":{"docs":{},"化":{"docs":{},"系":{"docs":{},"数":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}},"参":{"docs":{},"数":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}},"态":{"docs":{},"分":{"docs":{},"布":{"docs":{},"去":{"docs":{},"极":{"docs":{},"值":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}},"版":{"docs":{},"本":{"docs":{},"不":{"docs":{},"要":{"docs":{},"过":{"docs":{},"低":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}},"兼":{"docs":{},"容":{"docs":{},"的":{"docs":{},"问":{"docs":{},"题":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}},"：":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.006172839506172839}}}},"由":{"1":{"docs":{},"和":{"2":{"docs":{},"计":{"docs":{},"算":{"docs":{},"的":{"docs":{},"结":{"docs":{},"果":{"docs":{},"求":{"docs":{},"出":{"docs":{},"词":{"docs":{},"语":{"docs":{},"的":{"docs":{},"t":{"docs":{},"f":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}},"docs":{}}},"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}},"于":{"docs":{},"随":{"docs":{},"机":{"docs":{},"梯":{"docs":{},"度":{"docs":{},"下":{"docs":{},"降":{"docs":{},"法":{"docs":{},"本":{"docs":{},"质":{"docs":{},"上":{"docs":{},"利":{"docs":{},"用":{"docs":{},"每":{"docs":{},"个":{"docs":{},"样":{"docs":{},"本":{"docs":{},"的":{"docs":{},"损":{"docs":{},"失":{"docs":{},"来":{"docs":{},"更":{"docs":{},"新":{"docs":{},"参":{"docs":{},"数":{"docs":{},"，":{"docs":{},"而":{"docs":{},"不":{"docs":{},"用":{"docs":{},"每":{"docs":{},"次":{"docs":{},"求":{"docs":{},"出":{"docs":{},"全":{"docs":{},"部":{"docs":{},"的":{"docs":{},"损":{"docs":{},"失":{"docs":{},"和":{"docs":{},"，":{"docs":{},"因":{"docs":{},"此":{"docs":{},"使":{"docs":{},"用":{"docs":{},"s":{"docs":{},"g":{"docs":{},"d":{"docs":{},"时":{"docs":{},"：":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"p":{"docs":{},"矩":{"docs":{},"阵":{"docs":{},"和":{"docs":{},"q":{"docs":{},"矩":{"docs":{},"阵":{"docs":{},"是":{"docs":{},"两":{"docs":{},"个":{"docs":{},"不":{"docs":{},"同":{"docs":{},"的":{"docs":{},"矩":{"docs":{},"阵":{"docs":{},"，":{"docs":{},"通":{"docs":{},"常":{"docs":{},"分":{"docs":{},"别":{"docs":{},"采":{"docs":{},"取":{"docs":{},"不":{"docs":{},"同":{"docs":{},"的":{"docs":{},"正":{"docs":{},"则":{"docs":{},"参":{"docs":{},"数":{"docs":{},"，":{"docs":{},"如":{"docs":{},"\\":{"docs":{},"l":{"docs":{},"a":{"docs":{},"m":{"docs":{},"b":{"docs":{},"d":{"docs":{},"a":{"docs":{},"_":{"1":{"docs":{},"和":{"docs":{},"\\":{"docs":{},"l":{"docs":{},"a":{"docs":{},"m":{"docs":{},"b":{"docs":{},"d":{"docs":{},"a":{"docs":{},"_":{"2":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}},"docs":{}}}}}}}}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"m":{"docs":{},"l":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}},"是":{"docs":{},"给":{"docs":{},"所":{"docs":{},"有":{"docs":{},"用":{"docs":{},"户":{"docs":{},"进":{"docs":{},"行":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"，":{"docs":{},"此":{"docs":{},"处":{"docs":{},"运":{"docs":{},"算":{"docs":{},"时":{"docs":{},"间":{"docs":{},"也":{"docs":{},"较":{"docs":{},"长":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}},"运":{"docs":{},"算":{"docs":{},"时":{"docs":{},"间":{"docs":{},"比":{"docs":{},"较":{"docs":{},"长":{"docs":{},"，":{"docs":{},"所":{"docs":{},"以":{"docs":{},"这":{"docs":{},"里":{"docs":{},"先":{"docs":{},"将":{"docs":{},"结":{"docs":{},"果":{"docs":{},"存":{"docs":{},"储":{"docs":{},"起":{"docs":{},"来":{"docs":{},"，":{"docs":{},"供":{"docs":{},"后":{"docs":{},"续":{"docs":{},"其":{"docs":{},"他":{"docs":{},"操":{"docs":{},"作":{"docs":{},"使":{"docs":{},"用":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"该":{"docs":{},"思":{"docs":{},"想":{"docs":{},"正":{"docs":{},"好":{"docs":{},"和":{"docs":{},"热":{"docs":{},"独":{"docs":{},"编":{"docs":{},"码":{"docs":{},"实":{"docs":{},"现":{"docs":{},"方":{"docs":{},"法":{"docs":{},"一":{"docs":{},"样":{"docs":{},"，":{"docs":{},"因":{"docs":{},"此":{"docs":{},"这":{"docs":{},"里":{"docs":{},"直":{"docs":{},"接":{"docs":{},"使":{"docs":{},"用":{"docs":{},"热":{"docs":{},"独":{"docs":{},"编":{"docs":{},"码":{"docs":{},"方":{"docs":{},"式":{"docs":{},"处":{"docs":{},"理":{"docs":{},"数":{"docs":{},"据":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"前":{"docs":{},"面":{"docs":{},"使":{"docs":{},"用":{"docs":{},"的":{"docs":{},"是":{"docs":{},"o":{"docs":{},"u":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"方":{"docs":{},"式":{"docs":{},"合":{"docs":{},"并":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"产":{"docs":{},"生":{"docs":{},"了":{"docs":{},"部":{"docs":{},"分":{"docs":{},"空":{"docs":{},"值":{"docs":{},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"这":{"docs":{},"里":{"docs":{},"必":{"docs":{},"须":{"docs":{},"先":{"docs":{},"剔":{"docs":{},"除":{"docs":{},"掉":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"这":{"docs":{},"里":{"docs":{},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{},"其":{"docs":{},"实":{"docs":{},"很":{"docs":{},"少":{"docs":{},"，":{"docs":{},"所":{"docs":{},"以":{"docs":{},"我":{"docs":{},"们":{"docs":{},"再":{"docs":{},"直":{"docs":{},"接":{"docs":{},"转":{"docs":{},"成":{"docs":{},"p":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"a":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}}}}}}}}}}}}},"t":{"docs":{},"f":{"docs":{},"和":{"docs":{},"i":{"docs":{},"d":{"docs":{},"f":{"docs":{},"计":{"docs":{},"算":{"docs":{},"词":{"docs":{},"语":{"docs":{},"的":{"docs":{},"权":{"docs":{},"重":{"docs":{},"为":{"docs":{},"：":{"docs":{},"w":{"docs":{},"_":{"docs":{},"{":{"docs":{},"i":{"docs":{},"j":{"docs":{},"}":{"docs":{},"=":{"docs":{},"t":{"docs":{},"f":{"docs":{},"_":{"docs":{},"{":{"docs":{},"i":{"docs":{},"j":{"docs":{},"}":{"docs":{},"·":{"docs":{},"i":{"docs":{},"d":{"docs":{},"f":{"docs":{},"_":{"docs":{},"{":{"docs":{},"i":{"docs":{},"}":{"docs":{},"=":{"docs":{},"\\":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"c":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},"管":{"docs":{},"理":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{},"自":{"docs":{},"身":{"docs":{},"管":{"docs":{},"理":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}}}},"冒":{"docs":{},"号":{"docs":{},":":{"docs":{"Hive&HBase/06_hbase数据模型.html":{"ref":"Hive&HBase/06_hbase数据模型.html","tf":0.020833333333333332}}}}}},"示":{"docs":{},"例":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938},"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}},"：":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208},"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.01875}}}}},"经":{"docs":{},"过":{"docs":{},"交":{"docs":{},"替":{"docs":{},"最":{"docs":{},"小":{"docs":{},"二":{"docs":{},"乘":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}},"处":{"docs":{},"理":{"docs":{},"计":{"docs":{},"算":{"docs":{},"后":{"docs":{},"再":{"docs":{},"导":{"docs":{},"出":{"docs":{},"给":{"docs":{},"应":{"docs":{},"用":{"docs":{},"程":{"docs":{},"序":{"docs":{},"使":{"docs":{},"用":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}}}}}}}}}}},"训":{"docs":{},"练":{"docs":{},"集":{"docs":{},"的":{"docs":{},"比":{"docs":{},"例":{"docs":{},"，":{"docs":{},"如":{"docs":{},"x":{"docs":{},"=":{"0":{"docs":{},".":{"8":{"docs":{},"，":{"docs":{},"则":{"0":{"docs":{},".":{"2":{"docs":{},"是":{"docs":{},"测":{"docs":{},"试":{"docs":{},"集":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0011435105774728416}}}}}}},"docs":{}}},"docs":{}}}},"docs":{}}},"docs":{}}}}}}}}},"t":{"docs":{},"f":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0038910505836575876}}}},"分":{"docs":{},"类":{"docs":{},"模":{"docs":{},"型":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}},"样":{"docs":{},"本":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},"个":{"docs":{},"数":{"docs":{},"：":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}},"：":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}},"c":{"docs":{},"t":{"docs":{},"r":{"docs":{},"m":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"l":{"docs":{},"_":{"docs":{},"n":{"docs":{},"o":{"docs":{},"r":{"docs":{},"m":{"docs":{},"a":{"docs":{},"l":{"docs":{},"：":{"docs":{},"直":{"docs":{},"接":{"docs":{},"将":{"docs":{},"对":{"docs":{},"应":{"docs":{},"的":{"docs":{},"特":{"docs":{},"征":{"docs":{},"的":{"docs":{},"特":{"docs":{},"征":{"docs":{},"值":{"docs":{},"组":{"docs":{},"合":{"docs":{},"成":{"docs":{},"对":{"docs":{},"应":{"docs":{},"的":{"docs":{},"特":{"docs":{},"征":{"docs":{},"向":{"docs":{},"量":{"docs":{},"进":{"docs":{},"行":{"docs":{},"训":{"docs":{},"练":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{},":":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}},"模":{"docs":{},"型":{"docs":{},"时":{"docs":{},"，":{"docs":{},"通":{"docs":{},"过":{"docs":{},"对":{"docs":{},"类":{"docs":{},"别":{"docs":{},"特":{"docs":{},"征":{"docs":{},"数":{"docs":{},"据":{"docs":{},"进":{"docs":{},"行":{"docs":{},"处":{"docs":{},"理":{"docs":{},"，":{"docs":{},"一":{"docs":{},"定":{"docs":{},"程":{"docs":{},"度":{"docs":{},"达":{"docs":{},"到":{"docs":{},"提":{"docs":{},"高":{"docs":{},"了":{"docs":{},"模":{"docs":{},"型":{"docs":{},"的":{"docs":{},"效":{"docs":{},"果":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"详":{"docs":{},"见":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208},"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}},"细":{"docs":{},"使":{"docs":{},"用":{"docs":{},"方":{"docs":{},"法":{"docs":{},"：":{"docs":{},"p":{"docs":{},"y":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},".":{"docs":{},"m":{"docs":{},"l":{"docs":{},".":{"docs":{},"r":{"docs":{},"e":{"docs":{},"c":{"docs":{},"o":{"docs":{},"m":{"docs":{},"m":{"docs":{},"e":{"docs":{},"n":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},".":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"语":{"docs":{},"法":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208},"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938},"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}},"，":{"docs":{},"提":{"docs":{},"供":{"docs":{},"快":{"docs":{},"速":{"docs":{},"开":{"docs":{},"发":{"docs":{},"的":{"docs":{},"能":{"docs":{},"力":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}}}},"料":{"docs":{},"（":{"docs":{},"c":{"docs":{},"o":{"docs":{},"r":{"docs":{},"p":{"docs":{},"u":{"docs":{},"s":{"docs":{},"）":{"docs":{},"：":{"docs":{},"一":{"docs":{},"组":{"docs":{},"原":{"docs":{},"始":{"docs":{},"文":{"docs":{},"本":{"docs":{},"的":{"docs":{},"集":{"docs":{},"合":{"docs":{},"，":{"docs":{},"在":{"docs":{},"g":{"docs":{},"e":{"docs":{},"n":{"docs":{},"s":{"docs":{},"i":{"docs":{},"m":{"docs":{},"中":{"docs":{},"，":{"docs":{},"c":{"docs":{},"o":{"docs":{},"r":{"docs":{},"p":{"docs":{},"u":{"docs":{},"s":{"docs":{},"通":{"docs":{},"常":{"docs":{},"是":{"docs":{},"一":{"docs":{},"个":{"docs":{},"可":{"docs":{},"迭":{"docs":{},"代":{"docs":{},"的":{"docs":{},"对":{"docs":{},"象":{"docs":{},"（":{"docs":{},"比":{"docs":{},"如":{"docs":{},"列":{"docs":{},"表":{"docs":{},"）":{"docs":{},"。":{"docs":{},"每":{"docs":{},"一":{"docs":{},"次":{"docs":{},"迭":{"docs":{},"代":{"docs":{},"返":{"docs":{},"回":{"docs":{},"一":{"docs":{},"个":{"docs":{},"可":{"docs":{},"用":{"docs":{},"于":{"docs":{},"表":{"docs":{},"达":{"docs":{},"文":{"docs":{},"本":{"docs":{},"对":{"docs":{},"象":{"docs":{},"的":{"docs":{},"（":{"docs":{},"稀":{"docs":{},"疏":{"docs":{},"）":{"docs":{},"向":{"docs":{},"量":{"docs":{},"。":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"句":{"docs":{},"转":{"docs":{},"换":{"docs":{},"为":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}},"调":{"docs":{},"用":{"docs":{},"s":{"docs":{},"g":{"docs":{},"d":{"docs":{},"方":{"docs":{},"法":{"docs":{},"训":{"docs":{},"练":{"docs":{},"模":{"docs":{},"型":{"docs":{},"参":{"docs":{},"数":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.002287021154945683}}}}}}}}}}}},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"t":{"docs":{},"e":{"docs":{},"x":{"docs":{},"t":{"docs":{},"的":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}}}}}}}}}},"文":{"docs":{},"件":{"docs":{},"系":{"docs":{},"统":{"docs":{},"(":{"docs":{},"f":{"docs":{},"s":{"docs":{},")":{"docs":{},"s":{"docs":{},"h":{"docs":{},"e":{"docs":{},"l":{"docs":{},"l":{"docs":{},"命":{"docs":{},"令":{"docs":{},"应":{"docs":{},"使":{"docs":{},"用":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625}}}}}}}}}}}}}}}}}}}},"执":{"docs":{},"行":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}},"方":{"docs":{},"法":{"docs":{},"例":{"docs":{},"如":{"docs":{},"：":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},".":{"docs":{},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"d":{"docs":{},".":{"docs":{},"x":{"docs":{},"x":{"docs":{},"x":{"docs":{},"方":{"docs":{},"法":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}}}}}}}},"节":{"docs":{},"指":{"docs":{},"标":{"docs":{},"对":{"docs":{},"公":{"docs":{},"司":{"docs":{},"进":{"docs":{},"行":{"docs":{},"管":{"docs":{},"理":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}}}}},"转":{"docs":{},"换":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}},"为":{"docs":{},"列":{"docs":{},"表":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"，":{"docs":{},"再":{"docs":{},"从":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"到":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}}}}}}}}}}}}}}}},"p":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"a":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}},"普":{"docs":{},"通":{"docs":{},"的":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"类":{"docs":{},"型":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}},"来":{"docs":{},"输":{"docs":{},"出":{"docs":{},"列":{"docs":{},"表":{"docs":{},"。":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}}}}},"化":{"docs":{},"率":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.008403361344537815},"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}},"指":{"docs":{},"的":{"docs":{},"是":{"docs":{},"从":{"docs":{},"状":{"docs":{},"态":{"docs":{},"a":{"docs":{},"进":{"docs":{},"入":{"docs":{},"到":{"docs":{},"状":{"docs":{},"态":{"docs":{},"b":{"docs":{},"的":{"docs":{},"概":{"docs":{},"率":{"docs":{},"，":{"docs":{},"电":{"docs":{},"商":{"docs":{},"的":{"docs":{},"转":{"docs":{},"化":{"docs":{},"率":{"docs":{},"通":{"docs":{},"常":{"docs":{},"是":{"docs":{},"指":{"docs":{},"到":{"docs":{},"达":{"docs":{},"网":{"docs":{},"站":{"docs":{},"后":{"docs":{},"，":{"docs":{},"进":{"docs":{},"而":{"docs":{},"有":{"docs":{},"成":{"docs":{},"交":{"docs":{},"记":{"docs":{},"录":{"docs":{},"的":{"docs":{},"用":{"docs":{},"户":{"docs":{},"比":{"docs":{},"率":{"docs":{},"，":{"docs":{},"如":{"docs":{},"用":{"docs":{},"户":{"docs":{},"成":{"docs":{},"交":{"docs":{},"量":{"docs":{},"/":{"docs":{},"用":{"docs":{},"户":{"docs":{},"访":{"docs":{},"问":{"docs":{},"量":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"过":{"docs":{},"程":{"docs":{},":":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}},"成":{"docs":{},"j":{"docs":{},"s":{"docs":{},"o":{"docs":{},"n":{"docs":{},"字":{"docs":{},"符":{"docs":{},"串":{"docs":{},"再":{"docs":{},"保":{"docs":{},"存":{"docs":{},"，":{"docs":{},"能":{"docs":{},"保":{"docs":{},"证":{"docs":{},"数":{"docs":{},"据":{"docs":{},"再":{"docs":{},"次":{"docs":{},"倒":{"docs":{},"出":{"docs":{},"来":{"docs":{},"时":{"docs":{},"，":{"docs":{},"能":{"docs":{},"有":{"docs":{},"效":{"docs":{},"的":{"docs":{},"转":{"docs":{},"换":{"docs":{},"成":{"docs":{},"p":{"docs":{},"y":{"docs":{},"t":{"docs":{},"h":{"docs":{},"o":{"docs":{},"n":{"docs":{},"类":{"docs":{},"型":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"返":{"docs":{},"回":{"docs":{},"一":{"docs":{},"个":{"docs":{},"对":{"docs":{},"象":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}},"l":{"docs":{},"i":{"docs":{},"s":{"docs":{},"t":{"docs":{},"，":{"docs":{},"l":{"docs":{},"i":{"docs":{},"s":{"docs":{},"t":{"docs":{},"中":{"docs":{},"包":{"docs":{},"含":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}}}}}},"p":{"docs":{},"y":{"docs":{},"t":{"docs":{},"h":{"docs":{},"o":{"docs":{},"n":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"类":{"docs":{},"型":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}},"，":{"docs":{},"此":{"docs":{},"时":{"docs":{},"还":{"docs":{},"没":{"docs":{},"开":{"docs":{},"始":{"docs":{},"计":{"docs":{},"算":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}},"值":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938},"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}},"：":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.025}}}},"列":{"docs":{},"表":{"docs":{},"。":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}},"迭":{"docs":{},"代":{"docs":{},"器":{"docs":{},"。":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}},"函":{"docs":{},"数":{"docs":{},"计":{"docs":{},"算":{"docs":{},"结":{"docs":{},"果":{"docs":{},"。":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}}}}}}},"结":{"docs":{},"果":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}},"为":{"docs":{},"连":{"docs":{},"接":{"docs":{},"参":{"docs":{},"数":{"docs":{},"产":{"docs":{},"生":{"docs":{},"的":{"docs":{},"字":{"docs":{},"符":{"docs":{},"串":{"docs":{},"。":{"docs":{},"如":{"docs":{},"有":{"docs":{},"任":{"docs":{},"何":{"docs":{},"一":{"docs":{},"个":{"docs":{},"参":{"docs":{},"数":{"docs":{},"为":{"docs":{},"n":{"docs":{},"u":{"docs":{},"l":{"docs":{},"l":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"中":{"docs":{},"元":{"docs":{},"素":{"docs":{},"的":{"docs":{},"个":{"docs":{},"数":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}},"的":{"docs":{},"前":{"docs":{},"n":{"docs":{},"个":{"docs":{},"元":{"docs":{},"素":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}},"第":{"docs":{},"一":{"docs":{},"个":{"docs":{},"元":{"docs":{},"素":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}}},"的":{"docs":{},"是":{"docs":{},"一":{"docs":{},"个":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"，":{"docs":{},"这":{"docs":{},"里":{"docs":{},"的":{"docs":{},"c":{"docs":{},"o":{"docs":{},"u":{"docs":{},"n":{"docs":{},"t":{"docs":{},"计":{"docs":{},"算":{"docs":{},"的":{"docs":{},"是":{"docs":{},"每":{"docs":{},"一":{"docs":{},"个":{"docs":{},"分":{"docs":{},"组":{"docs":{},"的":{"docs":{},"个":{"docs":{},"数":{"docs":{},"，":{"docs":{},"但":{"docs":{},"当":{"docs":{},"前":{"docs":{},"还":{"docs":{},"没":{"docs":{},"有":{"docs":{},"进":{"docs":{},"行":{"docs":{},"计":{"docs":{},"算":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"字":{"docs":{},"段":{"docs":{},"p":{"docs":{},"i":{"docs":{},"d":{"docs":{},"_":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"是":{"docs":{},"一":{"docs":{},"个":{"docs":{},"稀":{"docs":{},"疏":{"docs":{},"向":{"docs":{},"量":{"docs":{},"类":{"docs":{},"型":{"docs":{},"数":{"docs":{},"据":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}},"模":{"docs":{},"型":{"docs":{},"中":{"docs":{},"关":{"docs":{},"于":{"docs":{},"用":{"docs":{},"户":{"docs":{},"的":{"docs":{},"所":{"docs":{},"有":{"docs":{},"属":{"docs":{},"性":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}},"迭":{"docs":{},"代":{"docs":{},"更":{"docs":{},"新":{"docs":{},"b":{"docs":{},"u":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}},"次":{"docs":{},"数":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}}}},"首":{"docs":{},"先":{"docs":{},"计":{"docs":{},"算":{"docs":{},"出":{"docs":{},"整":{"docs":{},"个":{"docs":{},"评":{"docs":{},"分":{"docs":{},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{},"的":{"docs":{},"平":{"docs":{},"均":{"docs":{},"评":{"docs":{},"分":{"docs":{},"\\":{"docs":{},"m":{"docs":{},"u":{"docs":{},"​":{"docs":{},"是":{"3":{"docs":{},".":{"5":{"docs":{},"分":{"docs":{"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"ref":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","tf":0.0005717552887364208}}}},"docs":{}}},"docs":{}}}}}}}}}}}}}}}}}}}}},"执":{"docs":{},"行":{"docs":{},"逻":{"docs":{},"辑":{"docs":{},"执":{"docs":{},"行":{"docs":{},"计":{"docs":{},"划":{"docs":{},"，":{"docs":{},"然":{"docs":{},"后":{"docs":{},"转":{"docs":{},"换":{"docs":{},"为":{"docs":{},"物":{"docs":{},"理":{"docs":{},"执":{"docs":{},"行":{"docs":{},"计":{"docs":{},"划":{"docs":{},"(":{"docs":{},"选":{"docs":{},"择":{"docs":{},"成":{"docs":{},"本":{"docs":{},"最":{"docs":{},"小":{"docs":{},"的":{"docs":{},")":{"docs":{},"，":{"docs":{},"通":{"docs":{},"过":{"docs":{},"c":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"，":{"docs":{},"要":{"docs":{},"定":{"docs":{},"义":{"docs":{},"一":{"docs":{},"个":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"，":{"docs":{},"可":{"docs":{},"以":{"docs":{},"是":{"docs":{},"任":{"docs":{},"意":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"类":{"docs":{},"型":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}}}}}}}}}}}}}}}}}},"借":{"docs":{},"鉴":{"docs":{},"线":{"docs":{},"性":{"docs":{},"回":{"docs":{},"归":{"docs":{},"的":{"docs":{},"思":{"docs":{},"想":{"docs":{},"，":{"docs":{},"通":{"docs":{},"过":{"docs":{},"最":{"docs":{},"小":{"docs":{},"化":{"docs":{},"观":{"docs":{},"察":{"docs":{},"数":{"docs":{},"据":{"docs":{},"的":{"docs":{},"平":{"docs":{},"方":{"docs":{},"来":{"docs":{},"寻":{"docs":{},"求":{"docs":{},"最":{"docs":{},"优":{"docs":{},"的":{"docs":{},"用":{"docs":{},"户":{"docs":{},"和":{"docs":{},"项":{"docs":{},"目":{"docs":{},"的":{"docs":{},"隐":{"docs":{},"含":{"docs":{},"向":{"docs":{},"量":{"docs":{},"表":{"docs":{},"示":{"docs":{},"。":{"docs":{},"同":{"docs":{},"时":{"docs":{},"为":{"docs":{},"了":{"docs":{},"避":{"docs":{},"免":{"docs":{},"过":{"docs":{},"度":{"docs":{},"拟":{"docs":{},"合":{"docs":{},"（":{"docs":{},"o":{"docs":{},"v":{"docs":{},"e":{"docs":{},"r":{"docs":{},"f":{"docs":{},"i":{"docs":{},"t":{"docs":{},"t":{"docs":{},"i":{"docs":{},"n":{"docs":{},"g":{"docs":{},"）":{"docs":{},"观":{"docs":{},"测":{"docs":{},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"又":{"docs":{},"提":{"docs":{},"出":{"docs":{},"了":{"docs":{},"带":{"docs":{},"有":{"docs":{},"l":{"2":{"docs":{},"正":{"docs":{},"则":{"docs":{},"项":{"docs":{},"的":{"docs":{},"f":{"docs":{},"u":{"docs":{},"n":{"docs":{},"k":{"docs":{},"s":{"docs":{},"v":{"docs":{},"d":{"docs":{},"，":{"docs":{},"上":{"docs":{},"公":{"docs":{},"式":{"docs":{},"：":{"docs":{"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html":{"ref":"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html","tf":0.037037037037037035}}}}}}}}}}}}}}}}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"函":{"docs":{},"数":{"docs":{},"式":{"docs":{},"编":{"docs":{},"程":{"docs":{},"方":{"docs":{},"式":{"docs":{"day03_Hadoop/ha3.2.html":{"ref":"day03_Hadoop/ha3.2.html","tf":0.02040816326530612}}}}}}}}}}},"刚":{"docs":{},"才":{"docs":{},"提":{"docs":{},"到":{"docs":{},"的":{"docs":{},"t":{"docs":{},"r":{"docs":{},"a":{"docs":{},"d":{"docs":{},"i":{"docs":{},"t":{"docs":{"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html":{"ref":"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html","tf":0.037037037037037035}}}}}}}}}}}}},"它":{"docs":{},"基":{"docs":{},"于":{"docs":{},"的":{"docs":{},"假":{"docs":{},"设":{"docs":{},"和":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"i":{"docs":{},"n":{"docs":{},"e":{"docs":{},"基":{"docs":{},"准":{"docs":{},"预":{"docs":{},"测":{"docs":{},"是":{"docs":{},"一":{"docs":{},"样":{"docs":{},"的":{"docs":{},"，":{"docs":{},"但":{"docs":{},"这":{"docs":{},"里":{"docs":{},"将":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"l":{"docs":{},"i":{"docs":{},"n":{"docs":{},"e":{"docs":{},"的":{"docs":{},"偏":{"docs":{},"置":{"docs":{},"引":{"docs":{},"入":{"docs":{},"到":{"docs":{},"了":{"docs":{},"矩":{"docs":{},"阵":{"docs":{},"分":{"docs":{},"解":{"docs":{},"中":{"docs":{"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html":{"ref":"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html","tf":0.037037037037037035}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"表":{"docs":{},"示":{"docs":{},"的":{"docs":{},"是":{"docs":{},"数":{"docs":{},"据":{"docs":{},"可":{"docs":{},"以":{"docs":{},"保":{"docs":{},"存":{"docs":{},"在":{"docs":{},"磁":{"docs":{},"盘":{"docs":{},"，":{"docs":{},"也":{"docs":{},"可":{"docs":{},"以":{"docs":{},"保":{"docs":{},"存":{"docs":{},"在":{"docs":{},"内":{"docs":{},"存":{"docs":{},"中":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}}}}}}}}}}}}}}}}}}}},"是":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"中":{"docs":{},"用":{"docs":{},"于":{"docs":{},"处":{"docs":{},"理":{"docs":{},"结":{"docs":{},"构":{"docs":{},"化":{"docs":{},"数":{"docs":{},"据":{"docs":{},"的":{"docs":{},"一":{"docs":{},"个":{"docs":{},"模":{"docs":{},"块":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}}}}}}}}}}}}}}}}}}}}}},"一":{"docs":{},"个":{"docs":{},"可":{"docs":{},"扩":{"docs":{},"展":{"docs":{},"，":{"docs":{},"高":{"docs":{},"吞":{"docs":{},"吐":{"docs":{},"具":{"docs":{},"有":{"docs":{},"容":{"docs":{},"错":{"docs":{},"性":{"docs":{},"的":{"docs":{},"流":{"docs":{},"式":{"docs":{},"计":{"docs":{},"算":{"docs":{},"框":{"docs":{},"架":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}}}}}}}}}}}}}}}}}}}}}},"将":{"docs":{},"足":{"docs":{},"够":{"docs":{},"多":{"docs":{},"的":{"docs":{},"信":{"docs":{},"息":{"docs":{},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},"c":{"docs":{},"k":{"docs":{},"p":{"docs":{},"o":{"docs":{},"i":{"docs":{},"n":{"docs":{},"t":{"docs":{},"到":{"docs":{},"某":{"docs":{},"些":{"docs":{},"具":{"docs":{},"备":{"docs":{},"容":{"docs":{},"错":{"docs":{},"性":{"docs":{},"的":{"docs":{},"存":{"docs":{},"储":{"docs":{},"系":{"docs":{},"统":{"docs":{},"如":{"docs":{},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},"上":{"docs":{},"，":{"docs":{},"以":{"docs":{},"便":{"docs":{},"出":{"docs":{},"错":{"docs":{},"时":{"docs":{},"能":{"docs":{},"够":{"docs":{},"迅":{"docs":{},"速":{"docs":{},"恢":{"docs":{},"复":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"很":{"docs":{},"显":{"docs":{},"然":{"docs":{},"我":{"docs":{},"们":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"其":{"docs":{},"实":{"docs":{},"绝":{"docs":{},"大":{"docs":{},"多":{"docs":{},"数":{"docs":{},"情":{"docs":{},"况":{"docs":{},"下":{"docs":{},"都":{"docs":{},"是":{"docs":{},"稀":{"docs":{},"疏":{"docs":{},"的":{"docs":{},"，":{"docs":{},"因":{"docs":{},"此":{"docs":{},"如":{"docs":{},"果":{"docs":{},"要":{"docs":{},"使":{"docs":{},"用":{"docs":{},"t":{"docs":{},"r":{"docs":{},"a":{"docs":{},"d":{"docs":{},"i":{"docs":{},"t":{"docs":{"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html":{"ref":"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html","tf":0.037037037037037035}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"多":{"docs":{},"运":{"docs":{},"营":{"docs":{},"管":{"docs":{},"理":{"docs":{},"人":{"docs":{},"员":{"docs":{},",":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}},"少":{"docs":{},"使":{"docs":{},"用":{"docs":{},"全":{"docs":{},"表":{"docs":{},"查":{"docs":{},"询":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}},")":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938},"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.005668934240362812},"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}},")":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.007590132827324478},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.004629629629629629}}},",":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.004545454545454545}}},"]":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}},".":{"docs":{},"a":{"docs":{},"l":{"docs":{},"i":{"docs":{},"a":{"docs":{},"s":{"docs":{},"(":{"docs":{},"c":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}},"q":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.013282732447817837},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.006944444444444444}},"[":{"docs":{},"i":{"docs":{},"i":{"docs":{},"d":{"docs":{},"]":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.007590132827324478},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.004629629629629629}}}}}}},"_":{"docs":{},"i":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.003795066413662239},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}},")":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.003795066413662239},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}},"{":{"docs":{},"i":{"docs":{},"k":{"docs":{},"}":{"docs":{},"&":{"docs":{},":":{"docs":{},"=":{"docs":{},"q":{"docs":{},"_":{"docs":{},"{":{"docs":{},"i":{"docs":{},"k":{"docs":{},"}":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}}}}}}}}}},"]":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.003795066413662239},"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.004629629629629629}}}}}}}},"矩":{"docs":{},"阵":{"docs":{},"是":{"docs":{},"l":{"docs":{},"f":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}},"正":{"docs":{},"则":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}},",":{"docs":{"day02_推荐算法/06_BiasSVD算法实现.html":{"ref":"day02_推荐算法/06_BiasSVD算法实现.html","tf":0.0023148148148148147}}},"u":{"docs":{},"e":{"docs":{},"r":{"docs":{},"y":{"docs":{},".":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}},"a":{"docs":{},"l":{"docs":{},"i":{"docs":{},"f":{"docs":{},"i":{"docs":{"Hive&HBase/06_hbase数据模型.html":{"ref":"Hive&HBase/06_hbase数据模型.html","tf":0.041666666666666664}},"e":{"docs":{},"r":{"docs":{},")":{"docs":{"Hive&HBase/06_hbase数据模型.html":{"ref":"Hive&HBase/06_hbase数据模型.html","tf":0.041666666666666664}}}}}}}}},"n":{"docs":{},"t":{"docs":{},"i":{"docs":{},"l":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}},"e":{"docs":{},"s":{"docs":{},"[":{"0":{"docs":{},"]":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.0045662100456621}}}},"1":{"docs":{},"]":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.0045662100456621}}}},"docs":{}}}}}}}}},"u":{"docs":{},"x":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00851063829787234}}}}}},"代":{"docs":{},"表":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196},"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}},"一":{"docs":{},"个":{"docs":{},"连":{"docs":{},"续":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"流":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}}}}}}}}},"码":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577},"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}},"到":{"docs":{},"物":{"docs":{},"品":{"docs":{},"矩":{"docs":{},"阵":{"docs":{},"里":{"docs":{},"获":{"docs":{},"取":{"docs":{},"物":{"docs":{},"品":{"docs":{},"向":{"docs":{},"量":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}}}}}}}},"用":{"docs":{},"户":{"docs":{},"矩":{"docs":{},"阵":{"docs":{},"中":{"docs":{},"获":{"docs":{},"取":{"docs":{},"用":{"docs":{},"户":{"docs":{},"向":{"docs":{},"量":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}}}}}}}}}},"每":{"docs":{},"一":{"docs":{},"个":{"docs":{},"分":{"docs":{},"量":{"docs":{},"相":{"docs":{},"乘":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}}}},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"k":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}},"w":{"docs":{},"o":{"docs":{},"r":{"docs":{},"k":{"docs":{},"e":{"docs":{},"r":{"docs":{},"上":{"docs":{},"会":{"docs":{},"有":{"docs":{},"多":{"docs":{},"个":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"k":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}}}}}}}}}}}}}}},"条":{"docs":{},"数":{"docs":{},"据":{"docs":{},"都":{"docs":{},"会":{"docs":{},"去":{"docs":{},"查":{"docs":{},"询":{"docs":{},"i":{"docs":{},"p":{"docs":{},"表":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}}}}}}}}}},"个":{"docs":{},"应":{"docs":{},"用":{"docs":{},"程":{"docs":{},"序":{"docs":{},"对":{"docs":{},"应":{"docs":{},"一":{"docs":{},"个":{"docs":{},"：":{"docs":{},"m":{"docs":{},"r":{"docs":{},"、":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"，":{"docs":{},"负":{"docs":{},"责":{"docs":{},"应":{"docs":{},"用":{"docs":{},"程":{"docs":{},"序":{"docs":{},"的":{"docs":{},"管":{"docs":{},"理":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"人":{"docs":{},"分":{"docs":{},"得":{"docs":{},"一":{"docs":{},"堆":{"docs":{},"钞":{"docs":{},"票":{"docs":{},"，":{"docs":{},"数":{"docs":{},"出":{"docs":{},"各":{"docs":{},"种":{"docs":{},"面":{"docs":{},"值":{"docs":{},"有":{"docs":{},"多":{"docs":{},"少":{"docs":{},"张":{"docs":{"day03_Hadoop/ha3.2.html":{"ref":"day03_Hadoop/ha3.2.html","tf":0.02040816326530612}}}}}}}}}}}}}}}}}}}},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"n":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"写":{"docs":{},"完":{"docs":{},"一":{"docs":{},"个":{"docs":{},"块":{"docs":{},"后":{"docs":{},"，":{"docs":{},"会":{"docs":{},"返":{"docs":{},"回":{"docs":{},"确":{"docs":{},"认":{"docs":{},"信":{"docs":{},"息":{"docs":{},"。":{"docs":{"day03_Hadoop/ha4.2.html":{"ref":"day03_Hadoop/ha4.2.html","tf":0.0196078431372549}}}}}}}}}}}}}}}}}}}}}}}}}},"次":{"docs":{},"点":{"docs":{},"击":{"docs":{},",":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}},"秒":{"docs":{},"产":{"docs":{},"生":{"docs":{},"订":{"docs":{},"单":{"docs":{},"数":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}},"隔":{"docs":{},"g":{"docs":{},"秒":{"docs":{},"，":{"docs":{},"统":{"docs":{},"计":{"docs":{},"最":{"docs":{},"近":{"docs":{},"l":{"docs":{},"秒":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}}}}}}}},"求":{"docs":{},"和":{"docs":{"day02_推荐算法/05_LFM算法实现.html":{"ref":"day02_推荐算法/05_LFM算法实现.html","tf":0.0018975332068311196}}}},"冷":{"docs":{},"启":{"docs":{},"动":{"docs":{"day02_推荐算法/07_基于内容的推荐算法.html":{"ref":"day02_推荐算法/07_基于内容的推荐算法.html","tf":0.03125}},"问":{"docs":{},"题":{"docs":{"day02_推荐算法/07_基于内容的推荐算法.html":{"ref":"day02_推荐算法/07_基于内容的推荐算法.html","tf":0.03125}}}}}}},"画":{"docs":{},"像":{"docs":{},"构":{"docs":{},"建":{"docs":{},"。":{"docs":{},"顾":{"docs":{},"名":{"docs":{},"思":{"docs":{},"义":{"docs":{},"，":{"docs":{},"画":{"docs":{},"像":{"docs":{},"就":{"docs":{},"是":{"docs":{},"刻":{"docs":{},"画":{"docs":{},"物":{"docs":{},"品":{"docs":{},"或":{"docs":{},"用":{"docs":{},"户":{"docs":{},"的":{"docs":{},"特":{"docs":{},"征":{"docs":{},"。":{"docs":{},"本":{"docs":{},"质":{"docs":{},"上":{"docs":{},"就":{"docs":{},"是":{"docs":{},"给":{"docs":{},"用":{"docs":{},"户":{"docs":{},"或":{"docs":{},"物":{"docs":{},"品":{"docs":{},"贴":{"docs":{},"标":{"docs":{},"签":{"docs":{},"。":{"docs":{"day02_推荐算法/07_基于内容的推荐算法.html":{"ref":"day02_推荐算法/07_基于内容的推荐算法.html","tf":0.03125}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},",":{"2":{"docs":{},".":{"0":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}}},"docs":{}}},"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938},"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112},"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.011538461538461539},"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653}},"{":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.004405286343612335}},"s":{"docs":{},"=":{"docs":{},">":{"docs":{},"'":{"docs":{},"列":{"docs":{},"族":{"docs":{},"名":{"docs":{},":":{"docs":{},"列":{"docs":{},"名":{"docs":{},"'":{"docs":{},"}":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}}}}}}}}}}}}},"三":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},"方":{"docs":{},"库":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}},"采":{"docs":{},"集":{"docs":{},"数":{"docs":{},"据":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}},"会":{"docs":{},"根":{"docs":{},"据":{"docs":{},"提":{"docs":{},"供":{"docs":{},"的":{"docs":{},"函":{"docs":{},"数":{"docs":{},"对":{"docs":{},"指":{"docs":{},"定":{"docs":{},"序":{"docs":{},"列":{"docs":{},"做":{"docs":{},"映":{"docs":{},"射":{"docs":{},"。":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}}},"自":{"docs":{},"动":{"docs":{},"重":{"docs":{},"新":{"docs":{},"调":{"docs":{},"度":{"docs":{},"作":{"docs":{},"业":{"docs":{},"计":{"docs":{},"算":{"docs":{"day03_Hadoop/ha1.3.html":{"ref":"day03_Hadoop/ha1.3.html","tf":0.07692307692307693}}}}}}}}}},"创":{"docs":{},"建":{"docs":{},"一":{"docs":{},"个":{"docs":{},"与":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}}}}},"走":{"docs":{},"到":{"docs":{},"同":{"docs":{},"一":{"docs":{},"个":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}}}}}},"保":{"docs":{},"留":{"docs":{},"多":{"docs":{},"个":{"docs":{},"版":{"docs":{},"本":{"docs":{},"数":{"docs":{},"据":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}},"但":{"docs":{},"另":{"docs":{},"外":{"docs":{},"一":{"docs":{},"些":{"docs":{},"特":{"docs":{},"征":{"docs":{},"，":{"docs":{},"比":{"docs":{},"如":{"docs":{},"电":{"docs":{},"影":{"docs":{},"的":{"docs":{},"内":{"docs":{},"容":{"docs":{},"简":{"docs":{},"介":{"docs":{},"、":{"docs":{},"电":{"docs":{},"影":{"docs":{},"的":{"docs":{},"影":{"docs":{},"评":{"docs":{},"、":{"docs":{},"图":{"docs":{},"书":{"docs":{},"的":{"docs":{},"摘":{"docs":{},"要":{"docs":{},"等":{"docs":{},"文":{"docs":{},"本":{"docs":{},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"这":{"docs":{},"些":{"docs":{},"被":{"docs":{},"称":{"docs":{},"为":{"docs":{},"非":{"docs":{},"结":{"docs":{},"构":{"docs":{},"化":{"docs":{},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"首":{"docs":{},"先":{"docs":{},"他":{"docs":{},"们":{"docs":{},"本":{"docs":{},"应":{"docs":{},"该":{"docs":{},"也":{"docs":{},"属":{"docs":{},"于":{"docs":{},"物":{"docs":{},"品":{"docs":{},"的":{"docs":{},"一":{"docs":{},"个":{"docs":{},"特":{"docs":{},"征":{"docs":{},"标":{"docs":{},"签":{"docs":{},"，":{"docs":{},"但":{"docs":{},"是":{"docs":{},"这":{"docs":{},"样":{"docs":{},"的":{"docs":{},"特":{"docs":{},"征":{"docs":{},"标":{"docs":{},"签":{"docs":{},"进":{"docs":{},"行":{"docs":{},"量":{"docs":{},"化":{"docs":{},"时":{"docs":{},"，":{"docs":{},"也":{"docs":{},"就":{"docs":{},"是":{"docs":{},"计":{"docs":{},"算":{"docs":{},"它":{"docs":{},"的":{"docs":{},"特":{"docs":{},"征":{"docs":{},"向":{"docs":{},"量":{"docs":{},"时":{"docs":{},"是":{"docs":{},"很":{"docs":{},"难":{"docs":{},"去":{"docs":{},"定":{"docs":{},"义":{"docs":{},"的":{"docs":{},"。":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"是":{"docs":{},"需":{"docs":{},"要":{"docs":{},"利":{"docs":{},"用":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},"写":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{},"e":{"docs":{},"代":{"docs":{},"码":{"docs":{},",":{"docs":{},"m":{"docs":{},"r":{"docs":{},"j":{"docs":{},"o":{"docs":{},"b":{"docs":{},"是":{"docs":{},"很":{"docs":{},"好":{"docs":{},"的":{"docs":{},"选":{"docs":{},"择":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"关":{"docs":{},"一":{"docs":{},"个":{"docs":{},"再":{"docs":{},"开":{"docs":{},"下":{"docs":{},"一":{"docs":{},"个":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}}}}}}}}}},"不":{"docs":{},"能":{"docs":{},"创":{"docs":{},"建":{"docs":{},"删":{"docs":{},"除":{"docs":{},"表":{"docs":{},",":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}}}}},"使":{"docs":{},"用":{"docs":{},"方":{"docs":{},"式":{"docs":{},"比":{"docs":{},"t":{"docs":{},"h":{"docs":{},"r":{"docs":{},"i":{"docs":{},"f":{"docs":{},"t":{"docs":{},"简":{"docs":{},"单":{"docs":{},",":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}}}}}}}}}}}},"在":{"docs":{},"企":{"docs":{},"业":{"docs":{},"中":{"docs":{},"存":{"docs":{},"在":{"docs":{},"很":{"docs":{},"多":{"docs":{},"实":{"docs":{},"时":{"docs":{},"性":{"docs":{},"处":{"docs":{},"理":{"docs":{},"的":{"docs":{},"需":{"docs":{},"求":{"docs":{},"，":{"docs":{},"例":{"docs":{},"如":{"docs":{},"：":{"docs":{},"双":{"docs":{},"十":{"docs":{},"一":{"docs":{},"的":{"docs":{},"京":{"docs":{},"东":{"docs":{},"阿":{"docs":{},"里":{"docs":{},"，":{"docs":{},"通":{"docs":{},"常":{"docs":{},"会":{"docs":{},"做":{"docs":{},"一":{"docs":{},"个":{"docs":{},"实":{"docs":{},"时":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"大":{"docs":{},"屏":{"docs":{},"，":{"docs":{},"显":{"docs":{},"示":{"docs":{},"实":{"docs":{},"时":{"docs":{},"订":{"docs":{},"单":{"docs":{},"。":{"docs":{},"这":{"docs":{},"种":{"docs":{},"情":{"docs":{},"况":{"docs":{},"下":{"docs":{},"，":{"docs":{},"对":{"docs":{},"数":{"docs":{},"据":{"docs":{},"实":{"docs":{},"时":{"docs":{},"性":{"docs":{},"要":{"docs":{},"求":{"docs":{},"较":{"docs":{},"高":{"docs":{},"，":{"docs":{},"仅":{"docs":{},"仅":{"docs":{},"能":{"docs":{},"够":{"docs":{},"容":{"docs":{},"忍":{"docs":{},"到":{"docs":{},"延":{"docs":{},"迟":{"1":{"docs":{},"分":{"docs":{},"钟":{"docs":{},"或":{"docs":{},"几":{"docs":{},"秒":{"docs":{},"钟":{"docs":{},"。":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}}}}}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"好":{"docs":{},"在":{"docs":{},"我":{"docs":{},"们":{"docs":{},"训":{"docs":{},"练":{"docs":{},"a":{"docs":{},"l":{"docs":{},"s":{"docs":{},"模":{"docs":{},"型":{"docs":{},"时":{"docs":{},"，":{"docs":{},"不":{"docs":{},"需":{"docs":{},"要":{"docs":{},"转":{"docs":{},"换":{"docs":{},"为":{"docs":{},"u":{"docs":{},"s":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}},"由":{"docs":{},"于":{"docs":{},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"i":{"docs":{},"d":{"docs":{},"字":{"docs":{},"段":{"docs":{},"过":{"docs":{},"多":{"docs":{},"，":{"docs":{},"这":{"docs":{},"里":{"docs":{},"运":{"docs":{},"算":{"docs":{},"量":{"docs":{},"比":{"docs":{},"很":{"docs":{},"大":{"docs":{},"，":{"docs":{},"机":{"docs":{},"器":{"docs":{},"内":{"docs":{},"存":{"docs":{},"要":{"docs":{},"求":{"docs":{},"很":{"docs":{},"高":{"docs":{},"才":{"docs":{},"能":{"docs":{},"执":{"docs":{},"行":{"docs":{},"，":{"docs":{},"否":{"docs":{},"则":{"docs":{},"无":{"docs":{},"法":{"docs":{},"完":{"docs":{},"成":{"docs":{},"任":{"docs":{},"务":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"该":{"docs":{},"方":{"docs":{},"法":{"docs":{},"其":{"docs":{},"实":{"docs":{},"指":{"docs":{},"标":{"docs":{},"不":{"docs":{},"治":{"docs":{},"本":{"docs":{},"，":{"docs":{},"因":{"docs":{},"为":{"docs":{},"无":{"docs":{},"法":{"docs":{},"防":{"docs":{},"止":{"docs":{},"内":{"docs":{},"存":{"docs":{},"溢":{"docs":{},"出":{"docs":{},"，":{"docs":{},"所":{"docs":{},"以":{"docs":{},"还":{"docs":{},"是":{"docs":{},"会":{"docs":{},"报":{"docs":{},"错":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"这":{"docs":{},"里":{"docs":{},"我":{"docs":{},"们":{"docs":{},"将":{"docs":{},"使":{"docs":{},"用":{"docs":{},"的":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"的":{"docs":{},"a":{"docs":{},"l":{"docs":{},"s":{"docs":{},"模":{"docs":{},"型":{"docs":{},"进":{"docs":{},"行":{"docs":{},"c":{"docs":{},"f":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"，":{"docs":{},"因":{"docs":{},"此":{"docs":{},"注":{"docs":{},"意":{"docs":{},"这":{"docs":{},"里":{"docs":{},"数":{"docs":{},"据":{"docs":{},"输":{"docs":{},"入":{"docs":{},"不":{"docs":{},"需":{"docs":{},"要":{"docs":{},"提":{"docs":{},"前":{"docs":{},"转":{"docs":{},"换":{"docs":{},"为":{"docs":{},"矩":{"docs":{},"阵":{"docs":{},"，":{"docs":{},"直":{"docs":{},"接":{"docs":{},"是":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"根":{"docs":{},"据":{"docs":{},"我":{"docs":{},"们":{"docs":{},"的":{"docs":{},"经":{"docs":{},"验":{"docs":{},"，":{"docs":{},"我":{"docs":{},"们":{"docs":{},"的":{"docs":{},"广":{"docs":{},"告":{"docs":{},"推":{"docs":{},"荐":{"docs":{},"其":{"docs":{},"实":{"docs":{},"和":{"docs":{},"用":{"docs":{},"户":{"docs":{},"的":{"docs":{},"消":{"docs":{},"费":{"docs":{},"水":{"docs":{},"平":{"docs":{},"、":{"docs":{},"用":{"docs":{},"户":{"docs":{},"所":{"docs":{},"在":{"docs":{},"城":{"docs":{},"市":{"docs":{},"等":{"docs":{},"级":{"docs":{},"都":{"docs":{},"有":{"docs":{},"比":{"docs":{},"较":{"docs":{},"大":{"docs":{},"的":{"docs":{},"关":{"docs":{},"联":{"docs":{},"，":{"docs":{},"因":{"docs":{},"此":{"docs":{},"在":{"docs":{},"这":{"docs":{},"里":{"docs":{},"p":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"、":{"docs":{},"n":{"docs":{},"e":{"docs":{},"w":{"docs":{},"_":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"c":{"docs":{},"l":{"docs":{},"a":{"docs":{},"s":{"docs":{},"s":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"都":{"docs":{},"是":{"docs":{},"比":{"docs":{},"较":{"docs":{},"重":{"docs":{},"要":{"docs":{},"的":{"docs":{},"特":{"docs":{},"征":{"docs":{},"，":{"docs":{},"我":{"docs":{},"们":{"docs":{},"不":{"docs":{},"考":{"docs":{},"虑":{"docs":{},"舍":{"docs":{},"弃":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"倒":{"docs":{},"排":{"docs":{},"索":{"docs":{},"引":{"docs":{},"介":{"docs":{},"绍":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}},"前":{"docs":{},"面":{"docs":{},"提":{"docs":{},"到":{"docs":{},"，":{"docs":{},"物":{"docs":{},"品":{"docs":{},"画":{"docs":{},"像":{"docs":{},"的":{"docs":{},"特":{"docs":{},"征":{"docs":{},"标":{"docs":{},"签":{"docs":{},"主":{"docs":{},"要":{"docs":{},"都":{"docs":{},"是":{"docs":{},"指":{"docs":{},"的":{"docs":{},"如":{"docs":{},"电":{"docs":{},"影":{"docs":{},"的":{"docs":{},"导":{"docs":{},"演":{"docs":{},"、":{"docs":{},"演":{"docs":{},"员":{"docs":{},"、":{"docs":{},"图":{"docs":{},"书":{"docs":{},"的":{"docs":{},"作":{"docs":{},"者":{"docs":{},"、":{"docs":{},"出":{"docs":{},"版":{"docs":{},"社":{"docs":{},"等":{"docs":{},"结":{"docs":{},"构":{"docs":{},"话":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"也":{"docs":{},"就":{"docs":{},"是":{"docs":{},"他":{"docs":{},"们":{"docs":{},"的":{"docs":{},"特":{"docs":{},"征":{"docs":{},"提":{"docs":{},"取":{"docs":{},"，":{"docs":{},"尤":{"docs":{},"其":{"docs":{},"是":{"docs":{},"体":{"docs":{},"征":{"docs":{},"向":{"docs":{},"量":{"docs":{},"的":{"docs":{},"计":{"docs":{},"算":{"docs":{},"是":{"docs":{},"比":{"docs":{},"较":{"docs":{},"简":{"docs":{},"单":{"docs":{},"的":{"docs":{},"，":{"docs":{},"如":{"docs":{},"直":{"docs":{},"接":{"docs":{},"给":{"docs":{},"作":{"docs":{},"品":{"docs":{},"的":{"docs":{},"分":{"docs":{},"类":{"docs":{},"定":{"docs":{},"义":{"0":{"docs":{},"或":{"docs":{},"者":{"1":{"docs":{},"的":{"docs":{},"状":{"docs":{},"态":{"docs":{},"。":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}},"docs":{}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"我":{"docs":{},"们":{"docs":{},"处":{"docs":{},"理":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"实":{"docs":{},"际":{"docs":{},"上":{"docs":{},"都":{"docs":{},"是":{"docs":{},"已":{"docs":{},"经":{"docs":{},"被":{"docs":{},"处":{"docs":{},"理":{"docs":{},"好":{"docs":{},"的":{"docs":{},"规":{"docs":{},"整":{"docs":{},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"但":{"docs":{},"是":{"docs":{},"在":{"docs":{},"大":{"docs":{},"数":{"docs":{},"据":{"docs":{},"整":{"docs":{},"个":{"docs":{},"生":{"docs":{},"产":{"docs":{},"过":{"docs":{},"程":{"docs":{},"中":{"docs":{},"，":{"docs":{},"需":{"docs":{},"要":{"docs":{},"先":{"docs":{},"对":{"docs":{},"数":{"docs":{},"据":{"docs":{},"进":{"docs":{},"行":{"docs":{},"数":{"docs":{},"据":{"docs":{},"清":{"docs":{},"洗":{"docs":{},"，":{"docs":{},"将":{"docs":{},"杂":{"docs":{},"乱":{"docs":{},"无":{"docs":{},"章":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"整":{"docs":{},"理":{"docs":{},"为":{"docs":{},"符":{"docs":{},"合":{"docs":{},"后":{"docs":{},"面":{"docs":{},"处":{"docs":{},"理":{"docs":{},"要":{"docs":{},"求":{"docs":{},"的":{"docs":{},"规":{"docs":{},"整":{"docs":{},"数":{"docs":{},"据":{"docs":{},"。":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"分":{"docs":{},"析":{"docs":{},"的":{"docs":{},"以":{"docs":{},"下":{"docs":{},"几":{"docs":{},"个":{"docs":{},"分":{"docs":{},"类":{"docs":{},"特":{"docs":{},"征":{"docs":{},"值":{"docs":{},"个":{"docs":{},"数":{"docs":{},"情":{"docs":{},"况":{"docs":{},":":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}},"缀":{"docs":{},"过":{"docs":{},"滤":{"docs":{},"器":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}},"七":{"docs":{},"天":{"docs":{},"为":{"docs":{},"训":{"docs":{},"练":{"docs":{},"数":{"docs":{},"据":{"docs":{},"、":{"docs":{},"最":{"docs":{},"后":{"docs":{},"一":{"docs":{},"天":{"docs":{},"为":{"docs":{},"测":{"docs":{},"试":{"docs":{},"数":{"docs":{},"据":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}},"匿":{"docs":{},"名":{"docs":{},"函":{"docs":{},"数":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938},"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}}}},"安":{"docs":{},"装":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938},"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}},"前":{"docs":{},"需":{"docs":{},"要":{"docs":{},"安":{"docs":{},"装":{"docs":{},"好":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}},"部":{"docs":{},"署":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196},"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}},"及":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"n":{"docs":{},"d":{"docs":{},"a":{"docs":{},"l":{"docs":{},"o":{"docs":{},"n":{"docs":{},"e":{"docs":{},"模":{"docs":{},"式":{"docs":{},"介":{"docs":{},"绍":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}}}}}}}}}}}}}}}}},"h":{"docs":{},"a":{"docs":{},"p":{"docs":{},"p":{"docs":{},"i":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}}}}},"并":{"docs":{},"将":{"docs":{},"电":{"docs":{},"影":{"docs":{},"的":{"docs":{},"分":{"docs":{},"类":{"docs":{},"词":{"docs":{},"直":{"docs":{},"接":{"docs":{},"作":{"docs":{},"为":{"docs":{},"每":{"docs":{},"部":{"docs":{},"电":{"docs":{},"影":{"docs":{},"的":{"docs":{},"画":{"docs":{},"像":{"docs":{},"标":{"docs":{},"签":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}}}}}},"文":{"docs":{},"件":{"docs":{},"内":{"docs":{},"容":{"docs":{},"映":{"docs":{},"射":{"docs":{},"到":{"docs":{},"表":{"docs":{},"中":{"docs":{},"。":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}}}}}}},"在":{"docs":{},"多":{"docs":{},"台":{"docs":{},"机":{"docs":{},"器":{"docs":{},"上":{"docs":{},"保":{"docs":{},"存":{"docs":{},"多":{"docs":{},"个":{"docs":{},"副":{"docs":{},"本":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282}}}}}}}}}}}}}},"解":{"docs":{},"压":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}},"存":{"docs":{},"储":{"docs":{},"索":{"docs":{},"引":{"docs":{},",":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}},"向":{"docs":{},"h":{"docs":{},"m":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"汇":{"docs":{},"报":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}}}}}},"行":{"docs":{},"计":{"docs":{},"算":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}},"描":{"docs":{},"述":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938},"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}},"数":{"docs":{},"据":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{"day03_Hadoop/ha2.4.html":{"ref":"day03_Hadoop/ha2.4.html","tf":0.03333333333333333}}}}}}}}},"放":{"docs":{},"到":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428}},"一":{"docs":{},"个":{"docs":{},"t":{"docs":{},"u":{"docs":{},"p":{"docs":{},"l":{"docs":{},"e":{"docs":{},"中":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},"中":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"k":{"docs":{},"s":{"docs":{},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"l":{"docs":{},"e":{"docs":{},"r":{"docs":{},"中":{"docs":{},"。":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}}}}}}}}}}}}}}}},"入":{"docs":{},"购":{"docs":{},"物":{"docs":{},"车":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}},"文":{"docs":{},"本":{"docs":{},"特":{"docs":{},"征":{"docs":{},"提":{"docs":{},"取":{"docs":{},"有":{"docs":{},"两":{"docs":{},"个":{"docs":{},"非":{"docs":{},"常":{"docs":{},"重":{"docs":{},"要":{"docs":{},"的":{"docs":{},"模":{"docs":{},"型":{"docs":{},"：":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}}},"件":{"docs":{},"内":{"docs":{},"容":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625}}}},"名":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625}}},"大":{"docs":{},"小":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625}}}},"夹":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625}}}},"章":{"docs":{},"数":{"docs":{},"据":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}},"表":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}},"档":{"docs":{},"地":{"docs":{},"址":{"docs":{},"：":{"docs":{},"h":{"docs":{},"t":{"docs":{},"t":{"docs":{},"p":{"docs":{},"s":{"docs":{},":":{"docs":{},"/":{"docs":{},"/":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},".":{"docs":{},"a":{"docs":{},"p":{"docs":{},"a":{"docs":{},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},".":{"docs":{},"o":{"docs":{},"r":{"docs":{},"g":{"docs":{},"/":{"docs":{},"d":{"docs":{},"o":{"docs":{},"c":{"docs":{},"s":{"docs":{},"/":{"2":{"docs":{},".":{"2":{"docs":{},".":{"2":{"docs":{},"/":{"docs":{},"a":{"docs":{},"p":{"docs":{},"i":{"docs":{},"/":{"docs":{},"p":{"docs":{},"y":{"docs":{},"t":{"docs":{},"h":{"docs":{},"o":{"docs":{},"n":{"docs":{},"/":{"docs":{},"p":{"docs":{},"y":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},".":{"docs":{},"m":{"docs":{},"l":{"docs":{},".":{"docs":{},"h":{"docs":{},"t":{"docs":{},"m":{"docs":{},"l":{"docs":{},"?":{"docs":{},"h":{"docs":{},"i":{"docs":{},"g":{"docs":{},"h":{"docs":{},"l":{"docs":{},"i":{"docs":{},"g":{"docs":{},"h":{"docs":{},"t":{"docs":{},"=":{"docs":{},"v":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{},"s":{"docs":{},"#":{"docs":{},"m":{"docs":{},"o":{"docs":{},"d":{"docs":{},"u":{"docs":{},"l":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{}}},"docs":{}}},"docs":{},"l":{"docs":{},"a":{"docs":{},"t":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{},"/":{"docs":{},"a":{"docs":{},"p":{"docs":{},"i":{"docs":{},"/":{"docs":{},"p":{"docs":{},"y":{"docs":{},"t":{"docs":{},"h":{"docs":{},"o":{"docs":{},"n":{"docs":{},"/":{"docs":{},"p":{"docs":{},"y":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},".":{"docs":{},"m":{"docs":{},"l":{"docs":{},".":{"docs":{},"h":{"docs":{},"t":{"docs":{},"m":{"docs":{},"l":{"docs":{},"?":{"docs":{},"h":{"docs":{},"i":{"docs":{},"g":{"docs":{},"h":{"docs":{},"l":{"docs":{},"i":{"docs":{},"g":{"docs":{},"h":{"docs":{},"t":{"docs":{},"=":{"docs":{},"v":{"docs":{},"e":{"docs":{},"c":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{},"s":{"docs":{},"#":{"docs":{},"m":{"docs":{},"o":{"docs":{},"d":{"docs":{},"u":{"docs":{},"l":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"权":{"docs":{},"重":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}},"限":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.0125}}}},"第":{"docs":{},"一":{"docs":{},"个":{"docs":{},"参":{"docs":{},"数":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}},"步":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676},"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}},"行":{"docs":{},"数":{"docs":{},"据":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}},"二":{"docs":{},"步":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}},"个":{"docs":{},"特":{"docs":{},"征":{"docs":{},"是":{"docs":{},"分":{"docs":{},"类":{"docs":{},"的":{"docs":{},":":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}},"而":{"docs":{},"倒":{"docs":{},"排":{"docs":{},"索":{"docs":{},"引":{"docs":{},"就":{"docs":{},"是":{"docs":{},"用":{"docs":{},"物":{"docs":{},"品":{"docs":{},"的":{"docs":{},"其":{"docs":{},"他":{"docs":{},"数":{"docs":{},"据":{"docs":{},"作":{"docs":{},"为":{"docs":{},"索":{"docs":{},"引":{"docs":{},"，":{"docs":{},"去":{"docs":{},"提":{"docs":{},"取":{"docs":{},"它":{"docs":{},"们":{"docs":{},"对":{"docs":{},"应":{"docs":{},"的":{"docs":{},"物":{"docs":{},"品":{"docs":{},"的":{"docs":{},"i":{"docs":{},"d":{"docs":{},"列":{"docs":{},"表":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"只":{"docs":{},"选":{"docs":{},"取":{"docs":{},"p":{"docs":{},"r":{"docs":{},"i":{"docs":{},"c":{"docs":{},"e":{"docs":{},"作":{"docs":{},"为":{"docs":{},"特":{"docs":{},"征":{"docs":{},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"因":{"docs":{},"为":{"docs":{},"价":{"docs":{},"格":{"docs":{},"本":{"docs":{},"身":{"docs":{},"是":{"docs":{},"一":{"docs":{},"个":{"docs":{},"统":{"docs":{},"计":{"docs":{},"类":{"docs":{},"型":{"docs":{},"连":{"docs":{},"续":{"docs":{},"数":{"docs":{},"值":{"docs":{},"型":{"docs":{},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"且":{"docs":{},"能":{"docs":{},"很":{"docs":{},"好":{"docs":{},"的":{"docs":{},"体":{"docs":{},"现":{"docs":{},"广":{"docs":{},"告":{"docs":{},"的":{"docs":{},"价":{"docs":{},"值":{"docs":{},"属":{"docs":{},"性":{"docs":{},"特":{"docs":{},"征":{"docs":{},"，":{"docs":{},"通":{"docs":{},"常":{"docs":{},"也":{"docs":{},"不":{"docs":{},"需":{"docs":{},"要":{"docs":{},"做":{"docs":{},"其":{"docs":{},"他":{"docs":{},"处":{"docs":{},"理":{"docs":{},"(":{"docs":{},"离":{"docs":{},"散":{"docs":{},"化":{"docs":{},"、":{"docs":{},"归":{"docs":{},"一":{"docs":{},"化":{"docs":{},"、":{"docs":{},"标":{"docs":{},"准":{"docs":{},"化":{"docs":{},"等":{"docs":{},")":{"docs":{},"，":{"docs":{},"所":{"docs":{},"以":{"docs":{},"这":{"docs":{},"里":{"docs":{},"直":{"docs":{},"接":{"docs":{},"将":{"docs":{},"当":{"docs":{},"做":{"docs":{},"特":{"docs":{},"征":{"docs":{},"数":{"docs":{},"据":{"docs":{},"来":{"docs":{},"使":{"docs":{},"用":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"经":{"docs":{},"过":{"docs":{},"热":{"docs":{},"独":{"docs":{},"编":{"docs":{},"码":{"docs":{},"，":{"docs":{},"数":{"docs":{},"据":{"docs":{},"会":{"docs":{},"变":{"docs":{},"成":{"docs":{},"稀":{"docs":{},"疏":{"docs":{},"的":{"docs":{},"，":{"docs":{},"方":{"docs":{},"便":{"docs":{},"分":{"docs":{},"类":{"docs":{},"器":{"docs":{},"处":{"docs":{},"理":{"docs":{},"：":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}},"自":{"docs":{},"然":{"docs":{},"语":{"docs":{},"言":{"docs":{},"处":{"docs":{},"理":{"docs":{},"利":{"docs":{},"器":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}},"那":{"docs":{},"么":{"docs":{},"最":{"docs":{},"终":{"docs":{},"得":{"docs":{},"出":{"docs":{},"预":{"docs":{},"测":{"docs":{},"值":{"docs":{},"后":{"docs":{},"，":{"docs":{},"需":{"docs":{},"要":{"docs":{},"对":{"docs":{},"应":{"docs":{},"+":{"1":{"docs":{},"才":{"docs":{},"能":{"docs":{},"还":{"docs":{},"原":{"docs":{},"回":{"docs":{},"来":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}},"docs":{}}}}}}}}}}}}}}}}}},"定":{"docs":{},"义":{"docs":{},"函":{"docs":{},"数":{"docs":{},"和":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}},"的":{"docs":{},"汇":{"docs":{},"总":{"docs":{},"方":{"docs":{},"法":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.006993006993006993}}}}}}}}}},"词":{"docs":{},"袋":{"docs":{},"模":{"docs":{},"型":{"docs":{},"（":{"docs":{},"b":{"docs":{},"o":{"docs":{},"w":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}},"：":{"docs":{},"在":{"docs":{},"词":{"docs":{},"集":{"docs":{},"的":{"docs":{},"基":{"docs":{},"础":{"docs":{},"上":{"docs":{},"如":{"docs":{},"果":{"docs":{},"一":{"docs":{},"个":{"docs":{},"单":{"docs":{},"词":{"docs":{},"在":{"docs":{},"文":{"docs":{},"档":{"docs":{},"中":{"docs":{},"出":{"docs":{},"现":{"docs":{},"不":{"docs":{},"止":{"docs":{},"一":{"docs":{},"次":{"docs":{},"，":{"docs":{},"统":{"docs":{},"计":{"docs":{},"其":{"docs":{},"出":{"docs":{},"现":{"docs":{},"的":{"docs":{},"次":{"docs":{},"数":{"docs":{},"（":{"docs":{},"频":{"docs":{},"数":{"docs":{},"）":{"docs":{},"。":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"集":{"docs":{},"模":{"docs":{},"型":{"docs":{},"：":{"docs":{},"单":{"docs":{},"词":{"docs":{},"构":{"docs":{},"成":{"docs":{},"的":{"docs":{},"集":{"docs":{},"合":{"docs":{},"，":{"docs":{},"集":{"docs":{},"合":{"docs":{},"自":{"docs":{},"然":{"docs":{},"每":{"docs":{},"个":{"docs":{},"元":{"docs":{},"素":{"docs":{},"都":{"docs":{},"只":{"docs":{},"有":{"docs":{},"一":{"docs":{},"个":{"docs":{},"，":{"docs":{},"也":{"docs":{},"即":{"docs":{},"词":{"docs":{},"集":{"docs":{},"中":{"docs":{},"的":{"docs":{},"每":{"docs":{},"个":{"docs":{},"单":{"docs":{},"词":{"docs":{},"都":{"docs":{},"只":{"docs":{},"有":{"docs":{},"一":{"docs":{},"个":{"docs":{},"。":{"docs":{"day02_推荐算法/08_物品画像.html":{"ref":"day02_推荐算法/08_物品画像.html","tf":0.0019455252918287938}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"频":{"docs":{},"统":{"docs":{},"计":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}},"案":{"docs":{},"例":{"docs":{"day03_Hadoop/ha3.2.html":{"ref":"day03_Hadoop/ha3.2.html","tf":0.02040816326530612}}}}}}}},"中":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}},"的":{"docs":{},"函":{"docs":{},"数":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}},"元":{"docs":{},"数":{"docs":{},"据":{"docs":{},"包":{"docs":{},"括":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}},"创":{"docs":{},"建":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625}}}},"。":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}},"包":{"docs":{},"含":{"docs":{},"以":{"docs":{},"下":{"docs":{},"数":{"docs":{},"据":{"docs":{},"模":{"docs":{},"型":{"docs":{},"：":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}},"所":{"docs":{},"有":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"都":{"docs":{},"存":{"docs":{},"储":{"docs":{},"在":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}},"表":{"docs":{},"现":{"docs":{},"为":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.006172839506172839}},"同":{"docs":{},"一":{"docs":{},"个":{"docs":{},"表":{"docs":{},"目":{"docs":{},"录":{"docs":{},"下":{"docs":{},"根":{"docs":{},"据":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}}},"所":{"docs":{},"属":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}},"，":{"docs":{},"并":{"docs":{},"在":{"docs":{},"随":{"docs":{},"后":{"docs":{},"由":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}},"没":{"docs":{},"有":{"docs":{},"专":{"docs":{},"门":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"存":{"docs":{},"储":{"docs":{},"格":{"docs":{},"式":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}}}}},"位":{"docs":{},"数":{"docs":{},"绝":{"docs":{},"对":{"docs":{},"偏":{"docs":{},"差":{"docs":{},"去":{"docs":{},"极":{"docs":{},"值":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}}},"出":{"docs":{},"现":{"docs":{},"次":{"docs":{},"数":{"docs":{},"最":{"docs":{},"多":{"docs":{},"的":{"docs":{},"词":{"docs":{},"权":{"docs":{},"重":{"docs":{},"为":{"1":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}},"docs":{}}}}}}}}}},"的":{"docs":{},"次":{"docs":{},"数":{"docs":{"day02_推荐算法/09_用户画像.html":{"ref":"day02_推荐算法/09_用户画像.html","tf":0.005263157894736842}}}}}}},"二":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}},"者":{"docs":{},"都":{"docs":{},"考":{"docs":{},"虑":{"docs":{"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}}}}}}},"娱":{"docs":{},"乐":{"docs":{},"(":{"docs":{},"王":{"docs":{},"思":{"docs":{},"聪":{"docs":{},")":{"docs":{"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}}}}}}}}},"王":{"docs":{},"思":{"docs":{},"聪":{"docs":{"day02_推荐算法/10_TOPN用户推荐.html":{"ref":"day02_推荐算法/10_TOPN用户推荐.html","tf":0.011235955056179775}}}}},"仅":{"docs":{},"限":{"docs":{},"于":{"docs":{},"数":{"docs":{},"据":{"docs":{},"库":{"docs":{},",":{"docs":{},"受":{"docs":{},"数":{"docs":{},"据":{"docs":{},"量":{"docs":{},"和":{"docs":{},"计":{"docs":{},"算":{"docs":{},"能":{"docs":{},"力":{"docs":{},"的":{"docs":{},"限":{"docs":{},"制":{"docs":{},",":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}}}}}}}}}}}}}}}}}},"会":{"docs":{},"删":{"docs":{},"除":{"docs":{},"元":{"docs":{},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},"上":{"docs":{},"的":{"docs":{},"文":{"docs":{},"件":{"docs":{},"并":{"docs":{},"不":{"docs":{},"会":{"docs":{},"被":{"docs":{},"删":{"docs":{},"除":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}}}}}}}}}}}}}}}}}},"作":{"docs":{},"者":{"docs":{},"：":{"docs":{},"d":{"docs":{},"o":{"docs":{},"u":{"docs":{},"g":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}}}}},"用":{"docs":{},"：":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}},"允":{"docs":{},"许":{"docs":{},"使":{"docs":{},"用":{"docs":{},"简":{"docs":{},"单":{"docs":{},"的":{"docs":{},"编":{"docs":{},"程":{"docs":{},"模":{"docs":{},"型":{"docs":{},"跨":{"docs":{},"计":{"docs":{},"算":{"docs":{},"机":{"docs":{},"集":{"docs":{},"群":{"docs":{},"分":{"docs":{},"布":{"docs":{},"式":{"docs":{},"处":{"docs":{},"理":{"docs":{},"大":{"docs":{},"型":{"docs":{},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}}}}}}}}}}}}}}}}}}}}}}}},"远":{"docs":{},"程":{"docs":{},"客":{"docs":{},"户":{"docs":{},"端":{"docs":{},"使":{"docs":{},"用":{"docs":{},"多":{"docs":{},"种":{"docs":{},"编":{"docs":{},"程":{"docs":{},"语":{"docs":{},"言":{"docs":{},"如":{"docs":{},"j":{"docs":{},"a":{"docs":{},"v":{"docs":{},"a":{"docs":{},"、":{"docs":{},"p":{"docs":{},"y":{"docs":{},"t":{"docs":{},"h":{"docs":{},"o":{"docs":{},"n":{"docs":{},"向":{"docs":{},"h":{"docs":{},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{},"提":{"docs":{},"交":{"docs":{},"请":{"docs":{},"求":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"啤":{"docs":{},"酒":{"docs":{},"尿":{"docs":{},"不":{"docs":{},"湿":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}}}},"天":{"docs":{},"猫":{"docs":{},"精":{"docs":{},"灵":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}}},"小":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}},"爱":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}},"文":{"docs":{},"件":{"docs":{},"存":{"docs":{},"储":{"docs":{"day03_Hadoop/ha2.4.html":{"ref":"day03_Hadoop/ha2.4.html","tf":0.03333333333333333}}}}}}},"广":{"docs":{},"义":{"docs":{},"大":{"docs":{},"数":{"docs":{},"据":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}},"的":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{"day03_Hadoop/ha4.1.html":{"ref":"day03_Hadoop/ha4.1.html","tf":0.023809523809523808}},"：":{"docs":{},"指":{"docs":{},"的":{"docs":{},"是":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},"生":{"docs":{},"态":{"docs":{},"系":{"docs":{},"统":{"docs":{},"，":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},"生":{"docs":{},"态":{"docs":{},"系":{"docs":{},"统":{"docs":{},"是":{"docs":{},"一":{"docs":{},"个":{"docs":{},"很":{"docs":{},"庞":{"docs":{},"大":{"docs":{},"的":{"docs":{},"概":{"docs":{},"念":{"docs":{},"，":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},"是":{"docs":{},"其":{"docs":{},"中":{"docs":{},"最":{"docs":{},"重":{"docs":{},"要":{"docs":{},"最":{"docs":{},"基":{"docs":{},"础":{"docs":{},"的":{"docs":{},"一":{"docs":{},"个":{"docs":{},"部":{"docs":{},"分":{"docs":{},"，":{"docs":{},"生":{"docs":{},"态":{"docs":{},"系":{"docs":{},"统":{"docs":{},"中":{"docs":{},"每":{"docs":{},"一":{"docs":{},"子":{"docs":{},"系":{"docs":{},"统":{"docs":{},"只":{"docs":{},"解":{"docs":{},"决":{"docs":{},"某":{"docs":{},"一":{"docs":{},"个":{"docs":{},"特":{"docs":{},"定":{"docs":{},"的":{"docs":{},"问":{"docs":{},"题":{"docs":{},"域":{"docs":{},"（":{"docs":{},"甚":{"docs":{},"至":{"docs":{},"可":{"docs":{},"能":{"docs":{},"更":{"docs":{},"窄":{"docs":{},"）":{"docs":{},"，":{"docs":{},"不":{"docs":{},"搞":{"docs":{},"统":{"docs":{},"一":{"docs":{},"型":{"docs":{},"的":{"docs":{},"全":{"docs":{},"能":{"docs":{},"系":{"docs":{},"统":{"docs":{},"，":{"docs":{},"而":{"docs":{},"是":{"docs":{},"小":{"docs":{},"而":{"docs":{},"精":{"docs":{},"的":{"docs":{},"多":{"docs":{},"个":{"docs":{},"小":{"docs":{},"系":{"docs":{},"统":{"docs":{},"；":{"docs":{"day03_Hadoop/ha4.1.html":{"ref":"day03_Hadoop/ha4.1.html","tf":0.023809523809523808}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"播":{"docs":{},"变":{"docs":{},"量":{"docs":{},"的":{"docs":{},"使":{"docs":{},"用":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}}}},"告":{"docs":{},"/":{"docs":{},"用":{"docs":{},"户":{"docs":{},"特":{"docs":{},"征":{"docs":{},"(":{"docs":{},"缓":{"docs":{},"存":{"docs":{},")":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}}}}},"基":{"docs":{},"本":{"docs":{},"信":{"docs":{},"息":{"docs":{},"表":{"docs":{},"a":{"docs":{},"d":{"docs":{},"_":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}}}}}}}}}},"推":{"docs":{},"荐":{"docs":{},"结":{"docs":{},"果":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}},"特":{"docs":{},"征":{"docs":{},"数":{"docs":{},"据":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}},"i":{"docs":{},"d":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"展":{"docs":{},"示":{"docs":{},"位":{"docs":{},"p":{"docs":{},"i":{"docs":{},"d":{"docs":{},"情":{"docs":{},"况":{"docs":{},"：":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}},"点":{"docs":{},"击":{"docs":{},"数":{"docs":{},"据":{"docs":{},"情":{"docs":{},"况":{"docs":{},"c":{"docs":{},"l":{"docs":{},"k":{"docs":{},"：":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}},"价":{"docs":{},"格":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"资":{"docs":{},"源":{"docs":{},"位":{"docs":{},"，":{"docs":{},"属":{"docs":{},"于":{"docs":{},"场":{"docs":{},"景":{"docs":{},"特":{"docs":{},"征":{"docs":{},"，":{"docs":{},"也":{"docs":{},"就":{"docs":{},"是":{"docs":{},"说":{"docs":{},"，":{"docs":{},"每":{"docs":{},"一":{"docs":{},"种":{"docs":{},"广":{"docs":{},"告":{"docs":{},"通":{"docs":{},"常":{"docs":{},"是":{"docs":{},"可":{"docs":{},"以":{"docs":{},"防":{"docs":{},"止":{"docs":{},"在":{"docs":{},"多":{"docs":{},"种":{"docs":{},"资":{"docs":{},"源":{"docs":{},"外":{"docs":{},"下":{"docs":{},"的":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"曾":{"docs":{},"经":{"docs":{},"进":{"docs":{},"行":{"docs":{},"数":{"docs":{},"分":{"docs":{},"析":{"docs":{},"与":{"docs":{},"统":{"docs":{},"计":{"docs":{},"时":{"docs":{},",":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}}}}}}}}}}},"机":{"docs":{},"器":{"docs":{},"学":{"docs":{},"习":{"docs":{},"时":{"docs":{},"代":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}}}}},"标":{"docs":{},"准":{"docs":{},"排":{"docs":{},"序":{"docs":{},"(":{"1":{"0":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}},"docs":{}},"docs":{}}}}},"记":{"docs":{},"点":{"docs":{},"是":{"docs":{},"与":{"docs":{},"标":{"docs":{},"签":{"docs":{},"/":{"docs":{},"响":{"docs":{},"应":{"docs":{},"相":{"docs":{},"关":{"docs":{},"联":{"docs":{},"的":{"docs":{},"密":{"docs":{},"集":{"docs":{},"或":{"docs":{},"稀":{"docs":{},"疏":{"docs":{},"的":{"docs":{},"局":{"docs":{},"部":{"docs":{},"矢":{"docs":{},"量":{"docs":{},"。":{"docs":{},"在":{"docs":{},"m":{"docs":{},"l":{"docs":{},"l":{"docs":{},"i":{"docs":{},"b":{"docs":{},"中":{"docs":{},"，":{"docs":{},"标":{"docs":{},"记":{"docs":{},"点":{"docs":{},"用":{"docs":{},"于":{"docs":{},"监":{"docs":{},"督":{"docs":{},"学":{"docs":{},"习":{"docs":{},"算":{"docs":{},"法":{"docs":{},"。":{"docs":{},"我":{"docs":{},"们":{"docs":{},"使":{"docs":{},"用":{"docs":{},"d":{"docs":{},"o":{"docs":{},"u":{"docs":{},"b":{"docs":{},"l":{"docs":{},"e":{"docs":{},"来":{"docs":{},"存":{"docs":{},"储":{"docs":{},"标":{"docs":{},"签":{"docs":{},"，":{"docs":{},"因":{"docs":{},"此":{"docs":{},"我":{"docs":{},"们":{"docs":{},"可":{"docs":{},"以":{"docs":{},"在":{"docs":{},"回":{"docs":{},"归":{"docs":{},"和":{"docs":{},"分":{"docs":{},"类":{"docs":{},"中":{"docs":{},"使":{"docs":{},"用":{"docs":{},"标":{"docs":{},"记":{"docs":{},"点":{"docs":{},"。":{"docs":{},"对":{"docs":{},"于":{"docs":{},"二":{"docs":{},"分":{"docs":{},"类":{"docs":{},"情":{"docs":{},"况":{"docs":{},"，":{"docs":{},"目":{"docs":{},"标":{"docs":{},"值":{"docs":{},"应":{"docs":{},"为":{"0":{"docs":{},"（":{"docs":{},"负":{"docs":{},"）":{"docs":{},"或":{"1":{"docs":{},"（":{"docs":{},"正":{"docs":{},"）":{"docs":{},"。":{"docs":{},"对":{"docs":{},"于":{"docs":{},"多":{"docs":{},"分":{"docs":{},"类":{"docs":{},"，":{"docs":{},"标":{"docs":{},"签":{"docs":{},"应":{"docs":{},"该":{"docs":{},"是":{"docs":{},"从":{"docs":{},"零":{"docs":{},"开":{"docs":{},"始":{"docs":{},"的":{"docs":{},"类":{"docs":{},"索":{"docs":{},"引":{"docs":{},"：":{"0":{"docs":{},",":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{}}}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"表":{"docs":{},"示":{"docs":{},"为":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}},"淘":{"docs":{},"宝":{"docs":{},"开":{"docs":{},"始":{"docs":{},"投":{"docs":{},"入":{"docs":{},"研":{"docs":{},"究":{"docs":{},"基":{"docs":{},"于":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},"的":{"docs":{},"系":{"docs":{},"统":{"docs":{},"–":{"docs":{},"云":{"docs":{},"梯":{"docs":{},"。":{"docs":{},"云":{"docs":{},"梯":{"docs":{},"总":{"docs":{},"容":{"docs":{},"量":{"docs":{},"约":{"9":{"docs":{},".":{"3":{"docs":{},"p":{"docs":{},"b":{"docs":{},"，":{"docs":{},"共":{"docs":{},"有":{"1":{"1":{"0":{"0":{"docs":{},"台":{"docs":{},"机":{"docs":{},"器":{"docs":{},"，":{"docs":{},"每":{"docs":{},"天":{"docs":{},"处":{"docs":{},"理":{"1":{"8":{"0":{"0":{"0":{"docs":{},"道":{"docs":{},"作":{"docs":{},"业":{"docs":{},"，":{"docs":{},"扫":{"docs":{},"描":{"5":{"0":{"0":{"docs":{},"t":{"docs":{},"b":{"docs":{},"数":{"docs":{},"据":{"docs":{},"。":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}}}}},"docs":{}},"docs":{}},"docs":{}}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}}}},"docs":{}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}},"卖":{"docs":{},"家":{"docs":{},"量":{"docs":{},"子":{"docs":{},"魔":{"docs":{},"方":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}},"双":{"1":{"1":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}},"docs":{}},"docs":{}},"网":{"docs":{},"站":{"docs":{},"中":{"docs":{},"随":{"docs":{},"机":{"docs":{},"抽":{"docs":{},"样":{"docs":{},"了":{"1":{"1":{"4":{"docs":{},"万":{"docs":{},"用":{"docs":{},"户":{"8":{"docs":{},"天":{"docs":{},"内":{"docs":{},"的":{"docs":{},"广":{"docs":{},"告":{"docs":{},"展":{"docs":{},"示":{"docs":{},"/":{"docs":{},"点":{"docs":{},"击":{"docs":{},"日":{"docs":{},"志":{"docs":{},"（":{"2":{"6":{"0":{"0":{"docs":{},"万":{"docs":{},"条":{"docs":{},"记":{"docs":{},"录":{"docs":{},"）":{"docs":{},"，":{"docs":{},"构":{"docs":{},"成":{"docs":{},"原":{"docs":{},"始":{"docs":{},"的":{"docs":{},"样":{"docs":{},"本":{"docs":{},"骨":{"docs":{},"架":{"docs":{},"。":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}}}}}}}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}}}}},"docs":{}}}}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}},"统":{"docs":{},"计":{"docs":{},"等":{"docs":{},"业":{"docs":{},"务":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}},"数":{"docs":{},"据":{"docs":{},"中":{"docs":{},"出":{"docs":{},"现":{"docs":{},"次":{"docs":{},"数":{"docs":{},"最":{"docs":{},"多":{"docs":{},"的":{"docs":{},"前":{"docs":{},"n":{"docs":{},"个":{"docs":{},"数":{"docs":{},"据":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}}}}}}}}}}},"分":{"docs":{},"析":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}},"指":{"docs":{},"标":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}},"函":{"docs":{},"数":{"docs":{},":":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}},"信":{"docs":{},"息":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.006993006993006993}}}},"每":{"docs":{},"个":{"docs":{},"用":{"docs":{},"户":{"docs":{},"对":{"docs":{},"各":{"docs":{},"个":{"docs":{},"品":{"docs":{},"牌":{"docs":{},"的":{"docs":{},"p":{"docs":{},"v":{"docs":{},"、":{"docs":{},"f":{"docs":{},"a":{"docs":{},"v":{"docs":{},"、":{"docs":{},"c":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{},"、":{"docs":{},"b":{"docs":{},"u":{"docs":{},"y":{"docs":{},"数":{"docs":{},"量":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}},"并":{"docs":{},"保":{"docs":{},"存":{"docs":{},"结":{"docs":{},"果":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}}}},"类":{"docs":{},"商":{"docs":{},"品":{"docs":{},"的":{"docs":{},"p":{"docs":{},"v":{"docs":{},"、":{"docs":{},"f":{"docs":{},"a":{"docs":{},"v":{"docs":{},"、":{"docs":{},"c":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{},"、":{"docs":{},"b":{"docs":{},"u":{"docs":{},"y":{"docs":{},"数":{"docs":{},"量":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"赢":{"docs":{},"得":{"docs":{},"世":{"docs":{},"界":{"docs":{},"最":{"docs":{},"快":{"1":{"docs":{},"t":{"docs":{},"b":{"docs":{},"数":{"docs":{},"据":{"docs":{},"排":{"docs":{},"序":{"docs":{},"在":{"9":{"0":{"0":{"docs":{},"个":{"docs":{},"节":{"docs":{},"点":{"docs":{},"上":{"docs":{},"用":{"docs":{},"时":{"2":{"0":{"9":{"docs":{},"秒":{"docs":{},"。":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}}}},"docs":{}},"docs":{}},"docs":{}}}}}}}},"docs":{}},"docs":{}},"docs":{}}}}}}}}},"docs":{}}}}}}},"集":{"docs":{},"群":{"docs":{},")":{"docs":{"day03_Hadoop/ha1.1.html":{"ref":"day03_Hadoop/ha1.1.html","tf":0.009009009009009009}}},"可":{"docs":{},"以":{"docs":{},"使":{"docs":{},"用":{"docs":{},"廉":{"docs":{},"价":{"docs":{},"机":{"docs":{},"器":{"docs":{},"，":{"docs":{},"成":{"docs":{},"本":{"docs":{},"低":{"docs":{"day03_Hadoop/ha1.3.html":{"ref":"day03_Hadoop/ha1.3.html","tf":0.07692307692307693}}}}}}}}}}}}}},"相":{"docs":{},"关":{"docs":{},"概":{"docs":{},"念":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}}}}},"合":{"docs":{},"函":{"docs":{},"数":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}},"图":{"docs":{},"中":{"docs":{},"对":{"docs":{},"于":{"docs":{},"文":{"docs":{},"件":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282}}}}}}}},"源":{"docs":{},"于":{"docs":{},"g":{"docs":{},"o":{"docs":{},"o":{"docs":{},"g":{"docs":{},"l":{"docs":{},"e":{"docs":{},"的":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{},"e":{"docs":{},"论":{"docs":{},"文":{"docs":{},"，":{"docs":{},"论":{"docs":{},"文":{"docs":{},"发":{"docs":{},"表":{"docs":{},"于":{"2":{"0":{"0":{"4":{"docs":{},"年":{"1":{"2":{"docs":{},"月":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282}}}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}}}},"(":{"2":{"0":{"0":{"4":{"docs":{},"年":{"1":{"2":{"docs":{},"月":{"docs":{},")":{"docs":{"day03_Hadoop/ha3.2.html":{"ref":"day03_Hadoop/ha3.2.html","tf":0.02040816326530612}}}}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}}}}}}}}}}}}}}}}}},"自":{"docs":{},"于":{"docs":{},"g":{"docs":{},"o":{"docs":{},"o":{"docs":{},"g":{"docs":{},"l":{"docs":{},"e":{"docs":{},"的":{"docs":{},"g":{"docs":{},"f":{"docs":{},"s":{"docs":{},"论":{"docs":{},"文":{"docs":{},",":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282}}}}}}}}}}}}}}}}}},"解":{"docs":{},"决":{"docs":{},"分":{"docs":{},"布":{"docs":{},"式":{"docs":{},"存":{"docs":{},"储":{"docs":{},"问":{"docs":{},"题":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282}}}}}},"计":{"docs":{},"算":{"docs":{},"问":{"docs":{},"题":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282}}}}}}}}},"数":{"docs":{},"据":{"docs":{},"可":{"docs":{},"以":{"docs":{},"切":{"docs":{},"割":{"docs":{},"进":{"docs":{},"行":{"docs":{},"计":{"docs":{},"算":{"docs":{},"的":{"docs":{},"应":{"docs":{},"用":{"docs":{"day03_Hadoop/ha3.2.html":{"ref":"day03_Hadoop/ha3.2.html","tf":0.02040816326530612}}}}}}}}}}}}}}}},"压":{"docs":{},"后":{"docs":{},"的":{"docs":{},"h":{"docs":{},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{},"目":{"docs":{},"录":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}},"释":{"docs":{},"器":{"docs":{},"、":{"docs":{},"编":{"docs":{},"译":{"docs":{},"器":{"docs":{},"、":{"docs":{},"优":{"docs":{},"化":{"docs":{},"器":{"docs":{},"、":{"docs":{},"执":{"docs":{},"行":{"docs":{},"器":{"docs":{},":":{"docs":{},"完":{"docs":{},"成":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}}}}}}}}}}},"论":{"docs":{},"文":{"docs":{},"发":{"docs":{},"表":{"docs":{},"于":{"2":{"0":{"0":{"3":{"docs":{},"年":{"1":{"0":{"docs":{},"月":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282}}}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}}}},"负":{"docs":{},"责":{"docs":{},"整":{"docs":{},"个":{"docs":{},"集":{"docs":{},"群":{"docs":{},"资":{"docs":{},"源":{"docs":{},"的":{"docs":{},"管":{"docs":{},"理":{"docs":{},"和":{"docs":{},"调":{"docs":{},"度":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282}}}}}}}}}}}}}},"元":{"docs":{},"数":{"docs":{},"据":{"docs":{},"（":{"docs":{},"文":{"docs":{},"件":{"docs":{},"的":{"docs":{},"名":{"docs":{},"称":{"docs":{},"、":{"docs":{},"副":{"docs":{},"本":{"docs":{},"系":{"docs":{},"数":{"docs":{},"、":{"docs":{},"b":{"docs":{},"l":{"docs":{},"o":{"docs":{},"c":{"docs":{},"k":{"docs":{},"存":{"docs":{},"放":{"docs":{},"的":{"docs":{},"d":{"docs":{},"n":{"docs":{},"）":{"docs":{},"的":{"docs":{},"管":{"docs":{},"理":{"docs":{"day03_Hadoop/ha2.4.html":{"ref":"day03_Hadoop/ha2.4.html","tf":0.03333333333333333}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"客":{"docs":{},"户":{"docs":{},"端":{"docs":{},"请":{"docs":{},"求":{"docs":{},"的":{"docs":{},"响":{"docs":{},"应":{"docs":{"day03_Hadoop/ha2.4.html":{"ref":"day03_Hadoop/ha2.4.html","tf":0.03333333333333333}}}}}}}}}}}},"超":{"docs":{},"算":{"docs":{"day03_Hadoop/ha1.2.html":{"ref":"day03_Hadoop/ha1.2.html","tf":0.01282051282051282}}},"时":{"docs":{},"未":{"docs":{},"发":{"docs":{},"送":{"docs":{},"心":{"docs":{},"跳":{"docs":{},",":{"docs":{"day03_Hadoop/ha4.2.html":{"ref":"day03_Hadoop/ha4.2.html","tf":0.0196078431372549}}}}}}}}},"出":{"docs":{},"这":{"docs":{},"个":{"docs":{},"范":{"docs":{},"围":{"docs":{},"的":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}},"某":{"docs":{},"个":{"docs":{},"节":{"docs":{},"点":{"docs":{},"崩":{"docs":{},"溃":{"docs":{},",":{"docs":{"day03_Hadoop/ha1.3.html":{"ref":"day03_Hadoop/ha1.3.html","tf":0.07692307692307693}}}}}}}},"电":{"docs":{},"商":{"docs":{},"网":{"docs":{},"站":{"docs":{},",":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}},"启":{"docs":{},"动":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{"day03_Hadoop/ha2.1.html":{"ref":"day03_Hadoop/ha2.1.html","tf":0.0136986301369863},"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428}}}},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}},"（":{"docs":{},"启":{"docs":{},"动":{"docs":{},"的":{"docs":{},"h":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"的":{"docs":{},"时":{"docs":{},"候":{"docs":{},"要":{"docs":{},"保":{"docs":{},"证":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},"集":{"docs":{},"群":{"docs":{},"已":{"docs":{},"经":{"docs":{},"启":{"docs":{},"动":{"docs":{},"）":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"启":{"docs":{},"动":{"docs":{},"y":{"docs":{},"a":{"docs":{},"r":{"docs":{},"n":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428}}}}}}}},"y":{"docs":{},"a":{"docs":{},"r":{"docs":{},"n":{"docs":{},"相":{"docs":{},"关":{"docs":{},"的":{"docs":{},"进":{"docs":{},"程":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}}}}}}},"d":{"docs":{},"o":{"docs":{},"c":{"docs":{},"k":{"docs":{},"e":{"docs":{},"r":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}},"m":{"docs":{},"y":{"docs":{},"s":{"docs":{},"q":{"docs":{},"l":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}}}}}},"即":{"docs":{},"可":{"docs":{},"使":{"docs":{},"用":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}},"p":{"docs":{},"y":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}}}}}}},"s":{"docs":{},"l":{"docs":{},"a":{"docs":{},"v":{"docs":{},"e":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}}}},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"集":{"docs":{},"群":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}}}}}}}}},"执":{"docs":{},"行":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"r":{"docs":{},"t":{"docs":{"day03_Hadoop/ha2.1.html":{"ref":"day03_Hadoop/ha2.1.html","tf":0.0136986301369863}}}}}}},"延":{"docs":{},"迟":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}},"…":{"docs":{},"]":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.0125}}},".":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}},"。":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"修":{"docs":{},"改":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.017857142857142856}},"日":{"docs":{},"期":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.0125}}}},"时":{"docs":{},"间":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.0125}}}},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428}}}}}}},"d":{"docs":{},"f":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428}}}}},"y":{"docs":{},"a":{"docs":{},"r":{"docs":{},"n":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428}}}}}},"配":{"docs":{},"置":{"docs":{},"文":{"docs":{},"件":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428},"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196},"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}}}},"会":{"docs":{},"将":{"docs":{},"修":{"docs":{},"改":{"docs":{},"直":{"docs":{},"接":{"docs":{},"同":{"docs":{},"步":{"docs":{},"给":{"docs":{},"元":{"docs":{},"数":{"docs":{},"据":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}}}}}}}},"可":{"docs":{},"以":{"docs":{},"显":{"docs":{},"示":{"docs":{},"的":{"docs":{},"版":{"docs":{},"本":{"docs":{},"数":{"docs":{},"量":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}},"数":{"docs":{},"据":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}},"删":{"docs":{},"除":{"docs":{},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},"中":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625}}}}}}},"指":{"docs":{},"定":{"docs":{},"的":{"docs":{},"文":{"docs":{},"件":{"docs":{},"。":{"docs":{},"只":{"docs":{},"删":{"docs":{},"除":{"docs":{},"非":{"docs":{},"空":{"docs":{},"目":{"docs":{},"录":{"docs":{},"和":{"docs":{},"文":{"docs":{},"件":{"docs":{},"。":{"docs":{},"请":{"docs":{},"参":{"docs":{},"考":{"docs":{},"r":{"docs":{},"m":{"docs":{},"r":{"docs":{},"命":{"docs":{},"令":{"docs":{},"了":{"docs":{},"解":{"docs":{},"递":{"docs":{},"归":{"docs":{},"删":{"docs":{},"除":{"docs":{},"。":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"时":{"docs":{},"影":{"docs":{},"响":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}},"表":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676},"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}},"查":{"docs":{},"看":{"docs":{},"结":{"docs":{},"果":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}},"中":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}},"一":{"docs":{},"张":{"docs":{},"表":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}},"列":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}},"记":{"docs":{},"录":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}},"数":{"docs":{},"据":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}},"某":{"docs":{},"些":{"docs":{},"字":{"docs":{},"段":{"docs":{},"值":{"docs":{},"完":{"docs":{},"全":{"docs":{},"一":{"docs":{},"样":{"docs":{},"的":{"docs":{},"重":{"docs":{},"复":{"docs":{},"记":{"docs":{},"录":{"docs":{},"，":{"docs":{},"s":{"docs":{},"u":{"docs":{},"b":{"docs":{},"s":{"docs":{},"e":{"docs":{},"t":{"docs":{},"参":{"docs":{},"数":{"docs":{},"定":{"docs":{},"义":{"docs":{},"这":{"docs":{},"些":{"docs":{},"字":{"docs":{},"段":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"把":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}},"t":{"docs":{},"e":{"docs":{},"x":{"docs":{},"t":{"docs":{},".":{"docs":{},"t":{"docs":{},"x":{"docs":{},"t":{"docs":{},"文":{"docs":{},"件":{"docs":{},"上":{"docs":{},"传":{"docs":{},"到":{"docs":{},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},"中":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625}}}}}}}}}}}}}}}}}}}},"集":{"docs":{},"群":{"docs":{},"中":{"docs":{},"j":{"docs":{},"a":{"docs":{},"r":{"docs":{},"包":{"docs":{},"的":{"docs":{},"位":{"docs":{},"置":{"docs":{},"添":{"docs":{},"加":{"docs":{},"到":{"docs":{},"h":{"docs":{},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{},"中":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}}}}}}}}}}}}}}},"变":{"docs":{},"量":{"docs":{},"映":{"docs":{},"射":{"docs":{},"到":{"docs":{},"高":{"docs":{},"维":{"docs":{},"空":{"docs":{},"间":{"docs":{},"：":{"docs":{},"如":{"docs":{},"p":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"的":{"1":{"docs":{},"维":{"docs":{},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"转":{"docs":{},"换":{"docs":{},"成":{"docs":{},"是":{"docs":{},"否":{"1":{"docs":{},"、":{"docs":{},"是":{"docs":{},"否":{"2":{"docs":{},"、":{"docs":{},"是":{"docs":{},"否":{"3":{"docs":{},"、":{"docs":{},"是":{"docs":{},"否":{"docs":{},"缺":{"docs":{},"失":{"docs":{},"的":{"4":{"docs":{},"维":{"docs":{},"数":{"docs":{},"据":{"docs":{},"；":{"docs":{},"这":{"docs":{},"样":{"docs":{},"保":{"docs":{},"证":{"docs":{},"了":{"docs":{},"所":{"docs":{},"有":{"docs":{},"原":{"docs":{},"始":{"docs":{},"数":{"docs":{},"据":{"docs":{},"不":{"docs":{},"变":{"docs":{},"，":{"docs":{},"同":{"docs":{},"时":{"docs":{},"能":{"docs":{},"提":{"docs":{},"高":{"docs":{},"精":{"docs":{},"确":{"docs":{},"度":{"docs":{},"，":{"docs":{},"但":{"docs":{},"这":{"docs":{},"样":{"docs":{},"会":{"docs":{},"导":{"docs":{},"致":{"docs":{},"数":{"docs":{},"据":{"docs":{},"变":{"docs":{},"得":{"docs":{},"比":{"docs":{},"较":{"docs":{},"稀":{"docs":{},"疏":{"docs":{},"，":{"docs":{},"如":{"docs":{},"果":{"docs":{},"样":{"docs":{},"本":{"docs":{},"量":{"docs":{},"很":{"docs":{},"小":{"docs":{},"，":{"docs":{},"反":{"docs":{},"而":{"docs":{},"会":{"docs":{},"导":{"docs":{},"致":{"docs":{},"样":{"docs":{},"本":{"docs":{},"效":{"docs":{},"果":{"docs":{},"较":{"docs":{},"差":{"docs":{},"，":{"docs":{},"因":{"docs":{},"此":{"docs":{},"也":{"docs":{},"不":{"docs":{},"能":{"docs":{},"滥":{"docs":{},"用":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{}}}}}}}},"docs":{}}}}},"docs":{}}}}},"docs":{}}}}}}}}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}},"的":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.006172839506172839},"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}},"形":{"docs":{},"式":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.00625}}}},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"f":{"docs":{},"i":{"docs":{},"g":{"docs":{},"u":{"docs":{},"r":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428}}}}}}}}}},"数":{"docs":{},"据":{"docs":{},"存":{"docs":{},"储":{"docs":{},"位":{"docs":{},"置":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428}}}}}}}},"各":{"docs":{},"个":{"docs":{},"组":{"docs":{},"件":{"docs":{},"配":{"docs":{},"合":{"docs":{},"是":{"docs":{},"有":{"docs":{},"不":{"docs":{},"会":{"docs":{},"有":{"docs":{},"兼":{"docs":{},"容":{"docs":{},"性":{"docs":{},"问":{"docs":{},"题":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}}}}}}}}}},"一":{"docs":{},"个":{"docs":{},"数":{"docs":{},"据":{"docs":{},"仓":{"docs":{},"库":{"docs":{},"工":{"docs":{},"具":{"docs":{},"，":{"docs":{},"可":{"docs":{},"以":{"docs":{},"将":{"docs":{},"结":{"docs":{},"构":{"docs":{},"化":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"映":{"docs":{},"射":{"docs":{},"为":{"docs":{},"一":{"docs":{},"张":{"docs":{},"数":{"docs":{},"据":{"docs":{},"库":{"docs":{},"表":{"docs":{},"，":{"docs":{},"并":{"docs":{},"提":{"docs":{},"供":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"关":{"docs":{},"系":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}},"用":{"docs":{},"户":{"docs":{},"很":{"docs":{},"方":{"docs":{},"便":{"docs":{},"地":{"docs":{},"利":{"docs":{},"用":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}},"安":{"docs":{},"装":{"docs":{},"与":{"docs":{},"s":{"docs":{},"h":{"docs":{},"e":{"docs":{},"l":{"docs":{},"l":{"docs":{},"操":{"docs":{},"作":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}}},"时":{"docs":{},"候":{"docs":{},"可":{"docs":{},"以":{"docs":{},"指":{"docs":{},"定":{"docs":{},"分":{"docs":{},"区":{"docs":{},"数":{"docs":{},"量":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}}}}}}},"容":{"docs":{},"错":{"docs":{},"机":{"docs":{},"制":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}},"组":{"docs":{},"i":{"docs":{},"d":{"docs":{"day03_Hadoop/ha2.2.html":{"ref":"day03_Hadoop/ha2.2.html","tf":0.0125}}}},"件":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}},"成":{"docs":{},",":{"docs":{"Hive&HBase/06_hbase数据模型.html":{"ref":"Hive&HBase/06_hbase数据模型.html","tf":0.020833333333333332}}}}},"容":{"docs":{},"易":{"docs":{},"扩":{"docs":{},"展":{"docs":{},"，":{"docs":{},"为":{"docs":{},"用":{"docs":{},"户":{"docs":{},"提":{"docs":{},"供":{"docs":{},"性":{"docs":{},"能":{"docs":{},"不":{"docs":{},"错":{"docs":{},"的":{"docs":{},"文":{"docs":{},"件":{"docs":{},"存":{"docs":{},"储":{"docs":{},"服":{"docs":{},"务":{"docs":{"day03_Hadoop/ha2.3.html":{"ref":"day03_Hadoop/ha2.3.html","tf":0.1111111111111111}}}}}}}}}}}}}}}}}}}}}},"器":{"docs":{},":":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}},"适":{"docs":{},"合":{"docs":{},"运":{"docs":{},"行":{"docs":{},"在":{"docs":{},"通":{"docs":{},"用":{"docs":{},"硬":{"docs":{},"件":{"docs":{},"(":{"docs":{},"c":{"docs":{},"o":{"docs":{},"m":{"docs":{},"m":{"docs":{},"o":{"docs":{},"d":{"docs":{"day03_Hadoop/ha2.3.html":{"ref":"day03_Hadoop/ha2.3.html","tf":0.1111111111111111}}}}}}}}}}}}}}}},"存":{"docs":{},"储":{"docs":{},"大":{"docs":{},"文":{"docs":{},"件":{"docs":{"day03_Hadoop/ha2.4.html":{"ref":"day03_Hadoop/ha2.4.html","tf":0.03333333333333333}}}}}}},"大":{"docs":{},"规":{"docs":{},"模":{"docs":{},"海":{"docs":{},"量":{"docs":{},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"p":{"docs":{},"b":{"docs":{},"级":{"docs":{},"数":{"docs":{},"据":{"docs":{},"；":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}}}}}}}}}}},"用":{"docs":{},"二":{"docs":{},"维":{"docs":{},"表":{"docs":{},"来":{"docs":{},"展":{"docs":{},"示":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}}}}}}},"非":{"docs":{},"结":{"docs":{},"构":{"docs":{},"化":{"docs":{},"数":{"docs":{},"据":{"docs":{},"存":{"docs":{},"储":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}}}}}},"用":{"docs":{},"于":{"docs":{},"廉":{"docs":{},"价":{"docs":{},"设":{"docs":{},"备":{"docs":{},"；":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}}}}},"优":{"docs":{},"点":{"docs":{"day03_Hadoop/ha2.4.html":{"ref":"day03_Hadoop/ha2.4.html","tf":0.03333333333333333}}},"化":{"docs":{},"引":{"docs":{},"擎":{"docs":{},"：":{"docs":{},"类":{"docs":{},"似":{"docs":{},"m":{"docs":{},"y":{"docs":{},"s":{"docs":{},"q":{"docs":{},"l":{"docs":{},"等":{"docs":{},"关":{"docs":{},"系":{"docs":{},"型":{"docs":{},"数":{"docs":{},"据":{"docs":{},"库":{"docs":{},"基":{"docs":{},"于":{"docs":{},"成":{"docs":{},"本":{"docs":{},"的":{"docs":{},"优":{"docs":{},"化":{"docs":{},"器":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}}}}}}}}}}}}}}}}}}}}}}}}}}},"器":{"docs":{},"（":{"docs":{},"c":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"l":{"docs":{},"y":{"docs":{},"s":{"docs":{},"t":{"docs":{},"）":{"docs":{},"的":{"docs":{},"优":{"docs":{},"化":{"docs":{},"，":{"docs":{},"即":{"docs":{},"使":{"docs":{},"你":{"docs":{},"写":{"docs":{},"的":{"docs":{},"程":{"docs":{},"序":{"docs":{},"或":{"docs":{},"s":{"docs":{},"q":{"docs":{},"l":{"docs":{},"不":{"docs":{},"仅":{"docs":{},"高":{"docs":{},"效":{"docs":{},"，":{"docs":{},"也":{"docs":{},"可":{"docs":{},"以":{"docs":{},"运":{"docs":{},"行":{"docs":{},"的":{"docs":{},"很":{"docs":{},"快":{"docs":{},"。":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"带":{"docs":{"day03_Hadoop/ha2.4.html":{"ref":"day03_Hadoop/ha2.4.html","tf":0.03333333333333333}},"有":{"docs":{},"嵌":{"docs":{},"套":{"docs":{},"结":{"docs":{},"构":{"docs":{},"的":{"docs":{},"j":{"docs":{},"s":{"docs":{},"o":{"docs":{},"n":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}}}}}}}}},"监":{"docs":{},"控":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"n":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"健":{"docs":{},"康":{"docs":{},"状":{"docs":{},"况":{"docs":{"day03_Hadoop/ha2.4.html":{"ref":"day03_Hadoop/ha2.4.html","tf":0.03333333333333333}}}}}}}}}}}}}},"我":{"docs":{},"们":{"docs":{},"的":{"docs":{},"n":{"docs":{},"m":{"docs":{},"，":{"docs":{},"一":{"docs":{},"旦":{"docs":{},"某":{"docs":{},"个":{"docs":{},"n":{"docs":{},"m":{"docs":{},"挂":{"docs":{},"了":{"docs":{},"，":{"docs":{},"那":{"docs":{},"么":{"docs":{},"该":{"docs":{},"n":{"docs":{},"m":{"docs":{},"上":{"docs":{},"运":{"docs":{},"行":{"docs":{},"的":{"docs":{},"任":{"docs":{},"务":{"docs":{},"需":{"docs":{},"要":{"docs":{},"告":{"docs":{},"诉":{"docs":{},"我":{"docs":{},"们":{"docs":{},"的":{"docs":{},"a":{"docs":{},"m":{"docs":{},"来":{"docs":{},"如":{"docs":{},"何":{"docs":{},"进":{"docs":{},"行":{"docs":{},"处":{"docs":{},"理":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"宣":{"docs":{},"传":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}},"集":{"docs":{},"群":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}}}}}}}},"测":{"docs":{},"到":{"docs":{},"本":{"docs":{},"机":{"docs":{},"的":{"docs":{},"某":{"docs":{},"块":{"docs":{},"磁":{"docs":{},"盘":{"docs":{},"损":{"docs":{},"坏":{"docs":{"day03_Hadoop/ha4.2.html":{"ref":"day03_Hadoop/ha4.2.html","tf":0.0196078431372549}}}}}}}}}}}}},"听":{"docs":{},"网":{"docs":{},"络":{"docs":{},"端":{"docs":{},"口":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"每":{"docs":{},"隔":{"3":{"docs":{},"秒":{"docs":{},"统":{"docs":{},"计":{"docs":{},"前":{"6":{"docs":{},"秒":{"docs":{},"出":{"docs":{},"现":{"docs":{},"的":{"docs":{},"单":{"docs":{},"词":{"docs":{},"数":{"docs":{},"量":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}}},"docs":{}}}}}},"docs":{}}}}}}}}}}}}},"硬":{"docs":{},"件":{"docs":{},"容":{"docs":{},"错":{"docs":{"day03_Hadoop/ha2.4.html":{"ref":"day03_Hadoop/ha2.4.html","tf":0.03333333333333333}}}}}},"缺":{"docs":{},"点":{"docs":{"day03_Hadoop/ha2.4.html":{"ref":"day03_Hadoop/ha2.4.html","tf":0.03333333333333333}},"：":{"docs":{},"不":{"docs":{},"同":{"docs":{},"路":{"docs":{},"径":{"docs":{},"启":{"docs":{},"动":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}},"失":{"docs":{},"值":{"docs":{},"处":{"docs":{},"理":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105},"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},"方":{"docs":{},"案":{"docs":{},"：":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}},"率":{"docs":{},"低":{"docs":{},"于":{"1":{"0":{"docs":{},"%":{"docs":{},"：":{"docs":{},"可":{"docs":{},"直":{"docs":{},"接":{"docs":{},"进":{"docs":{},"行":{"docs":{},"相":{"docs":{},"应":{"docs":{},"的":{"docs":{},"填":{"docs":{},"充":{"docs":{},"，":{"docs":{},"如":{"docs":{},"默":{"docs":{},"认":{"docs":{},"值":{"docs":{},"、":{"docs":{},"均":{"docs":{},"值":{"docs":{},"、":{"docs":{},"算":{"docs":{},"法":{"docs":{},"拟":{"docs":{},"合":{"docs":{},"等":{"docs":{},"等":{"docs":{},"；":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{}},"docs":{}}}}}},"要":{"docs":{},"定":{"docs":{},"期":{"docs":{},"向":{"docs":{},"n":{"docs":{},"n":{"docs":{},"发":{"docs":{},"送":{"docs":{},"心":{"docs":{},"跳":{"docs":{},"信":{"docs":{},"息":{"docs":{},"，":{"docs":{},"汇":{"docs":{},"报":{"docs":{},"本":{"docs":{},"身":{"docs":{},"及":{"docs":{},"其":{"docs":{},"所":{"docs":{},"有":{"docs":{},"的":{"docs":{},"b":{"docs":{},"l":{"docs":{},"o":{"docs":{},"c":{"docs":{},"k":{"docs":{},"信":{"docs":{},"息":{"docs":{},"，":{"docs":{},"健":{"docs":{},"康":{"docs":{},"状":{"docs":{},"况":{"docs":{"day03_Hadoop/ha2.4.html":{"ref":"day03_Hadoop/ha2.4.html","tf":0.03333333333333333}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"统":{"docs":{},"计":{"docs":{},"i":{"docs":{},"p":{"docs":{},"所":{"docs":{},"对":{"docs":{},"应":{"docs":{},"的":{"docs":{},"经":{"docs":{},"纬":{"docs":{},"度":{"docs":{},",":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}}}}}}}}}}},"~":{"docs":{},"/":{"docs":{},".":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"h":{"docs":{},"_":{"docs":{},"p":{"docs":{},"r":{"docs":{},"o":{"docs":{},"f":{"docs":{},"i":{"docs":{},"l":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.017857142857142856},"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.006172839506172839}}}}}}}}}}}}}},"a":{"docs":{},"p":{"docs":{},"p":{"docs":{},"/":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428},"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}},"目":{"docs":{},"录":{"docs":{},"下":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428}}}}}}}},"s":{"docs":{},"o":{"docs":{},"f":{"docs":{},"t":{"docs":{},"w":{"docs":{},"a":{"docs":{},"r":{"docs":{},"e":{"docs":{},"目":{"docs":{},"录":{"docs":{},"下":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428}}}}}}}}}}}}}},"]":{"docs":{},"$":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}},"压":{"docs":{},"缩":{"docs":{},"包":{"docs":{},"名":{"docs":{},"字":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428}}}}}}},"节":{"docs":{},"点":{"docs":{},"中":{"docs":{},"添":{"docs":{},"加":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.026785714285714284}}}}},"管":{"docs":{},"理":{"docs":{},"器":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}},"。":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}},"和":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}},"，":{"docs":{},"相":{"docs":{},"当":{"docs":{},"于":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}}}}}},"进":{"docs":{},"入":{"docs":{},"到":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428},"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}},"解":{"docs":{},"压":{"docs":{},"后":{"docs":{},"的":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},"目":{"docs":{},"录":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428}}}}}}}}}}}}}},"$":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"_":{"docs":{},"h":{"docs":{},"o":{"docs":{},"m":{"docs":{},"e":{"docs":{},"/":{"docs":{},"s":{"docs":{},"b":{"docs":{},"i":{"docs":{},"n":{"docs":{},"目":{"docs":{},"录":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}}}}}}}}}}}}}}}}}}},"p":{"docs":{},"y":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"环":{"docs":{},"境":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}}}}}}},"程":{"docs":{},"和":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}},"通":{"docs":{},"信":{"docs":{},"，":{"docs":{},"根":{"docs":{},"据":{"docs":{},"集":{"docs":{},"群":{"docs":{},"资":{"docs":{},"源":{"docs":{},"，":{"docs":{},"为":{"docs":{},"用":{"docs":{},"户":{"docs":{},"程":{"docs":{},"序":{"docs":{},"分":{"docs":{},"配":{"docs":{},"第":{"docs":{},"一":{"docs":{},"个":{"docs":{},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"t":{"docs":{},"a":{"docs":{},"i":{"docs":{},"n":{"docs":{},"e":{"docs":{},"r":{"docs":{},"(":{"docs":{},"容":{"docs":{},"器":{"docs":{},")":{"docs":{},"，":{"docs":{},"并":{"docs":{},"将":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"行":{"docs":{},"行":{"docs":{},"间":{"docs":{},"隔":{"docs":{},",":{"docs":{"Hive&HBase/06_hbase数据模型.html":{"ref":"Hive&HBase/06_hbase数据模型.html","tf":0.020833333333333332}}}}}}}},"配":{"docs":{},"置":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428}}}}}}}},"s":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"的":{"docs":{},"地":{"docs":{},"址":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}},"端":{"docs":{},"口":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}}}}}}}}},"y":{"docs":{},"a":{"docs":{},"r":{"docs":{},"n":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428}}}}}},"文":{"docs":{},"件":{"docs":{},"作":{"docs":{},"用":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428}}}}}},"环":{"docs":{},"境":{"docs":{},"变":{"docs":{},"量":{"docs":{"day03_Hadoop/ha2.5.html":{"ref":"day03_Hadoop/ha2.5.html","tf":0.008928571428571428},"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}},"h":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.004405286343612335}}}}}}},"伪":{"docs":{},"分":{"docs":{},"布":{"docs":{},"式":{"docs":{},"环":{"docs":{},"境":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}},"j":{"docs":{},"a":{"docs":{},"v":{"docs":{},"a":{"docs":{},"环":{"docs":{},"境":{"docs":{},"变":{"docs":{},"量":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}}}}}}}},"p":{"docs":{},"y":{"docs":{},"t":{"docs":{},"h":{"docs":{},"o":{"docs":{},"n":{"docs":{},"环":{"docs":{},"境":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}}}}}}}},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.2.html","tf":0.01098901098901099},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.007633587786259542},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}},"环":{"docs":{},"境":{"docs":{},"变":{"docs":{},"量":{"docs":{"day05_Spark_core/spark_core_6.html":{"ref":"day05_Spark_core/spark_core_6.html","tf":0.010752688172043012}}}}}}}}}}}},"合":{"docs":{},"使":{"docs":{},"用":{"docs":{},",":{"docs":{},"将":{"docs":{},"一":{"docs":{},"行":{"docs":{},"数":{"docs":{},"据":{"docs":{},"拆":{"docs":{},"分":{"docs":{},"成":{"docs":{},"多":{"docs":{},"行":{"docs":{},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"在":{"docs":{},"此":{"docs":{},"基":{"docs":{},"础":{"docs":{},"上":{"docs":{},"可":{"docs":{},"以":{"docs":{},"对":{"docs":{},"拆":{"docs":{},"分":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"进":{"docs":{},"行":{"docs":{},"聚":{"docs":{},"合":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"另":{"docs":{},"一":{"docs":{},"种":{"docs":{},"资":{"docs":{},"源":{"docs":{},"协":{"docs":{},"调":{"docs":{},"者":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}},"调":{"docs":{},"度":{"docs":{},"器":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}}}}}},"技":{"docs":{},"术":{"docs":{},"只":{"docs":{},"有":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{},",":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}}}}}}}}},"既":{"docs":{},"负":{"docs":{},"责":{"docs":{},"进":{"docs":{},"行":{"docs":{},"计":{"docs":{},"算":{"docs":{},"作":{"docs":{},"业":{"docs":{},"又":{"docs":{},"处":{"docs":{},"理":{"docs":{},"服":{"docs":{},"务":{"docs":{},"器":{"docs":{},"集":{"docs":{},"群":{"docs":{},"资":{"docs":{},"源":{"docs":{},"调":{"docs":{},"度":{"docs":{},"管":{"docs":{},"理":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}}}}}}}}}}}}}}}}}}}}},"请":{"docs":{},"求":{"docs":{},"k":{"docs":{},"i":{"docs":{},"l":{"docs":{},"l":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}}},"谨":{"docs":{},"慎":{"docs":{},"使":{"docs":{},"用":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}},"资":{"docs":{},"源":{"docs":{},"利":{"docs":{},"用":{"docs":{},"率":{"docs":{},"低":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}},"管":{"docs":{},"理":{"docs":{},"器":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}},"调":{"docs":{},"度":{"docs":{},"框":{"docs":{},"架":{"docs":{"day03_Hadoop/ha3.1.html":{"ref":"day03_Hadoop/ha3.1.html","tf":0.008547008547008548}}}}}},"位":{"docs":{},"。":{"docs":{},"该":{"docs":{},"特":{"docs":{},"征":{"docs":{},"属":{"docs":{},"于":{"docs":{},"分":{"docs":{},"类":{"docs":{},"特":{"docs":{},"征":{"docs":{},"，":{"docs":{},"只":{"docs":{},"有":{"docs":{},"两":{"docs":{},"类":{"docs":{},"取":{"docs":{},"值":{"docs":{},"，":{"docs":{},"因":{"docs":{},"此":{"docs":{},"考":{"docs":{},"虑":{"docs":{},"进":{"docs":{},"行":{"docs":{},"热":{"docs":{},"编":{"docs":{},"码":{"docs":{},"处":{"docs":{},"理":{"docs":{},"即":{"docs":{},"可":{"docs":{},"，":{"docs":{},"分":{"docs":{},"为":{"docs":{},"是":{"docs":{},"否":{"docs":{},"在":{"docs":{},"资":{"docs":{},"源":{"docs":{},"位":{"1":{"docs":{},"、":{"docs":{},"是":{"docs":{},"否":{"docs":{},"在":{"docs":{},"资":{"docs":{},"源":{"docs":{},"位":{"2":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}},"docs":{}}}}}}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"的":{"docs":{},"特":{"docs":{},"征":{"docs":{},"向":{"docs":{},"量":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}},"合":{"docs":{},"：":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{"day03_Hadoop/ha3.2.html":{"ref":"day03_Hadoop/ha3.2.html","tf":0.02040816326530612}}}}}}}}},"汇":{"docs":{},"总":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}},"，":{"docs":{},"每":{"docs":{},"个":{"docs":{},"人":{"docs":{},"负":{"docs":{},"责":{"docs":{},"统":{"docs":{},"计":{"docs":{},"一":{"docs":{},"种":{"docs":{},"面":{"docs":{},"值":{"docs":{"day03_Hadoop/ha3.2.html":{"ref":"day03_Hadoop/ha3.2.html","tf":0.02040816326530612}}}}}}}}}}}}}},"报":{"docs":{},"告":{"docs":{},",":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}},"海":{"docs":{},"量":{"docs":{},"数":{"docs":{},"据":{"docs":{},"离":{"docs":{},"线":{"docs":{},"处":{"docs":{},"理":{"docs":{},"&":{"docs":{},"易":{"docs":{},"开":{"docs":{},"发":{"docs":{"day03_Hadoop/ha3.2.html":{"ref":"day03_Hadoop/ha3.2.html","tf":0.02040816326530612}}}}}}}}}},"中":{"docs":{},"的":{"docs":{},"查":{"docs":{},"询":{"docs":{},"，":{"docs":{},"相":{"docs":{},"当":{"docs":{},"于":{"docs":{},"分":{"docs":{},"布":{"docs":{},"式":{"docs":{},"文":{"docs":{},"件":{"docs":{},"系":{"docs":{},"统":{"docs":{},"中":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"库":{"docs":{"day03_Hadoop/ha4.1.html":{"ref":"day03_Hadoop/ha4.1.html","tf":0.023809523809523808}}}}}}}}}}}}}}}}}}}}}}}}}},"编":{"docs":{},"程":{"docs":{},"模":{"docs":{},"型":{"docs":{"day03_Hadoop/ha3.2.html":{"ref":"day03_Hadoop/ha3.2.html","tf":0.02040816326530612}}}}},"写":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"风":{"docs":{},"格":{"docs":{},"脚":{"docs":{},"本":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}}}}}},"!":{"docs":{},"=":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269},"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577},"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.0045662100456621}}}},"或":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}},"敲":{"docs":{},"如":{"docs":{},"下":{"docs":{},"命":{"docs":{},"令":{"docs":{},":":{"docs":{"day03_Hadoop/ha3.3.html":{"ref":"day03_Hadoop/ha3.3.html","tf":0.005847953216374269}}}}}}}},"囊":{"docs":{},"括":{"docs":{},"了":{"docs":{},"大":{"docs":{},"数":{"docs":{},"据":{"docs":{},"处":{"docs":{},"理":{"docs":{},"的":{"docs":{},"方":{"docs":{},"方":{"docs":{},"面":{"docs":{},"面":{"docs":{"day03_Hadoop/ha4.1.html":{"ref":"day03_Hadoop/ha4.1.html","tf":0.023809523809523808}}}}}}}}}}}}}}},"开":{"docs":{},"源":{"docs":{},"、":{"docs":{},"社":{"docs":{},"区":{"docs":{},"活":{"docs":{},"跃":{"docs":{"day03_Hadoop/ha4.1.html":{"ref":"day03_Hadoop/ha4.1.html","tf":0.023809523809523808}}}}}}},"社":{"docs":{},"区":{"docs":{},"版":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}},"发":{"docs":{},"效":{"docs":{},"率":{"docs":{},"更":{"docs":{},"高":{"docs":{},"。":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}},"消":{"docs":{},"息":{"docs":{},"队":{"docs":{},"列":{"docs":{"day03_Hadoop/ha4.1.html":{"ref":"day03_Hadoop/ha4.1.html","tf":0.023809523809523808}}}}}},"狭":{"docs":{},"义":{"docs":{},"的":{"docs":{},"h":{"docs":{},"a":{"docs":{},"d":{"docs":{},"o":{"docs":{},"o":{"docs":{},"p":{"docs":{"day03_Hadoop/ha4.1.html":{"ref":"day03_Hadoop/ha4.1.html","tf":0.023809523809523808}}}}}}}}}}},"上":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"块":{"docs":{},"，":{"docs":{},"计":{"docs":{},"算":{"docs":{},"并":{"docs":{},"存":{"docs":{},"储":{"docs":{},"校":{"docs":{},"验":{"docs":{},"和":{"docs":{},"（":{"docs":{},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},"c":{"docs":{},"k":{"docs":{},"s":{"docs":{},"u":{"docs":{},"m":{"docs":{},")":{"docs":{"day03_Hadoop/ha4.2.html":{"ref":"day03_Hadoop/ha4.2.html","tf":0.0196078431372549}}}}}}}}}}}}}}}}}}}}}}}},"结":{"docs":{},"构":{"docs":{},"化":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},",":{"docs":{},"是":{"docs":{},"一":{"docs":{},"款":{"docs":{},"基":{"docs":{},"于":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}}}}}},"班":{"docs":{},"后":{"docs":{},"就":{"docs":{},"会":{"docs":{},"登":{"docs":{},"陆":{"docs":{},"后":{"docs":{},"台":{"docs":{},"数":{"docs":{},"据":{"docs":{},"系":{"docs":{},"统":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}}}}}},"。":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}},"传":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}},"述":{"docs":{},"三":{"docs":{},"种":{"docs":{},"操":{"docs":{},"作":{"docs":{},"的":{"docs":{},"核":{"docs":{},"心":{"docs":{},"都":{"docs":{},"是":{"docs":{},"：":{"docs":{},"通":{"docs":{},"过":{"docs":{},"原":{"docs":{},"始":{"docs":{},"数":{"docs":{},"据":{"docs":{},"设":{"docs":{},"定":{"docs":{},"一":{"docs":{},"个":{"docs":{},"正":{"docs":{},"常":{"docs":{},"的":{"docs":{},"范":{"docs":{},"围":{"docs":{},"，":{"docs":{},"超":{"docs":{},"过":{"docs":{},"此":{"docs":{},"范":{"docs":{},"围":{"docs":{},"的":{"docs":{},"就":{"docs":{},"是":{"docs":{},"一":{"docs":{},"个":{"docs":{},"异":{"docs":{},"常":{"docs":{},"值":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"写":{"docs":{},"完":{"docs":{},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"关":{"docs":{},"闭":{"docs":{},"输":{"docs":{},"输":{"docs":{},"出":{"docs":{},"流":{"docs":{},"。":{"docs":{"day03_Hadoop/ha4.2.html":{"ref":"day03_Hadoop/ha4.2.html","tf":0.0196078431372549}}}}}}}}}}}}},"模":{"docs":{},"式":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}},"入":{"docs":{},"数":{"docs":{},"据":{"docs":{},"时":{"docs":{},"才":{"docs":{},"开":{"docs":{},"始":{"docs":{},"计":{"docs":{},"算":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}},"发":{"docs":{},"送":{"docs":{},"完":{"docs":{},"成":{"docs":{},"信":{"docs":{},"号":{"docs":{},"给":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"n":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"。":{"docs":{"day03_Hadoop/ha4.2.html":{"ref":"day03_Hadoop/ha4.2.html","tf":0.0196078431372549}}}}}}}}}}}}}}}}},"现":{"docs":{},"日":{"docs":{},"活":{"docs":{},"没":{"docs":{},"有":{"docs":{},"明":{"docs":{},"显":{"docs":{},"下":{"docs":{},"降":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}},"p":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"和":{"docs":{},"n":{"docs":{},"e":{"docs":{},"w":{"docs":{},"_":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"c":{"docs":{},"l":{"docs":{},"a":{"docs":{},"s":{"docs":{},"s":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"存":{"docs":{},"在":{"docs":{},"空":{"docs":{},"值":{"docs":{},"：":{"docs":{},"（":{"docs":{},"注":{"docs":{},"意":{"docs":{},"此":{"docs":{},"处":{"docs":{},"的":{"docs":{},"n":{"docs":{},"u":{"docs":{},"l":{"docs":{},"l":{"docs":{},"表":{"docs":{},"示":{"docs":{},"空":{"docs":{},"值":{"docs":{},"，":{"docs":{},"而":{"docs":{},"如":{"docs":{},"果":{"docs":{},"是":{"docs":{},"n":{"docs":{},"u":{"docs":{},"l":{"docs":{},"l":{"docs":{},"，":{"docs":{},"则":{"docs":{},"往":{"docs":{},"往":{"docs":{},"表":{"docs":{},"示":{"docs":{},"是":{"docs":{},"一":{"docs":{},"个":{"docs":{},"字":{"docs":{},"符":{"docs":{},"串":{"docs":{},"）":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"客":{"docs":{},"户":{"docs":{},"端":{"docs":{},"向":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"n":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"发":{"docs":{},"出":{"docs":{},"写":{"docs":{},"文":{"docs":{},"件":{"docs":{},"请":{"docs":{},"求":{"docs":{},"。":{"docs":{"day03_Hadoop/ha4.2.html":{"ref":"day03_Hadoop/ha4.2.html","tf":0.0196078431372549}}}}}}}}}}}}}}}}}}}}},"单":{"docs":{},"价":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}},"报":{"docs":{},"告":{"docs":{},"给":{"docs":{"day03_Hadoop/ha4.2.html":{"ref":"day03_Hadoop/ha4.2.html","tf":0.0196078431372549}}}}},"校":{"docs":{},"验":{"docs":{},"不":{"docs":{},"正":{"docs":{},"确":{"docs":{},"抛":{"docs":{},"出":{"docs":{},"异":{"docs":{},"常":{"docs":{},",":{"docs":{"day03_Hadoop/ha4.2.html":{"ref":"day03_Hadoop/ha4.2.html","tf":0.0196078431372549}}}}}}}}}}}},"检":{"docs":{},"查":{"docs":{},"是":{"docs":{},"否":{"docs":{},"已":{"docs":{},"存":{"docs":{},"在":{"docs":{},"文":{"docs":{},"件":{"docs":{},"、":{"docs":{},"检":{"docs":{},"查":{"docs":{},"权":{"docs":{},"限":{"docs":{},"。":{"docs":{},"若":{"docs":{},"通":{"docs":{},"过":{"docs":{},"检":{"docs":{},"查":{"docs":{},"，":{"docs":{},"直":{"docs":{},"接":{"docs":{},"先":{"docs":{},"将":{"docs":{},"操":{"docs":{},"作":{"docs":{},"写":{"docs":{},"入":{"docs":{},"e":{"docs":{},"d":{"docs":{},"i":{"docs":{},"t":{"docs":{},"l":{"docs":{},"o":{"docs":{},"g":{"docs":{},"，":{"docs":{},"并":{"docs":{},"返":{"docs":{},"回":{"docs":{},"输":{"docs":{},"出":{"docs":{},"流":{"docs":{},"对":{"docs":{},"象":{"docs":{},"。":{"docs":{"day03_Hadoop/ha4.2.html":{"ref":"day03_Hadoop/ha4.2.html","tf":0.0196078431372549}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"这":{"docs":{},"些":{"docs":{},"数":{"docs":{},"据":{"docs":{},"块":{"docs":{},"在":{"docs":{},"哪":{"docs":{},"些":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"n":{"docs":{},"o":{"docs":{},"d":{"docs":{},"e":{"docs":{},"上":{"docs":{},"有":{"docs":{},"备":{"docs":{},"份":{"docs":{},",":{"docs":{"day03_Hadoop/ha4.2.html":{"ref":"day03_Hadoop/ha4.2.html","tf":0.0196078431372549}}}}}}}}}}}}}}}}}}}}}}}}},"磁":{"docs":{},"盘":{"docs":{},"介":{"docs":{},"质":{"docs":{},"在":{"docs":{},"存":{"docs":{},"储":{"docs":{},"过":{"docs":{},"程":{"docs":{},"中":{"docs":{},"受":{"docs":{},"环":{"docs":{},"境":{"docs":{},"或":{"docs":{},"者":{"docs":{},"老":{"docs":{},"化":{"docs":{},"影":{"docs":{},"响":{"docs":{},",":{"docs":{},"数":{"docs":{},"据":{"docs":{},"可":{"docs":{},"能":{"docs":{},"错":{"docs":{},"乱":{"docs":{"day03_Hadoop/ha4.2.html":{"ref":"day03_Hadoop/ha4.2.html","tf":0.0196078431372549}}}}}}}}}}}}}}}}}}}}}}}}}},"故":{"docs":{},"障":{"docs":{},"容":{"docs":{},"错":{"docs":{"day03_Hadoop/ha4.2.html":{"ref":"day03_Hadoop/ha4.2.html","tf":0.0196078431372549}}}}}}}},"重":{"docs":{},"新":{"docs":{},"计":{"docs":{},"算":{"docs":{},"读":{"docs":{},"取":{"docs":{},"出":{"docs":{},"来":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"校":{"docs":{},"验":{"docs":{},"和":{"docs":{},",":{"docs":{"day03_Hadoop/ha4.2.html":{"ref":"day03_Hadoop/ha4.2.html","tf":0.0196078431372549}}}}}}}}}}}}}}}},"写":{"docs":{},"覆":{"docs":{},"盖":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}},"要":{"docs":{},"的":{"docs":{},"方":{"docs":{},"法":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}}},"也":{"docs":{},"许":{"docs":{},"可":{"docs":{},"以":{"docs":{},"直":{"docs":{},"接":{"docs":{},"用":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}},"必":{"docs":{},"须":{"docs":{},"要":{"docs":{},"通":{"docs":{},"过":{"docs":{},"h":{"docs":{},"q":{"docs":{},"l":{"docs":{},"添":{"docs":{},"加":{"docs":{},"分":{"docs":{},"区":{"docs":{},",":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}}}}}}}}},"不":{"docs":{},"能":{"docs":{},"更":{"docs":{},"改":{"docs":{},"表":{"docs":{},"结":{"docs":{},"构":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}}},"可":{"docs":{},"以":{"docs":{},"复":{"docs":{},"制":{"docs":{},"到":{"docs":{},"磁":{"docs":{},"盘":{"docs":{},"的":{"docs":{},"其":{"docs":{},"它":{"docs":{},"节":{"docs":{},"点":{"docs":{},"上":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}}}}},"缓":{"docs":{},"存":{"docs":{},"到":{"docs":{},"磁":{"docs":{},"盘":{"docs":{},"上":{"docs":{},"，":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}},"接":{"docs":{},"收":{"docs":{},"一":{"docs":{},"个":{"docs":{},"字":{"docs":{},"典":{"docs":{},"｛":{"docs":{},"列":{"docs":{},"名":{"docs":{},"：":{"docs":{},"值":{"docs":{},"｝":{"docs":{},"这":{"docs":{},"样":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}}}}}}}}},"会":{"docs":{},"关":{"docs":{},"闭":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"t":{"docs":{},"e":{"docs":{},"x":{"docs":{},"t":{"docs":{},"对":{"docs":{},"象":{"docs":{},",":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}}}}}}}}}}}}}}}}}},"支":{"docs":{},"持":{"docs":{},"m":{"docs":{},"i":{"docs":{},"c":{"docs":{},"r":{"docs":{},"o":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}}}}}}}},"于":{"docs":{},"是":{"docs":{},"将":{"docs":{},"问":{"docs":{},"题":{"docs":{},"提":{"docs":{},"交":{"docs":{},"给":{"docs":{},"技":{"docs":{},"术":{"docs":{},"部":{"docs":{},"门":{"docs":{},"调":{"docs":{},"查":{"docs":{},"，":{"docs":{},"工":{"docs":{},"程":{"docs":{},"师":{"docs":{},"查":{"docs":{},"看":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}}}}}}}}}}}}}},"互":{"docs":{},"联":{"docs":{},"网":{"docs":{},"产":{"docs":{},"品":{"docs":{},"要":{"docs":{},"求":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}},"大":{"docs":{},"数":{"docs":{},"据":{"docs":{},"平":{"docs":{},"台":{"docs":{},"架":{"docs":{},"构":{"docs":{},":":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}}}}},"亚":{"docs":{},"马":{"docs":{},"逊":{"docs":{},"提":{"docs":{},"前":{"docs":{},"发":{"docs":{},"货":{"docs":{},"系":{"docs":{},"统":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}}},"价":{"docs":{},"格":{"docs":{},"异":{"docs":{},"常":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}},"低":{"docs":{},"于":{"1":{"docs":{},"的":{"docs":{},"条":{"docs":{},"目":{"docs":{},"个":{"docs":{},"数":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}},"docs":{}}},"高":{"docs":{},"于":{"1":{"docs":{},"w":{"docs":{},"的":{"docs":{},"条":{"docs":{},"目":{"docs":{},"个":{"docs":{},"数":{"docs":{},"：":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}},"docs":{}}}}},"任":{"docs":{},"务":{"docs":{},"调":{"docs":{},"度":{"docs":{},"系":{"docs":{},"统":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}},"运":{"docs":{},"行":{"docs":{},"，":{"docs":{},"使":{"docs":{},"不":{"docs":{},"熟":{"docs":{},"悉":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}},"意":{"docs":{},"指":{"docs":{},"定":{"docs":{},"路":{"docs":{},"径":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}},"何":{"docs":{},"对":{"docs":{},"d":{"docs":{},"s":{"docs":{},"t":{"docs":{},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"m":{"docs":{},"s":{"docs":{},"的":{"docs":{},"操":{"docs":{},"作":{"docs":{},"都":{"docs":{},"转":{"docs":{},"换":{"docs":{},"成":{"docs":{},"了":{"docs":{},"对":{"docs":{},"d":{"docs":{},"s":{"docs":{},"t":{"docs":{},"r":{"docs":{},"e":{"docs":{},"a":{"docs":{},"m":{"docs":{},"s":{"docs":{},"隐":{"docs":{},"含":{"docs":{},"的":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"的":{"docs":{},"操":{"docs":{},"作":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"及":{"docs":{},"时":{"docs":{},"调":{"docs":{},"整":{"docs":{},"运":{"docs":{},"营":{"docs":{},"和":{"docs":{},"产":{"docs":{},"品":{"docs":{},"策":{"docs":{},"略":{"docs":{},",":{"docs":{},"是":{"docs":{},"大":{"docs":{},"数":{"docs":{},"据":{"docs":{},"技":{"docs":{},"术":{"docs":{},"的":{"docs":{},"关":{"docs":{},"键":{"docs":{},"价":{"docs":{},"值":{"docs":{},"之":{"docs":{},"一":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}}}}}}}}}}}}}}}}}}},"反":{"docs":{},"应":{"docs":{},"网":{"docs":{},"站":{"docs":{},"应":{"docs":{},"收":{"docs":{},"能":{"docs":{},"力":{"docs":{},"的":{"docs":{},"重":{"docs":{},"要":{"docs":{},"指":{"docs":{},"标":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}}}}}}},"各":{"docs":{},"项":{"docs":{},"指":{"docs":{},"标":{"docs":{},"相":{"docs":{},"对":{"docs":{},"稳":{"docs":{},"定":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}},"垂":{"docs":{},"直":{"docs":{},"领":{"docs":{},"域":{"docs":{},"领":{"docs":{},"头":{"docs":{},"羊":{"docs":{},",":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}},"埋":{"docs":{},"点":{"docs":{},"采":{"docs":{},"集":{"docs":{},"数":{"docs":{},"据":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}},"延":{"docs":{},"迟":{"docs":{},"高":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}},"：":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}}}}}}}},"总":{"docs":{},"访":{"docs":{},"问":{"docs":{},"用":{"docs":{},"户":{"docs":{},"数":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}},"结":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}},"：":{"docs":{},"可":{"docs":{},"以":{"docs":{},"发":{"docs":{},"现":{"docs":{},"由":{"docs":{},"于":{"docs":{},"这":{"docs":{},"两":{"docs":{},"个":{"docs":{},"字":{"docs":{},"段":{"docs":{},"的":{"docs":{},"缺":{"docs":{},"失":{"docs":{},"过":{"docs":{},"多":{"docs":{},"，":{"docs":{},"所":{"docs":{},"以":{"docs":{},"预":{"docs":{},"测":{"docs":{},"出":{"docs":{},"来":{"docs":{},"的":{"docs":{},"值":{"docs":{},"已":{"docs":{},"经":{"docs":{},"大":{"docs":{},"大":{"docs":{},"失":{"docs":{},"真":{"docs":{},"，":{"docs":{},"但":{"docs":{},"如":{"docs":{},"果":{"docs":{},"缺":{"docs":{},"失":{"docs":{},"率":{"docs":{},"在":{"1":{"0":{"docs":{},"%":{"docs":{},"以":{"docs":{},"下":{"docs":{},"，":{"docs":{},"这":{"docs":{},"种":{"docs":{},"方":{"docs":{},"法":{"docs":{},"是":{"docs":{},"比":{"docs":{},"较":{"docs":{},"有":{"docs":{},"效":{"docs":{},"的":{"docs":{},"一":{"docs":{},"种":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}},"docs":{}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"的":{"docs":{},"条":{"docs":{},"目":{"docs":{},"数":{"docs":{},"，":{"docs":{},"查":{"docs":{},"看":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"i":{"docs":{},"s":{"docs":{},"中":{"docs":{},"总":{"docs":{},"的":{"docs":{},"条":{"docs":{},"目":{"docs":{},"数":{"docs":{},"是":{"docs":{},"否":{"docs":{},"一":{"docs":{},"致":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}}}}}}}}}}}}}},"广":{"docs":{},"告":{"docs":{},"条":{"docs":{},"数":{"docs":{},"：":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}},"智":{"docs":{},"能":{"docs":{},"推":{"docs":{},"荐":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}},"月":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.01680672268907563}},"活":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}},"比":{"docs":{},"如":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}},"获":{"docs":{},"取":{"docs":{},"h":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"中":{"docs":{},"所":{"docs":{},"有":{"docs":{},"的":{"docs":{},"表":{"docs":{},":":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}}}}}}}}}}},"，":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.004545454545454545}}}},"较":{"docs":{},"的":{"docs":{},"时":{"docs":{},"候":{"docs":{},"采":{"docs":{},"用":{"docs":{},"二":{"docs":{},"分":{"docs":{},"法":{"docs":{},"查":{"docs":{},"找":{"docs":{},"，":{"docs":{},"找":{"docs":{},"到":{"docs":{},"对":{"docs":{},"应":{"docs":{},"的":{"docs":{},"经":{"docs":{},"度":{"docs":{},"和":{"docs":{},"纬":{"docs":{},"度":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}}}}}}}}}}}}}}}}}}}}},"毫":{"docs":{},"秒":{"docs":{},"级":{"docs":{},"响":{"docs":{},"应":{"docs":{},"(":{"1":{"docs":{},"秒":{"docs":{},"以":{"docs":{},"内":{"docs":{},"完":{"docs":{},"成":{"docs":{},")":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}},"docs":{}}}}}}},"活":{"docs":{},"跃":{"docs":{},"用":{"docs":{},"户":{"docs":{},"数":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}},"浏":{"docs":{},"览":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}},"搜":{"docs":{},"索":{"docs":{},"结":{"docs":{},"果":{"docs":{},"列":{"docs":{},"表":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}}}}},"爬":{"docs":{},"虫":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}},"背":{"docs":{},"景":{"docs":{},":":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}},"订":{"docs":{},"单":{"docs":{},"活":{"docs":{},"跃":{"docs":{},"转":{"docs":{},"化":{"docs":{},"率":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}}}},"量":{"docs":{"day03_Hadoop/ha4.3.html":{"ref":"day03_Hadoop/ha4.3.html","tf":0.004201680672268907}}}}},"事":{"docs":{},"务":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}},"支":{"docs":{},"持":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}},"功":{"docs":{},"能":{"docs":{},"扩":{"docs":{},"展":{"docs":{},"很":{"docs":{},"方":{"docs":{},"便":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}},"原":{"docs":{},"子":{"docs":{},"数":{"docs":{},"据":{"docs":{},"类":{"docs":{},"型":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}},"始":{"docs":{},"数":{"docs":{},"据":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}},"样":{"docs":{},"本":{"docs":{},"骨":{"docs":{},"架":{"docs":{},"r":{"docs":{},"a":{"docs":{},"w":{"docs":{},"_":{"docs":{},"s":{"docs":{},"a":{"docs":{},"m":{"docs":{},"p":{"docs":{},"l":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}}}}}}}}}}},"命":{"docs":{},"令":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}},"行":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}},"表":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}},"达":{"docs":{},"式":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}},"外":{"docs":{},"部":{"docs":{},"表":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}},"(":{"docs":{},"e":{"docs":{},"x":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"n":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}}}},"分":{"docs":{},"区":{"docs":{},"表":{"docs":{},"即":{"docs":{},"使":{"docs":{},"有":{"docs":{},"分":{"docs":{},"区":{"docs":{},"的":{"docs":{},"目":{"docs":{},"录":{"docs":{},"结":{"docs":{},"构":{"docs":{},",":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}}}}}}}}}}}},"子":{"docs":{},"查":{"docs":{},"询":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}},"密":{"docs":{},"码":{"docs":{},"：":{"docs":{},"p":{"docs":{},"a":{"docs":{},"s":{"docs":{},"s":{"docs":{},"w":{"docs":{},"o":{"docs":{},"r":{"docs":{},"d":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}}}},"就":{"docs":{},"可":{"docs":{},"以":{"docs":{},"映":{"docs":{},"射":{"docs":{},"成":{"docs":{},"功":{"docs":{},"，":{"docs":{},"解":{"docs":{},"析":{"docs":{},"数":{"docs":{},"据":{"docs":{},"。":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}}}}},"是":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{"Hive&HBase/06_hbase数据模型.html":{"ref":"Hive&HBase/06_hbase数据模型.html","tf":0.020833333333333332}}}}},"一":{"docs":{},"个":{"docs":{},"a":{"docs":{},"c":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"操":{"docs":{},"作":{"docs":{},"，":{"docs":{},"使":{"docs":{},"用":{"docs":{},"某":{"docs":{},"个":{"docs":{},"函":{"docs":{},"数":{"docs":{},"聚":{"docs":{},"合":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"所":{"docs":{},"有":{"docs":{},"元":{"docs":{},"素":{"docs":{},"的":{"docs":{},"操":{"docs":{},"作":{"docs":{},"，":{"docs":{},"并":{"docs":{},"返":{"docs":{},"回":{"docs":{},"最":{"docs":{},"终":{"docs":{},"计":{"docs":{},"算":{"docs":{},"结":{"docs":{},"果":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"不":{"docs":{},"能":{"docs":{},"再":{"docs":{},"次":{"docs":{},"调":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}}}}}},"属":{"docs":{},"于":{"docs":{},"内":{"docs":{},"嵌":{"docs":{},"模":{"docs":{},"式":{"docs":{},"。":{"docs":{},"实":{"docs":{},"际":{"docs":{},"生":{"docs":{},"产":{"docs":{},"环":{"docs":{},"境":{"docs":{},"中":{"docs":{},"则":{"docs":{},"使":{"docs":{},"用":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}}}}}}}}}},"托":{"docs":{},"管":{"docs":{},"表":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}},"拥":{"docs":{},"有":{"docs":{},"一":{"docs":{},"套":{"docs":{},"自":{"docs":{},"己":{"docs":{},"的":{"docs":{},"元":{"docs":{},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"无":{"docs":{},"法":{"docs":{},"共":{"docs":{},"享":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}}}}}}}},"操":{"docs":{},"作":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}},"接":{"docs":{},"口":{"docs":{},"采":{"docs":{},"用":{"docs":{},"类":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}},"保":{"docs":{},"证":{"docs":{},"完":{"docs":{},"全":{"docs":{},"的":{"docs":{"Hive&HBase/06_hbase数据模型.html":{"ref":"Hive&HBase/06_hbase数据模型.html","tf":0.020833333333333332}}}}}}},"列":{"docs":{},"簇":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}},"表":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}},"，":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"创":{"docs":{},"建":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{},"将":{"docs":{},"用":{"docs":{},"于":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{},"e":{"docs":{},"，":{"docs":{},"m":{"docs":{},"a":{"docs":{},"p":{"docs":{},"阶":{"docs":{},"段":{"docs":{},"的":{"docs":{},"结":{"docs":{},"果":{"docs":{},"不":{"docs":{},"会":{"docs":{},"返":{"docs":{},"回":{"docs":{},"，":{"docs":{},"仅":{"docs":{},"会":{"docs":{},"返":{"docs":{},"回":{"docs":{},"r":{"docs":{},"e":{"docs":{},"d":{"docs":{},"u":{"docs":{},"c":{"docs":{},"e":{"docs":{},"结":{"docs":{},"果":{"docs":{},"。":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"把":{"docs":{},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{},"中":{"docs":{},"的":{"docs":{},"每":{"docs":{},"一":{"docs":{},"个":{"docs":{},"元":{"docs":{},"素":{"docs":{},"传":{"docs":{},"给":{"docs":{},"一":{"docs":{},"个":{"docs":{},"函":{"docs":{},"数":{"docs":{},"并":{"docs":{},"返":{"docs":{},"回":{"docs":{},"一":{"docs":{},"个":{"docs":{},"新":{"docs":{},"的":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{},"，":{"docs":{},"代":{"docs":{},"表":{"docs":{},"t":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"s":{"docs":{},"f":{"docs":{},"o":{"docs":{},"r":{"docs":{},"m":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"操":{"docs":{},"作":{"docs":{},"的":{"docs":{},"结":{"docs":{},"果":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"细":{"docs":{},"节":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}},"散":{"docs":{},"列":{"docs":{},"之":{"docs":{},"后":{"docs":{},"的":{"docs":{},"多":{"docs":{},"个":{"docs":{},"文":{"docs":{},"件":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}},"架":{"docs":{},"构":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}},"图":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}},"类":{"docs":{},"似":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}},"型":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}},"转":{"docs":{},"换":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}},"别":{"docs":{},"型":{"docs":{},"特":{"docs":{},"征":{"docs":{},"，":{"2":{"docs":{},"个":{"docs":{},"分":{"docs":{},"类":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.004228329809725159}}}}}},"3":{"docs":{},"个":{"docs":{},"分":{"docs":{},"类":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}},"7":{"docs":{},"个":{"docs":{},"分":{"docs":{},"类":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}},"docs":{},"约":{"1":{"3":{"docs":{},"个":{"docs":{},"分":{"docs":{},"类":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}},"docs":{}},"docs":{}}}}}}}},"索":{"docs":{},"引":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196},"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}},"表":{"docs":{},"的":{"docs":{},"列":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}},"名":{"docs":{},"字":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}},"属":{"docs":{},"性":{"docs":{},"（":{"docs":{},"是":{"docs":{},"否":{"docs":{},"为":{"docs":{},"外":{"docs":{},"部":{"docs":{},"表":{"docs":{},"等":{"docs":{},"）":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}}}},"数":{"docs":{},"据":{"docs":{},"所":{"docs":{},"在":{"docs":{},"目":{"docs":{},"录":{"docs":{},"等":{"docs":{},"。":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}}}}}},"结":{"docs":{},"构":{"docs":{},"修":{"docs":{},"改":{"docs":{},"时":{"docs":{},"影":{"docs":{},"响":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}},"和":{"docs":{},"分":{"docs":{},"区":{"docs":{},"进":{"docs":{},"行":{"docs":{},"修":{"docs":{},"改":{"docs":{},"，":{"docs":{},"则":{"docs":{},"需":{"docs":{},"要":{"docs":{},"修":{"docs":{},"复":{"docs":{},"（":{"docs":{},"m":{"docs":{},"s":{"docs":{},"c":{"docs":{},"k":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}}}}}}}}}}}}}}}},"关":{"docs":{},"系":{"docs":{},"复":{"docs":{},"杂":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"模":{"docs":{},"型":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}}}}}},"(":{"docs":{},"t":{"docs":{},"a":{"docs":{},"b":{"docs":{},"l":{"docs":{},"e":{"docs":{},")":{"docs":{},"：":{"docs":{},"用":{"docs":{},"于":{"docs":{},"存":{"docs":{},"储":{"docs":{},"管":{"docs":{},"理":{"docs":{},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"具":{"docs":{},"有":{"docs":{},"稀":{"docs":{},"疏":{"docs":{},"的":{"docs":{},"、":{"docs":{},"面":{"docs":{},"向":{"docs":{},"列":{"docs":{},"的":{"docs":{},"特":{"docs":{},"点":{"docs":{},"。":{"docs":{},"h":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"中":{"docs":{},"的":{"docs":{},"每":{"docs":{},"一":{"docs":{},"张":{"docs":{},"表":{"docs":{},"，":{"docs":{},"就":{"docs":{},"是":{"docs":{},"所":{"docs":{},"谓":{"docs":{},"的":{"docs":{},"大":{"docs":{},"表":{"docs":{},"(":{"docs":{},"b":{"docs":{},"i":{"docs":{},"g":{"docs":{},"t":{"docs":{},"a":{"docs":{},"b":{"docs":{},"l":{"docs":{},"e":{"docs":{},")":{"docs":{},"，":{"docs":{},"可":{"docs":{},"以":{"docs":{},"有":{"docs":{},"上":{"docs":{},"亿":{"docs":{},"行":{"docs":{},"，":{"docs":{},"上":{"docs":{},"百":{"docs":{},"万":{"docs":{},"列":{"docs":{},"。":{"docs":{},"对":{"docs":{},"于":{"docs":{},"为":{"docs":{},"值":{"docs":{},"为":{"docs":{},"空":{"docs":{},"的":{"docs":{},"列":{"docs":{},"，":{"docs":{},"并":{"docs":{},"不":{"docs":{},"占":{"docs":{},"用":{"docs":{},"存":{"docs":{},"储":{"docs":{},"空":{"docs":{},"间":{"docs":{},"，":{"docs":{},"因":{"docs":{},"此":{"docs":{},"表":{"docs":{},"可":{"docs":{},"以":{"docs":{},"设":{"docs":{},"计":{"docs":{},"的":{"docs":{},"非":{"docs":{},"常":{"docs":{},"稀":{"docs":{},"疏":{"docs":{},"。":{"docs":{"Hive&HBase/06_hbase数据模型.html":{"ref":"Hive&HBase/06_hbase数据模型.html","tf":0.020833333333333332}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"以":{"docs":{},"保":{"docs":{},"证":{"docs":{},"数":{"docs":{},"据":{"docs":{},"正":{"docs":{},"常":{"docs":{},"访":{"docs":{},"问":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}}}}},"地":{"docs":{},"址":{"docs":{},"、":{"docs":{},"h":{"docs":{},"m":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"地":{"docs":{},"址":{"docs":{},"；":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}}}}}}}}},"示":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"避":{"docs":{},"免":{"docs":{},"了":{"docs":{},"去":{"docs":{},"写":{"docs":{"Hive&HBase/01_hive介绍.html":{"ref":"Hive&HBase/01_hive介绍.html","tf":0.0030864197530864196}}}}}}},"创":{"docs":{},"建":{"docs":{},"一":{"docs":{},"个":{"docs":{},"外":{"docs":{},"部":{"docs":{},"表":{"docs":{},"s":{"docs":{},"t":{"docs":{},"u":{"docs":{},"d":{"docs":{},"e":{"docs":{},"n":{"docs":{},"t":{"2":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}},"docs":{}}}}}}}}}}}}},"分":{"docs":{},"区":{"docs":{},"表":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}},"数":{"docs":{},"据":{"docs":{},"库":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}},"表":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.007407407407407408},"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112},"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.004405286343612335}},"时":{"docs":{},"无":{"docs":{},"e":{"docs":{},"x":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"n":{"docs":{},"a":{"docs":{},"l":{"docs":{},"修":{"docs":{},"饰":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}}}}}}},"被":{"docs":{},"e":{"docs":{},"x":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"n":{"docs":{},"a":{"docs":{},"l":{"docs":{},"修":{"docs":{},"饰":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}}}}}}}},"的":{"docs":{},"时":{"docs":{},"候":{"docs":{},"添":{"docs":{},"加":{"docs":{},"n":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"c":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}}}}}},"之":{"docs":{},"后":{"docs":{},"可":{"docs":{},"以":{"docs":{},"传":{"docs":{},"入":{"docs":{},"表":{"docs":{},"名":{"docs":{},"获":{"docs":{},"取":{"docs":{},"到":{"docs":{},"t":{"docs":{},"a":{"docs":{},"b":{"docs":{},"l":{"docs":{},"e":{"docs":{},"类":{"docs":{},"的":{"docs":{},"实":{"docs":{},"例":{"docs":{},":":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}}}}}}}}}}}}}}}}}}}},"非":{"docs":{},"临":{"docs":{},"时":{"docs":{},"自":{"docs":{},"定":{"docs":{},"义":{"docs":{},"函":{"docs":{},"数":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}}}}},"外":{"docs":{},"部":{"docs":{},"表":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}},"名":{"docs":{},"称":{"docs":{},"空":{"docs":{},"间":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}},"和":{"docs":{},"h":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"的":{"docs":{},"连":{"docs":{},"接":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0056657223796034}}}}}}}}}}},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"t":{"docs":{},"e":{"docs":{},"x":{"docs":{},"t":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}},"的":{"docs":{},"时":{"docs":{},"候":{"docs":{},"需":{"docs":{},"要":{"docs":{},"一":{"docs":{},"个":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{},"c":{"docs":{},"o":{"docs":{},"n":{"docs":{},"f":{"docs":{},"，":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}},"逻":{"docs":{},"辑":{"docs":{},"回":{"docs":{},"归":{"docs":{},"训":{"docs":{},"练":{"docs":{},"器":{"docs":{},"，":{"docs":{},"并":{"docs":{},"训":{"docs":{},"练":{"docs":{},"模":{"docs":{},"型":{"docs":{},"：":{"docs":{},"l":{"docs":{},"o":{"docs":{},"g":{"docs":{},"i":{"docs":{},"s":{"docs":{},"t":{"docs":{},"i":{"docs":{},"c":{"docs":{},"r":{"docs":{},"e":{"docs":{},"g":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"s":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"、":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"动":{"docs":{},"态":{"docs":{},"分":{"docs":{},"区":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}},"j":{"docs":{},"s":{"docs":{},"o":{"docs":{},"n":{"docs":{},"数":{"docs":{},"据":{"docs":{},"的":{"docs":{},"读":{"docs":{},"取":{"docs":{},"和":{"docs":{},"操":{"docs":{},"作":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}}}}}}}}}}}},"导":{"docs":{},"入":{"docs":{},"数":{"docs":{},"据":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}},"才":{"docs":{},"能":{"docs":{},"看":{"docs":{},"到":{"docs":{},"相":{"docs":{},"应":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}}}}},"概":{"docs":{},"念":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}},"述":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}}}},"此":{"docs":{},"时":{"docs":{},"再":{"docs":{},"次":{"docs":{},"查":{"docs":{},"看":{"docs":{},"才":{"docs":{},"能":{"docs":{},"看":{"docs":{},"到":{"docs":{},"新":{"docs":{},"加":{"docs":{},"入":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}}}}}}}}}},"查":{"docs":{},"看":{"docs":{},"表":{"docs":{},"中":{"docs":{},"数":{"docs":{},"据":{"docs":{},"发":{"docs":{},"现":{"docs":{},"数":{"docs":{},"据":{"docs":{},"并":{"docs":{},"没":{"docs":{},"有":{"docs":{},"变":{"docs":{},"化":{"docs":{},",":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}}}}}}}}}}}},"还":{"docs":{},"没":{"docs":{},"有":{"docs":{},"开":{"docs":{},"始":{"docs":{},"计":{"docs":{},"算":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}},"外":{"docs":{},"，":{"docs":{},"h":{"docs":{},"r":{"docs":{},"e":{"docs":{},"g":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"v":{"docs":{},"e":{"docs":{},"r":{"docs":{},"管":{"docs":{},"理":{"docs":{},"一":{"docs":{},"系":{"docs":{},"列":{"docs":{},"h":{"docs":{},"r":{"docs":{},"e":{"docs":{},"g":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"对":{"docs":{},"象":{"docs":{},"，":{"docs":{},"每":{"docs":{},"个":{"docs":{},"h":{"docs":{},"r":{"docs":{},"e":{"docs":{},"g":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"对":{"docs":{},"应":{"docs":{},"t":{"docs":{},"a":{"docs":{},"b":{"docs":{},"l":{"docs":{},"e":{"docs":{},"中":{"docs":{},"一":{"docs":{},"个":{"docs":{},"r":{"docs":{},"e":{"docs":{},"g":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"，":{"docs":{},"h":{"docs":{},"r":{"docs":{},"e":{"docs":{},"g":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"由":{"docs":{},"多":{"docs":{},"个":{"docs":{},"h":{"docs":{},"s":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{},"e":{"docs":{},"组":{"docs":{},"成":{"docs":{},"，":{"docs":{},"每":{"docs":{},"个":{"docs":{},"h":{"docs":{},"s":{"docs":{},"t":{"docs":{},"o":{"docs":{},"r":{"docs":{},"e":{"docs":{},"对":{"docs":{},"应":{"docs":{},"t":{"docs":{},"a":{"docs":{},"b":{"docs":{},"l":{"docs":{},"e":{"docs":{},"中":{"docs":{},"一":{"docs":{},"个":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"处":{"docs":{},"训":{"docs":{},"练":{"docs":{},"时":{"docs":{},"间":{"docs":{},"较":{"docs":{},"长":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}}}}}}}},"装":{"docs":{},"载":{"docs":{},"数":{"docs":{},"据":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}},"跟":{"docs":{},"s":{"docs":{},"q":{"docs":{},"l":{"docs":{},"类":{"docs":{},"似":{"docs":{"Hive&HBase/02_hive的shell操作.html":{"ref":"Hive&HBase/02_hive的shell操作.html","tf":0.003703703703703704}}}}}}}},"字":{"docs":{},"符":{"docs":{},"串":{"docs":{},"函":{"docs":{},"数":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}},"段":{"docs":{},"说":{"docs":{},"明":{"docs":{},"如":{"docs":{},"下":{"docs":{},"：":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}}},"官":{"docs":{},"方":{"docs":{},"文":{"docs":{},"档":{"docs":{},"》":{"docs":{},"》":{"docs":{},")":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}}}},"逻":{"docs":{},"辑":{"docs":{},"运":{"docs":{},"算":{"docs":{},"符":{"docs":{"Hive&HBase/03_Hive的函数和自定义函数.html":{"ref":"Hive&HBase/03_Hive的函数和自定义函数.html","tf":0.004016064257028112}}}}}}},"所":{"docs":{},"有":{"docs":{},"的":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}}}}}},"t":{"docs":{},"r":{"docs":{},"a":{"docs":{},"n":{"docs":{},"s":{"docs":{},"f":{"docs":{},"o":{"docs":{},"r":{"docs":{},"m":{"docs":{},"a":{"docs":{},"t":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"操":{"docs":{},"作":{"docs":{},"都":{"docs":{},"是":{"docs":{},"惰":{"docs":{},"性":{"docs":{},"的":{"docs":{},"（":{"docs":{},"l":{"docs":{},"a":{"docs":{},"z":{"docs":{},"y":{"docs":{},"）":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"特":{"docs":{},"征":{"docs":{},"的":{"docs":{},"特":{"docs":{},"征":{"docs":{},"向":{"docs":{},"量":{"docs":{},"已":{"docs":{},"经":{"docs":{},"汇":{"docs":{},"总":{"docs":{},"到":{"docs":{},"在":{"docs":{},"f":{"docs":{},"e":{"docs":{},"a":{"docs":{},"t":{"docs":{},"u":{"docs":{},"r":{"docs":{},"e":{"docs":{},"s":{"docs":{},"字":{"docs":{},"段":{"docs":{},"中":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}}}}}}},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"k":{"docs":{},"都":{"docs":{},"会":{"docs":{},"去":{"docs":{},"复":{"docs":{},"制":{"docs":{},"i":{"docs":{},"p":{"docs":{},"表":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}}}}}}}}}},"用":{"docs":{},"户":{"docs":{},"的":{"docs":{},"i":{"docs":{},"d":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}},"类":{"docs":{},"别":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}},"以":{"docs":{},"这":{"docs":{},"份":{"docs":{},"数":{"docs":{},"据":{"docs":{},"可":{"docs":{},"以":{"docs":{},"共":{"docs":{},"享":{"docs":{},",":{"docs":{},"没":{"docs":{},"必":{"docs":{},"要":{"docs":{},"每":{"docs":{},"个":{"docs":{},"t":{"docs":{},"a":{"docs":{},"s":{"docs":{},"k":{"docs":{},"复":{"docs":{},"制":{"docs":{},"一":{"docs":{},"份":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}}}}}}}}}}}}}}}}}}}}},"里":{"docs":{},"我":{"docs":{},"们":{"docs":{},"除":{"docs":{},"了":{"docs":{},"需":{"docs":{},"要":{"docs":{},"我":{"docs":{},"们":{"docs":{},"训":{"docs":{},"练":{"docs":{},"的":{"docs":{},"a":{"docs":{},"l":{"docs":{},"s":{"docs":{},"模":{"docs":{},"型":{"docs":{},"以":{"docs":{},"外":{"docs":{},"，":{"docs":{},"还":{"docs":{},"需":{"docs":{},"要":{"docs":{},"有":{"docs":{},"一":{"docs":{},"个":{"docs":{},"广":{"docs":{},"告":{"docs":{},"和":{"docs":{},"类":{"docs":{},"别":{"docs":{},"的":{"docs":{},"对":{"docs":{},"应":{"docs":{},"关":{"docs":{},"系":{"docs":{},"。":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"拆":{"docs":{},"分":{"docs":{"Hive&HBase/04_hive综合案例.html":{"ref":"Hive&HBase/04_hive综合案例.html","tf":0.0011337868480725624}},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}},"丰":{"docs":{},"富":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"类":{"docs":{},"型":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}}}},"传":{"docs":{},"统":{"docs":{},"关":{"docs":{},"系":{"docs":{},"数":{"docs":{},"据":{"docs":{},"库":{"docs":{},"的":{"docs":{},"区":{"docs":{},"别":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}}}}},"变":{"docs":{},"化":{"docs":{},"后":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"不":{"docs":{},"是":{"docs":{},"连":{"docs":{},"续":{"docs":{},"的":{"docs":{},"，":{"docs":{},"而":{"docs":{},"是":{"docs":{},"随":{"docs":{},"机":{"docs":{},"分":{"docs":{},"配":{"docs":{},"的":{"docs":{},"，":{"docs":{},"不":{"docs":{},"容":{"docs":{},"易":{"docs":{},"应":{"docs":{},"用":{"docs":{},"在":{"docs":{},"分":{"docs":{},"类":{"docs":{},"器":{"docs":{},"中":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"：":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}},"入":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}},"办":{"docs":{},"公":{"docs":{},"文":{"docs":{},"档":{"docs":{},"、":{"docs":{},"文":{"docs":{},"本":{"docs":{},"、":{"docs":{},"图":{"docs":{},"片":{"docs":{},"、":{"docs":{},"x":{"docs":{},"m":{"docs":{},"l":{"docs":{},",":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}}}}}}}}}}}},"吞":{"docs":{},"吐":{"docs":{},"量":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}},"：":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}}}}},"单":{"docs":{},"位":{"docs":{},"时":{"docs":{},"间":{"docs":{},"内":{"docs":{},"成":{"docs":{},"功":{"docs":{},"传":{"docs":{},"输":{"docs":{},"数":{"docs":{},"据":{"docs":{},"的":{"docs":{},"数":{"docs":{},"量":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}}}}}}}}}}}}}}}}}},"易":{"docs":{},"于":{"docs":{},"扩":{"docs":{},"展":{"docs":{},"，":{"docs":{},"支":{"docs":{},"持":{"docs":{},"动":{"docs":{},"态":{"docs":{},"伸":{"docs":{},"缩":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}}}}}}}},"百":{"docs":{},"万":{"docs":{},"写":{"docs":{},"入":{"docs":{},"/":{"docs":{},"秒":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}}},"瞬":{"docs":{},"间":{"docs":{},"写":{"docs":{},"入":{"docs":{},"量":{"docs":{},"很":{"docs":{},"大":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}}}},"面":{"docs":{},"向":{"docs":{},"列":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"库":{"docs":{"Hive&HBase/05_hBase简介与环境部署.html":{"ref":"Hive&HBase/05_hBase简介与环境部署.html","tf":0.008695652173913044}}}}}}}}},"列":{"docs":{},"(":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{},")":{"docs":{},":":{"docs":{"Hive&HBase/06_hbase数据模型.html":{"ref":"Hive&HBase/06_hbase数据模型.html","tf":0.020833333333333332}}}}}}}}}}},"修":{"docs":{},"饰":{"docs":{},"符":{"docs":{},"(":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{"Hive&HBase/06_hbase数据模型.html":{"ref":"Hive&HBase/06_hbase数据模型.html","tf":0.041666666666666664}}}}}}}}}}}},"族":{"docs":{},"(":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{},"f":{"docs":{},"a":{"docs":{},"m":{"docs":{},"i":{"docs":{},"l":{"docs":{},"y":{"docs":{},")":{"docs":{},"：":{"docs":{},"是":{"docs":{},"列":{"docs":{},"的":{"docs":{},"集":{"docs":{},"合":{"docs":{},"。":{"docs":{},"列":{"docs":{},"族":{"docs":{},"在":{"docs":{},"表":{"docs":{},"定":{"docs":{},"义":{"docs":{},"时":{"docs":{},"需":{"docs":{},"要":{"docs":{},"指":{"docs":{},"定":{"docs":{},"，":{"docs":{},"而":{"docs":{},"列":{"docs":{},"在":{"docs":{},"插":{"docs":{},"入":{"docs":{},"数":{"docs":{},"据":{"docs":{},"时":{"docs":{},"动":{"docs":{},"态":{"docs":{},"指":{"docs":{},"定":{"docs":{},"。":{"docs":{},"列":{"docs":{},"中":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"都":{"docs":{},"是":{"docs":{},"以":{"docs":{},"二":{"docs":{},"进":{"docs":{},"制":{"docs":{},"形":{"docs":{},"式":{"docs":{},"存":{"docs":{},"在":{"docs":{},"，":{"docs":{},"没":{"docs":{},"有":{"docs":{},"数":{"docs":{},"据":{"docs":{},"类":{"docs":{},"型":{"docs":{},"。":{"docs":{},"在":{"docs":{},"物":{"docs":{},"理":{"docs":{},"存":{"docs":{},"储":{"docs":{},"结":{"docs":{},"构":{"docs":{},"上":{"docs":{},"，":{"docs":{},"每":{"docs":{},"个":{"docs":{},"表":{"docs":{},"中":{"docs":{},"的":{"docs":{},"每":{"docs":{},"个":{"docs":{},"列":{"docs":{},"族":{"docs":{},"单":{"docs":{},"独":{"docs":{},"以":{"docs":{},"一":{"docs":{},"个":{"docs":{},"文":{"docs":{},"件":{"docs":{},"存":{"docs":{},"储":{"docs":{},"。":{"docs":{},"一":{"docs":{},"个":{"docs":{},"表":{"docs":{},"可":{"docs":{},"以":{"docs":{},"有":{"docs":{},"多":{"docs":{},"个":{"docs":{},"列":{"docs":{},"簇":{"docs":{},"。":{"docs":{"Hive&HBase/06_hbase数据模型.html":{"ref":"Hive&HBase/06_hbase数据模型.html","tf":0.020833333333333332}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"中":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"通":{"docs":{},"过":{"docs":{},"列":{"docs":{},"标":{"docs":{},"识":{"docs":{},"来":{"docs":{},"进":{"docs":{},"行":{"docs":{},"映":{"docs":{},"射":{"docs":{},",":{"docs":{"Hive&HBase/06_hbase数据模型.html":{"ref":"Hive&HBase/06_hbase数据模型.html","tf":0.020833333333333332}}}}}}}}}}}}}}}}}}},"区":{"docs":{},"域":{"docs":{},"(":{"docs":{},"r":{"docs":{},"e":{"docs":{},"g":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},")":{"docs":{},"：":{"docs":{},"h":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"自":{"docs":{},"动":{"docs":{},"把":{"docs":{},"表":{"docs":{},"水":{"docs":{},"平":{"docs":{},"划":{"docs":{},"分":{"docs":{},"成":{"docs":{},"的":{"docs":{},"多":{"docs":{},"个":{"docs":{},"区":{"docs":{},"域":{"docs":{},"，":{"docs":{},"划":{"docs":{},"分":{"docs":{},"的":{"docs":{},"区":{"docs":{},"域":{"docs":{},"随":{"docs":{},"着":{"docs":{},"数":{"docs":{},"据":{"docs":{},"的":{"docs":{},"增":{"docs":{},"大":{"docs":{},"而":{"docs":{},"增":{"docs":{},"多":{"docs":{},"。":{"docs":{"Hive&HBase/06_hbase数据模型.html":{"ref":"Hive&HBase/06_hbase数据模型.html","tf":0.020833333333333332}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"时":{"docs":{},"间":{"docs":{},"戳":{"docs":{},"(":{"docs":{},"t":{"docs":{},"i":{"docs":{},"m":{"docs":{},"e":{"docs":{},"s":{"docs":{},"t":{"docs":{},"a":{"docs":{},"m":{"docs":{},"p":{"docs":{},")":{"docs":{},"：":{"docs":{},"是":{"docs":{},"列":{"docs":{},"的":{"docs":{},"一":{"docs":{},"个":{"docs":{},"属":{"docs":{},"性":{"docs":{},"，":{"docs":{},"是":{"docs":{},"一":{"docs":{},"个":{"6":{"4":{"docs":{},"位":{"docs":{},"整":{"docs":{},"数":{"docs":{},"。":{"docs":{},"由":{"docs":{},"行":{"docs":{},"键":{"docs":{},"和":{"docs":{},"列":{"docs":{},"确":{"docs":{},"定":{"docs":{},"的":{"docs":{},"单":{"docs":{},"元":{"docs":{},"格":{"docs":{},"，":{"docs":{},"可":{"docs":{},"以":{"docs":{},"存":{"docs":{},"储":{"docs":{},"多":{"docs":{},"个":{"docs":{},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"每":{"docs":{},"个":{"docs":{},"数":{"docs":{},"据":{"docs":{},"含":{"docs":{},"有":{"docs":{},"时":{"docs":{},"间":{"docs":{},"戳":{"docs":{},"属":{"docs":{},"性":{"docs":{},"，":{"docs":{},"数":{"docs":{},"据":{"docs":{},"具":{"docs":{},"有":{"docs":{},"版":{"docs":{},"本":{"docs":{},"特":{"docs":{},"性":{"docs":{},"。":{"docs":{},"可":{"docs":{},"根":{"docs":{},"据":{"docs":{},"版":{"docs":{},"本":{"docs":{},"(":{"docs":{},"v":{"docs":{},"e":{"docs":{},"r":{"docs":{},"s":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"s":{"docs":{},")":{"docs":{},"或":{"docs":{},"时":{"docs":{},"间":{"docs":{},"戳":{"docs":{},"来":{"docs":{},"指":{"docs":{},"定":{"docs":{},"查":{"docs":{},"询":{"docs":{},"历":{"docs":{},"史":{"docs":{},"版":{"docs":{},"本":{"docs":{},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"如":{"docs":{},"果":{"docs":{},"都":{"docs":{},"不":{"docs":{},"指":{"docs":{},"定":{"docs":{},"，":{"docs":{},"则":{"docs":{},"默":{"docs":{},"认":{"docs":{},"返":{"docs":{},"回":{"docs":{},"最":{"docs":{},"新":{"docs":{},"版":{"docs":{},"本":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"。":{"docs":{"Hive&HBase/06_hbase数据模型.html":{"ref":"Hive&HBase/06_hbase数据模型.html","tf":0.020833333333333332}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}},"字":{"docs":{},"段":{"docs":{},"，":{"docs":{},"划":{"docs":{},"分":{"docs":{},"训":{"docs":{},"练":{"docs":{},"集":{"docs":{},"和":{"docs":{},"测":{"docs":{},"试":{"docs":{},"集":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}},"‘":{"docs":{},"表":{"docs":{},"名":{"docs":{},"’":{"docs":{},"，":{"docs":{},"‘":{"docs":{},"r":{"docs":{},"o":{"docs":{},"w":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{},"的":{"docs":{},"值":{"docs":{},"’":{"docs":{},"，":{"docs":{},"’":{"docs":{},"列":{"docs":{},"族":{"docs":{},"：":{"docs":{},"列":{"docs":{},"标":{"docs":{},"识":{"docs":{},"符":{"docs":{},"‘":{"docs":{},"，":{"docs":{},"’":{"docs":{},"值":{"docs":{},"‘":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"名":{"docs":{},"称":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}},"展":{"docs":{},"示":{"docs":{},"现":{"docs":{},"有":{"docs":{},"名":{"docs":{},"称":{"docs":{},"空":{"docs":{},"间":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"默":{"docs":{},"认":{"docs":{},"前":{"2":{"0":{"docs":{},"条":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.0004079135223332653},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}},"docs":{}},"docs":{}}}}}}}}},"插":{"docs":{},"入":{"docs":{},"数":{"docs":{},"据":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676},"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}},"清":{"docs":{},"空":{"docs":{},"数":{"docs":{},"据":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}},"环":{"docs":{},"境":{"docs":{},"变":{"docs":{},"量":{"docs":{},"配":{"docs":{},"置":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}},"配":{"docs":{},"置":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}}},"等":{"docs":{},"条":{"docs":{},"件":{"docs":{},"缩":{"docs":{},"小":{"docs":{},"查":{"docs":{},"询":{"docs":{},"范":{"docs":{},"围":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}},"待":{"docs":{},"r":{"docs":{},"e":{"docs":{},"g":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"v":{"docs":{},"e":{"docs":{},"r":{"docs":{},"汇":{"docs":{},"报":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}}}}}}}}}}}},"连":{"docs":{},"接":{"docs":{},"集":{"docs":{},"群":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}},"池":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}},"限":{"docs":{},"制":{"docs":{},"起":{"docs":{},"始":{"docs":{},"的":{"docs":{},"r":{"docs":{},"o":{"docs":{},"w":{"docs":{},"k":{"docs":{},"e":{"docs":{},"y":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}}}}},"输":{"docs":{},"出":{"docs":{},"两":{"docs":{},"行":{"docs":{"Hive&HBase/07_hbase的安装与shell操作.html":{"ref":"Hive&HBase/07_hbase的安装与shell操作.html","tf":0.0022026431718061676}}}}}}}},"已":{"docs":{},"被":{"docs":{},"广":{"docs":{},"泛":{"docs":{},"应":{"docs":{},"用":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0028328611898017}}}}}}},"经":{"docs":{},"为":{"docs":{},"我":{"docs":{},"们":{"docs":{},"创":{"docs":{},"建":{"docs":{},"好":{"docs":{},"了":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}}}}}},"缩":{"docs":{},"小":{"docs":{},"查":{"docs":{},"询":{"docs":{},"范":{"docs":{},"围":{"docs":{"Hive&HBase/08_HappyBase操作HBase.html":{"ref":"Hive&HBase/08_HappyBase操作HBase.html","tf":0.0056657223796034}}}}}}}},"①":{"docs":{},"与":{"docs":{},"z":{"docs":{},"o":{"docs":{},"o":{"docs":{},"k":{"docs":{},"e":{"docs":{},"e":{"docs":{},"p":{"docs":{},"e":{"docs":{},"r":{"docs":{},"通":{"docs":{},"信":{"docs":{},",":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}}}}}}}}},"为":{"docs":{},"r":{"docs":{},"e":{"docs":{},"g":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}}},"保":{"docs":{},"证":{"docs":{},"任":{"docs":{},"何":{"docs":{},"时":{"docs":{},"候":{"docs":{},"，":{"docs":{},"集":{"docs":{},"群":{"docs":{},"中":{"docs":{},"只":{"docs":{},"有":{"docs":{},"一":{"docs":{},"个":{"docs":{},"r":{"docs":{},"u":{"docs":{},"n":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}}}}}}}}}}}}},"维":{"docs":{},"护":{"docs":{},"m":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"分":{"docs":{},"配":{"docs":{},"给":{"docs":{},"它":{"docs":{},"的":{"docs":{},"r":{"docs":{},"e":{"docs":{},"g":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"，":{"docs":{},"处":{"docs":{},"理":{"docs":{},"对":{"docs":{},"这":{"docs":{},"些":{"docs":{},"r":{"docs":{},"e":{"docs":{},"g":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"的":{"docs":{},"i":{"docs":{},"o":{"docs":{},"请":{"docs":{},"求":{"docs":{},"；":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"②":{"docs":{},"使":{"docs":{},"用":{"docs":{},"h":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}}},"存":{"docs":{},"贮":{"docs":{},"所":{"docs":{},"有":{"docs":{},"r":{"docs":{},"e":{"docs":{},"g":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"的":{"docs":{},"寻":{"docs":{},"址":{"docs":{},"入":{"docs":{},"口":{"docs":{},"，":{"docs":{},"包":{"docs":{},"括":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}}}}}}}}}}}}}},"负":{"docs":{},"责":{"docs":{},"r":{"docs":{},"e":{"docs":{},"g":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}},"切":{"docs":{},"分":{"docs":{},"在":{"docs":{},"运":{"docs":{},"行":{"docs":{},"过":{"docs":{},"程":{"docs":{},"中":{"docs":{},"变":{"docs":{},"得":{"docs":{},"过":{"docs":{},"大":{"docs":{},"的":{"docs":{},"r":{"docs":{},"e":{"docs":{},"g":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"。":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}}}}}}}}}}}}}}}}}}},"③":{"docs":{},"c":{"docs":{},"l":{"docs":{},"i":{"docs":{},"e":{"docs":{},"n":{"docs":{},"t":{"docs":{},"与":{"docs":{},"h":{"docs":{},"m":{"docs":{},"a":{"docs":{},"s":{"docs":{},"t":{"docs":{},"e":{"docs":{},"r":{"docs":{},"进":{"docs":{},"行":{"docs":{},"通":{"docs":{},"信":{"docs":{},"进":{"docs":{},"行":{"docs":{},"管":{"docs":{},"理":{"docs":{},"类":{"docs":{},"操":{"docs":{},"作":{"docs":{},"；":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}}}}}}}}}}}}}}}}}}}}}},"发":{"docs":{},"现":{"docs":{},"失":{"docs":{},"效":{"docs":{},"的":{"docs":{},"r":{"docs":{},"e":{"docs":{},"g":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}}}}}}},"实":{"docs":{},"时":{"docs":{},"监":{"docs":{},"控":{"docs":{},"r":{"docs":{},"e":{"docs":{},"g":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}}}}}}},"④":{"docs":{},"c":{"docs":{},"l":{"docs":{},"i":{"docs":{},"e":{"docs":{},"n":{"docs":{},"t":{"docs":{},"与":{"docs":{},"h":{"docs":{},"r":{"docs":{},"e":{"docs":{},"g":{"docs":{},"i":{"docs":{},"o":{"docs":{},"n":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"v":{"docs":{},"e":{"docs":{},"r":{"docs":{},"进":{"docs":{},"行":{"docs":{},"数":{"docs":{},"据":{"docs":{},"读":{"docs":{},"写":{"docs":{},"类":{"docs":{},"操":{"docs":{},"作":{"docs":{},"。":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"h":{"docs":{},"d":{"docs":{},"f":{"docs":{},"s":{"docs":{},"上":{"docs":{},"的":{"docs":{},"垃":{"docs":{},"圾":{"docs":{},"文":{"docs":{},"件":{"docs":{},"回":{"docs":{},"收":{"docs":{},"；":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}}}}}}}}},"存":{"docs":{},"储":{"docs":{},"h":{"docs":{},"b":{"docs":{},"a":{"docs":{},"s":{"docs":{},"e":{"docs":{},"的":{"docs":{},"s":{"docs":{},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},"m":{"docs":{},"a":{"docs":{},"，":{"docs":{},"包":{"docs":{},"括":{"docs":{},"有":{"docs":{},"哪":{"docs":{},"些":{"docs":{},"t":{"docs":{},"a":{"docs":{},"b":{"docs":{},"l":{"docs":{},"e":{"docs":{},"，":{"docs":{},"每":{"docs":{},"个":{"docs":{},"t":{"docs":{},"a":{"docs":{},"b":{"docs":{},"l":{"docs":{},"e":{"docs":{},"有":{"docs":{},"哪":{"docs":{},"些":{"docs":{},"c":{"docs":{},"o":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"n":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"⑤":{"docs":{},"处":{"docs":{},"理":{"docs":{},"用":{"docs":{},"户":{"docs":{},"对":{"docs":{},"表":{"docs":{},"的":{"docs":{},"增":{"docs":{},"删":{"docs":{},"改":{"docs":{},"查":{"docs":{},"操":{"docs":{},"作":{"docs":{},"。":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}}}}}}}}}}}},"角":{"docs":{},"色":{"docs":{},"功":{"docs":{},"能":{"docs":{},"：":{"docs":{"Hive&HBase/10_HBase组件.html":{"ref":"Hive&HBase/10_HBase组件.html","tf":0.012345679012345678}}}}}}},"入":{"docs":{},"门":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102}}}},"课":{"docs":{},"程":{"docs":{},"目":{"docs":{},"标":{"docs":{"day05_Spark_core/spark_core_3.html":{"ref":"day05_Spark_core/spark_core_3.html","tf":0.0022727272727272726},"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}},"：":{"docs":{"day05_Spark_core/spark_core_1.html":{"ref":"day05_Spark_core/spark_core_1.html","tf":0.010101010101010102},"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617},"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}}}},"`":{"docs":{},"/":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}},"弹":{"docs":{},"性":{"docs":{},":":{"docs":{},"并":{"docs":{},"不":{"docs":{},"是":{"docs":{},"指":{"docs":{},"他":{"docs":{},"可":{"docs":{},"以":{"docs":{},"动":{"docs":{},"态":{"docs":{},"扩":{"docs":{},"展":{"docs":{},"，":{"docs":{},"而":{"docs":{},"是":{"docs":{},"容":{"docs":{},"错":{"docs":{},"机":{"docs":{},"制":{"docs":{},"。":{"docs":{"day05_Spark_core/spark_core_2.html":{"ref":"day05_Spark_core/spark_core_2.html","tf":0.00425531914893617}}}}}}}}}}}}}}}}}}}}}}}},"包":{"docs":{},"含":{"docs":{},"了":{"docs":{},"i":{"docs":{},"p":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}},"：":{"docs":{},"缺":{"docs":{},"失":{"docs":{},"值":{"docs":{},"，":{"docs":{},"超":{"docs":{},"过":{"docs":{},"正":{"docs":{},"常":{"docs":{},"范":{"docs":{},"围":{"docs":{},"内":{"docs":{},"的":{"docs":{},"较":{"docs":{},"大":{"docs":{},"值":{"docs":{},"或":{"docs":{},"较":{"docs":{},"小":{"docs":{},"值":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}}}}}}}}}}}}}},"括":{"docs":{},"以":{"docs":{},"下":{"docs":{},"四":{"docs":{},"种":{"docs":{},"：":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}}},"拷":{"docs":{},"贝":{"docs":{},"到":{"docs":{},"p":{"docs":{},"y":{"docs":{},"c":{"docs":{},"h":{"docs":{},"a":{"docs":{},"r":{"docs":{},"m":{"docs":{},"使":{"docs":{},"用":{"docs":{},"的":{"docs":{},"：":{"docs":{},"x":{"docs":{},"x":{"docs":{},"x":{"docs":{},"\\":{"docs":{},"p":{"docs":{},"y":{"docs":{},"t":{"docs":{},"h":{"docs":{},"o":{"docs":{},"n":{"docs":{},"\\":{"docs":{},"p":{"docs":{},"y":{"docs":{},"t":{"docs":{},"h":{"docs":{},"o":{"docs":{},"n":{"3":{"6":{"docs":{},"\\":{"docs":{},"l":{"docs":{},"i":{"docs":{},"b":{"docs":{},"\\":{"docs":{},"s":{"docs":{},"i":{"docs":{},"t":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}}}}}}}},"docs":{}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"访":{"docs":{},"问":{"docs":{},"时":{"docs":{},"间":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}},"的":{"docs":{},"p":{"docs":{},"v":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}},"t":{"docs":{},"o":{"docs":{},"p":{"docs":{},"n":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}}},"u":{"docs":{},"v":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}},"地":{"docs":{},"址":{"docs":{},".":{"docs":{},".":{"docs":{},".":{"docs":{},"信":{"docs":{},"息":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}}}}}},"请":{"docs":{},"求":{"docs":{},"方":{"docs":{},"式":{"docs":{"day05_Spark_core/spark_core_4.html":{"ref":"day05_Spark_core/spark_core_4.html","tf":0.003355704697986577}}}}}}}}},"思":{"docs":{},"路":{"docs":{"day05_Spark_core/spark_core_5.html":{"ref":"day05_Spark_core/spark_core_5.html","tf":0.006622516556291391}}}},"序":{"docs":{},"列":{"docs":{},"化":{"docs":{},"和":{"docs":{},"反":{"docs":{},"序":{"docs":{},"列":{"docs":{},"化":{"docs":{},"开":{"docs":{},"销":{"docs":{},"大":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}}}}}}}}}}}}},"底":{"docs":{},"层":{"docs":{},"很":{"docs":{},"多":{"docs":{},"东":{"docs":{},"西":{"docs":{},"还":{"docs":{},"是":{"docs":{},"依":{"docs":{},"赖":{"docs":{},"于":{"docs":{},"h":{"docs":{},"i":{"docs":{},"v":{"docs":{},"e":{"docs":{},"，":{"docs":{},"修":{"docs":{},"改":{"docs":{},"了":{"docs":{},"内":{"docs":{},"存":{"docs":{},"管":{"docs":{},"理":{"docs":{},"、":{"docs":{},"物":{"docs":{},"理":{"docs":{},"计":{"docs":{},"划":{"docs":{},"、":{"docs":{},"执":{"docs":{},"行":{"docs":{},"三":{"docs":{},"个":{"docs":{},"模":{"docs":{},"块":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"频":{"docs":{},"繁":{"docs":{},"的":{"docs":{},"创":{"docs":{},"建":{"docs":{},"和":{"docs":{},"销":{"docs":{},"毁":{"docs":{},"对":{"docs":{},"象":{"docs":{},"造":{"docs":{},"成":{"docs":{},"大":{"docs":{},"量":{"docs":{},"的":{"docs":{},"g":{"docs":{},"c":{"docs":{"day06_Spark_sql&Spark_streaming/s1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.1.html","tf":0.015873015873015872}}}}}}}}}}}}}}}}}}},"<":{"docs":{},">":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}},"介":{"docs":{},"绍":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965},"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}},"右":{"docs":{},"侧":{"docs":{},"的":{"docs":{},"d":{"docs":{},"a":{"docs":{},"t":{"docs":{},"a":{"docs":{},"f":{"docs":{},"r":{"docs":{},"a":{"docs":{},"m":{"docs":{},"e":{"docs":{},"提":{"docs":{},"供":{"docs":{},"了":{"docs":{},"详":{"docs":{},"细":{"docs":{},"的":{"docs":{},"结":{"docs":{},"构":{"docs":{},"信":{"docs":{},"息":{"docs":{},"（":{"docs":{},"s":{"docs":{},"c":{"docs":{},"h":{"docs":{},"e":{"docs":{},"m":{"docs":{},"a":{"docs":{},"—":{"docs":{},"—":{"docs":{},"每":{"docs":{},"列":{"docs":{},"的":{"docs":{},"名":{"docs":{},"称":{"docs":{},"，":{"docs":{},"类":{"docs":{},"型":{"docs":{},"）":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"增":{"docs":{},"加":{"docs":{},"一":{"docs":{},"列":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}},"左":{"docs":{},"侧":{"docs":{},"的":{"docs":{},"r":{"docs":{},"d":{"docs":{},"d":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}},"测":{"docs":{},"试":{"docs":{},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{},"中":{"docs":{},"有":{"docs":{},"些":{"docs":{},"类":{"docs":{},"别":{"docs":{},"在":{"docs":{},"训":{"docs":{},"练":{"docs":{},"集":{"docs":{},"中":{"docs":{},"是":{"docs":{},"不":{"docs":{},"存":{"docs":{},"在":{"docs":{},"的":{"docs":{},"，":{"docs":{},"找":{"docs":{},"到":{"docs":{},"这":{"docs":{},"些":{"docs":{},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{},"做":{"docs":{},"后":{"docs":{},"续":{"docs":{},"处":{"docs":{},"理":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"存":{"docs":{},"储":{"docs":{},"的":{"docs":{},"模":{"docs":{},"型":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}}}}},"样":{"docs":{},"本":{"docs":{},"个":{"docs":{},"数":{"docs":{},"：":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}},"综":{"docs":{},"合":{"docs":{},"案":{"docs":{},"例":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}},"采":{"docs":{},"样":{"docs":{},"数":{"docs":{},"据":{"docs":{"day06_Spark_sql&Spark_streaming/s1.2.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.2.html","tf":0.0034965034965034965}}}}}},"嵌":{"docs":{},"套":{"docs":{},"结":{"docs":{},"构":{"docs":{},"的":{"docs":{},"j":{"docs":{},"s":{"docs":{},"o":{"docs":{},"n":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}}}}}}},"无":{"docs":{},"嵌":{"docs":{},"套":{"docs":{},"结":{"docs":{},"构":{"docs":{},"的":{"docs":{},"j":{"docs":{},"s":{"docs":{},"o":{"docs":{},"n":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}},"数":{"docs":{},"据":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}}}}}}}}},"状":{"docs":{},"态":{"docs":{},"：":{"docs":{},"指":{"docs":{},"的":{"docs":{},"是":{"docs":{},"每":{"docs":{},"个":{"docs":{},"时":{"docs":{},"间":{"docs":{},"片":{"docs":{},"段":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"之":{"docs":{},"间":{"docs":{},"是":{"docs":{},"没":{"docs":{},"有":{"docs":{},"关":{"docs":{},"联":{"docs":{},"的":{"docs":{},"。":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}}}}}}}}}}}}}}}}}}},"静":{"docs":{},"态":{"docs":{},"j":{"docs":{},"s":{"docs":{},"o":{"docs":{},"n":{"docs":{},"数":{"docs":{},"据":{"docs":{},"的":{"docs":{},"读":{"docs":{},"取":{"docs":{},"和":{"docs":{},"操":{"docs":{},"作":{"docs":{"day06_Spark_sql&Spark_streaming/s1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.3.html","tf":0.0038461538461538464}}}}}}}}}}}}}}}},"先":{"docs":{},"计":{"docs":{},"算":{"docs":{},"均":{"docs":{},"值":{"docs":{},"，":{"docs":{},"并":{"docs":{},"组":{"docs":{},"织":{"docs":{},"成":{"docs":{},"一":{"docs":{},"个":{"docs":{},"字":{"docs":{},"典":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}}}}}}},"异":{"docs":{},"常":{"docs":{},"值":{"docs":{},"处":{"docs":{},"理":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}},"：":{"docs":{},"不":{"docs":{},"属":{"docs":{},"于":{"docs":{},"正":{"docs":{},"常":{"docs":{},"的":{"docs":{},"值":{"docs":{"day06_Spark_sql&Spark_streaming/s1.4.html":{"ref":"day06_Spark_sql&Spark_streaming/s1.4.html","tf":0.00228310502283105}}}}}}}}}}}}},"之":{"docs":{},"前":{"docs":{},"我":{"docs":{},"们":{"docs":{},"接":{"docs":{},"触":{"docs":{},"的":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.1.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.1.html","tf":0.015625}}}}}}}}}}}}}},"典":{"docs":{},"型":{"docs":{},"案":{"docs":{},"例":{"docs":{},"：":{"docs":{},"热":{"docs":{},"点":{"docs":{},"搜":{"docs":{},"索":{"docs":{},"词":{"docs":{},"滑":{"docs":{},"动":{"docs":{},"统":{"docs":{},"计":{"docs":{},"，":{"docs":{},"每":{"docs":{},"隔":{"1":{"0":{"docs":{},"秒":{"docs":{},"，":{"docs":{},"统":{"docs":{},"计":{"docs":{},"最":{"docs":{},"近":{"6":{"0":{"docs":{},"秒":{"docs":{},"钟":{"docs":{},"的":{"docs":{},"搜":{"docs":{},"索":{"docs":{},"词":{"docs":{},"的":{"docs":{},"搜":{"docs":{},"索":{"docs":{},"频":{"docs":{},"次":{"docs":{},"，":{"docs":{},"并":{"docs":{},"打":{"docs":{},"印":{"docs":{},"出":{"docs":{},"最":{"docs":{},"靠":{"docs":{},"前":{"docs":{},"的":{"3":{"docs":{},"个":{"docs":{},"搜":{"docs":{},"索":{"docs":{},"词":{"docs":{},"出":{"docs":{},"现":{"docs":{},"次":{"docs":{},"数":{"docs":{},"。":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}}}},"docs":{}}}}}}}}}}}}}}}}}}}}}},"docs":{}},"docs":{}}}}}}}},"docs":{}},"docs":{}}}}}}}}}}}}}}}}}},"步":{"docs":{},"骤":{"docs":{},"：":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}},"滑":{"docs":{},"动":{"docs":{},"间":{"docs":{},"隔":{"docs":{},"g":{"docs":{},"：":{"docs":{},"控":{"docs":{},"制":{"docs":{},"每":{"docs":{},"隔":{"docs":{},"多":{"docs":{},"长":{"docs":{},"时":{"docs":{},"间":{"docs":{},"做":{"docs":{},"一":{"docs":{},"次":{"docs":{},"运":{"docs":{},"算":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}}}}}}}}}}}}},"窗":{"docs":{},"口":{"docs":{},"的":{"docs":{},"长":{"docs":{},"度":{"docs":{},"控":{"docs":{},"制":{"docs":{},"考":{"docs":{},"虑":{"docs":{},"前":{"docs":{},"几":{"docs":{},"批":{"docs":{},"次":{"docs":{},"数":{"docs":{},"据":{"docs":{},"量":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}}}}}}}},"长":{"docs":{},"度":{"docs":{},"l":{"docs":{},"：":{"docs":{},"运":{"docs":{},"算":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"量":{"docs":{"day06_Spark_sql&Spark_streaming/ss1.3.html":{"ref":"day06_Spark_sql&Spark_streaming/ss1.3.html","tf":0.003816793893129771}}}}}}}}}}}}}},"喜":{"docs":{},"欢":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}},"宝":{"docs":{},"贝":{"docs":{},"的":{"docs":{},"价":{"docs":{},"格":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}},"涉":{"docs":{},"及":{"docs":{},"技":{"docs":{},"术":{"docs":{},"：":{"docs":{},"f":{"docs":{},"l":{"docs":{},"u":{"docs":{},"m":{"docs":{},"e":{"docs":{},"、":{"docs":{},"k":{"docs":{},"a":{"docs":{},"f":{"docs":{},"k":{"docs":{},"a":{"docs":{},"、":{"docs":{},"s":{"docs":{},"p":{"docs":{},"a":{"docs":{},"r":{"docs":{},"k":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}}}}}}}}}}}}}}}}}},"缓":{"docs":{},"存":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.010526315789473684}}}},"脱":{"docs":{},"敏":{"docs":{},"过":{"docs":{},"的":{"docs":{},"品":{"docs":{},"牌":{"docs":{},"i":{"docs":{},"d":{"docs":{},"；":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}},"广":{"docs":{},"告":{"docs":{},"主":{"docs":{},"i":{"docs":{},"d":{"docs":{},"；":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}}}}}},"项":{"docs":{},"目":{"docs":{},"实":{"docs":{},"现":{"docs":{},"分":{"docs":{},"析":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}},"效":{"docs":{},"果":{"docs":{},"展":{"docs":{},"示":{"docs":{"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"ref":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","tf":0.005263157894736842}}}}}}}},"偏":{"docs":{},"好":{"docs":{},"打":{"docs":{},"分":{"docs":{},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}},"评":{"docs":{},"分":{"docs":{},"规":{"docs":{},"则":{"docs":{},"：":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}}}}}}},"判":{"docs":{},"断":{"docs":{},"数":{"docs":{},"据":{"docs":{},"是":{"docs":{},"否":{"docs":{},"有":{"docs":{},"空":{"docs":{},"值":{"docs":{},"：":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}}}}}}},"厚":{"docs":{},"厚":{"docs":{},"的":{"docs":{},"一":{"docs":{},"块":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}},"约":{"1":{"1":{"3":{"docs":{},"w":{"docs":{},"用":{"docs":{},"户":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}},"docs":{}},"2":{"9":{"6":{"8":{"docs":{},"类":{"docs":{},"别":{"docs":{},"i":{"docs":{},"d":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"2":{"6":{"0":{"0":{"docs":{},"w":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}},"docs":{}},"4":{"6":{"0":{"5":{"6":{"1":{"docs":{},"品":{"docs":{},"牌":{"docs":{},"i":{"docs":{},"d":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}}}}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"7":{"docs":{},"亿":{"docs":{},"条":{"docs":{},"目":{"7":{"2":{"3":{"2":{"6":{"8":{"1":{"3":{"4":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0008156606851549756}}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}},"docs":{}}}},"天":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}},"8":{"5":{"docs":{},"w":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}},"该":{"docs":{},"偏":{"docs":{},"好":{"docs":{},"权":{"docs":{},"重":{"docs":{},"比":{"docs":{},"例":{"docs":{},"，":{"docs":{},"次":{"docs":{},"数":{"docs":{},"上":{"docs":{},"限":{"docs":{},"仅":{"docs":{},"供":{"docs":{},"参":{"docs":{},"考":{"docs":{},"，":{"docs":{},"具":{"docs":{},"体":{"docs":{},"数":{"docs":{},"值":{"docs":{},"应":{"docs":{},"根":{"docs":{},"据":{"docs":{},"产":{"docs":{},"品":{"docs":{},"业":{"docs":{},"务":{"docs":{},"场":{"docs":{},"景":{"docs":{},"权":{"docs":{},"衡":{"docs":{"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"ref":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","tf":0.0016313213703099511}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"时":{"docs":{},"间":{"docs":{},"之":{"docs":{},"前":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"为":{"docs":{},"训":{"docs":{},"练":{"docs":{},"样":{"docs":{},"本":{"docs":{},"，":{"docs":{},"该":{"docs":{},"时":{"docs":{},"间":{"docs":{},"以":{"docs":{},"后":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"为":{"docs":{},"测":{"docs":{},"试":{"docs":{},"样":{"docs":{},"本":{"docs":{},"：":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"剔":{"docs":{},"除":{"docs":{},"掉":{"docs":{},"缺":{"docs":{},"失":{"docs":{},"值":{"docs":{},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"将":{"docs":{},"余":{"docs":{},"下":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"作":{"docs":{},"为":{"docs":{},"训":{"docs":{},"练":{"docs":{},"数":{"docs":{},"据":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}},"冗":{"docs":{},"余":{"docs":{},"、":{"docs":{},"不":{"docs":{},"需":{"docs":{},"要":{"docs":{},"的":{"docs":{},"字":{"docs":{},"段":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}},"空":{"docs":{},"值":{"docs":{},"数":{"docs":{},"据":{"docs":{},"后":{"docs":{},"，":{"docs":{},"还":{"docs":{},"剩":{"docs":{},"：":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}},"含":{"docs":{},"缺":{"docs":{},"失":{"docs":{},"值":{"docs":{},"的":{"docs":{},"特":{"docs":{},"征":{"docs":{},"情":{"docs":{},"况":{"docs":{},":":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}},"填":{"docs":{},"充":{"docs":{},"方":{"docs":{},"案":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},"：":{"docs":{},"结":{"docs":{},"合":{"docs":{},"用":{"docs":{},"户":{"docs":{},"的":{"docs":{},"其":{"docs":{},"他":{"docs":{},"特":{"docs":{},"征":{"docs":{},"值":{"docs":{},"，":{"docs":{},"利":{"docs":{},"用":{"docs":{},"随":{"docs":{},"机":{"docs":{},"森":{"docs":{},"林":{"docs":{},"算":{"docs":{},"法":{"docs":{},"进":{"docs":{},"行":{"docs":{},"预":{"docs":{},"测":{"docs":{},"；":{"docs":{},"但":{"docs":{},"产":{"docs":{},"生":{"docs":{},"了":{"docs":{},"大":{"docs":{},"量":{"docs":{},"人":{"docs":{},"为":{"docs":{},"构":{"docs":{},"建":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"一":{"docs":{},"定":{"docs":{},"程":{"docs":{},"度":{"docs":{},"上":{"docs":{},"增":{"docs":{},"加":{"docs":{},"了":{"docs":{},"数":{"docs":{},"据":{"docs":{},"的":{"docs":{},"噪":{"docs":{},"音":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"替":{"docs":{},"换":{"docs":{},"掉":{"docs":{},"n":{"docs":{},"u":{"docs":{},"l":{"docs":{},"l":{"docs":{},"字":{"docs":{},"符":{"docs":{},"串":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}},"，":{"docs":{},"替":{"docs":{},"换":{"docs":{},"掉":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266},"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}}}}}}}},"树":{"docs":{},"的":{"docs":{},"棵":{"docs":{},"数":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}},"样":{"docs":{},"本":{"docs":{},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{},"总":{"docs":{},"条":{"docs":{},"目":{"docs":{},"数":{"docs":{},"：":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}},"p":{"docs":{},"i":{"docs":{},"d":{"docs":{},"特":{"docs":{},"征":{"docs":{},"处":{"docs":{},"理":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}},"热":{"docs":{},"独":{"docs":{},"编":{"docs":{},"码":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}},"时":{"docs":{},"，":{"docs":{},"必":{"docs":{},"须":{"docs":{},"先":{"docs":{},"将":{"docs":{},"待":{"docs":{},"处":{"docs":{},"理":{"docs":{},"字":{"docs":{},"段":{"docs":{},"转":{"docs":{},"为":{"docs":{},"字":{"docs":{},"符":{"docs":{},"串":{"docs":{},"类":{"docs":{},"型":{"docs":{},"才":{"docs":{},"可":{"docs":{},"处":{"docs":{},"理":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}},"是":{"docs":{},"一":{"docs":{},"种":{"docs":{},"经":{"docs":{},"典":{"docs":{},"编":{"docs":{},"码":{"docs":{},"，":{"docs":{},"是":{"docs":{},"使":{"docs":{},"用":{"docs":{},"n":{"docs":{},"位":{"docs":{},"状":{"docs":{},"态":{"docs":{},"寄":{"docs":{},"存":{"docs":{},"器":{"docs":{},"(":{"docs":{},"如":{"0":{"docs":{},"和":{"1":{"docs":{},")":{"docs":{},"来":{"docs":{},"对":{"docs":{},"n":{"docs":{},"个":{"docs":{},"状":{"docs":{},"态":{"docs":{},"进":{"docs":{},"行":{"docs":{},"编":{"docs":{},"码":{"docs":{},"，":{"docs":{},"每":{"docs":{},"个":{"docs":{},"状":{"docs":{},"态":{"docs":{},"都":{"docs":{},"由":{"docs":{},"他":{"docs":{},"独":{"docs":{},"立":{"docs":{},"的":{"docs":{},"寄":{"docs":{},"存":{"docs":{},"器":{"docs":{},"位":{"docs":{},"，":{"docs":{},"并":{"docs":{},"且":{"docs":{},"在":{"docs":{},"任":{"docs":{},"意":{"docs":{},"时":{"docs":{},"候":{"docs":{},"，":{"docs":{},"其":{"docs":{},"中":{"docs":{},"只":{"docs":{},"有":{"docs":{},"一":{"docs":{},"位":{"docs":{},"有":{"docs":{},"效":{"docs":{},"。":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"docs":{}}},"docs":{}}}}}}}}}}}}}}}}}}}}}}}},"编":{"docs":{},"码":{"docs":{},"中":{"docs":{},"：":{"docs":{},"\"":{"docs":{},"p":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"\"":{"docs":{},"特":{"docs":{},"征":{"docs":{},"对":{"docs":{},"应":{"docs":{},"关":{"docs":{},"系":{"docs":{},":":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}}}}},"时":{"docs":{},"，":{"docs":{},"必":{"docs":{},"须":{"docs":{},"先":{"docs":{},"将":{"docs":{},"待":{"docs":{},"处":{"docs":{},"理":{"docs":{},"字":{"docs":{},"段":{"docs":{},"转":{"docs":{},"为":{"docs":{},"字":{"docs":{},"符":{"docs":{},"串":{"docs":{},"类":{"docs":{},"型":{"docs":{},"才":{"docs":{},"可":{"docs":{},"处":{"docs":{},"理":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}}}}}}}}}}},"空":{"docs":{},"值":{"docs":{},"占":{"docs":{},"比":{"docs":{},"：":{"3":{"2":{"docs":{},".":{"4":{"9":{"docs":{},"%":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}}},"docs":{}},"5":{"4":{"docs":{},".":{"2":{"4":{"docs":{},"%":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}},"docs":{}},"docs":{}}},"docs":{}},"docs":{}}}}}},"筛":{"docs":{},"选":{"docs":{},"出":{"docs":{},"缺":{"docs":{},"失":{"docs":{},"值":{"docs":{},"条":{"docs":{},"目":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}},"指":{"docs":{},"定":{"docs":{},"字":{"docs":{},"段":{"docs":{},"数":{"docs":{},"据":{"docs":{},"，":{"docs":{},"构":{"docs":{},"建":{"docs":{},"新":{"docs":{},"的":{"docs":{},"数":{"docs":{},"据":{"docs":{},"集":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}}}}}}}}}}}}}}}}},"还":{"docs":{},"原":{"docs":{},"预":{"docs":{},"测":{"docs":{},"值":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}},"除":{"docs":{},"了":{"docs":{},"前":{"docs":{},"面":{"docs":{},"处":{"docs":{},"理":{"docs":{},"的":{"docs":{},"p":{"docs":{},"v":{"docs":{},"a":{"docs":{},"l":{"docs":{},"u":{"docs":{},"e":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"和":{"docs":{},"n":{"docs":{},"e":{"docs":{},"w":{"docs":{},"_":{"docs":{},"u":{"docs":{},"s":{"docs":{},"e":{"docs":{},"r":{"docs":{},"_":{"docs":{},"c":{"docs":{},"l":{"docs":{},"a":{"docs":{},"s":{"docs":{},"s":{"docs":{},"_":{"docs":{},"l":{"docs":{},"e":{"docs":{},"v":{"docs":{},"e":{"docs":{},"l":{"docs":{},"需":{"docs":{},"要":{"docs":{},"作":{"docs":{},"为":{"docs":{},"特":{"docs":{},"征":{"docs":{},"以":{"docs":{},"外":{"docs":{},"，":{"docs":{},"(":{"docs":{},"能":{"docs":{},"体":{"docs":{},"现":{"docs":{},"出":{"docs":{},"用":{"docs":{},"户":{"docs":{},"的":{"docs":{},"购":{"docs":{},"买":{"docs":{},"力":{"docs":{},"特":{"docs":{},"征":{"docs":{},")":{"docs":{},"，":{"docs":{},"还":{"docs":{},"有":{"docs":{},"：":{"docs":{"day07_推荐系统案例/03_CTR预估数据准备.html":{"ref":"day07_推荐系统案例/03_CTR预估数据准备.html","tf":0.00020395676116663266}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}}},"四":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}},"年":{"docs":{},"龄":{"docs":{},"等":{"docs":{},"级":{"docs":{},"，":{"1":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517}}},"docs":{}}}}}},"载":{"docs":{},"入":{"docs":{},"训":{"docs":{},"练":{"docs":{},"好":{"docs":{},"的":{"docs":{},"模":{"docs":{},"型":{"docs":{"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"ref":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","tf":0.0003979307600477517},"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}}}}}}}},"×":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.003484320557491289}}},"五":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}},"手":{"docs":{},"动":{"docs":{},"释":{"docs":{},"放":{"docs":{},"一":{"docs":{},"些":{"docs":{},"内":{"docs":{},"存":{"docs":{"day07_推荐系统案例/05_离线推荐处理.html":{"ref":"day07_推荐系统案例/05_离线推荐处理.html","tf":0.0017421602787456446}}}}}}}}}},"六":{"docs":{"day07_推荐系统案例/06_实时推荐.html":{"ref":"day07_推荐系统案例/06_实时推荐.html","tf":0.0021141649048625794}}}},"length":9818},"corpusTokens":["!=","\"","\"\"\"{","\"\":","\")","\"))","\")).filter(lambda","\")).map(lambda","\",","\"./datasets/ml","\"/miniconda2/envs/py365/bin/python\"","\"/root/bigdata/spark\"","\"01001\",","\"01002\",","\"192.168.19.137\"","\"4\"),","\"430548_1007\")","\"430548_1007\"),","\"6g\"),","\"__main__\":","\"adgroupid\").\\","\"adgroupid\",","\"agawam\",","\"age_level\",","\"age_level\":","\"bob\",","\"bob\",\"stanley\",","\"brandid\").\\","\"campaignid\").\\","\"cateid\")","\"cateid\").\\","\"cateid\",","\"city\"","\"clk\",","\"cms_group_id\",","\"cms_group_id\":","\"cms_segid\",","\"columnprefixfilter('username')\"","\"copyright\",","\"credits\"","\"cushman\",","\"customerid\").\\","\"dnspod","\"features\"])","\"final_gender_code\",","\"final_gender_code\":","\"genres\",\"tags\"]","\"get","\"head","\"help\",","\"http://cos.name/category/software/packages/\"","\"http://www.angularjs.cn/a00n\"","\"id\"","\"iid\",","\"item","\"license\"","\"lily\",","\"ma\"","\"mae\":","\"mae:","\"movieid\",","\"movieid\":","\"mozilla/4.0","\"mozilla/5.0","\"new_user_class_level\"","\"new_user_class_level\":","\"nucl_onehot_value\"","\"nucl_onehot_value\"]).setoutputcol(\"features\").transform(user_profile_df3)","\"occupation\",","\"occupation\":","\"outer\")","\"peter\",","\"pid\",","\"pid_value\",","\"pl_onehot_value\",","\"pop\"","\"prediction\").sort(\"probability\").show(100)","\"preprocessingbehaviorlog\"","\"price\"","\"price\",","\"price\":","\"probability\",","\"profile\",","\"pvalue_level\",","\"pvalue_level\":","\"rating\":","\"rating\"])","\"rating\"]):","\"rmse\":","\"shopping_level\",","\"shopping_level\":","\"spark://192.168.19.137:7077\"","\"state\"","\"timestamp\").\\","\"timestamp\",","\"title\",","\"true\")","\"user2\",","\"user3\",","\"user4\",","\"user5\"]","\"userid\")","\"userid\").\\","\"userid\",","\"warn\".","\"weights\"])","\"well\",","\"well\"]","\"上海\",","\"动作\"、\"吴京\"、\"吴刚\"、\"张翰\"、\"大陆电影\"、\"国产\"、\"爱国\"、\"军事\"等等一系列标签是不是都可以贴上","\"华为\",","\"女\"][0,1]","\"小米\",","\"广州\"][0,1,2]","\"微软\"][0,1,2,3]","\"空值占比：%0.2f%%\"%(nul_na_count/t_count*100))","\"空值占比：%0.2f%%\"%(pl_na_count/t_count*100))","\"表名称\"","#","##","#((纬度,精度),1)","#00000000","#1101111100000000","#11011111111100110000000000000000","#================","#==================","#====================数据集拆成两部分","#api","#connecthbase()","#createtable()","#deletedata()","#deletetable()","#filter","#fraction：采样比例","#getquery()","#hbase中一般存储数据量都很大","#insertdata()","#limit=>2","#mappartit","#number_epoch","#result","#row_start","#seed：随机种子","#sort_valu","#startrow","#structtype：schema的整体结构，表示json的对象结构","#withreplacement：是否有放回的采样","#xxxstype:指的是某一列的数据类型","#‭","#‭11011111‬","#为数据添加列名","#介于mapper和reducer之间，用于临时的将mapper输出的数据进行统计","#传入两个step","#使用内部的schema","#依照已有的dataframe，创建一个临时的表(相当于mysql数据库中的一个表)，这样就可以用纯sql语句进行数据操作","#保存退出后","#全表查询","#关闭连接","#准备","#函数封装","#列名","#创建datafram","#创建udf，udf函数需要两个参数：","#创建一个广播变量","#创建和hbase的连接","#删除一列","#利用heapq将数据进行排序，将最大的2个取出","#到inverted_t","#加载csv类型的数据并转换为datafram","#参数2","#参数2：指定执行计算的时间间隔","#参数3","#参数4","#取出序列数据中的前n个","#只有被压缩后的json文件内容，才能被spark","#含有outer","#启动streamingcontext","#在dataframe中需要通过udf将自定义函数封装成udf函数再交给dataframe进行调用执行","#在rdd中可以直接定义函数，交给rdd的transformatioins方法进行执行","#如果操作的是原有列，可以替换原有列的数据","#定义state更新函数","#定义一个新的列，数据为其他某列数据的两倍","#定义一个方法，用于检测","#定义处理的时间间隔","#定义滑动时间间隔","#定义窗口长度","#定义结构类型","#实现steps方法用于指定自定义的mapper，comnbiner和reducer方法","#对数据进行累加，按照url出现次数的降序排列","#对每一行按照空格拆分，将ip地址取出","#对每一行按照空格拆分，将url数据取出，把每个url记为1","#封装函数","#将ip转换为特殊的数字形式","#将修改后的值设置回去","#将单词转换为(单词，1)的形式","#将数据按空格进行拆分为多个单词","#将电影的id","#开启检查点","#打印结果信息，会使得前面的transformation操作执行","#找到下面内容添加java","#把每一行数据记为(\"pv\",1)","#把每个ur记为1","#指定schema","#无意义重复数据去重：数据中行与行完全重复","#显示前10条数据","#显示数据结构","#显示结果","#有意义去重：删除除去无意义字段之外的完全重复的行数据","#查看两个数据集在类别上的差异","#根据单个ip获取对应经纬度信息","#根据取出对应的位置信息","#梯度下降优化损失函数","#每一行从line中输入","#每条数据代表一次访问记录","#注意：behavior_log_df.groupby(\"userid\").count()","#物品向量","#用户向量","#监听ip，端口上的上的数据","#等待计算结束","#统计单词个数","#统计总量","#获取hbase中的所有表","#获取streamingcontext","#计算损失","#计算某一列的描述信息","#计算用户之间相似度","#设置数据比例将数据划分为两部分","#调用reducebykeyandwindow，来进行窗口函数的调用","#调用函数并起一个别名","#输出处理结果信息","#返回结果如下","#返回结果：","#通过connection找到user表","#遍历","#遍历所有的最相似用户","#需要设置检查点","#首先找到这些类，整理到一个列表","%","%i\"","%s","&","&:=b_i","&:=b_u","&=","&=\\mu","&=\\vec","&=r_{ui}","&j(\\theta)=\\sum_{u,i\\in","&j(\\theta)=cost=f(b_u,","&p_{uk}:=p_{uk}+\\alpha","'''","'''pid和特征的对应关系","'''从hdfs中加载样本数据信息'''","'''对缺失数据进行特征热编码'''","'''特征处理'''","'''评分预测'''","'''预测测试集数据'''","','","',';","'/home/hadoop/tmp/student.txt'overwrit","'/root/tmp/employee.txt'","'/root/tmp/student.txt'","'/tmp/demo/article_keywords';","'/tmp/demo/user_action';","'/tmp/student';","'1',","'65536'}","'99999'),","'\\n'","'\\t'.join([fname,","'_/","'__main__':","'_missing')","'_o')","'abc","'age').show()","'age',","'age']","'age'])","'aus':","'australia'","'b',","'base_info',","'base_info:username'","'c',","'c'],","'d',","'delete'","'e',","'f'),","'f',","'f'],","'f2'","'false',","'forever',","'gender',","'gender'])","'gender']).topandas().to_dict('records')[0]","'h',","'hdfs:///user/hive/lib/h","'height',","'height':","'i',","'income'","'income'])","'ind':","'india'","'j']","'j']]","'m'),","'m',","'missing'","'none',","'org.apache.hadoop.hive.contrib.udf.udfrowsequence'","'outer')","'pid和特征的对应关系\\n430548_1007：0\\n430549_1007：1\\n'","'python","'rowkey_16',","'spark'.","'test'","'test:user','base_info'","'timestampsfilt","'unknown'","'usa'","'usa':","'user'","'user',","'user','base_info'","'user','rowkey_10','base_info:address','tokyo'","'user','rowkey_10','base_info:birthday','2014","'user','rowkey_10','base_info:sex','1'","'user','rowkey_10','base_info:username','tom'","'user','rowkey_10',{column=>'base_info:username',timestamp=>1558323904133}","'user','rowkey_10',{column=>'base_info:username',timestamp=>1558323918953}","'user','rowkey_10',{column=>'base_info:username',versions=>10}","'user','rowkey_10',{column=>'base_info:username',versions=>2,filt","'user','rowkey_10',{column=>'base_info:username',versions=>2,timerang","'user','rowkey_10',{column=>'base_info:username',versions=>2}","'user','rowkey_16'","'user','rowkey_16','base_info'","'user','rowkey_16','base_info:address','beijing'","'user','rowkey_16','base_info:birthday','2014","'user','rowkey_16','base_info:sex','1'","'user','rowkey_16','base_info:username'","'user','rowkey_16','base_info:username','mike'","'user','rowkey_22','base_info:address','newyork'","'user','rowkey_22','base_info:birthday','2014","'user','rowkey_22','base_info:sex','1'","'user','rowkey_22','base_info:username','jerry'","'user','rowkey_24','base_info:address','shanghai'","'user','rowkey_24','base_info:birthday','2014","'user','rowkey_24','base_info:sex','1'","'user','rowkey_24','base_info:username','nico'","'user','rowkey_25','base_info:address','soul'","'user','rowkey_25','base_info:birthday','2014","'user','rowkey_25','base_info:sex','1'","'user','rowkey_25','base_info:username','rose'","'user',name=>'base_info',versions=>10","'user',{column","'user',{filt","'weight').show()","'weight',","'weight':","'|'","'列族名1','列族名2','列族名n'","'列族名2'],","'名称空间:表名',","'行名','列名'","'表名'","'表名',","'表名','行名'","'表名','行名','列名:','值","'起始的rowkey'}","(","(\"a\",","(\"b\",","(\"movieid\",","(\"rating\",","(\"spark.app.name\",","(\"spark.dynamicallocation.enabled\",","(\"spark.dynamicallocation.initialexecutors\",","(\"spark.executor.cores\",","(\"spark.executor.memory\",","(\"spark.master\",","(\"spark.shuffle.service.enabled\",","('1',","('2',","('a',","('ab',","('abc',","('ac',","('b',","('bar',","('bc',","('bec',","('by',","('c',","('d',","('fleece',","('foo',","('had',","('hadoop',","('labs',","('lamb',","('little',","('me',","('quux',","('see',","('spark.sql.pivotmaxvalues',","('test',","('was',","('welcome',","('white',","('whose',","('you',","((city_broadcast_value[index][2],","(1,","(1558323139732,","(1558323904130,","(2,","(3,","(4,","(4,[0],[1.0])|","(4,[1,3],[3.0,4.0])","(4,[1],[1.0])|","(4,[2],[1.0])|","(5,","(5,[0],[1.0])|","(5,[0],[1.0])|(10,[0,1,5],[4.0,...|","(5,[0],[1.0])|(10,[0,2,5],[2.0,...|","(5,[0],[1.0])|(10,[0,2,5],[4.0,...|","(5,[1],[1.0])|","(5,[1],[1.0])|(10,[0,1,6],[3.0,...|","(5,[1],[1.0])|(10,[0,2,6],[2.0,...|","(5,[1],[1.0])|(10,[0,2,6],[4.0,...|","(5,[1],[1.0])|(10,[0,2,6],[5.0,...|","(5,[1],[1.0])|(10,[0,2,6],[6.0,...|","(5,[1],[1.0])|(10,[0,3,6],[2.0,...|","(5,[1],[1.0])|(18,[1,2,3,4,5,6,...|","(5,[1],[1.0])|(18,[1,2,4,5,6,7,...|","(5,[2],[1.0])|","(5,[2],[1.0])|(10,[0,1,7],[5.0,...|","(5,[2],[1.0])|(10,[0,3,7],[2.0,...|","(5,[3],[1.0])|","(5,[3],[1.0])|(10,[0,1,8],[5.0,...|","(5,[3],[1.0])|(10,[0,3,8],[1.0,...|","(5,[3],[1.0])|(10,[0,3,8],[4.0,...|","(5,[4],[1.0])|","(5,[4],[1.0])|(10,[0,2,9],[5.0,...|","(5,[4],[1.0])|(18,[1,2,3,4,5,6,...|","(6,","(7,","(\\mu+b_u+b_i)","(a.article_id","(classno","(compatible;)\"","(count(1)","(date1","(dct[x[0]],","(default,","(df_outliers[c]","(err","(error","(fn.count(c)","(fname,","(global_mean","(gross","(hdfs)成为hadoop项目的独立子项目。","(hdfs™):","(in","(key,","(khtml,","(lambda架构)","(last_sum","(manag","(master","(name","(negative)","(nullabl","(output,","(positive).","(pred_rat","(r_{ui}","(red","(reg_bi","(reg_bu","(select","(self.global_mean","(self.reg_bi","(self.reg_bu","(udafs)","(udfs)","(uid,","(video","(window","(word,","(x,","(x[0],","(x[2],","(yet","(业务角度)","(内容较多，见《hive","(内部表)","(海选)","(点击率预估","(理论角度)","(离散流)","(这个命令只运行一次)",")","))","),",").alias(c",")]","*","**","*********","*/","*[fn.mean(c).alias(c)","*ret)","+","+0000]","+=",",",",2.0",",{column",",{columns=>'列族名:列名'}","...","...)","...)。","......","...])","./hadoop","./pyspark","./start",".__/\\_,_/_/",".add(\"city\",",".add(\"id\",",".add(\"pop\"",".add(\"state\",stringtype())",".flatmap(lambda",".load(\"iris.csv\")",".map(lambda",".reducebykey(lambda",".reducebykeyandwindow(addfunc,",".updatestatebykey(updatefunc=updatefunc)#应用updatestatebykey函数",".withcolumn(\"new_user_class_level\",","/","/*","/_/","/_/\\_\\","/__","/hadoop001","/hadoop001/test","/hadoop001/test/","/hadoop001/test/test.txt","/hbase/bin/start","/home","/home/hadoop/app/hadoop","/home/p","/images/my.jpg","/nonexistentfil","/root/bigdata/hadoop/hdfs/data","/root/bigdata/hadoop/hdfs/nam","/root/bigdata/zookeep","/root/tmp/udf1.py;","/tmp/demo","/tmp/demo/user_act","/tmp/employee.txt","/user/hadoop/emptydir","/user/hadoop/file1","/user/hadoop/file2","/user/hadoop/hadoopdir","/user/hadoop/hadoopfil","/user/hive/lib","/user/hive/lib/","/user/hive/lib目录","/user/hive/warehouse/employee/date1=2018","/user/hive/warehouse/test.db/employee/date1=2018","/users/sameerp/data/part","/wp","0","0)","0),","0)]","0,","0,1,1]","0,1,2","0.","0.0,","0.0.0.0:","0.0000","0.01*v_puk)","0.01*v_qik)","0.01,","0.0120","0.01|","0.01正则化系数","0.02*(error*v_puk","0.02*(error*v_qik","0.02学习率","0.05)","0.0])","0.0|","0.0|(2,[0],[1.0])|","0.1,","0.1231","0.1612","0.24.2","0.3101","0.33","0.4276","0.4666","0.4677","0.4767","0.4781","0.4900","0.4])","0.4|[0.94045691149716...|","0.5)+1.2​，也就是4.2分。","0.5322","0.5817","0.5；","0.6415","0.6455","0.7071","0.7206","0.75],","0.7921","0.8528","0.8版本后加入位图索引","0.9001","0.9248=0.0752，即点击概率约为7.52%","0.9695","0.9x","0.]","00:04:12","01","01');","01_hadoop生态系统","01_hbase简介与环境部署","01_hdfs的使用","01_hive基本概念","01_spark","01_spark入门","01_个性化电商广告推荐系统介绍","01_什么是hadoop","01_资源调度框架yarn","02","02_dataframe介绍","02_hadoop核心组件","02_hbase数据模型","02_hdf","02_hdfs读写流程&高可用","02_hive的shell操作","02_rdd概念介绍","02_分布式计算框架mapreduc","02_根据用户行为数据创建als模型并召回商品","03","03_ctr预估数据准备","03_hadoop优势","03_hadoop发行版选择","03_hbase的安装与shell操作","03_hdfs设计思路","03_hive的函数和自定义函数","03_mapreduce实战","03_rdd常用算子练习","03_spark","04","04');","04/employee.txt","04_happybase操作hbas","04_hdfs架构","04_hive综合案例","04_mapreduce原理","04_spark","04_逻辑回归(lr)实现ctr预估","04spark","05","05_hbase组件","05_hdfs环境搭建","05_spark","05_离线推荐处理","06:01:10","06_spark","06_spark安装部署&standalone模式介绍","06_实时推荐","07","07:09:12","07:28:12","07:50:14","07_spark","09:07:12","09:08:12","09:21:12","0;","0|","0|(2,[1],[1.0])|","0|(2,[1],[1.0])|109.0|","0|(2,[1],[1.0])|176.0|","0|(2,[1],[1.0])|1880.0|","0|(2,[1],[1.0])|2200.0|","0|(2,[1],[1.0])|2360.0|","0|(2,[1],[1.0])|247.0|","0|(2,[1],[1.0])|5649.0|","0|(2,[1],[1.0])|697.0|","0|(2,[1],[1.0])|698.0|","0~255","0，其复制备份数设置为2,","1","1\")","1')","1)","1))","1),","1).collect()","1).where(\"new_user_class_level=","1).where(\"pvalue_level=","1)]","1)])","1)指定hadoop任务调度优先级(very_high|high),如：","1,","1,0,0,1,0,0,1,0,0]","1,1]或[0,1]之间,一般可以使用","1,1]来计算，","1,1之间","1,kw3:1,kw6:1","1.","1.0","1.0,","1.0000","1.0]","1.0e7|","1.0e8|","1.0e8|[0.86822033939259...|","1.0e8|[0.88410457194969...|","1.0e8|[0.89175497837562...|","1.0|","1.0|(2,[1],[1.0])|","1.0}))","1.1","1.1.0","1.1_推荐系统简介","1.2","1.2.0","1.2_推荐系统架构设计","1.3","1.3_推荐算法","1.4","1.4_案例","1.5","1.5_推荐系统评估","1.5e7|","1.5e7|[0.93741450446939...|","1.5e7|[0.93757135079959...|","1.5e7|[0.93834723093801...|","1.6","1.6_推荐系统的冷启动问题","1.x","1.删除重复数据","1.收集⽤户特征","1.计算每条记录的缺失值情况","1.首先删除完全一样的记录","1/3","10","10'","10,","10.0|[0.94045690659874...|","10.246621...|","10.89842]...|","100","100%","100).filter(lambda","100,","100000),","101","101,http://www.itcast.cn/1.html,kw8|kw1","101.226.68.137","10122|","102","102,http://www.itcast.cn/2.html,kw6|kw3","1024","102509|","1027.5|[0.94002127571285...|","103","103,http://www.itcast.cn/3.html,kw7","10305|","104","104,http://www.itcast.cn/4.html,kw5|kw1|kw4|kw9","1043|","1043|110616|","105","105,http://www.itcast.cn/5.html,","10539|","10549|","108.0|","108.0|[0.94045685659402...|","10812|","10856|","1088|[[104,","109.0|[0.94045685608377...|","10912|","1093|","109704|","1099.0|[0.93972095713786...|","10996|","10]","10])","10|","10分钟没有收到datanode报告认为datanode死掉了","11","11,","11,101,2018","11,104,2018","11.0,","11.699315...|","11.904813...|","11.968531...|","1101|","1101|365477|","11050|","110616|","110847|","11110011‬","11115|","11256|","113068|","11310|","1136340","1136|","113w","1141729","114532|","114w，略多余日志数据中用户数","11602|","11727|","11739|","118.0|[0.94045685149150...|","11800|","119.0|[0.93972146296721...|","11947|","11:00:12","11|","11|430539_1007|","11|430548_1007|","12","12,","12.0|[0.93999396767518...|","12.48068]...|","12.547271...|","12.652732...|","12.835257...|","120847|","12195|","122.5|[0.93999391088191...|","12276|","123242|","1234","1238|[[5631,","124.0|[0.94045684842999...|","124.1,","125.0|[0.93972145987031...|","125.0|[0.94045684791973...|","12549|","12620|","126746|","127.0|[0.94045684689923...|","129.0|[0.94045684587872...|","129.2,","129079|","12948","12955","12960","12968","129682|[8.5,1.0,0.0,1.0,...|","12:11:12","12:19:55","13","13.665942...|","132721|","133.2,","133457,","133457|[169.0,1.0,0.0,1....|","133457|[169.0,1.0,0.0,1....|[2.69019485098454...|[0.93644557925748...|","1342|[[5720,","135.0|[0.94045684281721...|","135256|","135610|","138.0|[0.93972145316035...|","138.0|[0.94021122658672...|","138953.0","139744|","139747|","13:37:12","13|","13|430539_1007|","13|430548_1007|","13维","14,","14.51981]...|","140008.0","140520|","143.5,","144.5,","14435|","14437|","14574|","145952|32.99|","148946|","148|[[3347,","149.0|[0.93999389726180...|","149.0|[0.94021122095226...|","149570|","149714|","14985|","14|","14|454237|249.0|","15","15,","15155|","152414|","15338,","15347|","154.2,","15455|","154623|","1558323139866)'}","1558323139866]}","1558323918954)'}","1558323918954]}","15783|","158.0|[0.93972144283734...|","158.0|[0.93999389263610...|","158.0|[0.94000390317214...|","158.0|[0.94045683108140...|","1580|[[5731,","159.0|[0.94021121583003...|","1591|[[1610,","15:43:53)","16,","16.9|[0.94045690307800...|","160.0|[0.94045683006090...|","162394|","163.177.71.12","1645|[[1610,","164807,","164807|[228.0,1.0,0.0,1....|[2.69019430490611...|[0.93644554675747...|","1661","1666","1669","167.2,","1670","1670|","16749|","168.0|[0.93972143767584...|","168.0|[0.94045682597887...|","1689.0|[0.94055856072019...|","169717|[2.20000004768371...|","16:42:12","17","17.576496...|","170121|","17054|1494691184|","172334|","173327,","173327|[356.0,1.0,0.0,1....|[2.69019312019358...|[0.93644547624893...|","174374|139.0|","176.0|[0.93972143354663...|","176.0|[0.93999388338470...|","176.0|[0.94045682189685...|","176076|","17788|","178.0|[0.94021120609778...|","179595|","179746|","18","18.0|[0.93972151509838...|","182415|","182966|","1829|[[1610,","183.49.46.228","185524","186334|[106.0,1.0,0.0,1....|","186847|","187.1|[0.94045681623305...|","188.0|[0.93972142735283...|","188.0|[0.94021120097554...|","188.0|[0.94031410659516...|","18:02:02","18:31:12","18:42:12","18]","18|","19.0|","19.0|[0.94045690200647...|","19.8|[0.93999396366625...|","19.9|[0.93999396361485...|","19/03/08","191.7]}","191036|","192.168.19.137","192.168.19.137:4040","192.168.19.137:50070","194.237.142.21","195.0|[0.94021119738998...|","1959|[[1610,","198.0|[0.94035413548387...|","198.0|[0.94045681067129...|","198424|","199.0|[0.94045681016104...|","19939","199445|[5.0,1.0,0.0,1.0,...|","19:10:12","19]","19|","1:","1:20","1:男,2:女；","1|","1|154436|","1|174374|139.0|","1|249.0|","1|368.0|","1|428.0|","1|430548_1007|","1|5.5555556e7|","1|5.5555556e7|[0.92481456486873...|","1|639.0|","1|8.8888888e7|","1|[[1610,","1、","1、2","1、spark","1、sparkstreaming概述","1、什么是spark","1、内嵌(","1、易整合","1、解决了rdd的缺点","1、速度快（比mapreduce在内存中快100倍，在磁盘中快10倍）","1。","1个namenode/nn(master)","1个执行器","1个文件会被拆分成多个block","1个虚拟变量，n为pvalue_level的取值范围","1来作为目标值","1用户对相似物品物品的评分","1表示强负相关，+1表示强正相关","1表示正相关","1表示负相关,","1）mapr","1，client提交作业请求","1，get_json_object","1，map结果写磁盘，reduce写hdfs，多个mr之间通过hdfs交换数据","1，创建dataframe的步骤","1，创建一个streamingcontext","1，通过反射自动推断，适合静态数据","1，需要安装一个nc工具：sudo","1：梯度下降法推导","2","2)","2),","2).collect()","2).show()","2)]","2)map及reduce任务个数限制，如：","2,","2.","2.0","2.0|","2.1","2.1_基于模型的协同过滤推荐","2.2","2.2_基于回归模型的协同过滤推荐","2.3","2.3.0","2.3429515...|","2.3_基于矩阵分解的协同过滤推荐","2.4","2.4.1","2.4_lfm算法实现","2.5","2.5_biassvd算法实现","2.6.0","2.6_基于内容的推荐算法","2.7_电影推荐(contentbased)物品画像","2.8_电影推荐(contentbased)用户画像","2.9_电影推荐(contentbased)top","2.x","2.其次，关键字段值完全一模一样的记录（在这个例子中，是指除了id之外的列一模一样）","2.处理缺失值","2.计算各列的缺失情况百分比","2/4","20","20)","200","200)","2003","2004年","2006年2月hadoop成为apache的独立开源项目(","2006年4月—","2008年4月—","2008年—","2009年3月—","2009年5月—","2009年7月—","201060|","2012年11月—","2014年6月1日的时候，spark宣布了不再开发shark，全面转向spark","20150623","2017","20170512），用第8天的做测试样本（20170513）","2018","2018,","201867,","201867|[179.0,1.0,0.0,1....|[2.69019475842887...|[0.93644557374900...|","2018年4月—","201|","202710|","20397|","205612|","206|","207754|","207800|199.0|","208.0|[0.94039204931181...|","208458|","209959|","20:09:11","20|","20个","21),","211292|","211816|","2122|[[1610,","213567|","2142|[[1610,","217512|","2181","218101|","218276|","218306|","218918|","22","22,102,2018","22,103,2018","22,104,2018","220.0|[0.93999386077017...|","220.0|[0.94028926340218...|","221585|[18.5,1.0,0.0,1.0...|","221714|[4.80000019073486...|","221720|","222.68.172.190","223.243.0.0|223.243.191.255|","2246|","227731,","227731|[199.0,1.0,0.0,1....|[2.69019457331754...|[0.93644556273205...|","229827,","229827|[238.0,1.0,0.0,1....|[2.69019421235044...|[0.93644554124900...|","23,","23.0|[0.94045689996546...|","23236|","23249291","234|","2366|[[1610,","237471.0","238761.0","238772.0","239302|","23:59:46","24*60*60))","240984|","241402,","241402|[269.0,1.0,0.0,1....|[2.69019392542787...|[0.93644552417271...|","243384|","24484|","248909|","25,","25.0|[0.93980311449212...|","25.0|[0.94021128446795...|","25.4989],...|","25.98|[0.94045689844491...|","25029435","2545|","255","255.255.255.255","25542,","25542|[176.0,1.0,0.0,1....|[2.69019478619557...|[0.93644557540155...|","255875","256","256.0|[0.94002167206744...|","258.0|[0.94002167103995...|","258.0|[0.94021116511987...|","258252|[7.59999990463256...|","259.0|[0.94021116460765...|","25]","262215|","265403,","265403|[198.0,1.0,0.0,1....|[2.69019458257311...|[0.93644556328290...|","26557961","2659|[[5607,","266086|","27.6|[0.93972151014334...|","270027|","270719|","27073","27151","273.0|[0.94002166333380...|","274795|","275.0|[0.94002166230631...|","275819,","275819|[3280.0,1.0,0.0,1...|[2.69016605691669...|[0.93644386554961...|","277335,","277335|[181.5,1.0,0.0,1....|[2.69019473528996...|[0.93644557237189...|","278.0|[0.93972138089925...|","278.0|[0.94002166076508...|","278.0|[0.94021115487539...|","278301|","28),","28)]","28,'m',","28.0|[0.93980311294563...|","28.0|[0.93999395945172...|","28145|","28589|","2866|[[1610,","2890.0|[0.94028789742257...|","289563|","290675|","290950|[6.5,1.0,0.0,1.0,...|","292027|[16.0,1.0,0.0,1.0...|","29466,","29466|[640.0,1.0,0.0,1....|[2.69019049161265...|[0.93644531980785...|","297.0|[0.94002165100394...|","298.0|[0.94002165049020...|","298.0|[0.94045675964600...|","299.0|[0.94002164997645...|","299.0|[0.94021114411869...|","2:","2\\lambda*b_u","2\\lambda{b_u}","2\\sum_{u,i\\in","2],","2^8","2|","2|145952|32.99|","2|293656|","2|324420|","2|[[5579,","2、","2、datafram","2、spark","2、丢失了rdd的优点","2、为什么要学习spark","2、易用性（可以通过java/scala/python/r开发spark应用程序）","2、本地(","2、统一的数据源访问","2维","2）yarn","2，get_json","2，resourcemanag","2，从streamingcontext中创建一个数据对象","2，任务调度和启动开销大","2，其他方式创建datafram","2，执行指令：nc","2，程序指定，适合程序运行中动态生成的数据","2：随机梯度下降","3","3)","3)).dropna()","3),","3)]","3,","3.","3.0])","3.0]))","3.0|","3.1","3.1.1","3.1.2","3.1.3","3.1.5","3.1节中的例子为通过反射自动推断schema，适合静态数据","3.2","3.2.1","3.2.2","3.3","3.3.1","3.3.2","3.3.3","3.4","3.4.14/datadir","3.5.0","3.5|[0.94045690991538...|","3.641833]...|","3.91","3.x","3.有意义的重复记录去重之后，再看某个无意义字段的值是否有重复（在这个例子中，是看id是否重复）","30.0|[0.93972150890458...|","30.0|[0.93980311191464...|","30.0|[0.93999395842379...|","300.0|[0.93972136954393...|","300556|","300681|","301299|","304","3088.0|[0.94055784801535...|","31.0|[0.94045689588345...|","310408|","311.0|[0.93972136386626...|","31183|","31239|","313","31314|[15.8000001907348...|","313401|","314","315371|","316.0|[0.94045675046144...|","3175|[[3347,","31899|","31|","32.0|[0.94045689537319...|","32.8|[0.93999395698469...|","32233|","326126|","32位2进制数","32位二进制数","33","33),","33,","33,101,2018","33,102,2018","33,103,2018","33.0|","33.0|[0.93972150735613...|","33.0|[0.93999395688189...|","3308670","335.0|[0.93999380166395...|","335413|","335495|","33756|","338.0|[0.93972134993018...|","339.0|[0.93999379960808...|","339334|","339382]","339382|[163.0,1.0,0.0,1....|[2.69019490651794...|[0.93644558256256...|","33|","342.3,","342.3|","344920","345870|","348.0|[0.94002162480299...|","348.0|[0.94045673413334...|","349.0|[0.94045673362308...|","35","35,102,2018","35,105,2018","35.0|[0.93972150632383...|","35.0|[0.94002178560473...|","35.0|[0.94021127934572...|","35.5|[0.93999395559698...|","351366,","351366|[246.0,1.0,0.0,1....|[2.69019413830591...|[0.93644553684221...|","35156|","352273.0","352666|","3644|","365477|","366.0|[0.94002161555560...|","368.0|[0.94002161452811...|","368.0|[0.94021110877521...|","369.0|[0.94002161401436...|","36963,","36|","37004|","3749|[[1610,","375706|","375920|","37665|","37759|","38.0|","38.0|[0.93972150477538...|","3800000.0|","383023|","385|","385|428950|","387991|","388.0|[0.94002160425322...|","39.6|[0.94045689149528...|","39.9|[0.94045689134220...|","3900000.0|","392038|","392|","395195|","3960.0|[0.94055740378069...|","398.0|[0.94000377983931...|","398.0|[0.94021109340848...|","3980000.0|","399.0|[0.94055921788912...|","3:","3]","3],","3|","3|173047|","3|430548_1007|","3|[[5607,","3、","3、hadoop(","3、json数据的处理","3、spark","3、spark特点","3、兼容hive","3、删除缺失值过于严重的列","3、异常值处理","3、通用性（可以使用spark","3日留存","3维","3，explod","3，在启动的container中创建applicationmast","3，对数据对象进行transformations操作","3，无法充分利用内存","3：算法实现","4","4)","4))","4),","4)]","4,","4.0","4.0,","4.0]))","4.0]).toarray())","4.0|","4.1","4.1利用pycharm编写spark","4.2","4.2hdf","4.3","4.4","4.5","4.6","4.63","4.8.5","4.8523283|","4.979195|","4.]","4.对于id这种无意义的列重复，添加另外一列自增id","40.0|[0.94045689129118...|","400","401433,","401433|[1200.0,1.0,0.0,1...|[2.69018530849532...|[0.93644501133142...|","403318|","405447|","406125|","406713|","410958|","4120.0|[0.94001968693052...|","41289|","413653|","416333|","417722|","417898|","41925|","42),","42,","42055|","420769|430548_1007|","422260|","423436","4251","4267|","427579|430548_1007|","4284|","428950|","43.98|[0.94045688926037...|","430.0|[0.94002158267595...|","430023|[34.2000007629394...|","430548_1007：0","430549_1007：1","431082|430548_1007|","4339|","43866|","43|","44.98|[0.93999395072458...|","440.0|[0.94002157753851...|","4416","44251|","443295|","445914|[9.89999961853027...|","448651|","44|","45),","45,","45,'m',","451004|430539_1007|","4520|","454237|249.0|","459.0|[0.94055918732327...|","460561","4610|","46239|","4631","463|[[1610,","467512.0","468.0|[0.94055918273839...|","468220|","46w","471|[[1610,","4760000.0|","4766|","4770","478.0|[0.94045666780037...|","4824|","485749|","49.0|[0.94004219516957...|","49.0|[0.94021127217459...|","494224,","494224|[169.0,1.0,0.0,1....|[2.69019485098454...|[0.93644557925748...|","494312|430548_1007|","496|[[1610,","498.0|[0.94002154774131...|","499.0|[0.94055916694603...|","49911|","49|","4:","4]).count()","4]).first()","4|","4|138833|","4|430548_1007|","4、兼容性（spark程序可以运行在standalone/yarn/mesos）","4、按照缺失值删除行（threshold是根据一行记录中，缺失字段的百分比的定义）","4、提供了标准的数据库连接（jdbc/odbc）","4、数据清洗","4个分区）如未指定分区数量，spark会自动设置分区数。","4）验证","4，applicationmaster启动后向resourcemanager注册进程,申请资源","4，不适合迭代计算（如机器学习、图计算等等），交互式处理（数据挖掘）","4，对相同的经度和纬度做累计求和","4，输出结果","5","5)","5),","5),...('white',","5)]","5,","5.*.jar","5.0776596|","5.1","5.1,","5.12694|","5.139578|","5.162216]...|","5.1701374|","5.1离线数据缓存之离线召回集","5.1通过spark实现ip地址查询","5.2","5.2,","5.245261|","5.2992325|","5.3","5.3,","5.4","5.4,","5.5,","5.6","5.6,","5.6804466|","5.6882005|","5.7,","5.7.0","5.838009|","5.9,","5.9051886...|","5.9155636|","50%","500","500.0|[0.94002154671382...|","500:","5032","509.0|[0.94002154209012...|","513942|","518883|","519.0|[0.94045664687995...|","523|","527|","529913|","5392|","54),","54,","546930|","552638|","554311|","55|","56.0|[0.93972149548468...|","5608.0|[0.94001892245145...|","561681|430548_1007|","563.0|[0.94002151434789...|","568.0|[0.94000369247841...|","568.0|[0.94021100633025...|","569939,","569939|[188.0,1.0,0.0,1....|[2.69019467512877...|[0.93644556879138...|","5718.0|[0.94001886593718...|","575633,","575633|[180.0,1.0,0.0,1....|[2.69019474917331...|[0.93644557319816...|","575917","5762","5777|","58.0|[0.93972149445238...|","58.0|[0.94021126756458...|","58.0|[0.94031417307687...|","58013|","582235|430548_1007|","583215,","583215|[3750.0,1.0,0.0,1...|[2.69016170680037...|[0.93644360664433...|","588664|430548_1007|","5888888.0|","59.98|[0.93972149343040...|","59.9|[0.93999394305620...|","590965|","593001|430548_1007|","5953|","59774|","598.0|[0.94002149636681...|","598.0|[0.94021099096349...|","599.0|[0.94055911600291...|","5]","5])","5|","5|430548_1007|","5、填充缺失值，可以用fillna来填充缺失值，","5日留存","5）停止yarn相关的进程","5，applicationmaster申请到资源后，向对应的nodemanager申请启动container,将要执行的程序分发到nodemanager上","5，不适合流式处理（点击日志分析）","5，开始和停止","6","6)","6),","6)]","6,","6.0623236|","6.1","6.1)","6.1000000000000005],","6.2145095|","6.6835284|","6.7|[0.93999397039920...|","6.886987],...|","60.0|[0.94045688108613...|","60.208.6.156","600195|430548_1007|","60214|","607788|","610","610*k","612|","6130|","618965|430548_1007|","6211|","62353|","624504|430539_1007|","6250|","6261|","629.0|[0.94055910071996...|","6306|","631204|[19888.0,1.0,0.0,...|[2.69001234046578...|[0.93643471623189...|","63133|","6355|","6379","639794|","6406|","6527","65726|","6580|","658722|","660.0|[0.94002146451460...|","6636|","6703|","672.0|[0.94002145834965...|","67558|","675674|430539_1007|","6766","6767","6768","6769","68.0|[0.93999393889308...|","68.0|[0.94031416796289...|","68.0|[0.94045687700412...|","6823|","684020|","685|","687854|430548_1007|","688.0|[0.93999362023323...|","69.0|[0.94045687649386...|","692672|[47.0,1.0,0.0,1.0...|","6972|","6]","6]).take(10)","6]).take(2)","6|","6|207800|199.0|","6，container启动后，执行对应的任务","6，mapreduce编程不够灵活，仅支持map和reduce两种操作","7","7),","7,","7.149538|","7.3395424|","7.4762917|","7.479664|","7.752719]...|","70206|","7032|","7043|","70894|","7185|","72.5|[0.94045687470798...|","7207|","7211|","7213|","7214|","72273|","723268134","7266|","7267|","7270|","7270|274795|","72781|","7282|","731,","735220|430548_1007|","737.0|[0.94055904570133...|","739|","742741|430548_1007|","745|","746178|[16.7999992370605...|","75.0|[0.93999393529532...|","756665|430548_1007|","76000),],","763027|[68.0,1.0,0.0,1.0...|","77","77,103,2018","77,104,2018","77.0|[0.94045687241185...|","77797|","78.0|[0.93972148412937...|","782038|430548_1007|","788.0|[0.94055901972029...|","79.0|[0.93999393323945...|","79.0|[0.94045687139134...|","79.8|[0.94045687098314...|","794890|","797982|[33.0,1.0,0.0,1.0...|","79971|","7|","7日留存","7，tast执行完毕之后，向applicationmaster返回结果","8","8),","8,","8.353473]...|","8.466453]...|","80548|","8071|","815219|[2.40000009536743...|","815312|[2.29999995231628...|","816999|[5.0,1.0,0.0,1.0,...|","817569|430548_1007|","818681.0","820018|430548_1007|","8237|","8237|301299|","83237|","833|[[5607,","838953.0","83948|","8401|","843456","845337.0","846728","846729","846810","846811","85373|","86243|","8655|","87331|199.0|","877|","88.0|[0.93999392861375...|","888.0|[0.94055896877705...|","8888.0|[0.94045237642030...|","8888888.0|","88975|","89.9|","89831|","8|","8|430548_1007|","8》\"等，我们是不是就可以分析出该用户的一些兴趣特征如：\"爱国\"、\"战争\"、\"赛车\"、\"动作\"、\"军事\"、\"吴京\"、\"韩三平\"等标签。","8位2进制数","8，applicationmaster向resourcemanag","9),","9)])","9,","9.065482]...|","9.250818]...|","9.917084|","90).take(3)","90141|","90351|","9086|","91286|","915.0|[0.94021082858784...|","92,","92241|","92560|","9293|","93.0],","932|","93]","9510|","95471|170.0|","9600000.0|","97","9700","9700*k","98.0|[0.94045686169655...|","989.0|[0.94002129549211...|","99","99),","99,102,2018","99,105,2018","99.0|","99.0|[0.93999392296012...|","99.0|[0.94045686118630...|","9900000.0|","9970|","998.0|[0.94055891273943...|","99815","9994|","9995|","9996|","9999","9999)","99|","9],","9|","9|186847|","9|430539_1007|","9|430548_1007|",":",":=",":param",":return:",":x[0]).collect()","<>","=","==","==1:","===========","==========================================","==================从csv读取======================","==================基本统计功能","================交叉表","================直接创建==========================","================采样数据","===============增加一列(或者替换)","===============提取部分列","==========删除一列","===>","==>","==>7维","=>","=>['base_info'],limit=>2,startrow=>'rowkey_16'}","=>['base_info'],limit=>2}","=[0,1,1,0,0,1,0,0,0]","=[1,0,0]","=\\cfrac{0.85*3+0.71*5}{0.85+0.71}",">",">(out_key,intermediate_value)",">.class",">.jar",">=",">>>",">>>def",">features(特征)",">jvm",">ml",">out_valu",">predict",">rdd2",">transform",">写入数据",">处理数据",">读取数据",">输出数据","[","[\"1\",\"1\",\"1\",\"1\",\"1\",\"2\",\"1\"]","[\"1\",\"1\",\"1\",\"1\",\"1\"]","[\"1\",\"kw3\",\"kw6\"]","[\"101\",\"101\",\"101\",\"104\"]","[\"101\",\"102\",\"103\"]","[\"101\",\"104\",\"101\",\"101\"]","[\"101\",\"104\"]","[\"102\",\"103\",\"103\",\"104\"]","[\"102\",\"103\",\"104\",\"103\"]","[\"102\",\"103\",\"104\"]","[\"102\",\"105\"]","[\"103\",\"102\",\"101\"]","[\"103\",\"104\"]","[\"105\",\"102\"]","[\"4\",\"1\",\"1\",\"3\",\"1\"]","[\"buy\",\"buy\",\"buy\",none,\"buy\"],","[\"buy\",none,\"buy\",\"buy\",none],","[\"buy\",none,\"buy\",none,none],","[\"buy\",none,none,\"buy\",\"buy\"],","[\"item","[\"kw1\",\"kw3\",\"kw4\",\"kw5\",\"kw6\",\"kw7\",\"kw9\"]","[\"kw1\",\"kw3\",\"kw6\",\"kw7\",\"kw8\"]","[\"kw1\",\"kw4\",\"kw5\",\"kw7\",\"kw9\"]","[\"kw1\",\"kw4\",\"kw5\",\"kw8\",\"kw9\"]","[\"kw5\",\"kw1\",\"kw4\",\"kw9\"]","[\"kw6\",\"kw3\"]","[\"kw7\"]","[\"kw8\",\"kw1\"]","[\"stanley\",","[\"user1\",","[\"userid\",","[\"北京\",","[\"女“，”北京“，”苹果“]","[\"男\",","[\"男“，”上海“，”小米“]=[","[\"苹果\",","['a',","['base_info:username','base_info:sex']}","['d',","['foo","['h',","['id',","['weight',","['列族名1',","[(","[(\"userid\",","[('1',","[('a',","[('ankit',25),('jalfaizy',22),('saurabh',20),('bala',26)]","[('b',","[('c',","[('mary',","[('python',","[(1,","[(r_{ui}","[(w,round(c/maxcount,","[0","[0,","[0,1,0,1,1],","[0.","[0.0.0.0]","[0.25,","[1,","[1,0,0,1,1],","[1,0,1,0,0],","[1,0,1,1,0],","[1,1,1,0,1],","[1,2,3,4,5])","[1,2,3]","[1,5,5,2,1],","[1.","[1.0,","[1558323139732,","[1558323904130,","[18/sep/2013:06:49:18","[18/sep/2013:06:49:23","[18/sep/2013:06:49:33","[18/sep/2013:06:49:36","[18/sep/2013:06:49:42","[18/sep/2013:06:49:45","[18/sep/2013:06:49:48","[18/sep/2013:06:49:57","[18/sep/2013:06:50:08","[2,","[3,","[3,1,2,3,3],","[3,3,1,5,4],","[3.0,","[4,","[4,3,4,3,5],","[4,5,6,7,8]","[4,5,6]","[4.499999999999999,","[5,3,4,4,none],","[6,","[631204,","[91,","[91.69999999999999,","[['a',","[\\sum_{u,i\\in","[]","[])","[]),","[_.userid==user_profile_df.userid]","[c","[dct.doc2bow(line)","[gcc","[hadoop00]","[hadoop@hadoop00","[hadoop@hadoop000","[i.adgroupid","[i.cateid","[i[0]","[none,\"buy\",none,\"buy\",\"buy\"],","[null,\"1\",\"1\"]","[r.cms_segid,","[raw_sample_df.adgroupid==ad_feature_df.adgroupid]","[row(adgroupid=631204),","[row(btag='buy',","[row(clk='0',","[row(pid='430548_1007',","[row(userid=1,","[uri","\\","\\/","\\\\","\\\\&=","\\\\&=2\\sum_{u,i\\in","\\\\&=\\mu","\\\\&=\\sum_{u,i\\in","\\\\&={\\sum_{k=1}}^k","\\\\&q_{ik}:=q_{ik}","\\\\+","\\alpha*(","\\alpha*((r_{ui}","\\alpha*(\\sum_{u,i\\in","\\alpha*(error","\\alpha[(r_{ui}","\\alpha[\\sum_{u,i\\in","\\alpha\\cfrac{\\parti","\\begin{split}","\\cfrac","\\cfrac{\\partial}{\\parti","\\end{split}","\\hat","\\hat{r}_{ui}","\\hat{r}_{ui})^2","\\lambda","\\lambda(\\sum_u{b_u}^2+\\sum_i{b_i}^2+\\sum_u{p_{uk}}^2+\\sum_i{q_{ik}}^2)","\\lambda(\\sum_u{p_{uk}}^2+\\sum_i{q_{ik}}^2)","\\lambda*","\\lambda*(\\sum_u","\\lambda*b_i)","\\lambda*b_i)\\\\","\\lambda*b_u)","\\lambda_1","\\lambda_2","\\lambda_3","\\lambda_4","\\mu","\\sum_i","\\sum_{u,i\\in","\\theta_j:=\\theta_j","\\theta_j}j(\\theta)","\\vec","\\vec{q_{k,1}}","]","])","],","_","_)","_,","_.append((mid,","_.append(interest_weight)","_.append(interest_weight*related_weight)","_.append(related_weight)","_.join(user_profile_df,","_.topandas()","_1","_1)","_2","_2)","_3","_3)","_4","_4)","_\\","__","__/","__/__","___","____","_____/","__init__(self,","__name__","__name__=='__main__':","_ad_feature_df","_ad_feature_df.\\","_ad_feature_df.adgroup_id.cast(integertype())).withcolumnrenamed(\"adgroup_id\",","_ad_feature_df.brand.cast(integertype())).withcolumnrenamed(\"brand\",","_ad_feature_df.campaign_id.cast(integertype())).withcolumnrenamed(\"campaign_id\",","_ad_feature_df.cate_id.cast(integertype())).withcolumnrenamed(\"cate_id\",","_ad_feature_df.customer.cast(integertype())).withcolumnrenamed(\"customer\",","_ad_feature_df.price.cast(floattype()))","_ad_feature_df.replace(\"null\",","_df","_df.sort_values(ascending=false)","_df_sort","_index","_init_matrix(self):","_mae_sum","_movie_profil","_movie_profile.append((mid,","_raw_sample_df1","_raw_sample_df1.\\","_raw_sample_df1.adgroup_id.cast(integertype())).withcolumnrenamed(\"adgroup_id\",","_raw_sample_df1.clk.cast(integertype()))","_raw_sample_df1.nonclk.cast(integertype())).\\","_raw_sample_df1.pid.cast(stringtype())).\\","_raw_sample_df1.show()","_raw_sample_df1.time_stamp.cast(longtype())).withcolumnrenamed(\"time_stamp\",","_raw_sample_df1.user.cast(integertype())).withcolumnrenamed(\"user\",","_raw_sample_df2","_raw_sample_df2.printschema()","_raw_sample_df2.show()","_rmse_sum","_sum","_tag","_tags.groupby(\"movieid\").agg(list)","_user_profile_df1","_user_profile_df1.na.fill(","_user_profile_df2","_user_profile_df2.new_user_class_level.cast(stringtype()))","_user_profile_df2.pvalue_level.cast(stringtype()))\\","_user_profile_df2.show()","_user_profile_df2.withcolumn(\"pvalue_level\",","_user_profile_df3","_user_profile_df3.printschema()","_user_profile_df4","_user_profile_df4.printschema()","_user_profile_df4.show()","_和user_profile_df合并条件","`/","a\",","a\"],","a,","a,b:a+b)","a,b:a+b).sortby(lambda","a.user_id,","a.user_id,b.kw","a.user_id,b.kw;","a.user_id,weight","a.user_id;","a/b测试","a1,","a2","ab","abc","abs(pred_r","ac","access","accuray(pred_results)","accuray(predict_results,","acid","acid只支持单个row级别","acid，即对行级别的","action","action操作","action算子","action：立即操作","actual","ad_featur","ad_feature.csv","ad_feature_df","ad_feature_df.foreachpartition(foreachpartition)","ad_feature_df.groupby(\"brandid\").count().count()","ad_feature_df.groupby(\"campaignid\").count().count()","ad_feature_df.groupby(\"cateid\").count().count()","ad_feature_df.groupby(\"customerid\").count().count()","ad_feature_df.printschema()","ad_feature_df.select(\"adgroupid\",","ad_feature_df.select(\"price\").filter(\"pric","ad_feature_df.select(\"price\").filter(\"price>10000\").count())","ad_feature_df.show()","ad_feature_df.sort(\"price\").show()","ad_feature_df.sort(\"price\",","add","add(x):","add(x,","addfunc","adgroup_id:","adgroup_id总数：","adgroup_id总数：\",","adgroup_id：脱敏过的广告id；","adgroup_id：脱敏过的广告单元id；","adgroupid","adgroupid))","adgroupid,","adgroupid:","adjust","age=int(x[1])))","age_level,","age_level:","age_level_valu","age_level_value[age_level_rela[int(features[\"age_level\"])]]","age_level：年龄层次；","aggreg","aggregation.","agg（aggregation聚合）","ahead","al","algorithm(选择算法训练模型)","algorithms.","ali_display_ad_click是阿里巴巴提供的一个淘宝展示广告点击率预估数据集","allow","alpha","alpha,","alpha学习率","als(self):","als(usercol='userid',","als.fit(brand_rating_df)","als.fit(cate_rating_df)","als_model","als_model.recommendforallusers(3)","als_model.transform(spark.createdataframe(cateid_df)).sort(\"prediction\",","als_model.userfactor","als_model.userfactors.select(\"id\").collect():","alsmodel","alsmodel.load(\"hdfs://localhost:9000/models/userbrandratingmodel.obj\")","alsmodel.load(\"hdfs://localhost:9000/models/usercateratingalsmodel.obj\")","als模型","als的意思是交替最小二乘法（altern","alter","anoth","ansi","apach","apache™","api","api.","api丰富","api实现","api或sql处理数据，会自动经过spark","api（如df.select())和sql(select","api，2.2.2版本中无法使用","api：比spark","app/web","applewebkit/537.36","applic","applicationmast","applicationmaster:","approxquantile方法接收三个参数：参数1，列名；参数2：想要计算的分位点，可以是一个点，也可以是一个列表（0和1之间的小数），第三个参数是能容忍的误差，如果是0，代表百分百精确计算。","array","array]","arraytype(doubletype())),","artical_id,artical_url,artical_keyword","articl","article_id","article_id,kw","articles(","articles;","ascending=false).na.drop()","ascending=false).na.drop().show()","ascending=false).show()","associ","assum","autoconnect=false)","automat","avail","availability)，而是在应用层检测和处理故障，从而在计算机集群之上提供高可用服务","avg","avg(),","b","b\",","b\"]))","b)","b).collect()","b.article_id)","b.kw","b.kw,count(1)","b:","b_i","b_i&:=b_i","b_i)","b_i)(","b_i)\\\\","b_i)^2","b_i)^2是用来寻找与已知评分数据拟合最好的b_u和b_i​","b_i)}{\\lambda_1","b_i:=b_i","b_i]","b_u","b_u&:=b_u","b_u)","b_u)\\\\","b_u)}{\\lambda_2","b_u:=b_u","b_u]","b_u}","b_u​更新(因为alpha可以人为控制，所以2可以省略掉)：","b_u和b_i​分别属于用户和物品的偏置，因此他们的正则参数可以分别设置两个独立的参数","b_{ui}","bag","baidu","bar","base","base_info:usernam","based）","baselinecfbyals(20,","baselinecfbyals(object):","baselinecfbysgd(20,","baselinecfbysgd(object):","baseline目标：","baseline设计思想基于以下的假设：","baseline：基准预测","basic","batch_interv","batch_interval)","batch方式（trident）","batch流式处理数据（spark","bc","bcf","bcf.fit(dataset)","bcf.fit(trainset)","bcf.test(testset)","bec","befor","behavior_log.csv","behavior_log_df","behavior_log_df.brandid).pivot(\"btag\",[\"pv\",\"fav\",\"cart\",\"buy\"]).count()","behavior_log_df.cateid).pivot(\"btag\",[\"pv\",\"fav\",\"cart\",\"buy\"]).count()","behavior_log_df.count()","behavior_log_df.count(),","behavior_log_df.dropna().count())","behavior_log_df.groupby(\"brandid\").count().count())","behavior_log_df.groupby(\"btag\").count().collect())","behavior_log_df.groupby(\"cateid\").count().count())","behavior_log_df.groupby(\"userid\").count().count())","behavior_log_df.groupby(behavior_log_df.userid,","behavior_log_df.show()","below","bi","bi[iid]","bi[iid])","biassvd","biassvd(0.02,","biassvd(object):","biassvd:","biassvd其实就是前面提到的funk","bigint","bigint)","bigint,","bigtabl","bigtable是google设计的分布式数据存储系统，用来处理海量的数据的一种非关系型的数据库。","bigtable的开源实现","bigtable：一个大型的分布式数据库","bin/hadoop","bin/hiv","binari","binary_search(ip_num,","bi的正则参数","block1的两个备份存储在datanode0和datanode2两个服务器上","block3的两个备份存储在datanode4和datanode6两个服务器上","blockcach","blockid","blocksiz","boolean","both","bound","bounds[c][1])","bounds[col]","brand:","brand_count_df","brand_count_df.show()","brand_count_df.write.csv(\"hdfs://localhost:9000/preprocessing_dataset/brand_count.csv\",","brand_id:","brand_id：脱敏过的品牌id；","brandid:","brandid数值个数：","brandid：脱敏过的品牌id；","brand|","brand|price|","break","bsvd","bsvd.fit(dataset)","btag:","btag：行为类型,","bu","bu,","bu[uid]","bu[uid])","bucket：在","buffer","builtin","buy","buy:","buy=none)","bu的正则参数","byte","by中的就是reducer。","by中的某列转为一个数组返回","by和clust","by和统计","b各自减去向量的均值后,","b的相似度","c","c!='id'])","c\",","c\",\"d","c01,n0101,82","c01,n0102,59","c01,n0103,65","c02,n0201,81","c02,n0202,82","c02,n0203,79","c03,n0301,56","c03,n0302,92","c03,n0306,72","cache_bloom","cache_index_on_writ","calculation)","call","campaign_id:","campaign_id：脱敏过的广告计划id；","campaignid:","campaignid数值个数：","campaignid：脱敏过的广告计划id；","cap定理","cart","cart:","cart=53,","case","cat","catalystoptimizer：catalyst优化器","cate","cate/brand","cate/brand评分数据","cate:","cate_count_df","cate_count_df.first()","cate_count_df.printschema()","cate_count_df.rdd.map(process_row).todf([\"userid\",","cate_count_df.write.csv(\"hdfs://localhost:9000/preprocessing_dataset/cate_count.csv\",","cate_id:","cate_id：脱敏过的商品类目id；","cate_rating_df","cate_rating_df.groupby(\"userid\").povit(\"cateid\").min(\"rating\")","cateid","cateid)，这里控制了userid一样，所以相当于是在求某用户对所有分类的兴趣程度","cateid:","cateid=4520,","cateid_df","cateid_df.insert(0,","cateid_list","cateid_list.head(20):","cateid数值个数：","cateid：脱敏过的商品类目id；","cate|","cc","cc.user_id,concat_ws(',',collect_set(cc.kw_w))","cc.user_id,str_to_map(concat_ws(',',collect_set(cc.kw_w)))","cc.user_id;","cd","cdh","cdh5.7.0","cdh5.7.0.jar","cdh5.7.0.jar';","cdh5.7.0.jar;","cdh5.7.0.tar.gz","cdh5.7.0/logs/hadoop","cdh:","cdh版本一致","cdh版本的这些组件","cell","cf","cf_list)","cf的评分结果也是存在差异的，因为严格意义上他们其实应当属于两种不同的推荐算法，各自在不同的领域不同场景下，都会比另一种的效果更佳，但具体哪一种更佳，必须经过合理的效果评估，因此在实现推荐系统时这两种算法往往都是需要去实现的，然后对产生的推荐效果进行评估分析选出更优方案。","cf算法做一个大致的分类：","cf预测评分和item","cf）","cf）的推荐系统算法。","cf）的推荐系统算法，也是目前spark内唯一一个推荐算法。","char","check","checkpointinterval=2)","checkpointinterval=5)","chrome/29.0.1547.66","chunk","chunk.to_csv('test4.csv',index","city_broadcast","city_broadcast.valu","city_broadcast_valu","city_broadcast_value)","city_broadcast_value[index][3]),","city_id_rdd","class","classification,","classification.","classno,count(score)","classno;","cli(command","click","client","client.hset(\"ad_features\",","client.hset(\"recall_cate\",","client.hset(\"user_features1\",","client.sadd(userid,","client:","client_of_featur","client_of_recal","client_of_recall.smembers(userid)","client将namenode返回的分配的可写的datanode列表和data数据一同发送给最近的第一个datanode节点，此后client端和namenode分配的多个datanode构成pipeline管道，client端向输出流对象中写数据。client每向第一个datanode写入一个packet，这个packet便会直接在pipeline里传给第二个、第三个…datanode。","client端按128mb的块切分文件。","client访问regionserver写入数据","client：客户端进程，负责提交作业到master。","cli、jdbc/odbc、webgui。","clk:","clk：为0代表没有点击；为1代表点击；","cloudera","cloudera推出cdh（cloudera’","cluster","cms_group_id,","cms_group_id:","cms_group_id_valu","cms_group_id_value[cms_group_id_rela[int(features[\"cms_group_id\"])]]","cms_group_id：cms_group_id；","cms_segid:","cms_segid：微群id；","cnt,word","code","col","collect","collect_list","collect_list不去重而collect_set去重","collect_set","collect_set/collect_list作用:","collections.counter(reduce(lambda","collections方式创建rdd","collect会将所有数据加载到内存，慎用","collect会把计算结果全部加载到内存，谨慎使用","cols:","column","column+cel","column=base_info:address,","column=base_info:birthday,","column=base_info:sex,","column=base_info:username,","columns=[\"movieid\",","columns=[\"movieid\"],values=\"rating\")","columns=[\"uid\",","columns=[\"userid\",","columns=items,","columns=users,","com.mysql.jdbc.driv","combiner(self,","combiner=self.combiner,","comment","common","common:","common;","compress","comput","concat","concat(str1,str2,…)","concat(user_id,article_id)","concat_ws(':',b.kw,cast","concat_ws(':',user_id,article_id)","concat_ws()","concat_ws:","concat：","condit","condition,","condition2","condition2,","conf","conf.setall(config)","conf/hiv","config","configur","configuration节点中添加","config对象","conf目录,","conn.delete_table(table_name,","connect","connecthbase():","connection.close()","connection.create_table('users',{'cf1':","connection.delete_table('users',disable=true)","connection.open()","connection.open():","connection.table('mytable')","connection.table('user')","connection.table('users')","connection.tables():","connector","consist","contain","content","content/uploads/2013/07/rcassandra.png","content/uploads/2013/07/rstudio","context","context上调用stop方法,","context处于活跃状态,","context对象(不关闭sparkcontext前提下),","context对象,设置stop()的可选参数为fals","context的start()),就不能有新的流算子(dstream)建立或者是添加到context中","context调用了stop方法之后","contrib","control","convers","convert","copyfromloc","core","core和spark","core实战","core实战_ip统计","core实战案例_pv&uv统计","core编写的rdd，不同的语言生成不同的rdd","core项目更名为hadoop","corpu","cost","cost=\\sum_{u,i\\in","count","count(),","count)","count))","count=10085063)]","count=1366056)]","count=15946033),","count=16472898),","count=25191905),","count=688904345)]","count=9115919),","count=9301837),","count=====","count>1","countdistinct(),","counter","counter(names)","counter.most_common(50)","country_nam","counts):","counts.collect()","counts.pprint()","count|","count案例","cp","creat","create_datasets(88,","create_datasets(userid,","create_inverted_table(movie_profile)","create_inverted_table(movie_profile):","create_movie_profile(movie_dataset)","create_movie_profile(movie_dataset):","create_namespac","create_user_profile()","create_user_profile():","createdataframe：panda","createtable():","creator","creator:jerri","creator:tom","crosstab=============","ctr_model","ctr_model.transform(datasets).sort(\"probability\")","ctr点击率预测模型","ctr预估","ctr预估数据准备","ctr预测模型","custom","customer:","customer_id:","customerid:","customerid:脱敏过的广告主id；","customerid数值个数：","cut","cutting等人实现了dfs和mapreduce机制)。","d","d\",","daemon.sh","dafaframe对象","dagscheduler：","dag引擎，较少多次计算之间中间结果写到hdfs的开销","data","data(数据)","data.","data.(分布式文件系统)","data[0]","data[1]","data[2]","data_block_encod","data_path","data_path:","data_split(\"datasets/ml","data_split(data_path,","databas","databases;","datafram","dataframe[id:","dataframe[userid:","dataframe、list、rdd","dataframe和dataset统一，dataframe只是dataset[row]的类型别名。由于python是弱类型语言，只能使用datafram","dataframe和普通的rdd的逻辑框架区别如下所示：","dataframe引入schema和off","dataframe数据合并：pyspark.sql.dataframe.join","dataframe来处理，因为方便，但注意如果数据量较大不推荐，因为这样会把全部数据加载到内存中","dataframe来处理，把数据载入内存","dataframe的抽象后，我们处理数据更加简单了，甚至可以用sql来处理数据了","dataframe相当于是一个带着schema的rdd","dataframe还引入了off","dataframe还配套了新的操作数据的方法，datafram","dataframe：分布式的row对象的集合，其提供了由列组成的详细模式信息，使得spark","datanod","datanode(dn)","datanode,","datanode/dn(slaves)","datanodes界面查看datanode的情况","datanode启动的日志信息","datanode故障容错","dataset","dataset):","dataset.groupby('movieid').agg([list])","dataset.groupby('userid').agg([list])","dataset.groupby(self.columns[0]).agg([list])[[self.columns[1],","dataset.groupby(self.columns[1]).agg([list])[[self.columns[0],","dataset.itertuples(index","dataset.itertuples(index=false):","dataset.withcolumnrenamed(\"_1\",","dataset:","dataset:一个数据集，简单的理解为集合，用于存放数据的","dataset['rating'].mean()","dataset]","datasets.printschema()","datasets.select(*useful_cols)","datasets.show()","datasets_1","datasets_1.count())","datasets_1.dropna()","datasets_1.filter(datasets_1.timestamp(1494691186","dataset）叫做弹性分布式数据集，是spark中最基本的数据抽象，它代表一个不可变、可分区、里面的元素可并行计算的集合.","data：输入数据","date","datetim","datetime.fromtimestamp(1494691186","datetime.fromtimestamp(1494691186)","db","db=10)","db=9)","db：在","dct","ddl","decim","def","default","defin","del","delet","delete_table(table_name):","deletedata():","deletetable():","delimit","denomin","dens","densevector","densevector([price]","derbi","desc","desc;","describe================","descript","design","dest_data","dest_data.mappartitions(lambda","dest_rdd","dest_rdd.reducebykey(lambda","detail","develop","devic","df","df.\\","df.adgroup_id.cast(integertype())).withcolumnrenamed(\"adgroup_id\",","df.agg(fn.count('sepalwidth').alias('width_count'),fn.countdistinct('cls').alias('distinct_cls_count')).show()","df.brand.cast(integertype())).withcolumnrenamed(\"brand\",","df.campaign_id.cast(integertype())).withcolumnrenamed(\"campaign_id\",","df.cate_id.cast(integertype())).withcolumnrenamed(\"cate_id\",","df.clk.cast(integertype()))","df.column","df.corr()","df.count()","df.count())","df.crosstab('cls','sepallength').show()","df.customer.cast(integertype())).withcolumnrenamed(\"customer\",","df.describe('cls').show()","df.describe().show()","df.drop('cls').show()","df.dropduplicates()","df.groupby(\"adgroup_id\").count().count())","df.groupby(\"clk\").count().collect())","df.groupby(\"pid\").count().collect())","df.groupby(\"user\").count().count())","df.groupby('cls').agg({'sepalwidth':'mean','sepallength':'max'}).show()","df.index:","df.ix[user].replace(0,np.nan).dropna().index:","df.nonclk.cast(integertype())).\\","df.pid.cast(stringtype())).\\","df.price.cast(floattype()))","df.printschema()","df.randomsplit([0.6,","df.randomsplit([0.99,0.01])","df.replace(\"null\",","df.sample(false,0.2,100)","df.select('cls').distinct().count()","df.select('sepallength','sepalwidth').show()","df.show()","df.show(10)","df.t.corr()","df.time_stamp.cast(longtype())).withcolumnrenamed(\"time_stamp\",","df.user.cast(integertype())).withcolumnrenamed(\"user\",","df.withcolumn('newwidth',df.sepalwidth","df2","df2.column","df2.dropduplicates(subset","df3","df3.agg(fn.count('id').alias('id_count'),fn.countdistinct('id').alias('distinct_id_count')).collect()","df3.withcolumn('new_id',fn.monotonically_increasing_id()).show()","df:","df[\"item","df_miss","df_miss.agg(*[(1","df_miss.column","df_miss.columns]).show()","df_miss.rdd.map(lambda","df_miss.select([","df_miss_no_incom","df_miss_no_income.agg(","df_miss_no_income.column","df_miss_no_income.dropna(thresh=3).show()","df_miss_no_income.fillna(means).show()","df_outlier","df_outliers.approxquantile(col,","df_outliers.filter('age_o').select('id',","df_outliers.filter('weight_o').select('id',","df_outliers.join(outliers,","df_outliers.select(*['id']","dfs.datanode.data.dir","dfs.namenode.name.dir","dfs.replic","dfs.sh","df的基础上直接替换掉列数据","df，且值很多时，需要修改，默认是10000","dict","dict()})","dict(map(lambda","dict(zip(","dict(zip(items_ratings.index,","dict(zip(items_ratings.index,np.random.rand(len(items_ratings),10).astype(np.float32)","dict(zip(self.items_ratings.index,","dict(zip(self.users_ratings.index,","dict(zip(users_ratings.index,","dict(zip(users_ratings.index,np.random.rand(len(users_ratings),10).astype(np.float32)","dictionari","dictionary(dataset)","diff_in_train_test","diff_in_train_test.distinct().count()","disabl","disk：将所有的\"小的数据\"进行合并。","distdata","distdata.reduce(lambda","distinct","distribut","distributed：dataframe和rdd一样都是分布式的","distributed：它的数据是分布式存储，并且可以做分布式的计算","dml","doc","docker","docs/stable/reference/groupby.html","document","done","doubl","double]","doubletype())","doug","driver和pyspark运行时，所使用的python解释器路径","driver：","drop","drop=========================","dsitribut","dstream","dstreams中的每个rdd都包含确定时间间隔内的数据","dstream由一系列连续的rdd组成","dtype","dtype:","dtype=dict(dtype))","dtype=dtype,","dtype={\"userid\":np.int32,","e","e\"]","e:","each","echart","ee可能带来的问题","ee问题实践","election机制保证总有一个master运行。","elif","else:","employe","employee2","employee;","enabl","encod","encoder])","enumerate(movie_dataset.index):","enumerate(movie_dataset.itertuples()):","env.sh","env.sh(需要将spark","env.sh.templ","env.sh.template重命名)","env.sh中指定hadoop的路径","er","err","error","error_list","error_list.append(err","etc/hadoop","evaluations:","evaluations：只有action才会触发transformation的执行","even","event","event_tim","evict_blocks_on_clos","exactli","except","exception(\"无法预测用户对电影的评分，因为训练集中缺失的数据\".format(uid=uid,","exception(\"用户没有相似的用户\"","executor","executor使用的cpu核心数","executor：即真正执行作业的地方，一个集群一般包含多个executor，每个executor接收driver的命令launch","exist","explod","explode(key_words)","explode(wm)","explode函数","explode把map中的数据转换成多列","exploit","exploitation(开发","exploit⼒度","explor","exploration(探测","export","export_java_home=/root/bigdata/jdk","extend","extern","f\",\"h","f(b_u,","facebook","facebook推出h","factor","fals","false)","false):","false|","false|false|","famili","family:","family。","family会更高效。","family就是一个集中的存储单元，故将具有相同io特性的column放在一个column","family的存储，column","family，就只有一个store。","fav","fav:","fav=none,","featur","feature_cols_from_ad","feature_cols_from_us","feature_df","feature_df.select(\"features\").show()","feature_df.show()","features.items():","features.update(ad_feature)","features.update(user_feature)","features:","features[k]","features_col","features|","field","file","file1.txt","file2.txt","file:///root/bigdata/data/spark_test.log","file:/root/bigdata/hadoop/tmp","file=sys.stderr)","filesystem","file，比如下面两种运行方式是等价的","fillna可以接收两种类型的参数：","filter","filter(func)","filtering）","final_gender_code,","final_gender_code:","final_gender_code_valu","final_gender_code_value[final_gender_code_rela[int(features[\"final_gender_code\"])]]","final_gender_code_value\\","final_gender_code：性别","finally_similar_item","finally_similar_items.iteritems():","finally_similar_us","finally_similar_users.iteritems():","firewalld","first","first(),","fit","fit(self,","flatmap","flatmap会先执行map的操作，再将所有对象合并为一个对象","flatmap和map的区别：flatmap在map的基础上将结果合并到一个list中","flink:","float","float(features[\"price\"])","float64","floattyp","floattype,","flume","flume*","flume:日志收集框架","flume：日志数据收集","flush成storefil","fn","fn.count('*'))).alias(c","fname","foo","foreachpartit","foreachpartition(partition):","foreachpartition2(partition):","foreachrdd","format","formul","framework","frequenc","frequency)","frequency，idf）两部分，由tf和idf的乘积来设置文档词语的权重。","frequency，idf）的乘积。tf指的是某一个给定的词语在该文件中出现的次数。这个数字通常会被正规化，以防止它偏向长的文件（同一个词语在长文件里可能会比短文件有更高的词频，而不管该词语重要与否）。idf是一个词语普遍重要性的度量，某一特定词语的idf，可以由总文件数目除以包含该词语之文件的数目，再将得到的商取对数得到。","frequency，tf）和逆转文档频率（invers","friendli","from(","fs","fs.defaultf","func:正向操作，类似于updatestatebykey","function","functions;","function（有两个参数）先对集合中的第","function）。","functool","funksvd（lfm）","g","gateway,","gb","gb每个节点)在188个节点上运行47.9个小时。","gc","gc.collect()","gecko)","generation最终生成为rdd","genr","genres:","gensim","gensim.corpora","gensim.model","gensim介绍","gensim基本概念","gen：代码生成器","get_countryname(line):","get_movie_dataset()","get_movie_dataset():","get_pos(x))","get_pos(x):","get_result(ip):","getquery():","gfs：google的分布式文件系统googl","git3.png","global_mean","gmv","gmv相关的指标:","go","google发表了三篇论文","group","groupbi","groupby('userid')","groupby().count()：可以看到数据的重复情况","groupby(colname).agg({'col':'fun','col2':'fun2'})","groupbykey","groupbykey之后的结果中","growth","h","hadoop","hadoop)方式","hadoop.tmp.dir","hadoop00.out","hadoop001/test","hadoop001/test/","hadoop001/test/test.txt","hadoop001/test/test.txt文件下载到cento","hadoop00:","hadoop1.x时并没有yarn，mapreduc","hadoop_home=/root/bigdata/hadoop","hadoop®","hadoop、spark","hadoop。配置好环境变量。","hadoop企业应用案例之消费大数据","hadoop企业案例之商业零售大数据","hadoop优势","hadoop发展史","hadoop发布的","hadoop发行版的选择","hadoop名字的由来","hadoop数据分布式存储（数据分块，冗余存储）","hadoop早期,","hadoop是所有搜索引擎的共性问题的廉价解决方案","hadoop核心组件","hadoop概念扩展","hadoop概述","hadoop生态圈","hadoop生态系统","hadoop生态系统成熟","hadoop生态系统的特点","hadoop的mapreduce是google论文的开源实现","hadoop的概念:","hadoop能做什么?","hadoop计算流程","hadoop项目作者的孩子给一个棕黄色的大象样子的填充玩具的命名","hadoop）","hand","happybas","happybase.connection('192.168.19.137')","happybase.connection('somehost')","happybase.connection('somehost',","happybase操作hbas","hardware)上的分布式文件系统","hash","hat","hbase","hbase.","hbase.cluster.distribut","hbase.mast","hbase.rootdir","hbase.sh","hbase.unsafe.stream.capability.enforc","hbase.zookeeper.property.clientport","hbase.zookeeper.property.datadir","hbase.zookeeper.quorum","hbase_home=/root/bigdata/hbas","hbase_manages_zk=fals","hbase不同于一般的关系数据库,","hbase不适合有join,","hbase与hdf","hbase中最核心的模块，主要负责响应用户i/o请求，向hdfs文件系统中读写数据。","hbase使用场景","hbase内部使用哈希表,","hbase可以存储超大数据并适合用来进行大数据的实时查询","hbase启动","hbase在hadoop生态中的地位","hbase基于hdfs进行数据存储","hbase存储的核心，由memstore和storefile组成。","hbase建立在hadoop文件系统上,","hbase提供对数据的随机实时读/写访问功能","hbase是apache基金会顶级项目","hbase是cap中的cp系统,即hbase是强一致性的","hbase是googl","hbase是一个分布式的、面向列的开源数据库","hbase模块协作","hbase的列由","hbase的安装","hbase的数据模型","hbase简介","hbase组件","hdf","hdfs:///output","hdfs:///test.txt","hdfs:///user/hive/lib/h","hdfs:///user/hive/lib/udf.py;","hdfs://hadoop","hdfs://host:port/dir1","hdfs://host:port/fil","hdfs://host:port/file1","hdfs://host:port/file2","hdfs://host:port/file3","hdfs://host:port/hadoop/hadoopfil","hdfs://host:port/user/hadoop/dir1","hdfs中任意位置","hdfs优缺点","hdfs如何实现高可用(ha)","hdfs是gfs的开源实现","hdfs架构","hdfs环境搭建","hdfs的使用","hdfs的特点:扩展性&容错性&海量数量存储","hdfs的设计目标","hdfs能提供高吞吐量的数据访问，非常适合大规模数据集上的应用","hdfs设计思路","hdfs读写流程","hdfs需要把数据导出交给应用程序,","hdfs：存储数据","hdp:","he_data_on_writ","header=true)","header=true,","heap(使用操作系统层面上的内存)","heap,意味着jvm堆以外的内存,","heapq","heapq.nlargest(2,word_cnts):","high","histori","hive","hive.exec.dynamic.partition.mode=nonstrict;","hive.exec.script.wrapp","hive.metastore.warehouse.dir","hive:数据仓库","hive>","hive>select","hive_home=/root/bigdata/h","hiveql","hiveserver2基于thrift,","hive。","hive不会对student.txt做任何格式处理，因为hive本身并不强调数据的存储格式。","hive中分区表实际就是对应hdfs文件系统上独立的文件夹，该文件夹内的文件是该分区所有数据文件。","hive中表的类型","hive从hdfs中加载python脚本","hive会自动添加分区列","hive可以在hadoop上运行sql操作,","hive启动","hive基本概念","hive安装目录的lib目录下","hive把查询的结果变成了mapreduce作业通过hadoop执行","hive支持的数据类型","hive是数据仓库工具，没有集群的概念，如果想提交hive作业只需要在hadoop集群","hive是目前大数据领域，事实上的数据仓库标准。","hive的metastore元数据服务","hive的内部表和外部表","hive目录下","hive简介","hive综合案例","hive，每一个","hlog","hmaster","hmaster启动,","hmaster失效","hmaster将失效regionserver上的region分配到其他节点","hmaster更新hbase:","home","hortonwork","host","hql","hql(hive","hql操作初体验","hregion","hregionserv","hstore","html、各类报表、图像和音频/视频信息等","http/1.0\"","http/1.1\"","http://192,168.19.137:8088","http://192.168.19.137:4040/","http://192.168.19.137:8080/","http://archive.cloudera.com/cdh5/cdh/5/","http://archive.cloudera.com/cdh5/cdh/5/hbas","http://hadoop.apache.org/docs/r1.0.4/cn/hdfs_shell.html","http://pandas.pydata.org/panda","http://www.itcast.cn/1.html","http://www.itcast.cn/2.html","http://www.itcast.cn/3.html","http://www.itcast.cn/4.html","http://www.itcast.cn/5.html","https://cwiki.apache.org/confluence/display/hive/languagemanual+udf","i)","i,","i_5)","i_{rated}}sim(i,j)*r_{uj}}{\\sum_{j\\in","i_{rated}}sim(i,j)}","id","id,","idf与词语在文档中的出现次数成正比，与该词在整个文档集中的出现次数成反比。","idf值","idf值。","idf值作为它们的权重按照对应的顺序依次排列，就得到这篇影评的特征向量，我们就用这个向量来代表这篇影评，向量中每一个维度的分量大小对应这个属性的重要性。","idf值得到top","idf值最大的k个数组成目标文档的特征向量用以表示文档。","idf是一个词语普遍重要性的度量。表示某一词语在整个文档集中出现的频率，由它计算的结果取对数得到关键词k_i的逆文档频率idf_i：idf_i=log\\frac","idf模型，即计算tf","idf的特征提取技术","idf算法便是其中一种在自然语言处理领域中应用比较广泛的一种算法。可用来提取目标文档中，并得到关键词用于计算对于目标文档的权重，并将这些权重组合到一起得到特征向量。","idf算法基于一个这样的假设：若一个词语在目标文档中出现的频率高而在其他文档中出现的频率低，那么这个词语就可以用来区分出目标文档。这个假设需要掌握的有两点：","idf算法的计算可以分为词频（term","idf结果，“海盗”为0，“船长”为0.0225，“自由”为0.05。","idf自然语言处理领域中计算文档中词或短语的权值的方法，是词频（term","idf计算出来并进行对比，取其中tf","idf，word2vec在内的多种主题模型算法","idf，以电影“加勒比海盗：黑珍珠号的诅咒”为例，假设它总共有1000篇影评，其中一篇影评的总词语数为200，其中出现最频繁的词语为“海盗”、“船长”、“自由”，分别是20、15、10次，并且这3个词在所有影评中被提及的次数分别为1000、500、100，就这3个词语作为关键词的顺序计算如下。","id|age|","id|weight_o|height_o|age_o|","id|weight|","iid","iid)","iid))","iid):","iid,","iid:","iid=iid))","iids,","immuatable：一旦rdd、dataframe被创建，就不能更改，只能通过transformation生成新的rdd、datafram","immutable：不可更改","import","in_memori","includ","independ","index","index=items)","index=users)","index_col=\"movieid\")","indic","infer","info","information.","initi","initializer])","inlin","inline)可以省略，输出文件使用","inline)方式","inpath","inplace=true)","input","input(\"iid:","input(\"uid:","input.txt","inputcol='nucl_onehot_feature',","inputcol='pid_feature',","inputcol='pl_onehot_feature',","inputformat：对数据进行切分，格式化处理","insert","insertdata():","instal","int","int(adgroupid)","int(broadcast_value[mid][0])","int(broadcast_value[mid][1]):","int(i)","int(iid)))","int(input(\"iid:","int(input(\"uid:","int)","int,","int]","integ","integertyp","integertype())","integertype()),","integertype,","intelligence，简称：bi)","interact","interest_weight","interest_word","interest_word,","interest_words:","interest_words[0][1]","interest_words]","interface)为","intersect","invaddfunc","invaddfunc,","inverted_t","inverted_table.get(tag,","inverted_table.setdefault(tag,","inverted_table[interest_word]","invfunc：反向操作","ip","ip.split(\".\")#[223,243,0,0]","ip_num","ip_transform(ip)","ip_transform(ip):","ips:","ip日志信息","iqr","iqr,","item","item_id","item_ids:","item_similar","item_similar)","item_similar):","item_similar.index:","item_similar.loc[i].drop([i])","item_similar:","item_similar[1].drop([1]).dropna()","itemcol='brandid',","itemcol='cateid',","items_r","items_ratings.itertuples(index=true):","item打分数据应该是通过一下方式进行处理转换为us","item的评分矩阵（稠密/稀疏）分解为p和q矩阵，然后利用p*q​还原出us","item矩阵，即隐含特征和物品的矩阵","item矩阵，有p*q得来","item评分矩阵r​。整个过程相当于降维处理，其中：","iter","iterable,","iterable[,","j\"])","j(\\theta)&=\\cfrac{\\partial}{\\parti","j(\\theta)=\\sum_{u,i\\in","jaccard","jaccard_similarity_scor","jake,11000","jar","jar包","java","java_home='/root/bigdata/jdk'","java_home=/root/bigdata/jdk","java_home=java_home_path","javax.jdo.option.connectiondrivernam","javax.jdo.option.connectionpassword","javax.jdo.option.connectionurlmysql","javax.jdo.option.connectionusernam","jdbc/odbc","jdbc:mysql://127.0.0.1:3306/hiv","jdbcdf","jdk","jerri","jerry,12000","job","jobconf","jobtracker:负责接收客户作业提交，负责任务到作业节点上运行，检查作业的状态","join","jp","jpg","jps查看进程","json","json.dumps(data))","json.loads(client_of_features.hget(\"ad_features\",","json.loads(client_of_features.hget(\"user_features\",","jsondf","jsondf.createorreplacetempview(\"tmp_table\")","jsondf.filter(jsondf.pop>4000).show(10)","jsondf.printschema()","jsondf.show()","jsondf.show(2)","jsonrdd","jsonschema","jsonstr","json数据","k","k,v","k.lower()).collect()","k:","kafka","kafka:","kafka：实时日志数据处理队列","key","key)来进行唯一标识的,","key,valu","key:value形式","key:value形式并按用户聚合","key=lambda","key_word","keyfunc=>)","keyfunc=lambda","keyword,weight;","kill","kpi压力大","kurtosis(),","kw)","kw1","kw1:1","kw1:1,kw3:1,kw4:1,kw5:1,kw6:1,kw7:2,kw9:1","kw1:1,kw3:1,kw6:1,kw7:1,kw8:1","kw1:1,kw4:1,kw5:1,kw7:1,kw9:1","kw1:4","kw1:4,kw4:1,kw5:1,kw8:3,kw9:1","kw3","kw3:1","kw4","kw4:1","kw5","kw5:1","kw6","kw6:1","kw7","kw7:1","kw7:2","kw8","kw8:1","kw8:3","kw9","kw9:1","kw;","kw_w","k个词，这里k设为100，作为用户的标签","k值","l","l_name","l_name)","lab","label","label,","label/response.","labeledpoint","labeledpoint(0.0,","labeledpoint(1.0,","labeledpoint.","labeledpoint。","label目标值字段","lambda","lambda架构图","lambda架构是由实时大数据处理框架storm的作者nathan","lambda架构的将离线计算和实时计算整合，设计出一个能满足实时大数据系统关键特性的架构，包括有：高容错、低延时和可扩展等。","languag","larg","last_sum):","later","latest","latest数据集中","lazi","learn","left","len(iids))","len(ret)","len(sys.argv)","len(uids))","length","length),","length,","length:","less","level","lf","lfm","lfm(0.02,","lfm(latent","lfm(object):","lfm.fit(dataset)","lfm也就是前面提到的funk","lfm原理解析","lf矩阵，即用户和隐含特征矩阵。lf有三个，表示共总有三个隐含特征。","lib/hiv","librari","limit","line","line):","line.split(\"","line.split('\\t')","line.split():","line.strip()","line.strip().split():","line:","lines.flatmap(lambda","lines.map(get_countryname)","linux","list","list()","list(_df_sorted.index[:2])","list(a1)","list(a2)","list(index[_index:])","list(result[2][1])","list(user_rating_data.index)","list(user_rating_data.index.values[index:])","list(x)+list(y),","list(zip(a,c))","list(zipped)","list_namespac","list_namespace_t","lk","lname","lname)","lname.upper()","load","local","local)方式","localfil","localfile1","localfile2","localhost:","local模式的启动","locat","log","logisticregress","logisticregression()","logisticregressionmodel","logisticregressionmodel.load(\"hdfs://localhost:9000/models/ctrmodel_allonehot.obj\")","logisticregressionmodel.load(\"hdfs://localhost:9000/models/ctrmodel_normal.obj\")","log，先写log，再写内存，因为editlog记录的是最新的hdfs客户端执行所有的写操作。如果后续真实写操作失败了，由于在真实写操作之前，操作就被写入editlog中了，故editlog中仍会有记录，我们不用担心后续client读不到相应的数据块，因为在第5步中datanode收到块后会有一返回确认信息，若没写成功，发送端没收到确认信息，会一直重试，直到成功）","long","longtyp","longtype())","longtype()),","longtype(),","longtype(),true)","longtype,","lr","lr.setlabelcol(\"clk\").setfeaturescol(\"features\").fit(train_datasets_1)","lr实现ctr预估","ls","m","m:","mae","mae(predict_results)","mae(predict_results):","mae)","mae评估指标","mahout:机器学习库","main()","main():","management.(资源调度系统)","map","map(","map()","map(fun,可迭代对象)","map(func)","map(function,","map(in_key,in_value)","map(lambda","map(square,","map(tuple,[get_result(ip)","map:","mapper","mapper(self,","mapr","mapreduc","mapreduce.framework.nam","mapreduce.job.priority=very_high。","mapreduce.map.tasks=2","mapreduce.reduce.tasks=5","mapreduce2.x架构","mapreduce:","mapreduce_shuffl","mapreduce中map和reduce任务都是以进程的方式运行着，而spark中的job是以线程方式运行在进程中。","mapreduce优点:","mapreduce分而治之的思想","mapreduce原理详解","mapreduce和hadoop","mapreduce实战","mapreduce是googlemapreduce的开源实现","mapreduce架构","mapreduce框架局限性","mapreduce特点:扩展性&容错性&海量数据离线处理","mapreduce编程分map和reduce阶段","mapreduce编程执行步骤","mapreduce编程模型","mapreduce缺点:","mapreduce，减少开发人员的学习成本","map函数","map就是一个transform","map返回的结果是rdd类型，需要调用todf方法转换为datafram","map阶段","map：将前面切分的数据做map处理(将数据进行分类，输出(k,v)键值对数据)","map：将数据进行处理","marz提出的一个实时大数据处理框架。","master","master.sh","master:2181","master:60000","master:9000","master:9000/hbas","master和work","master的地址","master节点上装hive就可以了","master节点选举","master，避免单点问题；","master：standalone模式中主控节点，负责接收client提交的作业，管理worker，并命令worker启动driver和executor。","math","matrix","matrix，所以这里可以不用运行","max(),","maxcount","mean","mean(),","means['gender']","memory：达到80%数据时，将数据锁在内存上，将这部分输出到磁盘上","merchandis","merg","meso","meta","metadata","metastor","method.lower()","method:","method=\"all\"):","metric=\"jaccard\")","mi","mid","mid,","mike,13000","min(),","mkdir","ml","mllib","mllib,","mllib的区别","mllib：rdd","ml、redi","ml的模型训练是基于内存的，如果数据过大，内存空间小，迭代次数过多的化，可能会造成内存溢出，报错","ml：dataframe，","ml：模型训练","model","model)隐语义模型核心思想是通过隐含特征联系用户和物品，如下图：","model.predict([0.0,","model.predict(rdd)","model.predict(rdd2)","model.recommendforallusers(3)","model.recommendforallusers(3).show()","model.recommendforallusers(n)","model.recommendforusersubset","model.recommendforusersubset(dataset,","model.save(\"hdfs://localhost:9000/models/ctrmodel_normal.obj\")","model.save(\"hdfs://localhost:9000/models/userbrandratingmodel.obj\")","model.save(\"hdfs://localhost:9000/models/usercateratingalsmodel.obj\")","model.transform","model.transform(test_datasets_1)","model2","model2.predict([0.0,","model[corpus[i]]","modul","modules.(hadoop的核心组件)","monitor/1.0\"","more","movi","movie_dataset","movie_dataset.set_index(\"movieid\",","movie_dataset:","movie_dataset[\"tags\"].valu","movie_profil","movie_profile.loc[list(mids)]","movie_profile.set_index(\"movieid\",","movie_profile[\"weights\"].iteritems():","movie_profile[mid]","movie_tag","movie_tags))","movies.join(new_tags)","movies[\"genres\"]","movies[\"genres\"].apply(lambda","movies_index","movie的评分矩阵","mr_word_count.pi","mrjob","mrjob,mrstep","mrjob.job","mrjob实现wordcount","mrjob是最简单的方式","mrjob程序可以在本地测试运行也可以部署到hadoop集群上运行","mrstep(mapper=self.mapper,","mrstep(reducer=self.top_n_reducer)","mrwordcount(mrjob):","mrwordcount.run()","multiclass","mv","my_file.txt","my_model","my_model.recommendforallusers(3).first()","mysql","mysql/derbi","mysql驱动到","n","name","name,salary,date1","name:","name:file1.txt","name:file2.jpg","namenod","namenode(nn)","namenode,","namenode会认为这个datanode已经宕机","namenode和","namenode故障容错","namenode查找这个datanode上有哪些数据块,","names_count","namespace:","nativ","nc","need","need))","neg","negoti","negotiator)","negotiator,","netflix","new_df","new_df.new_user_class_level.cast(stringtype()))","new_df.pvalue_level.cast(stringtype()))\\","new_df.show()","new_df.sort(\"timestamp\",","new_df.withcolumn(\"pvalue_level\",","new_tag","new_user_class_level","new_user_class_level:","new_user_class_level_valu","new_user_class_level_value)","new_user_class_level_value[new_user_class_level_rela[int(features[\"new_user_class_level\"])]]","new_user_class_level的空值情况：","new_user_class_level：城市层级","new_user_profile_df","new_user_profile_df.show()","new_version_b","nm","noclk：为1代表没有点击；为0代表点击；","nodemanag","nodemanager:","nodemanager：由resourcemanager指派任务，定期向resourcemanager汇报状态","nonclk:","nonclk和clk在这里是作为目标值，不做为特征","none","none),","none,","none,(sum(counts),word)","none:","nosql(hbase/cassandra)","not_exist_cl","not_exist_cls:","nov","now.'","np","np.array([8","np.array([userid","np.array(temp)","np.dot(p_u,","np.dot(v_pu,","np.dot(v_puk,v_qik)","np.float32(r_ui","np.float32)]","np.float32}","np.int32),","np.int32,","np.int32})","np.nan","np.random.choice(pdf.where(pdf.cateid==11156).dropna().adgroupid.astype(np.int64),","np.random.rand(len(self.items_ratings),","np.random.rand(len(self.users_ratings),","np.random.shuffle(index)","np.zeros(len(items_ratings))))","np.zeros(len(self.items_ratings))))","np.zeros(len(self.users_ratings))))","np.zeros(len(users_ratings))))","nt","nucl_onehot_feature:","nucl_onehot_value:","nul_na_count","nul_na_count,","nul_na_df","nul_na_df.rdd.map(row)","nul_na_df.show(10)","null","null|","null|249.0|","null|344920|","null|368.0|","null|428.0|","null|575917|","null|639.0|","null。","null，则结果为","number","number_epoch","number_epochs,","number_epochs=10,","number_latentfactor","number_latentfactors=10,","numer","numerator/denomin","numpartitions=none,","numpi","n个关键词作为电影画像标签","n个物品","n作为用户最终的画像标签","n关键词，构建电影画像","n列表","n推荐结果","n最相似的物品进行相关推荐：如与该商品相似的商品有哪些？与该文章相似文章有哪些？","n物品进行推荐","n物品，构建初始推荐结果","n用户推荐","n的关键词","n的推荐","n相似的人或物品","n相似结果，并进行推荐了","n结果生成初始推荐结果，然后过滤掉用户已经有过记录的物品或明确表示不感兴趣的物品","o","occupation:","occupation_valu","occupation_value[occupation_rela[int(features[\"occupation\"])]]","occupation作为特征值，pvalue_level作为目标值","occupation：是否大学生","offer","on","on='id')","onehotencod","onehotencoder(droplast=false,","onehotencoder：对特征列数据，进行热编码，通常需结合stringindexer一起使用","oozie:工作流引擎，管理作业执行顺序","option(\"header\",","order","os","os.environ[\"pyspark_driver_python\"]","os.environ[\"pyspark_python\"]","os.environ[\"spark_home\"]","os.environ['java_home']=java_hom","os系统上","outer","outlier","outliers.show()","output","output(预测输出)","output.txt","output1.txt","output:","outputcol='nucl_onehot_feature')","outputcol='nucl_onehot_value')","outputcol='pid_feature')","outputcol='pid_value')","outputcol='pl_onehot_feature')","outputcol='pl_onehot_value')","outputformat：格式化输出数据","overview界面查看整体情况","overwrit","overwrite\\into","p","p,","p[uid]","p_u","p_{uk}&:=p_{uk}+\\alpha","p_{uk}]","p_{uk}q_{ik}","p_{uk}q_{ik})","p_{uk}q_{ik})^2","p_{uk}q_{ik})p_{uk}","p_{uk}q_{ik})q_{ik}","packages目录下","page","pagerank","pair","pairs.","pairs.reducebykey(lambda","pairwise_dist","pairwise_distances(df,","pairwise_distances(df.t,","panda","pandas中corr方法可直接用于计算皮尔逊相关系数","pandas的数据分批读取","parallel","parallelcollectionrdd[0]","parallel：集群并行执行","parquetdf","partit","partition(date1)","partition(date1='2018","partition:","partitions：在磁盘上有很多\"小的数据\"，将这些数据进行归并排序。","partition：在","pass","password","path","path:/hom","path:/home/p","path=$hadoop_home/bin:$path","path=$hbase_home/bin:$path","path=$hive_home/bin:$path","path=$java_home/bin:$path","path=$path:$spark_home/bin","pb级别","pb级数据的存储","pd","pd.dataframe(","pd.dataframe(_movie_profile,","pd.dataframe(create_datasets(8,","pd.dataframe(dataset)","pd.dataframe(datasets,","pd.dataframe(item_similar,","pd.dataframe(pdf.cateid.unique(),columns=[\"cateid\"])","pd.dataframe(user_similar,","pd.read_csv(\"datasets/ml","pd.read_csv(\"ml","pd.read_csv('behavior_log.csv',chunksize=100,iterator=true)","pd.read_csv(data_path,","pdf","pdf.where(pdf.cateid==11156).dropna().adgroupid","pdf[\"pvalue_level\"]","peopl","perform","persist","persist操作用于将数据缓存","person类的内部结构。","pgc","pid","pid):","pid:","pid_feature:","pid_valu","pid_value:","pid_value[pid_rela[pid]]","pid_value|","pid_value|price|cms_segid|cms_group_id|final_gender_code|age_level|shopping_level|occupation|pl_onehot_value|nucl_onehot_value|","pid|nonclk|clk|","pid|nonclk|clk|pid_feature|","pid：资源位；","pig：脚本语言，跟hive类似","pip","pipelin","pipeline(stages=[stringindexer,","pipeline.fit(_raw_sample_df2)","pipeline.fit(_user_profile_df3)","pipeline.fit(_user_profile_df4)","pipeline.fit(raw_sample_df)","pipeline.fit(user_profile_df)","pipeline.fit(user_profile_df2)","pipeline_fit","pipeline_fit.transform(_raw_sample_df2)","pipeline_fit.transform(_user_profile_df3)","pipeline_fit.transform(_user_profile_df4)","pipeline_fit.transform(user_profile_df)","pipeline_fit.transform(user_profile_df2)","pipeline_model","pipeline_model.transform(raw_sample_df)","pipeline：让数据按顺序依次被处理，将前一次的处理结果作为下一次的输入","pivot透视操作，把某列里的字段值转换成行并进行聚合运算(pyspark.sql.groupeddata.pivot)","pl_na_count","pl_na_count,","pl_na_df","pl_na_df.rdd.map(row)","pl_na_df.show(10)","pl_na_df.topandas()","pl_onehot_feature:","pl_onehot_value:","pl_onehot_value列的值为稀疏向量，存储热独编码的结果","pl_onehot_value列的值为稀疏矩阵，存储热编码的结果","platform","platform...","plug","po","point","pool","pop>4000\")","port","port=6379,","port=port)","posit","possibl","pprint","pprint(create_movie_profile(movie_dataset))","pprint(inverted_table)","pprint(result)","pprint(rs_result)","pprint(rs_results)","pprint(topn_items)","pprint(topn_users)","pprint(user_profile)","pred(u,i)=\\hat{r}_{ui}=\\cfrac{\\sum_{j\\in","pred(u,i)=\\hat{r}_{ui}=\\cfrac{\\sum_{v\\in","pred(u_1,","pred_rat","pred_result","predict","predict(self,","predict(uid,","predict_all(1,","predict_all(uid,","predict_r","predict_rating))","predict_results:","prediction.select(\"adgroupid\").head(20)","prediction.select(\"adgroupid\").head(20)]","prediction.show()","predicts.count())","predicts.map(lambda","predicts2","predicts2.take(20)","pretty_print('delet","price","price:","price|","price|cms_segid|cms_group_id|final_gender_code|age_level|shopping_level|occupation|pl_onehot_value|nucl_onehot_value|","print","print(\"%s:","print(\"*********\")","print(\"age_level:","print(\"brandid数值个数：\",","print(\"campaignid数值个数：\",","print(\"cateid数值个数：\",","print(\"cms_group_id:","print(\"cms_segid:","print(\"customerid数值个数：\",","print(\"final_gender_code:","print(\"iter%d\"","print(\"iter%d\"%i)","print(\"new_user_class_level的空值情况：\",","print(\"occupation:","print(\"pvalue_level的空值情况：\",","print(\"rmse:","print(\"shopping_level:","print(\"top2相似物品：\")","print(\"top2相似用户：\")","print(\"usage:","print(\"价格低于1的条目个数\",","print(\"价格高于1w的条目个数：\",","print(\"分类特征值个数情况:","print(\"判断数据是否有空值：\",","print(\"剔除空值数据后，还剩：\",","print(\"含缺失值的特征情况:","print(\"完成数据集切分...\")","print(\"广告id","print(\"广告展示位pid情况：\",","print(\"广告点击数据情况clk：\",","print(\"开始切分数据集...\")","print(\"开始预测用户对电影的评分...\"%(uid,","print(\"总广告条数：\",df.count())","print(\"最终推荐结果：\")","print(\"查看brandid的数据情况：\",","print(\"查看btag的数据情况：\",","print(\"查看cateid的数据情况：\",","print(\"查看userid的数据情况：\",","print(\"样本数据集总条目数：\",","print(\"测试样本个数：\")","print(\"物品之间的两两相似度：\")","print(\"用户user总数：\",","print(\"用户之间的两两相似度：\")","print(\"该时间之前的数据为训练样本，该时间以后的数据为测试样本：\",","print(\"预测值总数\",","print(\"预测出用户对电影的评分：%0.2f\"","print('*'*10,i)","print(age_level_value)","print(bcf.predict(uid,","print(bsvd.predict(int(uid),","print(cms_group_id_value)","print(connection.tables())","print(datasets.count())","print(df)","print(e)","print(final_gender_code_value)","print(item_similar)","print(item_similar.round(4))","print(jaccard_similarity_score(df[\"item","print(key,value)","print(lfm.predict(int(uid),","print(movie_dataset)","print(new_df.select(\"pid_value\").first())","print(new_df.select(\"pid_value\").first().pid_value.toarray())","print(new_user_class_level_value)","print(np.sqrt(np.mean(error_list)))","print(occupation_value)","print(pid_value)","print(predicts.take(20))","print(pvalue_level_value)","print(result)","print(result_rdd.collect())","print(shopping_level_value)","print(sparsevector(4,","print(test_sample.count())","print(uid)","print(user_similar)","print(user_similar.round(4))","print(watch_record)","probability|prediction|","process","process_row(r):","profile画像建立：","projecttungsten：钨丝计划，为了提高rdd的效率而制定的计划","provid","put","pv","pv:","pv=2326,","pvalue_level","pvalue_level:","pvalue_level_valu","pvalue_level_value[pvalue_level_rela[int(features[\"pvalue_level\"])]]","pvalue_level的空值情况：","pvalue_level：消费档次，1:低档，2:中档，3:高档；","pv|","pv|11800|","pv：网站的总访问量","pyspark","pyspark.ml","pyspark.ml.classif","pyspark.ml.featur","pyspark.ml.linalg","pyspark.ml.linalg.sparsevector","pyspark.ml.recommend","pyspark.mllib.linalg","pyspark.mllib.regress","pyspark.mllib.tre","pyspark.sql","pyspark.sql.funct","pyspark.sql.sess","pyspark.sql.typ","pyspark.stream","pyspark_python","pyspark_python=/xx/pythonx_home/bin/pythonx","pyspark可以从hadoop支持的任何存储源创建rdd，包括本地文件系统，hdfs，cassandra，hbase，amazon","python","python']","pythonrdd.scala:175","python操作rdd，转换为可执行代码，运行在java虚拟机，涉及两个不同语言引擎之间的切换，进行进程间","python操作storm","p矩阵是user","p矩阵正则","q","q,","q[iid]","q_i","q_i)","q_{ik}&:=q_{ik}","q_{ik}]","qualifi","qualifier)","quantil","quantiles[0]","quantiles[1]","query.","quux","q矩阵是lf","q矩阵正则","r","r.adgroupid,","r.age_level,","r.cms_group_id,","r.cms_segid,","r.final_gender_code,","r.id","r.new_user_class_level","r.occup","r.occupation,","r.occupation])","r.price","r.pvalue_level,","r.shopping_level,","r.userid,","r:labeledpoint(r.new_user_class_level","r:labeledpoint(r.pvalue_level","r:数据分析","r_ui","r_{ui}","rais","random","random:","random=false):","random=true)","randomforest","randomforest.trainclassifier(train_data,","randomforest.trainclassifier(train_data2,","randomsplit","range(13)]","range(15):","range(2)]","range(2)]#[0,0]","range(3)]","range(4)]","range(5)]","range(6769)]))","range(7)]","range(number_epochs):","range(self.number_epochs):","range(self.number_of_latentfactors):","rate","rate)概念","rate)预测来实现","rate的数据","rating:","rating=10.35690689086914)]),","rating=11.770171165466309),","rating=13.665942192077637),","rating=20.736785888671875)]),","rating=24.901548385620117),","rating=25.498899459838867),","rating=5.2555742263793945)])]","rating=5.624575138092041),","rating=5.90518856048584),","ratingcol='rating',","ratings):","ratings.drop(testset_index)","ratings.groupby(\"userid\").any().index:","ratings.loc[testset_index]","ratings.pivot_table(index=[\"userid\"],","ratings.where(ratings[\"userid\"]==uid).dropna()","ratings_matrix","ratings_matrix,","ratings_matrix.column","ratings_matrix.corr()","ratings_matrix.ix[sim_uid].dropna()","ratings_matrix.t.corr()","ratings_matrix:","ratings_matrix[sim_iid].dropna()","rating字段的名称","raw","raw_sample.csv","raw_sample_df","raw_sample_df.filter(raw_sample_df.timestamp(1494691186","raw_sample_df.join(ad_feature_df,","raw_sample_df.printschema()","raw_sample_df.show()","raw_sample_df和ad_feature_df合并条件","rawprediction|","rdd","rdd,","rdd.map(lambda","rdd.reducebykey(lambda","rdd1","rdd1.collect()","rdd1.flatmap(lambda","rdd1.map(add)","rdd1.map(lambda","rdd1.reduce(lambda","rdd1.union(rdd2)","rdd2","rdd2.collect()","rdd2.distinct().map(lambda","rdd2.filter(lambda","rdd2.reducebykey(lambda","rdd3","rdd3.collect()","rdd3.groupbykey()","rdd3.intersection(rdd2)","rdd3.reducebykey(lambda","rdd3.take(5)","rdd4","rdd4.collect()","rdd4.saveastextfile(\"hdfs:///uv/result\")","rdd两类算子执行示意","rdd中的所有元素","rdd会在多个节点上存储，就和hdfs的分布式道理是一样的。hdfs文件被切分为多个block存储在各个节点上，而rdd是被切分为多个partition。不同的partition可能在不同的节点上","rdd具有面向对象编程的特性","rdd常用算子练习","rdd是分布式的java对象的集合。dataframe是分布式的row对象的集合。dataframe除了提供了比rdd更丰富的算子以外，更重要的特点是提升执行效率、减少数据读取以及执行计划的优化。","rdd概述","rdd的pv","rdd的word","rdd的创建","rdd编译时进行类型检查","rdd（resili","rdd：分布式的对象的集合，spark并不知道对象的详细模式信息","reader","reader.json('data/nest.json')","reader.json(jsonrdd)","reader:","real_rat","real_rating)","real_rating,","recall_cate_by_cf(partition):","recall_set","recall_sets:","recommendations=[row(cateid=1610,","recommendations=[row(cateid=5579,","recommendations=[row(cateid=5607,","recommendations|","record_movie_prifol","record_movie_prifole[\"profile\"].values))","redi","redis.connectionpool(host=host,","redis.redis(connection_pool=pool)","redis.strictredis(host=\"192.168.19.137\",","redis.strictredis(host=\"192.168.199.188\",","redis/memcach","redis：缓存","reduc","reduce()","reduce(add,","reduce(function,","reduce(lambda","reduce(out_key,intermediate_value)","reducebykey","reducebykeyandwindow(func,invfunc,windowlength,slideinterval,[num,tasks])","reducer(self,","reducer=self.reducer_sum),","reducer_sum(self,","reduce函数","reduce处理","reduce将rdd中元素两两传递给输入函数，同时产生一个新的值，新产生的值与rdd中下一个元素再被传递给输入函数直到最后只有一个值为止。","reduce阶段:","reduce：不同的reduce任务，会从map中对应的任务中copy数据","reduce：将map输出的数据进行hash计算，对每个map数据进行统计计算","refer","reg","reg,","reg_bi","reg_bi,","reg_bu","reg_bu,","reg_p","reg_p,","reg_q","reg_q,","regionserver失效","regionserver注册到zookeeper,","region会随着插入的数据越来越多，会进行拆分。默认大小是10g一个。","regress","regression)这样的机器学习算法，而推荐算法则是一些基于协同过滤推荐、基于内容的推荐等思想实现的算法","related_movi","related_movies:","related_weight","releases.","repair","repres","resilient：弹性的","resourc","resourcemanag","resourcemanager:","resourcemanager：负责资源的管理，负责提交任务到nodemanager所在的节点运行，检查节点的状态","result","result.append((userid,","result.count()","result.foreachpartition(recall_cate_by_cf)","result.show()","result[2]","result[2][1]","result_1","result_1.filter(result_1.clk==1).select(\"clk\",","result_1.select(\"clk\",","result_rdd","result_t","result_table.get(mid,","result_table.items())","result_table.setdefault(mid,","resultdf","resultdf.show()","resultdf.show(10)","ret","ret.collect()","ret.itertuples())","ret.select(\"recommendations\").show()","ret.show()","ret.union(np.random.choice(pdf.where(pdf.cateid==i.cateid).adgroupid.dropna().astype(np.int64),","return","reverse=true)[:100]","reverse=true)[:30]","reverse=true)[:k]","rich","rm","rmse","rmse(predict_results)","rmse(predict_results):","rmse,","rmse_mae(predict_results)","rmse_mae(predict_results):","rmse和mae评估指标","rmse评估指标","rob,10000","root","round(_mae_sum","round(len(user_rating_data)","round(np.sqrt(_rmse_sum","round(predict_rating,","row","row(adgroupid=133457),","row(adgroupid=164807),","row(adgroupid=173327),","row(adgroupid=201867),","row(adgroupid=227731),","row(adgroupid=229827),","row(adgroupid=241402),","row(adgroupid=25542),","row(adgroupid=265403),","row(adgroupid=275819),","row(adgroupid=277335),","row(adgroupid=29466),","row(adgroupid=339382)]","row(adgroupid=351366),","row(adgroupid=401433),","row(adgroupid=494224),","row(adgroupid=569939),","row(adgroupid=575633),","row(adgroupid=583215),","row(btag='cart',","row(btag='fav',","row(btag='pv',","row(cateid=1610,","row(cateid=2447,","row(cateid=3347,","row(cateid=5690,","row(cateid=5737,","row(clk='1',","row(name=x[0],","row(pid='430539_1007',","row(pid_value=sparsevector(2,","row(r):","row(s)","row(userid=1061650,","row(userid=2,","row(userid=3,","row.","row.recommendations])","row.userid,","row:(row['id'],sum([c==non","row]))).collect()","row_sequ","row_sequence(),*","rowkey","rowkey_10","rowkey_16","rowkey_22","rowprefixfilt","rows,","rpc机制与hmaster和hregionserver进行通信；","rs_result","rs_result.union(set(df.ix[sim_user].replace(0,np.nan).dropna().index))","rs_result.union(topn_items[item])","rs_results[user]","run","r}","r}(r_{ui}","r矩阵是user","s3等","safari/537.36\"","sample===========","save","sbin","sbin/start","sbin/stop","sbin]$","sc","sc.broadcast(city_id_rdd.collect())","sc.parallelize([\"a","sc.parallelize([(\"a\",","sc.parallelize([(\"a\",1),(\"b\",2)])","sc.parallelize([(\"c\",1),(\"b\",3)])","sc.parallelize([1,2,3,4,5,6,7,8,9],3)","sc.parallelize([1,2,3,4,5])","sc.parallelize([2,","sc.parallelize(data)","sc.parallelize(data,5)","sc.parallelize(jsonstring)","sc.parallelize(l)","sc.parallelize(range(100),","sc.parallelize(tmp).sortbykey().first()","sc.parallelize(tmp).sortbykey(true,","sc.parallelize(tmp2).sortbykey(true,","sc.setloglevel(newlevel).","sc.stop()","sc.textfile(\"file:///root/bigdata/data/access.log\")","sc.textfile(\"file:///root/tmp/20090121000132.394251.http.format\").map(","sc.textfile(\"file:///root/tmp/ip.txt\").map(lambda","sc.textfile('file:///home/hadoop/tmp/word.txt')","sc.textfile('file:///root/tmp/word.txt')","sc.textfile(sys.argv[1])","scala","scan","scanquery()","scanquery():","scan会加上一些条件限制","scan查询中添加限制条件","scan查询添加过滤器","schedul","schema","schema=schema)","schema=schema))","schemapeopl","score","score>=60","script","sdf","se',","search","second","secondari","secondarynamenod","secondarynamenode,","seconds)","see","select","select==============","selection）","self._init_matrix()","self.alpha","self.alpha*(err*v_pu[k]","self.alpha*(err*v_qi[k]","self.als()","self.bi","self.bi[iid]","self.bu,","self.bu[uid]","self.column","self.columns[2]]]","self.dataset","self.dataset.itertuples(index=false):","self.dataset[self.columns[2]].mean()","self.global_mean","self.globalmean","self.items_r","self.items_ratings.index,","self.items_ratings.index:","self.items_ratings.itertuples(index=true):","self.number_epoch","self.number_latentfactor","self.number_latentfactors).astype(np.float32)","self.p,","self.p[uid]","self.predict(uid,","self.q","self.q,","self.q[iid]","self.reg","self.reg_bi","self.reg_bu","self.reg_p","self.reg_p*v_pu[k])","self.reg_q","self.reg_q*v_qi[k])","self.sgd()","self.users_r","self.users_ratings.index","self.users_ratings.index,","self.users_ratings.itertuples(index=true):","separ","server","server分配region；","server的socket链接.","server的上线和下线信息，实时通知给master；","server的状态，将region","server的负载均衡；","serve并重新分配其上的region；","servic","session","set","set()","set(df.ix[user].replace(0,np.nan).dropna().index)","set(movies.index)","set(ratings_matrix.ix[1].dropna().index)&set(similar_items.index)","set(ratings_matrix[1].dropna().index)&set(similar_users.index)","set(ratings_matrix[iid].dropna().index)&set(similar_users.index)","set(tags.index)","setloglevel(newlevel).","sets.","setups,","sgd(self):","shark：shark底层使用spark的基于内存的计算模型，从而让性能比hive提升了数倍到上百倍。","shell","shell中","shell操作","shell操作练习","shell的优缺点","shell的基本使用","shell（进入shell命令行）","shopping_level,","shopping_level:","shopping_level_valu","shopping_level_value[shopping_level_rela[int(features[\"shopping_level\"])]]","shopping_level：购物深度，1:浅层用户,2:中度用户,3:深度用户","should_remove(x):","show","shuffl","shuffle&sort:将相同的数据放在一起，并对数据进行排序处理","shuffle方法作用，所以需要强行转换为列表","sim_iid,","sim_item_rated_movi","sim_item_rated_movies[1]","sim_item_rating_from_us","sim_uid,","sim_us","sim_user_rated_movi","sim_user_rated_movies[1]","sim_user_rated_movies[iid]","sim_user_rating_for_item","sim_users:","similar","similar_item","similar_items.ix[list(ids)]","similar_items.where(similar_items>0).dropna()","similar_us","similar_users.empti","similar_users.ix[list(1)]","similar_users.ix[list(ids)]","similar_users.where(similar_users>0).dropna()","simplifi","singl","siri","site.xml","site.xml.templ","size","size:1024","size:5032","skewness(),","sklearn.metr","sklearn.metrics.pairwis","slave","slave.sh","slave结构)","small","small.zip，数据量小，便于我们单机使用和运行","small/al","small/movies.csv\",","small/ratings.csv\"","small/ratings.csv\",","smallint","small中标签数据太多，因此借助其来扩充","smart","socket","sort","sort_array:","sortbykey","sortbykey(ascending=true,","sorted(results,","sorted(rs_result,","sorted(vector,","sourc","spark","spark'","spark.conf.set(\"spark.sql.shuffle.partitions\",","spark.createdataframe([","spark.createdataframe([[1],[2],[3]])","spark.createdataframe(pdf)","spark.createdataframe(people)","spark.read.csv(\"hdfs://localhost:8020/csv/user_profile.csv\",","spark.read.csv(\"hdfs://localhost:9000/data/ad_feature.csv\",","spark.read.csv(\"hdfs://localhost:9000/data/behavior_log.csv\",","spark.read.csv(\"hdfs://localhost:9000/data/raw_sample.csv\",","spark.read.csv(\"hdfs://localhost:9000/data/user_profile.csv\",","spark.read.csv(\"hdfs://localhost:9000/datasets/ad_feature.csv\",","spark.read.csv(\"hdfs://localhost:9000/datasets/user_profile.csv\",","spark.read.csv(\"hdfs://localhost:9000/preprocessing_dataset/brand_count.csv\",","spark.read.csv(\"hdfs://localhost:9000/preprocessing_dataset/cate_count.csv\",","spark.read.format(\"csv\").","spark.read.format(\"jdbc\").option(\"url\",\"jdbc:mysql://localhost:3306/db_name\").option(\"dbtable\",\"table_name\").option(\"user\",\"xxx\").option(\"password\",\"xxx\").load()","spark.read.format('json').load('xxx.json')","spark.read.json(\"xxx.json\")","spark.read.json(jsonrdd)","spark.read.parquet(\"xxx.parquet\")","spark.read.schema(jsonschema)","spark.read.schema(jsonschema).json('xxx.json')","spark.sparkcontext","spark.sparkcontext.setcheckpointdir(\"hdfs://localhost:9000/checkpoint/\")","spark.sql(\"select","spark:","spark://192.168.19.137:7077","spark_app_nam","spark_app_name),","spark_hom","spark_home=/xxx/spark2.x","spark_master_host=nod","spark_master_port=7077","spark_url","spark_url),","sparkconf","sparkconf()","sparkconf().setappname(appname).setmaster(master)","sparkcontext","sparkcontext(\"local[2]\",appname=\"networkwordcount\")","sparkcontext(conf=conf)","sparkcontext,","sparkcontext.broadcast(要共享的数据)","sparkcontext代表了和spark集群的链接,","sparkr,","sparksess","sparksession.builder.appname(\"pv\").getorcreate()","sparksession.builder.appname(\"test\").getorcreate()","sparksession.builder.appname(\"topn\").getorcreate()","sparksession.builder.appname('json_demo').getorcreate()","sparksession.builder.appname('test').getorcreate()","sparksession.builder.config(conf=conf).getorcreate()","sparksession.builder.master(\"local[2]\").getorcreate()","sparksession.read.json","sparksql特性","sparkstreaming(秒)","sparkstreaming是什么","sparkstreaming的组件","spark中使用热独编码","spark中的job中间结果可以不落地，可以存放在内存中。","spark作业相关概念","spark几秒钟","spark启动（local模式）和wordcount(演示)","spark将为群集的每个分区（partition）运行一个任务（task）。","spark框架本身不了解","spark概述","spark的缺点是：吃内存，不太稳定","spark程序的入口.","spark计算结束，一般会把数据做持久化到hive，hbase，hdfs等等。我们就拿hdfs举例，将rdd持久化到hdfs上，rdd的每个partition就会存成一个文件，如果文件小于128m，就可以理解为一个partition对应hdfs的一个block。反之，如果大于128m，就会被且分为多个block，这样，一个partition就会对应多个block。","spark读取hdfs的场景下，spark把hdfs的block读到内存就会抽象为spark的partition。","spark逻辑回归(lr)训练点击率预测模型","spark配置信息","spark集群架构(standalone模式)","spars","sparse,","sparsevector","sparsevector(3,","sql","sql)查询功能，底层数据是存储在","sql/spark","sql、spark","sql优势","sql历史","sql可以进行某些形式的执行优化。","sql对json数据的处理","sql来进行离线批处理","sql案例数据清洗","sql概念","sql正确读取，否则格式化后的数据读取会出现问题","sql的开发","sql的相关概念","sql简介","sql编写转换的rdd慢，涉及到执行计划","sql能够自动将json数据集以结构化的形式加载为一个datafram","sql进行数据清洗","sql都是处理属于离线批处理任务，数据一般都是在固定位置上，通常我们写好一个脚本，每天定时去处理数据，计算，保存数据结果。这类任务通常是t+1(一天一个任务)，对实时性要求不高。","sql：离线处理","sqoop:数据交换框架，例如：关系型数据库与hdfs之间的数据交换","square(x)","squares），是spark2.*中加入的进行基于模型的协同过滤（model","squares），是spark中进行基于模型的协同过滤（model","ssc","ssc.awaittermination()","ssc.checkpoint(\"checkpoint\")","ssc.sockettextstream(\"localhost\",","ssc.sockettextstream('localhost',","ssc.sockettextstream('localhost',9999)","ssc.start()","stage：一个spark作业一般包含一到多个stage。","standalone模式启动","standalone模式的启动","standard","start","start())","startrow","statement","stddev(),","stddev_pop(),","stddev_samp(),","steaming的状态操作","step","steps(self):","stop","store","storm","storm(毫秒)","storm:","str(l_name)])","stream","streaming/mlib/graphx）","streaming/storm/flink","streamingcontext","streamingcontext(sc,","streaming中存在两种状态操作","streaming中提供这种状态保护机制，即updatestatebykey","streaming优于storm","streaming实现wordcount","streaming实现实时数据处理","streaming实现的是一个实时批处理操作，每隔一段时间将数据进行打包，封装成rdd，是无状态的。","streaming差于storm","streaming的特点","streaming的状态操作","streaming的状态操作解决实际问题","streaming简介","streaming编码实践","streaming编码步骤：","streaming）","streming\\hdfs、spark","string","string)","string))","string);","string,","string,lnam","string,salari","stringindex","stringindexer(inputcol='new_user_class_level',","stringindexer(inputcol='pid',","stringindexer(inputcol='pvalue_level',","stringindexer对指定字符串列进行特征处理","stringindexer：对指定字符串列数据进行特征处理，如将性别数据“男”、“女”转化为0和1","stringjsonrdd","stringtyp","stringtype())","stringtype()),","stringtype(),","stringtype(),true)","stringtype,","struct","structfield(\"age_level\",","structfield(\"brandid\",","structfield(\"btag\",","structfield(\"buy\",","structfield(\"cart\",","structfield(\"cateid\",","structfield(\"city\",","structfield(\"cms_group_id\",","structfield(\"cms_segid\",","structfield(\"fav\",","structfield(\"final_gender_code\",","structfield(\"id\",","structfield(\"loc\"","structfield(\"new_user_class_level\",","structfield(\"occupation\",","structfield(\"pop\",","structfield(\"pv\",","structfield(\"pvalue_level\",","structfield(\"shopping_level\",","structfield(\"state\",","structfield(\"timestamp\",","structfield(\"userid\",","structfield,","structtype()","structtype([","structtype,","structur","student","student(classno","student2","student2;","student;","stuno","submit","submit,","subtract，确保训练数据集覆盖了所有分类","sum","sum(),","sum(counts)","sum(new_values)","sum(x[1])),","sum_up/sum_down","sumdistinct(),","supervis","support","surface,","svd++:","svd++是基于这样的假设：在biassvd基础上，认为用户对于项目的历史浏览记录、购买记录、收听记录等可以从侧面反映用户的偏好。","svd:","svd也被称为最原始的lfm模型","svd分解的形式为3个矩阵相乘，中间矩阵为奇异值矩阵。如果想运用svd分解的话，有一个前提是要求矩阵是稠密的，即矩阵里的元素要非空，否则就不能运用svd分解。","svd分解降维，但这样做明显对数据的原始性造成一定影响。","svd的方法，它不在将矩阵分解为3个矩阵，而是分解为2个用户","svd矩阵分解","svd矩阵分解基础上加上了偏置项。","svd首先需要填充矩阵，然后再进行分解降维，同时存在计算复杂度高的问题，因为要分解成3个矩阵，所以后来提出了funk","svd（传统并经典着）其公式如下：","svd，一般的做法是先用均值或者其他统计学方法来填充矩阵，然后再运用tradit","sy","sys.exit(","sys.stdin:","system","systemctl","t","t_count","tabl","table(默认)","table)","table.delete('rk_01',['cf1:username'])","table.delete(row_key,","table.put('rk_01',{'cf1:address':'beijing'})","table.put(row_key,","table.row('row_key')","table.row('rowkey_22',columns=['base_info:username'])","table.rows(['rowkey_22','rowkey_16'],columns=['base_info:username'])","table.rows([row_keys])","table.scan()","table.scan():","table.scan(row_start='rowkey_10',filter=filter):","table_name)","table_name;","table_name;）","table类提供了大量api,","table：在","table：数据存放位置可以在","tag","tag,","tags.csv\",","tags.csv来自ml","tags.loc[list(movies_index)]","take","take(num)","tar","task","taskscheduler：实现task分配到executor上执行。","tasktracker：由jobtracker指派任务，定期向jobtracker汇报状态，在每一个工作节点上永远只会有一个tasktrack","task，一个executor可以执行一到多个task。","task：一个stage包含一到多个task，通过多个task实现并行运行的功能。","tb","tb的数据进行排序只花了62秒时间。","tcp/ip","teach","temp","temporari","termin","test","test',","test(self,testset):","test.txt","test;","test_datasets_1.show(5)","testdf","testdf.select('cls').subtract(traindf.select('cls'))","testset","testset.itertuples(index=false):","testset_index","text","textfile;","tf","tfidfmodel","tfidfmodel(corpus)","tf指的是一个词语在文档中的出现频率。假设文档集包含的文档数为n，文档集中包含关键词k_i的文档数为n_i，f_{ij}表示关键词k_i在文档d_j中出现的次数，f_{dj}表示文档d_j中出现的词语总数，k_i在文档dj中的词频tf_{ij}定义为：tf_{ij}=\\frac","therefor","thrift","thrift,","through","throughput","thru","time","time_stamp","time_stamp:","time_stamp为key，会有很多重复的记录；这是因为我们的不同的类型的行为数据是不同部门记录的，在打包到一起的时候，实际上会有小的偏差（即两个一样的time_stamp实际上是差异比较小的两个时间）","time_stamp：时间戳；","timerang","timestamp","timestamp:","timestamp=1558323139575,","timestamp=1558323139636,","timestamp=1558323139678,","timestamp=1558323139732,","timestamp=1558323139866,","timestamp=1558323139907,","timestamp=1558323139963,","timestamp=1558323140036,","timestamp=1558323140107,","timestamp=1558323140143,","timestamp=1558323140188,","timestamp=1558323758696,","timestamp=1558323904133,","timestamp=1558323918953,","timestamp|adgroupid|","timestamp|btag|cateid|brandid|","timestamp|clk|","tinyint","tip","titl","title,","tmp","tmp2","tmp2.extend([('whose',","tmp_tabl","tom","tom,4300","took","top","top2","top_k_rs_result(20)","top_k_rs_result(k):","top_n_reducer(self,_,word_cnts):","topn_item","topn_items[i]","topn_tag","topn_tags,","topn_tags_weight","topn_tags_weights))","topn_tags_weights.items()]","topn_tags_weights[g]","topn_us","topn_users.items():","topn_users[i]","topnwords(mrjob):","topnwords.run()","topn推荐","topn统计（实验）","touch","tradit","train_data","train_data2","train_datasets_1","train_datasets_1.show(5)","train_sampl","traindf,","traindf,testdf","traindf.select('cls').subtract(testdf.select('cls')).distinct().rdd.map(lambda","traindf.select('cls').subtract(testdf.select('cls')).distinct().show()","traindf.withcolumn('new_cls',check(traindf['cls'])).filter('new_cl","trainset","trainset,","transform","transform(fname,","transform,and","transformation:延迟性操作","transformation算子","transform中提供userid和cateid可以对打分进行预测，利用打分结果排序后","transform中提供userid和cateid可以对打分进行预测，利用打分结果排序后，同样可以实现top","true","true)","true),","true:","true|","truncat","try:","ttl","txt","type","type:jpg","type:txt","u(fnam","u2","u;","udaf","udaf:就是一个reducer，把一组输入数据映射为一条(或多条)输出数据。","udf","udf(should_remove,stringtype())","udf.pi","udf1.py'","udf================","udf示例(运行java已经编写好的udf)","udf：就是做一个mapper，对每一条输入数据，映射为一条输出数据。","udf：自定义函数","ue(前端界面)","ug","ugc","ui","uid","uid)","uid,","uid,iid,real_r","uid:","uids,","ui中看到当前的spark作业","ui中观察执行情况","ui查看spark集群及spark","unabl","union","unit","update\\insert\\delet","updatefunc(new_values,","updatestatebykey","uri","url","us","use:","usecols=range(1,","usecols=range(2),","usecols=range(2),dtype={\"userid\":","usecols=range(3))","usecols=range(3),","useful_col","user","user,","user1","user2","user3","user4","user5","user:","user_act","user_actions(","user_actions;","user_featur","user_id","user_id,","user_id,collect_list(article_id)","user_id,collect_set(article_id)","user_id,keyword,weight","user_id,map_keys(wm),map_values(wm)","user_id,sort_array(collect_list(article_id))","user_id;","user_id：脱敏过的用户id；","user_kw","user_kws;","user_profil","user_profile.csv","user_profile.items():","user_profile[uid]","user_profile_df","user_profile_df.count()","user_profile_df.dropna(subset=[\"new_user_class_level\"]).count()","user_profile_df.dropna(subset=[\"new_user_class_level\"]).rdd.map(","user_profile_df.dropna(subset=[\"pvalue_level\"]).count()","user_profile_df.dropna(subset=[\"pvalue_level\"]).rdd.map(","user_profile_df.dropna(subset=[\"pvalue_level\"]).unionall(spark.createdataframe(pdf,","user_profile_df.dropna(subset=[\"pvalue_level\"])：","user_profile_df.foreachpartition(foreachpartition2)","user_profile_df.groupby(\"age_level\").count().count())","user_profile_df.groupby(\"cms_group_id\").count().count())","user_profile_df.groupby(\"cms_segid\").count().count())","user_profile_df.groupby(\"final_gender_code\").count().count())","user_profile_df.groupby(\"new_user_class_level\").count().show()","user_profile_df.groupby(\"new_user_class_level\").min(\"nucl_onehot_feature\").show()","user_profile_df.groupby(\"occupation\").count().count())","user_profile_df.groupby(\"pvalue_level\").count().show()","user_profile_df.groupby(\"pvalue_level\").min(\"pl_onehot_feature\").show()","user_profile_df.groupby(\"shopping_level\").count().count())","user_profile_df.na.fill(","user_profile_df.new_user_class_level.cast(stringtype()))","user_profile_df.printschema()","user_profile_df.pvalue_level.cast(stringtype()))\\","user_profile_df.show()","user_profile_df.withcolumn(\"pvalue_level\",","user_profile_df2","user_profile_df2.printschema()","user_profile_df2.show()","user_profile_df3","user_profile_df3.show()","user_rating_data","user_similar","user_similar)","user_similar):","user_similar.index:","user_similar.loc[i].drop([i])","user_similar:","user_similar[1].drop([1]).dropna()","user_similar[uid].drop([uid]).dropna()","usercf","userid","userid))","userid:","userid、cataid的df，对应预测值进行排序","userid：脱敏过的用户id；","users_r","users_ratings.itertuples(index=true):","user|time_stamp|adgroup_id|","user|time_stamp|btag|","user：脱敏过的用户id；","util","util.nativecodeloader:","uv统计案例","uv：网站的独立用户访问量","u}sim(u,v)*r_{vi}}{\\sum_{v\\in","u}|sim(u,v)|}","v","v.s.","v_pu","v_pu)","v_pu[k]","v_puk","v_qi","v_qi)","v_qi))","v_qi[k]","v_qik","value)","value),","value=1","value=2014","value=beij","value=jerri","value=newyork","value=tokyo","value=tom","value=tom2","value=tom3","value=tom4","values('bill','clinton');","values('bill','gates');","values('george','bush');","values('george','washington');","value是一个iter","var_pop(),","var_samp()","varchar","variance()","vector","vector))","vector,","vector.","vectorassembl","vectorassembler().setinputcols([\"age_level\",","vectorassembler().setinputcols(useful_cols[2:]).setoutputcol(\"features\").transform(datasets_1)","version","versions=>'1'说明最多可以显示一个版本","vi","view","view)","volume)","volume电商网站成交金额)/视频网站vv","vs","w,c","want","warn","watch_record","watch_record.groupby(\"userid\").agg(list)","watch_record.itertuples():","way","wc.pi","web","webgui","web项目:","weight","weight))","weights.items():","welcom","window","window_count","window_counts.pprint()","window_length","window_length,","windows操作","window操作是基于窗口长度和滑动间隔来工作的","withcolumn(\"adgroup_id\",","withcolumn(\"brand\",","withcolumn(\"campaign_id\",","withcolumn(\"cate_id\",","withcolumn(\"clk\",","withcolumn(\"customer\",","withcolumn(\"nonclk\",","withcolumn(\"pid\",","withcolumn(\"price\",","withcolumn(\"time_stamp\",","withcolumn(\"user\",","withcolumn===========","wm","wm['kw1']","word","word,","word,1","word,cnt","word,sum(counts)","word:","word:(word,1))","word_count.pi","wordcount","wordcounts.pprint()","wordcount程序","words)","words.map(lambda","words），如“是”、“的”之类的，对于文档的中心思想表达没有意义的词，在分词时需要先过滤掉再计算其他词语的tf","word相同的","work","worker","worker：standalone模式中slave节点上的守护进程，负责管理本节点的资源，定期向master汇报心跳，接收master的命令，启动driver和executor。","write","x","x)","x+1","x+1)","x+y)","x+y,","x,","x,i","x,y:x+y)","x,y:x+y).collect()","x.split(\"|\"))","x.split(\"|\")[1])","x:","x:(\"pv\",1)).reducebykey(lambda","x:(\"uv\",1))","x:(dct[x[0]],","x:(x[10],1))","x:int(x)).collect()","x:len(x)>10).map(lambda","x:x*2)","x:x.split(\"","x:x.split(\"|\")).map(lambda","x:x>4)","x:x[0])","x:x[1],","x:x[1],ascending=false)","x=0.8,","x[13],","x[14]))","x[1]),","x[1],","x[2]+x[3])","x[2],","x[3]","x[3],","x])","xx_tabl","y","y)","y,","y:","yahoo","yahoo的团队使用hadoop对1","yarn","yarn&mapreduc","yarn.nodemanager.aux","yarn.sh","yarn:","yarn产生背景","yarn特点:扩展性&容错性&多框架资源统一调度","yarn环境搭建","yarn的架构和执行流程","yield","yum","zero:","zip","zip()","zip(*zip(a,b))","zip([iterable,","zip(a,b)","zip(iids,","zip(uids,","zk返回regionserver地址","zookeep","zookeeper:用户无感知，主节点挂掉选择从节点作为主的","zookeeper配合","zxvf","{","{\"1\":null,\"kw3\":\"1\",\"kw6\":\"1\"}","{\"kw1\":\"1\",\"kw3\":\"1\",\"kw4\":\"1\",\"kw5\":\"1\",\"kw6\":\"1\",\"kw7\":\"2\",\"kw9\":\"1\"}","{\"kw1\":\"1\",\"kw3\":\"1\",\"kw6\":\"1\",\"kw7\":\"1\",\"kw8\":\"1\"}","{\"kw1\":\"1\",\"kw4\":\"1\",\"kw5\":\"1\",\"kw7\":\"1\",\"kw9\":\"1\"}","{\"kw1\":\"4\",\"kw4\":\"1\",\"kw5\":\"1\",\"kw8\":\"3\",\"kw9\":\"1\"}","{\"userid\":","{'age':","{'cf:cq':'value'})","{0.97*5+0.58*4}{0.97+0.58}","{0:","{2:2,3:7}","{2:2}","{\\sum_{k=1}}^k","{\\sum_{u,i\\in","{b_i}^2)","{b_i}^2)​是正则化项，用于避免过拟合现象","{b_u}^2","{column","{f_{ij}}{f_{dj}}·log\\frac","{f_{ij}}{f_{dj}}。并且注意，这个数字通常会被正规化，以防止它偏向长的文件（指同一个词语在长文件里可能会比短文件有更高的词频，而不管该词语重要与否）。","{name","{n}{n_i}","{p_{uk}}\\cdot","{q_{ik}}","{q_{ki}}","{rowprefixfilter=>'rowkey_22'}","{r}_{ui}","{}","{},","|","|(10,[0,1,5],[4.0,...|","|(10,[0,1,6],[3.0,...|","|(10,[0,1,7],[5.0,...|","|(10,[0,1,8],[5.0,...|","|(10,[0,2,5],[2.0,...|","|(10,[0,2,5],[4.0,...|","|(10,[0,2,6],[2.0,...|","|(10,[0,2,6],[4.0,...|","|(10,[0,2,6],[5.0,...|","|(10,[0,2,6],[6.0,...|","|(10,[0,2,9],[5.0,...|","|(10,[0,3,6],[2.0,...|","|(10,[0,3,7],[2.0,...|","|(10,[0,3,8],[1.0,...|","|(10,[0,3,8],[4.0,...|","|117840|1494036743|","|1494261938|","|1494436784|","|1494553913|","|1494677292|","|1494684007|","|177002|1494691186|","|243671|1494691186|","|286630|1494218579|","|286630|1494289247|","|298139|1494462593|","|322244|1494691179|","|332634|1493809895|","|399907|1494302958|","|421590|1494034144|","|449818|1494638778|","|467042|1493772641|","|467042|1493772644|","|488527|1494691184|","|530454|1494293746|","|555266|1494307136|","|558157|1493741625|","|558157|1493741626|","|558157|1493741627|","|581738|1494137644|","|619381|1493774638|","|623911|1494451608|","|623911|1494625301|","|627200|1494691179|","|628137|1494524935|","|628998|1494691180|","|674444|1494691179|","|704223|1494691183|","|707120|1494220810|","|728690|1493776998|","|738335|1494691179|","|739815|1494115387|","|771431|1494153867|","|775475|1494561036|","|839493|1494691183|","|857237|1493816945|","|914836|1494650879|","|914836|1494651029|","|976358|1494156949|","|991528|1493780633|","|991528|1493780710|","|991528|1493780712|","|991528|1493780714|","|991528|1493780764|","|991528|1493780765|","|[[104,","|[[1610,","|[[3347,","|[[5607,","|[[5631,","|[[5720,","|[[5731,","|adgroup_id|cate_id|campaign_id|customer|","|adgroupid|cateid|campaignid|customerid|brandid|","|adgroupid|cateid|campaignid|customerid|brandid|price|","|clk|","|new_user_class_level|","|new_user_class_level|min(nucl_onehot_feature)|","|new_user_class_level|nucl_onehot_featur","|pvalue_level|","|pvalue_level|min(pl_onehot_feature)|","|pvalue_level|pl_onehot_featur","|r(i)|}","|r(u)|}","|userid|","|userid|adgroupid|","|userid|cateid|prediction|","|userid|cms_segid|cms_group_id|final_gender_code|age_level|pvalue_level|shopping_level|occupation|new_user_class_level","|userid|cms_segid|cms_group_id|final_gender_code|age_level|pvalue_level|shopping_level|occupation|new_user_class_level|","|userid|cms_segid|cms_group_id|final_gender_code|age_level|pvalue_level|shopping_level|occupation|new_user_class_level|pl_onehot_feature|pl_onehot_value|","|userid|cms_segid|cms_group_id|final_gender_code|age_level|pvalue_level|shopping_level|occupation|new_user_class_level|pl_onehot_feature|pl_onehot_value|nucl_onehot_feature|nucl_onehot_value|","}","}\"\"\"","}\"\"\",","}{\\partial","~/.bash_profil","~/app/","~/app目录下","~/software目录下","~]$","×","λ","λ正则化系数）","​","‘表名’，‘rowkey的值’，’列族：列标识符‘，’值‘","“new_user_class_level”的特征对应关系","“查询词(query)”，查询词和广告内容的匹配程度很大程度影响了点击概率，搜索广告的点击率普遍较高","“海盗”出现的词频为20/200＝0.1","“海盗”的idf为：log(1000/1000)=0","“自由”出现的词频为10/200=0.05；","“自由”的idf为：log(1000/100)=1","“船长”出现的词频为15/200=0.075","“船长”的idf为：log(1000/500)=0.3","“跟你喜好相似的人喜欢的东西你也很有可能喜欢”","“跟你喜欢的东西相似的东西你也很有可能喜欢","“阿甘正传”比较热门且备受好评，评分普遍比平均评分要高1.2分，“阿甘正传”的偏置是+1.2","”：基于物品的协同过滤推荐（item","•","….","…]","…。","≈","①与zookeeper通信,","①为region","①保证任何时候，集群中只有一个run","①维护master分配给它的region，处理对这些region的io请求；","②使用hbase","②存贮所有region的寻址入口，包括","②负责region","②负责切分在运行过程中变得过大的region。","③client与hmaster进行通信进行管理类操作；","③发现失效的region","③实时监控region","④client与hregionserver进行数据读写类操作。","④hdfs上的垃圾文件回收；","④存储hbase的schema，包括有哪些table，每个table有哪些column","⑤处理用户对表的增删改查操作。","⽤户冷启动：如何为新⽤户做个性化推荐","⽤户注册信息：性别、年龄、地域","⽤户留存","一","一一对应的datafram","一个region中会有个多个store，每个store用来存储一个列簇。如果只有一个column","一个sparkcontext创建一个stream","一个sparkcontext对象可以重复利用去创建多个stream","一个spark作业运行时包括一个driver进程，也是作业的主进程，负责作业的解析、生成stage并调度task到executor上。包括dagscheduler，taskscheduler。","一个人数所有的钞票，数出各种面值有多少张","一个或多个序列","一个数字、字符串，这时整个dataset中所有的缺失值都会被填充为相同的值。","一个给定的商品，可能被拥有类似品味或需求的用户购买","一个脚本至于是做mapper还是做reducer，又或者是做udf还是做udaf，取决于我们把它放在什么样的hive操作符中。放在select中的基本就是udf，放在distribut","一个表最开始存储的时候，是一个region。","一个集群中可以包含数以千计的节点","一些物品的评分普遍高于其他物品，一些物品的评分普遍低于其他物品。比如一些物品一被生产便决定了它的地位，有的比较受人们欢迎，有的则被人嫌弃。","一份广告基本信息数据ad_feature.csv：体现的是每个广告的类目(id)、品牌(id)、价格特征","一份广告点击的样本数据raw_sample.csv：体现的是用户对不同位置广告点击、没点击的情况","一份用户基本信息数据user_profile.csv：体现的是用户群组、性别、年龄、消费购物档次、所在城市级别等特征","一份用户行为日志数据behavior_log.csv：体现用户对商品类目(id)、品牌(id)的浏览、加购物车、收藏、购买等信息","一旦一个context已经停止,不能重新启动(stream","一旦一个context已经启动(调用了stream","一致性(所有节点在同一时间具有相同的数据)","一般超过一天都是用rdd或spark","三","三方库","三方采集数据","上。","上传","上班后就会登陆后台数据系统","上的数据块，计算并存储校验和（checksum)","上的结构化的数据,是一款基于","上述三种操作的核心都是：通过原始数据设定一个正常的范围，超过此范围的就是一个异常值","下载","下载hive的安装包","下载jdk","下载地址：movielen","下载安装包","下面我们来讲解如何进行程序指定schema","下面这张图是数据块多份复制存储的示意","不会立即计算结果","不依靠硬件来提供高可用性(high","不可变","不同数据源产生的数据质量可能差别很大","不同计算框架可以共享同一个hdfs集群上的数据，享受整体的资源调度","不完全支持","不损害用户体验","不插入数据直接查询查看结果","不支持(默认)","不方便用数据库二维逻辑表来表现","不确定思维","不算是一个标准的流式计算","不过先对向量做了中心化,","不适合计算布尔值向量之间的相关度","不适用于传统关系型数据的存储；","与","与传统数据库对比","与向量长度无关,余弦相似度计算要对向量长度归一化,","与非缺失数据进行拼接，完成pvalue_level的缺失值预测","且当前我们缺少对这些特征更加具体的信息，（如商品类目具体信息、品牌具体信息等），从而无法对这些特征的数据做聚类、降维处理","且数量会持续增长","且服务的物品对用户构成了信息过载,","且跟线上真实效果存在偏差","业务知识","两个向量只要方向一致,无论程度强弱,","两个向量的夹角为0是,余弦值为1,","两个物体,","两个特征","两个集合的交集元素个数在并集中所占的比例,","两数相加","两者本质上的区别，词袋是在词集的基础上增加了频率的维度，词集只关注有和没有，词袋还要关注有几个。","个元素进行操作，得到的结果再与第三个数据用","个性化","个性化推荐(推荐系统)经历了多年的发展，已经成为互联网产品的标配，也是ai成功落地的分支之一，在电商(淘宝/京东)、资讯(今日头条/微博)、音乐(网易云音乐/qq音乐)、短视频(抖音/快手)等热门应用中,推荐系统都是核心组件之一。","个性化电商广告推荐系统介绍","中","中。","中位数绝对偏差去极值","中创建","中包含以下数据模型：","中所有的数据都存储在","中的元数据包括","中的函数","中表现为","中表现为同一个表目录下根据","中表现所属","中，并在随后由","中，没有专门的数据存储格式","丰富的数据类型","为上层应用提供统一的资源管理和调度，为集群在利用率、资源统一管理和数据共享等方面带来了巨大好处","为了保证每个用户在测试集和训练集都有数据，因此按userid聚合","为了根据指定关键词迅速匹配到对应的电影，因此需要对物品画像的标签词，建立倒排索引","为什么使用","为什么要学习sparksql","为应用程序向rm申请资源（core、memory），分配给内部task","为异常值字段打标志","为机器学习提供燃料","为某一用户预测所有电影评分","为正则化系数）","为每个物品产生top","为每部电影匹配对应的标签数据，如果没有将会是nan","主从热备","主动","主要包括","主要用途：用来做离线数据分析，比直接用","举例","举例：词统计。","举例：通过baseline来预测用户a对电影“阿甘正传”的评分","之前我们接触的spark","也不能更改表结构","也会关闭sparkcontext对象,","也可以复制到磁盘的其它节点上","也可以接收一个字典｛列名：值｝这样","也可以缓存到磁盘上，","也必须要通过hql添加分区,","也支持micro","也许可以直接用","了解hbase的基本架构","了解hdfs读写流程","了解hive原理和架构","了解mapreduce概念","了解spark概念","了解spark的安装部署","了解yarn概念和产生背景","了解为什么使用hive","了解什么是hive","了解公司目前发展的状况","了解基于内容推荐算法概念","了解处理推荐系统冷启动的常用方法","了解推荐模型构建流程","了解推荐相关常用概念","了解推荐系统与web项目区别","了解推荐系统概念及产生背景","了解推荐系统的冷启动问题","了解推荐系统的常用评估指标","了解推荐系统的评估","了解推荐系统的评估方法","了解推荐系统要素","了解物品冷启动的推荐方法","了解物品画像，用户画像概念","事务","事务支持","二","二者都考虑","于是将问题提交给技术部门调查，工程师查看","互联网产品要求","互联网大数据平台架构:","五","亚马逊提前发货系统","交互式计算：impala、presto","交叉表","交替最小二乘法应用","交替最小二乘法推导","产品增长性的关键指标","产生推荐结果","产生的数据&日志同步到大数据系统","人们后来又提出了改进的biassvd，被称为svd++，该算法是在biassvd的基础上添加了用户的隐式反馈信息：","人员学习成本太高","人群算法:","什么是","什么是hadoop","什么是happybas","什么是hbase","什么是mapreduc","什么是rdd","什么是yarn","什么是分区表","什么是推荐系统","什么是非结构化数据存储","仅会删除元数据，hdfs上的文件并不会被删除","仅限于数据库,受数据量和计算能力的限制,","今日头条&抖音","介绍","从csv中读取数据","从hdfs中加载csv文件为datafram","从hdfs中加载广告基本信息数据","从hdfs中加载广告基本信息数据，返回spark","从hdfs中加载样本数据信息","从hdfs加载csv文件","从hdfs加载csv文件为datafram","从hdfs加载之前存储的模型","从hdfs加载数据为dataframe，并设置结构","从hdfs加载模型","从hdfs加载用户基本信息数据","从hdfs加载预处理好的品牌的统计数据","从iid物品的近邻相似物品中筛选出uid用户评分过的物品","从json到datafram","从json字符串数组得到datafram","从json字符串数组得到rdd有两种方法","从uid用户的近邻相似用户中筛选出对iid物品有评分记录的近邻用户","从一个已经存在的数据集创建一个新的数据集","从其它datanode上读取备份数据","从其它datanode服务器上复制数据","从前20个分类中选出500个进行召回","从单个服务器扩展到数千台计算机，每台计算机都提供本地计算和存储","从执行结果可以看出","从排序之后的结果中切片","从数据中筛选特征","从本地文件系统中复制单个或多个源路径到目标文件系统。也支持从标准输入中读取输入写入目标文件系统。","从标准输入中读取输入。","从模板文件复制","从用户1的近邻相似用户中筛选出对物品1有评分记录的近邻用户","从表中获取map中所有的key","从表中通过key查询map中的值","代码","代表","代表一个连续的数据流","以rdd为单位处理数据","以record为单位处理数据","以上两种最优化函数都可以通过梯度下降或者随机梯度下降法来寻求最优解。","以上四个特征均属于分类特征，但由于分类值个数均过于庞大，如果去做热独编码处理，会导致数据过于稀疏","以下三项配置，可以控制执行器数量","以下是一个简单的示例，数据集相当于一个用户对物品的购买记录表：打勾表示用户对物品的有购买记录","以二进制的字节来存储","以元组中的第0个元素作为key，进行分组，返回一个新的rdd","以参数序列中的每一个元素调用","以及","以及这些数据在其它datanode服务器上的存储情况","价格低于1的条目个数","价格异常","价格高于1w的条目个数：","任何对dstreams的操作都转换成了对dstreams隐含的rdd的操作","任务调度系统","任务运行，使不熟悉","任意指定路径","优化器（catalyst）的优化，即使你写的程序或sql不仅高效，也可以运行的很快。","优化引擎：类似mysql等关系型数据库基于成本的优化器","优点","会保留多个版本数据","会根据提供的函数对指定序列做映射。","会自动创建一个与","会自动重新调度作业计算","会走到同一个reduc","传入","传统关系数据库的区别","传统变化后的数据不是连续的，而是随机分配的，不容易应用在分类器中","传统变化：","估计用户是否会点击某个商品","但使用方式比thrift简单,","但另外一些特征，比如电影的内容简介、电影的影评、图书的摘要等文本数据，这些被称为非结构化数据，首先他们本应该也属于物品的一个特征标签，但是这样的特征标签进行量化时，也就是计算它的特征向量时是很难去定义的。","但在企业中存在很多实时性处理的需求，例如：双十一的京东阿里，通常会做一个实时的数据大屏，显示实时订单。这种情况下，对数据实时性要求较高，仅仅能够容忍到延迟1分钟或几秒钟。","但好在我们训练als模型时，不需要转换为us","但是不能创建删除表,","但是需要关一个再开下一个","但是需要利用hadoop写mapreduce代码,mrjob是很好的选择","但根据我们的经验，我们的广告推荐其实和用户的消费水平、用户所在城市等级都有比较大的关联，因此在这里pvalue_level、new_user_class_level都是比较重要的特征，我们不考虑舍弃","但由于cateid字段过多，这里运算量比很大，机器内存要求很高才能执行，否则无法完成任务","但该方法其实指标不治本，因为无法防止内存溢出，所以还是会报错","但这里我们将使用的spark的als模型进行cf推荐，因此注意这里数据输入不需要提前转换为矩阵，直接是","低","低延迟的数据访问","低维转高维方式","余弦相似度","余弦相似度/皮尔逊相关系数适合用户评分数据(实数值),","余弦相似度在度量文本相似度,","余弦相似度的特点,","作用：","作者：doug","使⽤单独的特征和模型预估","使⽤历史⾏为预测⽤户对某个物品的喜爱程度","使用","使用baseline的算法思想预测评分的步骤如下：","使用collections.counter类统计列表元素出现次数","使用dataframe.withcolumn更改df列数据结构；使用dataframe.withcolumnrenamed更改列名称","使用lr算法)","使用pip安装","使用pyspark中的als矩阵分解方法实现cf评分预测","使用python开发在hadoop上运行的程序,","使用tfidf，分析提取topn关键词","使用交替最小二乘法优化算法预测baseline偏置值","使用其它站点的行为数据,","使用动态分区需要设置参数","使用协同过滤推荐算法对用户进行评分预测","使用多线程模型来减少task启动开销，shuffle过程中避免不必要的sort操作以及减少磁盘io","使用方法：hadoop","使用有状态的transformation，需要开启checkpoint","使用热独编码转换pvalue_level的一维数据为多维，其中缺失值单独作为一个特征值","使用热编码转换new_user_class_level的一维数据为多维","使用热编码转换pvalue_level的一维数据为多维，增加n","使用用户行为数据描述商品","使用语法为：concat_ws(separator,str1,str2,…)","使用随机梯度下降优化算法预测baseline偏置值","使用随机梯度下降，优化结果","例如map","例如在热词时，在上一个窗口中可能是热词，这个一个窗口中可能不是热词，就需要在这个窗口中把该次剔除掉","例如腾讯视频&qq音乐","例如，假设已知电影a是一部喜剧，而恰巧我们得知某个用户喜欢看喜剧电影，那么我们基于这样的已知信息，就可以将电影a推荐给该用户。","例子","信任度","信息熵","信息过载","修改","修改hadoop","修改hdf","修改yarn","修改会将修改直接同步给元数据","修改可以显示的版本数量","修改数据","修改日期","修改时间","修改配置文件","倒排索引介绍","借鉴函数式编程方式","借鉴线性回归的思想，通过最小化观察数据的平方来寻求最优的用户和项目的隐含向量表示。同时为了避免过度拟合（overfitting）观测数据，又提出了带有l2正则项的funksvd，上公式：","假如叫做p,q,","假设有三组特征，分别表示年龄，城市，设备；","偏好打分数据集","偏好评分规则：","允许使用简单的编程模型跨计算机集群分布式处理大型数据集","允许远程客户端使用多种编程语言如java、python向hive提交请求","元数据","元数据存储：通常是存储在关系数据库如","元数据库信息(mysql安装见文档)","元素个数与最短的列表一致","先计算均值，并组织成一个字典","入门","全量上线","全面的acid支持,","公式第一部分","公式第二部分\\lambda*(\\sum_u","公式解析：","六","关于zip","关于协同过滤推荐算法使用的数据集","关于用户","关于相似度计算这里先用一个简单的思想：如有两个同学x和y，x同学爱好[足球、篮球、乒乓球]，y同学爱好[网球、足球、篮球、羽毛球]，可见他们的共同爱好有2个，那么他们的相似度可以用：2/3","关于评分预测的方法也有比较多的方案，下面介绍一种效果比较好的方案，该方案考虑了用户本身的评分评分以及近邻用户的加权平均相似度打分来进行预测：","关系型数据库","关系型数据库中数据示例","关系型数据库的\"数据库\"(database)","关系运算符","关联分析","关联分析,","关联对应的广告完成召回","关联广告","关联推荐","关闭连接","关闭防火墙","兴趣扩展:","其中|r(i)|表示物品i​收到的评分数量","其中|r(u)|表示用户u的有过评分数量","其中一个广告id对应一个商品（宝贝），一个宝贝属于一个类目，一个宝贝属于一个品牌。","其他渠道：如爬虫","其基于python","其实是先建一个df，不要缺失值的列","其次，要定义state更新函数","典型案例：热点搜索词滑动统计，每隔10秒，统计最近60秒钟的搜索词的搜索频次，并打印出最靠前的3个搜索词出现次数。","内存计算引擎，提供cache机制来支持需要反复迭代计算或者多次数据共享，减少数据读取的io开销","内容推荐数据处理","内容提供方的共赢","内置","内置函数","内置运算符","内部表(manag","再回头看看这些异常值的值，重新和原始数据关联","再次创建外部表","再求元素和","再计算余弦相似度","写入数据时才开始计算","写完数据，关闭输输出流。","写模式","决定了最终的推荐效果","冷启动","冷启动问题","准备mapper数据","准备mapreduce的输入数据","准备案例环境","准备空白dict用来保存推荐结果","准备要预测的物品的id列表","准实时","准确性","准确性指标计算方法","准确性指标评估","几分钟","几分钟~几小时(计算量和数据量不同)","出现次数最多的词权重为1","出现的次数","函数","函数会对参数序列中元素进行累积。","函数名;","函数将一个数据集合（链表，元组等）中的所有数据进行下列操作：用传给","函数求导：","函数用于将可迭代的对象作为参数，将对象中对应的元素打包成一个个元组，然后返回由这些元组组成的对象，这样做的好处是节约了不少的内存。","函数语法：","函数运算，最后得到一个结果。","函数返回值的新列表。","函数，有两个参数","函数，返回包含每次","分位数去极值","分别都是n个坐标,","分区仅仅是一个目录名","分区及其属性","分区可以理解为分类，通过分类把不同类型的数据放到不同的目录下。","分区字段不是表中的列,","分区容错性(系统中任意信息的丢失或失败不会影响系统的运行,系统如果不能在某个时限内达成数据一致性,就必须在上面两个操作之间做出选择)","分区表","分区表的意义在于优化查询。查询时尽量利用分区字段。如果不使用分区字段，就会全部扫描。","分发到这个容器上面","分子是两个布尔向量做点积计算,","分层架构","分布式、并发数据处理，效率极高；","分布式处理框架","分布式存储","分布式存储：","分布式文件系统","分布式文件系统的设计思路：","分布式的流式计算框架","分布式的计算框架基于内存","分布式系统执行任务瓶颈:","分布式系统的最大难点，就是各个节点的状态如何同步。cap","分布式计算","分布式计算框架","分布式计算：","分布式集群namenode和datanode部署在不同机器上","分析","分析函数","分析并预处理ad_feature数据集","分析并预处理raw_sample数据集","分析并预处理user_profile数据集","分析数据集字段的类型和格式","分母是两个布尔向量做或运算,","分治策略","分类⽬录（1990s）：覆盖少量热门⽹站。典型应用：hao123","分类特征值个数情况:","分类的标准就是分区字段，可以一个，也可以多个。","分组","分组查询group","分组查询每个用户的浏览记录","分组统计","分配region和meta信息","分：把复杂的问题分解为若干\"简单的任务\"","切分数据集，","列(column):","列修饰符(column","列族(columnfamily)：是列的集合。列族在表定义时需要指定，而列在插入数据时动态指定。列中的数据都是以二进制形式存在，没有数据类型。在物理存储结构上，每个表中的每个列族单独以一个文件存储。一个表可以有多个列簇。","列族中的数据通过列标识来进行映射,","刚才提到的tradit","创建datafram","创建rdd","创建spark","创建sparkcontext","创建sparkcontext的时候需要一个sparkconf，","创建一个外部表student2","创建分区表","创建名称空间","创建和hbase的连接","创建外部表","创建数据库","创建表","创建表之后可以传入表名获取到table类的实例:","创建表时无external修饰","创建表时被external修饰","创建表的时候添加namespac","创建逻辑回归训练器，并训练模型：logisticregression、","创建非临时自定义函数","初始化bu","初始化bu、bi的值，全部设为0","初始化p","初始化p和q矩阵，同时为设置0，1之间的随机值作为初始值","删除hdfs中","删除一列","删除一张表","删除指定的文件。只删除非空目录和文件。请参考rmr命令了解递归删除。","删除数据","删除时影响","删除某些字段值完全一样的重复记录，subset参数定义这些字段","删除表","删除表中的数据","删除表查看结果","删除记录","判断数据是否有空值：","利用","利用)：选择现在可能最佳的⽅案","利用als模型进行类别的召回","利用biassvd预测用户对物品的评分，k表示隐含特征数量：","利用config对象，创建spark","利用lfm预测用户对物品的评分，$k​$表示隐含特征数量：","利用mrjob编写和运行mapreduce代码","利用schema从hdfs加载","利用spark","利用sql进行数据统计","利用tags.csv中每部电影的标签作为电影的候选关键词","利用tf·idf计算每部电影的标签的tfidf值，选取top","利用top","利用了hdfs的容错能力","利用分区表方式减少查询时需要扫描的数据量","利用打分数据，训练als模型","利用模型，传入datasets(userid,","利用次数计算权重","利用物品画像计算物品间两两相似情况","利用物品的内容信息，将新物品先投放给曾经喜欢过和它内容相似的其他物品的用户。","利用矩阵分解技术，将原始user","利用管道对每一个数据进行热独编码处理","利用随机梯度下降，优化bu，bi的值","利用随机森林对new_user_class_level的缺失值进行预测","利用随机森林对pvalue_level的缺失值进行预测","到物品矩阵里获取物品向量","到用户矩阵中获取用户向量","前七天为训练数据、最后一天为测试数据","前缀过滤器","前面分析的以下几个分类特征值个数情况:","前面我们处理的数据实际上都是已经被处理好的规整数据，但是在大数据整个生产过程中，需要先对数据进行数据清洗，将杂乱无章的数据整理为符合后面处理要求的规整数据。","前面提到，物品画像的特征标签主要都是指的如电影的导演、演员、图书的作者、出版社等结构话的数据，也就是他们的特征提取，尤其是体征向量的计算是比较简单的，如直接给作品的分类定义0或者1的状态。","剔除冗余、不需要的字段","剔除掉缺失值数据，将余下的数据作为训练数据","剔除空值数据后，还剩：","办公文档、文本、图片、xml,","功能扩展很方便","加入l2正则化：","加入购物车","加权求和得到最终推荐结果","加购物车","加载als模型，注意必须先有spark上下文管理器，即sparkcontext，但这里sparksession创建后，自动创建了sparkcontext","加载json数据","加载ratings.csv，转换为用户","加载城市ip段信息，获取ip起始数字和结束数字，经度，纬度","加载基于所有电影的标签","加载数据到分区","加载数据集","加载数据，我们只用前三列数据，分别是用户id，电影id，已经用户对电影的对应评分","加载文件到hdf","加载日志数据，获取ip信息，然后转换为数字，和ip段比较","加载电影列表数据集","动态json数据的读取和操作","动态分区","包含了ip","包含：缺失值，超过正常范围内的较大值或较小值","包括以下四种：","区域(region)：hbase自动把表水平划分成的多个区域，划分的区域随着数据的增大而增多。","匿名函数","协同过滤","协同过滤召回","协同过滤推荐算法代码实现：","协同过滤算法","单机程序计算流程","单样本损失值：","单点策略","单节点","历史上所有订单","历史兴趣程度","历史推荐结果","历史数据","历史样本数据","历史订单","压缩包名字","厚厚的一块","原始数据","原始样本骨架raw_sampl","原子数据类型","参数","参数1","参数更新：","参数：维度、索引列表、值列表","参考：为什么spark中只有al","及时调整运营和产品策略,是大数据技术的关键价值之一","反应网站应收能力的重要指标","发现pvalue_level和new_user_class_level存在空值：（注意此处的null表示空值，而如果是null，则往往表示是一个字符串）","发现日活没有明显下降","发送完成信号给namenode。","取出出现次数最多的前50个词","取出出现次数最多的词","取出前两条（相似度最高的两个）","取出每一列数据，并删除自身，然后排序数据","取出每个用户当前已购物品列表","另一种资源协调者","另一种资源调度器","只支持row","只有action才会触发transformation的执行","只有两种广告展示位，占比约为六比四","只有四种类型数据：pv、fav、cart、buy","只有当数据量较小的时候使用collect","只有调用action一类的操作之后才会计算所有transform","只给部分用推荐，运算时间短","只考虑兴趣词与电影的关联程度","只考虑用户的兴趣程度","只能在用户看到过的候选集上做评估,","只能用在from子句中","只能评估少数指标","只记下应用于数据集的transformation操作","召回","召回决定了最终推荐结果的天花板","召回决定了最终推荐结果的天花板,","召回到redi","召回率","召回阶段","可以启动多个hmaster，通过zookeeper的mast","可以在spark","可以快速查找hdfs中数据","可以把运行日志,","可以理解为一个键值对(key","可以看到","可以看到与用户1最相似的是用户2和用户3；与物品a最相似的物品分别是物品e和物品d。","可以缓存在内存中","可以通过参数禁止自动链接,","可以通过广播变量,","可分区","可扩展:","可扩展性","可扩展的(scalable)分布式计算框架","可构建在廉价机器上","可水平扩展","可理解为解压，返回二维矩阵式","可用性(保证每个请求不管成功或失败都有响应,但不保证获取的数据的正确性)","可能导致用户流失","可视化查看效果：http://192.168.19.137:4040","可进行任何计算,","可迭代对象","可选，初始参数","可通过该方法获得","可靠的(reliable),","可靠的:","右侧的dataframe提供了详细的结构信息（schema——每列的名称，类型）","号操作符，可以将元组解压为列表。","号早晨发现","号的订单量没有恢复正常，运营人员开始尝试寻找原因","各项指标相对稳定","合：reduc","同svd，它也是一种矩阵分解技术，但理论上，als在海量数据的处理上要优于svd。","同svd，它也是一种矩阵分解技术，对数据进行降维处理。","同一时间只能有一个stream","同上","同样对于评分预测我们利用平方差来构建损失函数：","同样数据保存到列式数据库中","同样，损失函数：","同理可得，梯度下降更新b_i​:","同理可得：","同理：","名称","向朋友咨询,","向表中插入数据","向量a","向量乘法","向量（vector）：由一组文本特征构成的列表。是一段文本在gensim中的内部表达。","吞吐量","吞吐量：spark","吞吐量：单位时间内成功传输数据的数量","含缺失值的特征情况:","启动","启动docker","启动hbase","启动hbase（启动的hbase的时候要保证hadoop集群已经启动）","启动hdf","启动hive","启动master","启动mysql","启动pyspark","启动slave","启动spark集群","启动yarn相关的进程","启动即可使用","启动启动yarn","命令","命令行","命令表","命令表达式","和","和item","商业⽬标","商业智能(busi","商业智能通常被理解为将企业中现有的数据(订单、库存、交易账目、客户和供应商等数据)转化为知识，帮助企业做出明智的业务经营决策的工具。从技术层面上讲，是数据仓库、数据挖掘等技术的综合运用。","啤酒尿不湿","喜欢","囊括了大数据处理的方方面面","四","因为不可变类型不能被","因为前面提到广告的点击率一般都比较低，所以预测值通常都是0，因此通常需要反减得出点击的概率","因为所有的结果都会加载到内存中","因此在协同过滤推荐算法中其实会更多的利用用户对物品的“评分”数据来进行预测，通过评分数据集，我们可以预测用户对于他没有评分过的物品的评分。其实现原理和思想和都是一样的，只是使用的数据集是用户","因此就可以预测出用户a对电影“阿甘正传”的评分为：3.5+(","因此最终，我们的目标也就是要求出p矩阵和q矩阵及其当中的每一个值，然后再对用户","因此直接利用schema就可以加载进该数据，无需替换null值","因此这时就需要借助一些自然语言处理、信息检索等技术，将如用户的文本评论或其他文本内容信息的非结构化数据进行量化处理，从而实现更加完善的物品画像/用户画像。","因此这里不选取它们作为特征","因此这里对于pid，应该是由广告系统发起推荐请求时，向推荐系统明确要推荐的用户是谁，以及对应的资源位，或者说有哪些","因此这里经过map函数处理，将每一行数据转换为普通的列表数据","因此，tf","因此，我们需要通过日志信息（运行商或者网站自己生成）和城市ip段信息来判断用户的ip段，统计热点经纬度。","图中对于文件","在","在$spark_home/sbin目录下执行","在cento","在centos中为test.txt","在funksvd提出来之后，出现了很多变形版本，其中一个相对成功的方法是biassvd，顾名思义，即带有偏置项的svd分解：","在hdfs中创建","在hive","在hive中创建临时udf","在ip日志信息中，我们只需要关心ip这一个维度就可以了，其他的不做介绍","在jvm(java虚拟机)中,","在mapr","在panda","在rdd中无法看出，解释性不强，无法告诉引擎信息，没法详细优化。","在reduce中同样要进行merge操作","在sbin中","在spark","在spark语义中，dataframe是一个分布式的行集合，可以想象为一个关系型数据库的表，或者一个带有列名的excel表格。它和rdd一样，有这样一些特点：","在spark集群中通过sparkcontext来创建rdd","在stream","在上面的示例中，我们已经使用connection.tables（）方法查询hbase中的表。","在之前的案例中使用临时自定义函数(函数功能:","在互联网中，我们经常会见到城市热点图这样的报表数据，例如在百度统计中，会统计今年的热门旅游城市、热门报考学校等，会将这样的信息显示在热点图中。","在做两类决策的过程中，不断更新对所有决策的不确定性的认知，优化","在其他文档出现的频率低。","在内部,","在写入数据时自动创建分区(包括目录结构)","在分布式系统环境中，无法避免系统出错或者宕机，一旦hregionserver意外退出，memstore中的内存数据就会丢失，引入hlog就是防止这种情况。","在创建表时指定数据中的分隔符，hive","在前面的demo中，我们只是使用用户对物品的一个购买记录，类似也可以是比如浏览点击记录、收听记录等等。这样数据我们预测的结果其实相当于是在预测用户是否对某物品感兴趣，对于喜好程度不能很好的预测。","在新闻类网站中，经常要衡量一条网络新闻的页面访问量，最常见的就是uv和pv，如果在所有新闻中找到访问最多的前几条新闻，topn是最常见的指标。","在本地文件系统创建一个如下的文本文件：/home/hadoop/tmp/student.txt","在本文档出现的频率高；","在浏览器访问当前centos的4040端口","在特征中","在社区版的基础上做了一些修改","在系统上执行指令","在线处理业务流","在线推荐","在线评估:","在通过parallelize方法创建rdd","垂直领域领头羊,","埋点采集数据","基于rdd创建","基于spark的als隐因子模型进行cf评分预测","基于tf","基于tf·idf提取top","基于内存的计算引擎，它的计算速度非常快。但是仅仅只涉及到数据的计算，并没有涉及到数据的存储。","基于内容","基于内容推荐的算法流程：","基于内容的推荐","基于内容的推荐和协同过滤的推荐结果都计算出来","基于内容的推荐实现步骤","基于内容的推荐方法是非常直接的，它以物品的内容描述信息为依据来做出的推荐，本质上是基于对物品和用户自身的特征或属性的直接分析和计算。","基于内容的推荐算法（content","基于内容的推荐逐渐过渡到协同过滤","基于内容的电影推荐：为用户产生top","基于内容的电影推荐：物品画像","基于内容的电影推荐：用户画像","基于分类算法、回归算法、聚类算法","基于协同过滤的推荐","基于协同过滤的电影推荐","基于回归模型的协同过滤推荐","基于图模型算法","基于流行度的推荐","基于矩阵分解的cf算法","基于矩阵分解的cf算法实现（一）：lfm","基于矩阵分解的cf算法实现（二）：biassvd","基于矩阵分解的协同过滤推荐","基于矩阵分解的推荐","基于神经网络算法","基本判断,","基本操作","基本源","基本的协同过滤推荐算法基于以下假设：","基本统计功能","基础架构","填充方案","填充方案：结合用户的其他特征值，利用随机森林算法进行预测；但产生了大量人为构建的数据，一定程度上增加了数据的噪音","增加一列","处于backup状态的其他hmaster节点推选出一个转为active状态","处理","处理json数据","处理和计算","处理复杂业务逻辑，处理高并发，为用户构建一个稳定的信息流通服务","处理客户端的请求：","处理推荐系统冷启动问题的常用方法","处理数据所面临的问题：","处理数据规模","处理来自am的命令","处理每一行数据：r表示row对象","处理流式数据","复杂","复杂数据类型","复杂运算","外部分区表即使有分区的目录结构,","外部表","外部表(extern","多","多样性","多样性&新颖性&惊喜性","多样性：推荐列表中两两物品的不相似性。（相似性如何度量？","多级子目录","多级索引,","够满⾜他们兴趣和需求的信息。","大","大数据lambda架构","大数据产品与互联网产品结合","大数据存储与计算的核心","大数据平台","大数据平台(互联网企业)运行的绝大多数大数据计算都是关于数据分析的","大数据应用","大数据提高数据存储能力,","大数据资源管理产品","大致查看一下数据类型","大量数据需要长期保存,","大量的清洗,转化处理","天猫精灵","好的推荐系统可以实现用户,","如","如下转化公式:","如何使用happybas","如何存储持续增长的海量网页:","如何对持续增长的海量网页进行排序:","如何平衡大众口味和小众需求","如何平衡实时兴趣和长期兴趣","如何平衡短期产品体验和长期系统生态","如何选择余弦相似度","如果redis所在机器，内存不足，会抛出异常","如果uid或iid不在，我们使用全剧平均分作为预测结果返回","如果不想成为hadoop专家,","如果不足500个，那么随机选出need个广告","如果你是使用hbase自带的zk就是true，如果使用自己的zk就是fals","如果取不到就返回[]","如果只想仅关闭stream","如果各个迭代器的元素个数不一致，则返回列表长度与最短的对象相同，利用","如果我们将评分看作是一个连续的值而不是离散的值，那么就可以借助线性回归思想来预测目标用户对某物品的评分。其中一种实现策略被称为baseline（基准预测）。","如果数据量大，应考虑的是增加内存、或限制迭代次数和训练数据量级等","如果是文件，则按照如下格式返回文件信息：","如果是目录，则返回它直接子文件的一个列表，就像在unix中一样。目录返回列表的信息如下：","如果没有updatestatebykey，我们需要将每一秒的数据计算好放入mysql中取，再用mysql来进行统计计算","如果没有通用资源管理系统，只能为多个集群分别提供数据","如果电影没有标签数据，那么就替换为空列表","如果达到500个则退出","如果还没有任何表，可使用connection.create_table（）创建一个新表：","如果透视的字段中的不同属性值超过10000个，则需要设置spark.sql.pivotmaxvalues，否则计算过程中会出现错误。文档介绍。","如果重复加载同名文件，不会报错，会自动创建一个*_copy_1.txt","如果预测值是0，其概率是0.9248，那么反之可推出1的可能性就是1","如求b_u时，将b_i看作是已知；求b_i时，将b_u​看作是已知；如此反复交替，不断更新二者的值，求得最终的结果。这就是交替最小二乘法（als）","娱乐(王思聪)","子查询","字段说明如下：","字符串函数","存储","存储/计算资源不够时，可以横向的线性扩展机器","存储和分析某个窗口期内的数据（一段时间的热销排行，实时热搜等）","存储推荐结果","存储数据，利用","存储用户召回，使用redis第9号数据库，类型：sets类型","存储用户的文件对应的数据块(block)","存储的blockid分别为1、3。","学习率","学习目标","它基于的假设和baseline基准预测是一样的，但这里将baseline的偏置引入到了矩阵分解中","它将足够多的信息checkpoint到某些具备容错性的存储系统如hdfs上，以便出错时能够迅速恢复","它是spark中用于处理结构化数据的一个模块","它是一个可扩展，高吞吐具有容错性的流式计算框架","它表示的是数据可以保存在磁盘，也可以保存在内存中","安装","安装happi","安装前需要安装好","安装部署","安装部署及standalone模式介绍","完全支持","完善画像关键词","完成提交作业到yarn上执行","完整代码","官方文档》》)","定义了执行的顺序","定时向rm汇报本节点的资源使用情况","定期做问卷调查","定理是这方面的基本定理，也是理解分布式系统的起点。","宝贝的价格","实战案例","实时产生推荐结果","实时商品类别/品牌","实时处理层","实时广告召回集","实时性","实时数据分析","实时数据收集","实时流式计算","实时特征","实时行为日志","实时计算","实时计算框架对比","实现","实现协同过滤推荐有以下几个步骤：","实现复杂查询逻辑开发难度太大","实现将spark作业分解成一到多个stage，每个stage根据rdd的partition个数决定task的个数，然后生成相应的task","实现并开源，是基于","实现，与传统数据库jdbc","实践","实践:","实际上","实际上也是余弦相似度,","客单价","客户端向namenode发出写文件请求。","容器:","容易扩展，为用户提供性能不错的文件存储服务","密码：password","对pvalue_level进行热独编码，求值","对pvalue_level进行热编码，求值","对row和表","对两个rdd求交集","对两个rdd求并集","对于bool类型、或者分类类型，可以为缺失值单独设置一个类型，miss","对于存储在","对于所有电影的平均评分是直接能计算出的，因此问题在于要测出每个用户的评分偏置和每部电影的得分偏置。对于线性回归问题，我们可以利用平方差构建损失函数如下：","对于推荐越大越好","对于数值类型，可以用均值或者中位数等填充","对于最小过程的求解，我们一般采用随机梯度下降法或者交替最小二乘法来优化实现。","对于每个batch，spark都会为每个之前已经存在的key去应用一次state更新函数，无论这个key在batch中是否有新的数据。如果state更新函数返回none，那么key对应的state就会被删除","对于每个新出现的key，也会执行state更新函数","对于计算影评的tf","对各个regionserver(包括失效的)的数据进行整理,","对咨询信息分类统计后发现，新用户的咨询量几乎为","对处理出来的特征处理列进行，热独编码","对应关系型数据库的列","对应的召回集(缓存)","对推荐结果进行评估（评估方法后面章节介绍），评估通过后上线","对数据以空格进行拆分，分为多个单词","对数据清洗","对数组排序","对模型进行存储","对每一组特征，使用枚举类型，从0开始；","对每个分片的数据进行处理","对比可见，user","对比：","对结果有确定预期","对缺失值对应的行或列进行标记","对缺失值进行删除操作(行，列)","对缺失值进行填充操作(列的均值)","导入数据","封装了cpu、memory等资源的一个容器,是一个任务运行环境的抽象","封装成方法","将","将array","将func函数作用到数据集的每一个元素上，生成一个新的rdd返回","将group","将hdfs中","将key相同的键值对，按照function进行计算","将pvalue_level中的空值所在行数据剔除后的数据，作为训练样本","将spark目录下的python目录下的pyspark整体拷贝到pycharm使用的python环境下","将上面三个部分整合起来","将上面聚合结果转换成map","将下图中的pyspark","将代码上传到远程cent","将作业拆分成map阶段和reduce阶段","将元数据存储在数据库中。","将应用产生的数据导入到大数据系统,","将影评中出现的停用词过滤掉，计算其他词语的词频。以出现最多的三个词为例进行计算如下：","将总的影评集中所有的影评向量与特定的系数相乘求和，得到这部电影的综合影评向量，与电影的基本属性结合构建视频的物品画像，同理构建用户画像，可采用多种方法计算物品画像和用户画像之间的相似度，为用户做出推荐。","将所有用户行为合并在一起","将数据load到表中","将数据复制到其他服务器上","将文件从源路径移动到目标路径。这个命令允许有多个源路径，此时目标路径必须是一个目录。不允许在不同的文件系统间移动文件。","将文件切分成指定大小的数据块,","将模型进行存储","将每个用户的x比例的数据作为训练集，剩余的作为测试集","将源文件输出为文本格式。允许的格式是zip和textrecordinputstream。","将用户查看的关键字和频率合并成","将用户的阅读偏好结果保存到表中","将电影的分类词直接作为每部电影的画像标签","将类别词分开","将类别词的添加进去，并设置权重值为1.0","将该块磁盘上存储的所有","将问题交给数据分析团队","小","小文件存储","小爱","少","就不能再次调","就可以映射成功，解析数据。","就是key","就是一个action操作，使用某个函数聚合rdd所有元素的操作，并返回最终计算结果","展示数据，默认前20条","展示现有名称空间","属于内嵌模式。实际生产环境中则使用","嵌套结构的json","左侧的rdd","已经为我们创建好了","已被广泛应用","带","带有嵌套结构的json","常用操作","常用算法:","常用评估指标","平衡个性化推荐和热门推荐比例","年龄等级，1","并向hmaster汇报","并在多台机器上保存多个副本","并存储索引,","并将文件内容映射到表中。","并将电影的分类词直接作为每部电影的画像标签","并行计算","并解压","广义大数据","广义的hadoop","广义的hadoop：指的是hadoop生态系统，hadoop生态系统是一个很庞大的概念，hadoop是其中最重要最基础的一个部分，生态系统中每一子系统只解决某一个特定的问题域（甚至可能更窄），不搞统一型的全能系统，而是小而精的多个小系统；","广告/用户特征(缓存)","广告id","广告价格","广告基本信息表ad_featur","广告展示位pid情况：","广告推荐结果","广告点击数据情况clk：","广告特征数据","广告资源位，属于场景特征，也就是说，每一种广告通常是可以防止在多种资源外下的","广播变量的使用","序列化和反序列化开销大","应用基于物品的协同过滤实现电影评分预测","应用基于用户的协同过滤实现电影评分预测","应用杰卡德相似度实现简单协同过滤推荐案例","应用采集数据,数据库数据放到一起分析","底层很多东西还是依赖于hive，修改了内存管理、物理计划、执行三个模块","度量两个变量是不是同增同减","度量的是两个向量之间的夹角,","延迟高","延迟：spark","建立redi","建立redis客户端","建立tag","建立连接","建议下载ml","开发效率更高。","开源、社区活跃","开源社区版","异常值处理","异常值：不属于正常的值","引导用户填写兴趣","弱","弹性:并不是指他可以动态扩展，而是容错机制。","强","当","当前用户","当多个mapreduce任务要用到相同的hdfs数据，","当夹角为90度是余弦值为0,为180度是余弦值为","当存在多个版本时，不指定很可能会导致出错","当期新增用户数","当调用df.count()时才开始进行计算，这里的count计算的是dataframe的条目数，也就是共有多少个分组","当连接建立时,","当需要pivot","往往需要牺牲准确性","很多运营管理人员,","很少使用全表查询","很显然我们的数据其实绝大多数情况下都是稀疏的，因此如果要使用tradit","得到的就是交集元素的个数","得到需要的运营数据报告","快速满足","思路","性别与电视剧的关系","总广告条数：","总的条目数，查看redis中总的条目数是否一致","总结","总结：可以发现由于这两个字段的缺失过多，所以预测出来的值已经大大失真，但如果缺失率在10%以下，这种方法是比较有效的一种","总访问用户数","惊喜度","惊喜性：历史不相似（惊）但很满意（喜）","意图","成交总金额(gross","成功返回0，失败返回","成本高","成熟的生态圈","我","我们使用cms_segid,","我们只能对最重要的数据进行统计和分析(决策数据,财务相关)","我们可以使用","我们接下来采用将变量映射到高维空间的方法来处理数据，即将缺失项也当做一个单独的特征来对待，保证数据的原始性","我们是在对非搜索类型的广告进行点击率预测和推荐(没有搜索词、没有广告的内容特征信息)","我们的目标也就转化为寻找最优的b_u和","我们要预测用户1对物品e的评分，那么可以根据与物品e最近邻的物品a和物品d进行预测，计算如下：","我们要预测用户1对物品e的评分，那么可以根据与用户1最近邻的用户2和用户3进行预测，计算如下：","或","户的历史⾏为给⽤户的兴趣进⾏建模，从⽽主动给⽤户推荐能","所以这份数据可以共享,没必要每个task复制一份","所以这里我们除了需要我们训练的als模型以外，还需要有一个广告和类别的对应关系。","所有task都会去复制ip表","所有用户的id","所有的transformation操作都是惰性的（lazy）","所有的valu","所有的特征的特征向量已经汇总到在features字段中","所有类别","手动释放一些内存","才能看到相应的数据","打乱列表","打分","打分规则","打印df结构信息","打印当前dataframe的结构","打开app","打开产品就算活跃","打开以后是否频繁操作就用pv衡量,","打开使用产品的用户","打开命令行,","打开搜索引擎,","打开用户数","打点:","托管表","执行","执行start","执行延迟","批处理层","批处理计算框架","批处理：mapreduce、hive、pig","找出iid物品的相似物品","找出uid用户的相似用户","找出最相似的人或物品：top","找出每个用户普遍高于或低于他人的偏置值b_u","找出每件物品普遍高于或低于其他物品的偏置值b_i​","找到","找到一篇文本文档,","找到前","找到和自己历史兴趣相似的用户,","找到数据入口地址","技术只有hadoop,","把","把text.txt文件上传到hdfs中","把变量映射到高维空间：如pvalue_level的1维数据，转换成是否1、是否2、是否3、是否缺失的4维数据；这样保证了所有原始数据不变，同时能提高精确度，但这样会导致数据变得比较稀疏，如果样本量很小，反而会导致样本效果较差，因此也不能滥用","把集群中jar包的位置添加到hive中","报告给","拆分","拆分数据集","拥有一套自己的元数据，无法共享","拷贝到pycharm使用的：xxx\\python\\python36\\lib\\sit","持续服务","持续计算","指定dataframe的schema","指定hdfs的访问方式","指定namenod","指定一个函数如何使用之前的state和新值来更新st","指定了字段的分隔符为逗号，所以load数据的时候，load的文本也要为逗号，否则加载后为null。hive只支持单个字符的分隔符，hive默认的分隔符是\\001","指定具体时间戳的值","指定时间范围","指定显示多个版本","指定起始rowkey","指标方法，类型为字符串，rmse或mae，否则返回两者rmse和ma","按probability升序排列数据，probability表示预测结果的概率","按照tf","按照相似度降序排列","损失函数","损失函数偏导推导：","损失函数：","掌握happybase的常用api","掌握hbase的shell操作","掌握transformation和action算子的基本使用","排序","排序逼近这个极限,","排序阶段","探索与利用问题","探索伤害用户体验,","探索带来的长期收益(留存率)评估周期长,","接下来我们重点学习以下几种应用较多的方案：","接收并处理来自rm的各种命令：启动contain","推荐","推荐业务处理主要流程：","推荐任务处理","推荐任务部分：","推荐模型构建流程","推荐算法","推荐算法架构","推荐系统","推荐系统:","推荐系统冷启动概念","推荐系统和web项目的区别","推荐系统基础","推荐系统整体架构","推荐系统架构","推荐系统案例","推荐系统概念及产生背景","推荐系统的作用","推荐系统的冷启动问题","推荐系统的工作原理","推荐系统的工作原理及作用","推荐系统的应用场景","推荐系统的整体架构","推荐系统的评估指标","推荐系统简介","推荐系统算法","推荐系统要素","推荐系统设计","推荐系统评估","推荐系统评估方法","推荐系统（2010s）：不需要⽤户提供明确的需求，通过分析⽤","推荐结果存放在recommendations列中，","描述","描述数据的数据","提交作业","提供了两个列表，对相同位置的列表数据进行相加","提供的内置函数无法满足你的业务处理需要时，此时就可以考虑使用用户自定义函数（udf：us","提升活跃是网站运营的重要目标","提取用户观看列表","提取部分列","提高用户停留时间和用户活跃程度","插入数据","搜索","搜索)：选择现在不确定的⼀些⽅案，但未来可能会有⾼收益的⽅案","搜索中有很强的搜索信号","搜索关键词","搜索和非搜索广告点击率预测的区别","搜索引擎","搜索引擎时代","搜索引擎（2000s）：通过搜索词明确需求。典型应用：googl","搜索打开转化率","搜索用户数","搜索记录","搭建大型数据仓库","搭配推荐","播放/点击","操作","操作保证完全的","操作列簇","操作接口采用类","操作细节","操作表","操作，map创建的数据集将用于reduce，map阶段的结果不会返回，仅会返回reduce结果。","操作，把数据集中的每一个元素传给一个函数并返回一个新的rdd，代表transformation操作的结果","支付","支持","支持micro","支持两种类型的操作：","支持包括tf","支持压缩文件","支持多级分区,","支持整个目录、多文件、通配符","支持特定场景下的","支持随机读","放入购物车","放到","放到hdfs中","放到taskscheduler中。","放到一个tuple中","散列之后的多个文件","数千写入/秒","数据","数据上传hdf","数据不可变,","数据也是只需要进行查询操作的,","数据仓库时代","数据保存位置","数据冗余","数据分布式也是弹性的","数据分析","数据分析师分析可能性","数据分析案例","数据切分、多副本、容错等操作对用户是透明的","数据初始化","数据加载","数据加载初始化与之前完全相同","数据去重","数据同步后导入hdf","数据块多副本","数据处理","数据处理部分：","数据存储","数据存储:","数据存储故障容错","数据存入memstore，一直到memstore满","数据库","数据库,日志","数据库同步:sqoop","数据库大小","数据挖掘","数据挖掘时代","数据文件中没有对应的列","数据条数","数据来源","数据模型","数据清洗/数据处理","数据源","数据源：rdd、csv、json、parquet、orc、jdbc","数据示例","数据管理","数据类型","数据能正常读写,","数据规模大,","数据计算:","数据输出与展示","数据采集","数据量/数据能否满足要求","数据集下载","数据集中user","数据集介绍","数据集来源：天池竞赛","数据集路径","数据集：","数据需要写入数据库","数据驱动运营:","数组的元素之间用'|'分割","数组的数据类型","数量","数钱实例：一堆钞票，各种面值分别是多少","敲如下命令:","整个集群中有多个，负责自己本身节点资源管理和使用","整个集群分为","整个集群同一时间提供服务的rm只有一个，负责集群资源的统一管理和调度","整体封装","整合网站应用和大数据系统之间的差异,","文件内容","文件名","文件大小","文件夹","文本特征提取有两个非常重要的模型：","文档地址：https://spark.apache.org/docs/2.2.2/api/python/pyspark.ml.html?highlight=vectors#modul","文档地址：https://spark.apache.org/docs/latest/api/python/pyspark.ml.html?highlight=vectors#modul","文章数据","文章表","新⽤户在冷启动阶段更倾向于热门排⾏榜，⽼⽤户会更加需要长尾推荐","新增用户出现问题","新增用户数","新增访问网站(新下载app)的用户数","新老用户推荐策略的差异","新颖性","新颖性：未曾关注的类别、作者；推荐结果的平均流⾏度","方便练习可以对数据做拆分处理","方法一：随机梯度下降法优化","方法二：交替最小二乘法优化","方法并且传入已有的可迭代对象或者集合","无嵌套结构的json","无嵌套结构的json数据","无状态：指的是每个时间片段的数据之间是没有关联的。","既负责进行计算作业又处理服务器集群资源调度管理","日开始，网站的订单量连续四天明显下跌","日当天发布记录,发现有消息队列sdk更新","日志","日志分析","日志同步:flume","日志收集：","日期函数","日活","日订单量","时间字段，划分训练集和测试集","时间戳(timestamp)：是列的一个属性，是一个64位整数。由行键和列确定的单元格，可以存储多个数据，每个数据含有时间戳属性，数据具有版本特性。可根据版本(versions)或时间戳来指定查询历史版本数据，如果都不指定，则默认返回最新版本的数据。","明确","易于扩展，支持动态伸缩","是","是facebook员工开发的操作hbase的python库,","是rdd为基础的分布式数据集，类似于传统关系型数据库的二维表，dataframe记录了对应列的名称和类型","是一个开源的,","是一个欧式空间下度量距离的方法.","是否喜欢这个推荐","是否收藏,是否点击,是否加购物车)","是否有负面报道被扩散","是否某类商品缺货","是否竞争对手在做活动","是否随机切分，默认fals","是通过浏览器访问","显式反馈","显性数据","显示内容:","显示反馈指的用户的评分这样的行为，隐式反馈指用户的浏览记录、购买记录、收听记录等。","显示所有函数","显示所有数据库","显示效果:","显示某个名称空间下有哪些表","显示特征情况","显示结果:","显示表信息","智能推荐","更多了解：pyspark.ml.recommendation.","更多关于groupby的","更改df表结构：更改列类型和列名称","更改表结构，转换为对应的数据类型","更新","更新bu","更新记录","曾经进行数分析与统计时,","替换掉null字符串","替换掉null字符串，替换掉","最大值正无穷,","最大迭代次数","最小二乘法和梯度下降法一样，可以用于求极值。","最小二乘法思想：对损失函数求偏导，然后再使偏导为0","最感兴趣的类别","最新的hadoop版本都是从apach","最终预测出用户1对物品5的评分为3.91","最经典的推荐算法：协同过滤推荐算法（collabor","月","月活","有两个分类","有了两两的相似度，接下来就可以筛选top","有了数据集，接下来我们就可以进行相似度的计算，不过对于相似度的计算其实是有很多专门的相似度计算方法的，比如余弦相似度、皮尔逊相关系数、杰卡德相似度等等。这里我们选择使用杰卡德相似系数[0,1]","有些用户的评分普遍高于其他用户，有些用户的评分普遍低于其他用户。比如有些用户天生愿意给别人好评，心慈手软，比较好说话，而有的人就比较苛刻，总是评分不超过3分（5分满分）","有保存大量网页的需求(单机","有关api的更多详细信息，请参阅labeledpointpython文档。","有四种类型的运算符：","有复杂的索引","有效的帮助产品实现其商业价值","有明显降幅的是咨询详情转化率","有购买意向开始咨询","有购买行为的用户数","服务器集群资源调度管理和mapreduce执行过程耦合在一起带来的问题","服务层","服务提供方,","服务提供方设定的属性（服务提供方为物品附加的属性）：如短视频话题、微博话题（平台拟定）","本小节主要根据广告点击样本数据集(raw_sample)、广告基本特征数据集(ad_feature)、用户基本信息数据集(user_profile)构建出了一个完整的样本数据集，并按日期划分为了训练集(前七天)和测试集(最后一天)，利用逻辑回归进行训练。","本数据集无空值条目，可放心处理","本数据集涵盖了raw_sample中全部广告的基本信息(约80万条目)。字段说明如下：","本数据集涵盖了raw_sample中全部用户22天内的购物行为(共七亿条记录)。字段说明如下：","本数据集涵盖了raw_sample中全部用户的基本信息(约100多万用户)。字段说明如下：","本样本数据集共计8天数据","本质:","本质是推荐系统依赖历史数据，没有历史数据⽆法预测⽤户偏好","机器学习时代","权重","权限","来共享这个数据,避免数据的多次复制,可以大大降低内存的开销","来到$hadoop_home/sbin目录下","来到hadoop的bin目录","来自广告基本信息中","来表示。","来进行元数据的存储。","杰卡德相似度","杰卡德相似度适用于隐式反馈数据(0,1布尔值","杰卡德距离=杰卡德相似度","构建初始的推荐结果","构建推荐结果","构建数据集","构建数据集：","构建数据集：注意这里构建评分数据时，对于缺失的部分我们需要保留为none，如果设置为0那么会被当作评分值为0去对待","构建电影数据集，包含电影id、电影名称、类别、标签四个字段","构建结构对象","构建表结构schema对象","架构","架构图","某个节点崩溃,","某电商网站,","查看brandid的数据情况：","查看btag的数据情况：","查看cateid的数据情况：","查看dataframe，默认显示前20条","查看datasets条目数","查看datasets的结构","查看hdfs中","查看user的数据情况：","查看两个数据集在类别上的差异","查看前20条","查看各项数据的特征","查看所有记录","查看指定表指定列所有数据","查看数据","查看数据时,","查看日活数据,","查看是否有空值","查看更详细配置及说明：https://spark.apache.org/docs/latest/configuration.html","查看最大时间","查看某一列是否有重复值","查看样本中点击的被实际点击的条目的预测情况","查看每一篇文章的关键字","查看每列数据的类别情况","查看每列数据的类型","查看票房排行榜,","查看表中的记录总数","查看表的分区","查看记录","查看重复记录","查询一行","查询作业的运行进度,杀死作业","查询分析数据。","查询多行","查询操作","查询某个rowkey的数据","查询某个列簇的数据","查询表中的所有数据","查询表中的数据","查询语句从词法分析、语法分析、编译、优化以及查询计划的生成。生成的查询计划存储在","查询语言","标准排序(10","标记点是与标签/响应相关联的密集或稀疏的局部矢量。在mllib中，标记点用于监督学习算法。我们使用double来存储标签，因此我们可以在回归和分类中使用标记点。对于二分类情况，目标值应为0（负）或1（正）。对于多分类，标签应该是从零开始的类索引：0,","标记点表示为","树的棵数","校验不正确抛出异常,","样本数据pid特征处理","样本数据集总条目数：","根据pgc/ugc内容构建物品画像","根据pgc内容构建物品画像","根据pgc内容构建的物品画像的可以解决物品的冷启动问题","根据元数据存储的介质不同，分为下面两个版本，其中","根据关键词提取对应的名称","根据将每条数据，返回对应的词索引和词频","根据您统计的次数","根据指定的类别找到对应的广告","根据数据集建立词袋，并统计词频，将所有词放入一个词典，使用索引进行获取","根据文章id找到用户查看文章的关键字","根据文章id找到用户查看文章的关键字并统计频率","根据每个物品找出最相似的top","根据每条数据返回，向量","根据测试数据进行预测","根据特征字段计算出特征向量，并划分出训练数据集和测试数据集","根据特征字段计算特征向量","根据用户id分组","根据用户对品牌偏好打分训练als模型","根据用户对类目偏好打分训练als模型","根据用户画像从物品中寻找最匹配的top","根据用户的评分历史，结合物品画像，将有观影记录的电影的画像标签作为初始标签反打到用户身上","根据用户行为以及文章标签筛选出用户最感兴趣(阅读最多)的标签","根据用户行为数据创建als模型并召回商品","根据用户行为记录生成用户画像","根据相似的人或物品产生推荐结果","根据经验，以上几个分类特征都一定程度能体现用户在购物方面的特征，且类别都较少，都可以用来作为用户特征","根据经验，该数据集中，只有广告展示位pid对比较重要，且数据不同数据之间的占比约为6:4，因此pid可以作为一个关键特征","根据观看列表和物品画像为用户匹配关键词，并统计词频","根据评分为指定用户推荐topn个电影","根据词频排序，最多保留top","案例","案例：updatestatebykey","梯度下降优化损失函数","梯度下降参数更新原始公式：（公式中α为学习率）","梯度下降更新b_u:","梯度下降更新参数p_{uk}和q_{ik}​：（α学习率","梯度下降更新参数p_{uk}：","梯度下降最高迭代次数","检查是否已存在文件、检查权限。若通过检查，直接先将操作写入editlog，并返回输出流对象。","检查这些数据块在哪些datanode上有备份,","概念","概述","模型训练好后，调用方法进行使用，具体api查看","模型（model）","模式","模糊","欧氏距离,","欧氏距离不适用于布尔向量之间","欧氏距离的值是一个非负数,","正则化系数","正则参数","正态分布去极值","此处训练时间较长","此外，hregionserver管理一系列hregion对象，每个hregion对应table中一个region，hregion由多个hstore组成，每个hstore对应table中一个column","此时再次查看才能看到新加入的数据","此时查看表中数据发现数据并没有变化,","此时还没有开始计算","步骤：","每一个task","每一个worker上会有多个task,","每一个分量相乘","每一条数据都会去查询ip表","每个datanode写完一个块后，会返回确认信息。","每个人分得一堆钞票，数出各种面值有多少张","每个应用程序对应一个：mr、spark，负责应用程序的管理","每次点击,","每秒产生订单数","每隔g秒，统计最近l秒的数据","比如","比如获取hbase中所有的表:","比如，","比较的时候采用二分法查找，找到对应的经度和纬度","毫秒级响应(1秒以内完成)","求和","汇总","汇总报告,","汇总，每个人负责统计一种面值","没有全部开源","没有嵌套结构的json","没有找到原因,","没有明确需求的用户访问了我们的服务,","没有预定义的数据模型","注册到zookeeper,","注意+1","注意用法：https://spark.apache.org/docs/latest/api/python/pyspark.mllib.html?highlight=tree%20random#pyspark.mllib.tree.randomforestmodel.predict","注意随机森林输入数据时，由于label的分类数是从0开始的，但pvalue_level的目前只分别是1，2，3，所以需要对应分别","注意，一般情况下：","注意：","注意：recommendforusersubset","注意：todf不是每个rdd都有的方法，仅局限于此处的rdd","注意：unionall的使用，两个df的表结构必须完全一样","注意：我们在预测评分时，往往是通过与其有正相关的用户或物品进行预测，如果不存在正相关的情况，那么将无法做出预测。这一点尤其是在稀疏评分矩阵中尤为常见，因为稀疏评分矩阵中很难得出正相关系数。","注意：文档中存在的停用词（stop","注意：热编码只能对字符串类型的列数据进行处理","注意：由于数据量巨大，因此这里不考虑基于内存的cf算法","注意：由于数据量巨大，因此这里也不考虑基于内存的cf算法","注意：由于本数据集中存在null字样的数据，无法直接设置schema，只能先将null类型的数据处理掉，然后进行类型转换","注意：还需要加入广告基本特征和用户基本特征才能做程一份完整的样本数据集","注意：这里的null会直接被pyspark识别为none数据，也就是na数据，所以这里可以直接利用schema导入数据","注意：这里这是召回的是用户最感兴趣的n个类别","活跃用户数","流式处理,","流式计算","流式计算框架","流式计算：storm","流量分布","测试存储的模型","测试数据集中有些类别在训练集中是不存在的，找到这些数据集做后续处理","测试样本个数：","浏览","浏览搜索结果列表","海量数据中的查询，相当于分布式文件系统中的数据库","海量数据离线处理&易开发","消息队列","涉及技术：flume、kafka、spark","淘宝卖家量子魔方","淘宝双11","淘宝开始投入研究基于hadoop的系统–云梯。云梯总容量约9.3pb，共有1100台机器，每天处理18000道作业，扫描500tb数据。","淘宝网站中随机抽样了114万用户8天内的广告展示/点击日志（2600万条记录），构成原始的样本骨架。","添加test方法，然后使用之前实现accuary方法计算准确性指标","添加分区","添加到list中","添加到结果中","添加文本内容","添加自增长的行号)","添加记录","添加过滤器","清空数据","源于google的mapreduce论文(2004年12月)","源于google的mapreduce论文，论文发表于2004年12月","源自于google的gfs论文,","滑动间隔g：控制每隔多长时间做一次运算","满意度","灰度发布","点击","点击streaming，查看效果","点击商品访问详情","点击率","点击率排序","点击率预测","点击率预测(ctr","点击率预测使用的算法通常是如逻辑回归(logic","点击率预测是对每次广告的点击情况做出预测，可以判定这次为点击或不点击，也可以给出点击或不点击的概率","点击率预测需要给出精准的点击概率，比如广告a点击率0.5%、广告b的点击率0.12%等；而推荐算法很多时候只需要得出一个最优的次序a>b>c即可。","点和不点比率约：","热独编码","热独编码时，必须先将待处理字段转为字符串类型才可处理","热独编码是一种经典编码，是使用n位状态寄存器(如0和1)来对n个状态进行编码，每个状态都由他独立的寄存器位，并且在任意时候，其中只有一位有效。","热编码中：\"pvalue_level\"特征对应关系:","热编码时，必须先将待处理字段转为字符串类型才可处理","然后再需要连接是调用","然后添加其它的列","然后看看返回结果中还有什么电影是自己没看过的","然后解压到","爬虫","版本不兼容的问题","版本不要过低","版：","物品id","物品两两间的相似度","物品之间的两两相似度：","物品冷启动","物品冷启动处理：","物品冷启动：如何将新物品推荐给⽤户（协同过滤）","物品打分矩阵","物品画像","物品画像构建步骤：","物品画像：例如给电影《战狼2》贴标签，可以有哪些？","物品的倒排索引","物品的评分数据","物品的评分数据。","物品的评分矩阵，根据评分矩阵的稀疏程度会有不同的解决方案","物品的评分进行预测。","物品相似度的时候较为常用","物品自带的属性（物品一产生就具备的）：如电影的标题、导演、演员、类型等等","物品评分数据","物品评分矩阵","特征","特征中是否包含分类的特征","特征值","特征值字段","特征处理","特征处理，如1维转多维","特征工程","特征获取","特征选取","特征选取（featur","特征选择","特征选择就是选择那些靠谱的feature，去掉冗余的feature，对于搜索广告，query关键词和广告的匹配程度很重要；但对于展示广告，广告本身的历史表现，往往是最重要的feature。","特点是调试方便，启动单一进程模拟任务执行状态和结果，默认(","独立完成mrjob实现wordcount","独立实现ip地址查询","独立实现rdd的创建","独立实现spark","狭义的hadoop","王思聪","环境变量配置","环境配置","理解协同过滤原理","生成器，逐个返回预测评分","用later","用scala/python编写的rdd比spark","用tag作为key去取值","用于hadoop环境，支持hadoop运行调度控制参数，如：","用于本地模拟hadoop调试，与内嵌(inline)方式的区别是启动了多进程执行每一个任务。如：","用于海量数据的离线数据分析。","用任何语言编写生成的rdd都一样，而使用spark","用前面7天的做训练样本（20170506","用夹角的余弦值来度量相似的情况","用户","用户a比较苛刻，普遍比平均评分低0.5分，即用户a的偏置值b_i​是","用户id","用户user总数：","用户两两相似度矩阵","用户两两间的相似度","用户之间的两两相似度：","用户写入数据的流程为：client访问zk,","用户冷启动","用户只需要实现两个函数接口：","用户名：root","用户在享受服务过程中提供的物品的属性：如用户评论内容，微博话题（用户拟定）","用户在访问网站的过程中,转化出了问题","用户基本信息表user_profil","用户增长","用户对商品类别的打分数据","用户对应的行为次数","用户对类别的偏好打分数据","用户微群id","用户性别特征，[1,2]","用户接口：包括","用户特征","用户特征合并","用户特征数据","用户画像","用户画像/物品画像","用户画像建立","用户画像构建步骤：","用户画像：例如已知用户的观影历史是：\"《战狼1》\"、\"《战狼2》\"、\"《建党伟业》\"、\"《建军大业》\"、\"《建国大业》\"、\"《红海行动》\"、\"《速度与激情1","用户留存率","用户的行为日志behavior_log","用户相似度","用户组id","用户聚类","用户自己写的spark应用程序，批处理作业的集合。application的main方法为应用程序的入口，用户通过spark的api，定义了rdd和对rdd的操作。","用户行为数据拆分","用户行为表","用户评分数据","用户购买记录数据集","用户需求不明确","用数据表示特征","用来传递spark应用的基本信息","用边界值替换","用途：在目标文档中，提取关键词(特征标签)的方法就是将该文档所有词语的tf","由","由1和2计算的结果求出词语的tf","由hdfs管理","由hive自身管理","由tf和idf计算词语的权重为：w_{ij}=tf_{ij}·idf_{i}=\\frac","由于ml","由于p矩阵和q矩阵是两个不同的矩阵，通常分别采取不同的正则参数，如\\lambda_1和\\lambda_2","由于前面使用的是outer方式合并的数据，产生了部分空值数据，这里必须先剔除掉","由于是给所有用户进行推荐，此处运算时间也较长","由于该思想正好和热独编码实现方法一样，因此这里直接使用热独编码方式处理数据","由于运算时间比较长，所以这里先将结果存储起来，供后续其他操作使用","由于这里数据集其实很少，所以我们再直接转成panda","由于随机梯度下降法本质上利用每个样本的损失来更新参数，而不用每次求出全部的损失和，因此使用sgd时：","由冒号:","电商广告推荐通常使用广告点击率(ctr","电商网站统计营业额,","电影/书籍评分","电影id:[0.2,0.5,0.7]","电影评分矩阵并计算用户之间相似度","画像构建。顾名思义，画像就是刻画物品或用户的特征。本质上就是给用户或物品贴标签。","留存率/阅读时间/gmv","留存用户数","百万写入/秒","的","的configur","的一个数据仓库工具，可以将结构化的数据映射为一张数据库表，并提供","的关系","的各个组件配合是有不会有兼容性问题","的安装与shell操作","的容错机制","的形式","的数据存储位置","的时候可以指定分区数量","的用户很方便地利用","皮尔逊相似度计算结果在","皮尔逊相关系数pearson","皮尔逊相关系数度量的是两个变量的变化趋势是否一致,","监听网络端口的数据，每隔3秒统计前6秒出现的单词数量","监控datanode健康状况","监控spark","监控spark集群","监控宣传","监控我们的nm，一旦某个nm挂了，那么该nm上运行的任务需要告诉我们的am来如何进行处理","监测到本机的某块磁盘损坏","目录下一个文件夹","目录下的子目录","目录名","目标","目标值的分类个数","目的：预测用户1对物品e的评分","直接从文件生成datafram","直接使用","直接删除元数据（metadata）及存储数据","直接利用spark.createdataframe()，见后面例子","直接编写rdd也可以自实现优化代码，但是远不及sparksql前面的优化操作后转换的rdd效率高，快1倍左右","直接计算某两项的杰卡德相似系数","直接计算皮尔逊相关系数","相似度的计算方法","相似度计算(similar","相似物品筛选规则：正相关的物品","相似用户筛选规则：正相关的用户","相似话题,","相关函数","相关数据","相反，zip(*)","相当大的数量或部分","看看他们最近在看什么电影","瞬间写入量很大","知道ctr预估概念","知道hadoop生态组成","知道hadoop的优势","知道hadoop的概念及发展历史","知道hbase和关系型数据库的区别","知道hdf","知道hive的udf（自定义函数）","知道hive的内部表、外部表、分区表","知道hql和sql的区别","知道rdd的概念","知道spark作业提交集群的过程","知道spark的安装过程，知道standalone启动模式","知道spark的特点（与hadoop对比）","知道什么是hdf","知道列式数据库与行数据库的区别","知道协同过滤推荐的相关原理","知道基于回归模型的协同过滤推荐原理","知道基于矩阵分解的协同过滤推荐原理","知道常用的基于模型的推荐算法","知道推荐系统的工程架构和算法架构","知道推荐系统的常用算法","矩阵","矩阵值p_{11}​表示用户1对隐含特征1的权重值","矩阵值q_{11}​表示隐含特征1在物品1上的权重值","矩阵值r_{11}就表示预测的用户1对物品1的评分，且r_{11}=\\vec{p_{1,k}}\\cdot","矩阵分解发展史","硬件容错","确定","磁盘介质在存储过程中受环境或者老化影响,数据可能错乱","磁盘故障容错","示例","示例：","社交信息、推⼴素材、安装来源","社会化推荐","社会化推荐,","离线处理业务流","离线存储","离线推荐","离线推荐数据缓存","离线数据缓存之离线特征","离线计算","离线计算,","离线计算特点:","离线计算通常针对(某一类别)全体数据,","离线评估:","离线评估和在线评估结合,","稀疏评分矩阵","稠密评分矩阵","稳定的信息流通系统","空值占比：32.49%","空值占比：54.24%","窗口的长度控制考虑前几批次数据量","窗口长度l：运算的数据量","第一个参数","第一步","第一行数据","第二个特征是分类的:","第二步","等待regionserver汇报","等条件缩小查询范围","策略调整","筛选出缺失值条目","筛选指定字段数据，构建新的数据集","简介","简单函数:","简明","算术运算符","算法","算法举例","算法原理","算法实现","算法思想：物以类聚，人以群分","类似","类别型特征，2个分类","类别型特征，3个分类","类别型特征，7个分类","类别型特征，约13个分类","类型","类型转换","精准率","系统冷启动","系统冷启动：⽤户冷启动+物品冷启动","系统早期","系统过度强调实时性","系统通过一定的规则对物品进行排序,并将排在前面的物品展示给用户,这样的系统就是推荐系统","索引","约","约113w用户","约12968类别id","约2600w","约460561品牌id","约7亿条目723268134","约7天的数据","约85w","组id","组件","组成,","经过交替最小二乘","经过处理计算后再导出给应用程序使用","结合iid物品与其相似物品的相似度和uid用户对其相似物品的评分，预测uid对iid的评分","结合uid用户与其近邻用户的相似度预测uid用户对iid物品的评分","结构化数据","结构化数据，可以直接看出数据的详情","结果再保存到hdf","结果是概率问题","结果输出","结论：tf","给所有用户推荐top","给物品打标签","给用户推荐top","给运营和决策层提供各种统计报告,","给部分用户推荐top","统计信息","统计函数:","统计分析","统计指标","统计数据中出现次数最多的前n个数据","统计每个用户对各个品牌的pv、fav、cart、buy数量","统计每个用户对各个品牌的pv、fav、cart、buy数量并保存结果","统计每个用户对各类商品的pv、fav、cart、buy数量","统计等业务","综合案例","缓存","编写map风格脚本","编程模型","缩小查询范围","缺失值处理","缺失值处理方案：","缺失率低于10%：可直接进行相应的填充，如默认值、均值、算法拟合等等；","缺点","缺点：不同路径启动","美国录像带租赁","而倒排索引就是用物品的其他数据作为索引，去提取它们对应的物品的id列表","而只选取price作为特征数据，因为价格本身是一个统计类型连续数值型数据，且能很好的体现广告的价值属性特征，通常也不需要做其他处理(离散化、归一化、标准化等)，所以这里直接将当做特征数据来使用","而经过热独编码，数据会变成稀疏的，方便分类器处理：","背景:","能处理稀疏评分矩阵","能够应用spark","能够应用sparkmllib训练lr模型","能够应用sparkml训练als模型","能够掌握hdf","能够掌握hdfs的环境搭建","脱敏过的品牌id；","脱敏过的广告主id；","自定义函数和","自定义的汇总方法","自然语言处理利器","自然那么最终得出预测值后，需要对应+1才能还原回来","节点。","节点中添加","节点和","节点管理器","节点，相当于","获取对数据进行运算操作之后的结果","获取成本","获取最近多个版本的数据","获取用户召回集","获取用户特征","获取该广告的特征值","获得table对象","行(row)：在表里面,每一行代表着一个数据对象,每一行都是以一个行键(row","行为方式","行数据库&列数据库存储方式比较","行键(rowkey)：类似于mysql中的主键，hbase根据行键来快速检索数据，一个行键对应一条记录。与mysql主键不同的是，hbase的行键是天然固有的，每一行数据都存在行键。","行键并没有什么特定的数据类型,","表(table)：用于存储管理数据，具有稀疏的、面向列的特点。hbase中的每一张表，就是所谓的大表(bigtable)，可以有上亿行，上百万列。对于为值为空的列，并不占用存储空间，因此表可以设计的非常稀疏。","表以保证数据正常访问","表关系复杂的数据模型","表地址、hmaster地址；","表的列","表的名字","表的属性（是否为外部表等）","表的数据所在目录等。","表示","表结构修改时影响","表结构和分区进行修改，则需要修复（msck","被动","装载数据","要定期向nn发送心跳信息，汇报本身及其所有的block信息，健康状况","要统计ip所对应的经纬度,","覆盖度","覆盖率","视图存储数据库","角色功能：","解决分布式存储问题","解决分布式计算问题","解决数据可以切割进行计算的应用","解压后的hive目录","解释器、编译器、优化器、执行器:完成","计算item","计算全局平均分","计算其中一项，先固定其他未知参数，即看作其他未知参数为已知","计算分子的值","计算分母的值","计算列表各个元素的平方","计算列表和：1+2+3+4+5","计算平方数","计算所有电影的平均评分\\mu​（即全局平均评分）","计算所有的数据两两的杰卡德相似系数","计算时我们数据通常都需要对数据进行处理，或者编码，目的是为了便于我们对数据进行运算处理，比如这里是比较简单的情形，我们用1、0分别来表示用户的是否购买过该物品，则我们的数据集其实应该是这样的：","计算框架","计算框架都要用到服务器集群资源","计算每个用户评分与平均评分\\mu的偏置值b_u​","计算每部电影所接受的评分与平均评分\\mu的偏置值b_i","计算物品间相似度","计算用户间相似度","计算相似度：对于评分数据这里我们采用皮尔逊相关系数[","计算词语的逆文档频率如下：","计算预测的评分值","计算预测的评分值并返回","订单活跃转化率","订单量","让好友给自己推荐物品","让用户实时展示","训练ctrmodel_normal：直接将对应的特征的特征值组合成对应的特征向量进行训练","训练tf","训练分类模型","训练数据集:","训练样本","训练样本个数：","训练样本：","训练模型时，通过对类别特征数据进行处理，一定程度达到提高了模型的效果","训练的数据","训练集的比例，如x=0.8，则0.2是测试集","记忆推荐系统冷启动概念","记忆推荐系统工作原理及作用","记忆推荐系统架构","记忆相似度计算方法","论文发表于2003年10月","设备信息：定位、⼿机型号、app列表","设定范围","设置checkpoint的话，会把所有数据落盘，这样如果异常退出，下次重启后，可以接着上次的训练节点继续运行","设置spark","设置启动的spark的app名称，没有提供，将随机产生一个名称","设置目标字段、特征值字段并训练","设置要加载的数据字段的类型","设置该app启动时占用的内存用量，默认1g","访问时间","访问的pv","访问的topn","访问的uv","访问的地址...信息","访问的请求方式","评估指标","评估数据来源显示反馈和隐式反馈","评估方法","评分公式","评分数据","评分预测","评分预测公式的分子部分的值","评分预测公式的分母部分的值","评分预测：","评分预测：使用物品间的相似度进行预测","评分预测：使用用户间的相似度进行预测","评论","评论/评价","词袋模型（bow","词袋模型：在词集的基础上如果一个单词在文档中出现不止一次，统计其出现的次数（频数）。","词集模型：单词构成的集合，集合自然每个元素都只有一个，也即词集中的每个单词都只有一个。","词频统计","词频统计案例","该偏好权重比例，次数上限仅供参考，具体数值应根据产品业务场景权衡","该时间之前的数据为训练样本，该时间以后的数据为测试样本：","详细使用方法：pyspark.ml.recommendation.","详见","语句转换为","语料（corpus）：一组原始文本的集合，在gensim中，corpus通常是一个可迭代的对象（比如列表）。每一次迭代返回一个可用于表达文本对象的（稀疏）向量。","语法","语法，提供快速开发的能力","说出dataframe与rdd的联系","说出dstreaming的常见操作api","说出hadoop发行版本的选择","说出hadoop的核心组件","说出hdfs的架构","说出mapreduce原理","说出rdd的三类算子","说出spark","说出sparkml","说出yarn执行流程","说出处理缺失值的常件办法","说出广播变量的概念","说明","说明启动成功","请求kill","请谨慎使用","读写流程&","读取一个json文件可以用sparksession.read.json方法","读取批处理层和实时处理层结果并对其归并","读取数据的时候,","读取数据进行计算","读模式","课程目标","课程目标：","调用sgd方法训练模型参数","调用sparkcontext的","调用执行","调用文件系统(fs)shell命令应使用","调用方法例如：spark.read.xxx方法","调节指标对公司进行管理","负责元数据（文件的名称、副本系数、block存放的dn）的管理","负责客户端请求的响应","负责整个集群资源的管理和调度","购买","资源位。该特征属于分类特征，只有两类取值，因此考虑进行热编码处理即可，分为是否在资源位1、是否在资源位2","资源位的特征向量","资源利用率低","资源管理器","资源调度框架","赢得世界最快1tb数据排序在900个节点上用时209秒。","超出这个范围的","超时未发送心跳,","超算","跟sql类似","转化率","转化率指的是从状态a进入到状态b的概率，电商的转化率通常是指到达网站后，进而有成交记录的用户比率，如用户成交量/用户访问量","转化过程:","转成json字符串再保存，能保证数据再次倒出来时，能有效的转换成python类型","转换","转换为panda","转换为rdd，再从rdd到datafram","转换为列表","转换为普通的rdd类型","转换来输出列表。","载入训练好的模型","输入hbase","输入数据","输入自己喜欢的演员的名字,","输出结果：","过滤","过滤掉已经购买过的物品","过滤掉用户已购的物品","运维成本高","运营人员发现从","运营常用数据指标","运营数据是公司管理的基础","运营数据的获取需要大数据平台的支持","运行mrjob的不同方式","运行wordcount代码","运行时间长","运行结果：","运行过程是先将pvalue_level转换为一列新的特征数据，然后对该特征数据求出的热编码值，存在了新的一列数据中，类型为一个稀疏矩阵","近线：最近1天、3天、7天","近邻物品的评分数据","近邻用户对iid物品的评分","近邻用户的评分数据","返回rdd中元素的个数","返回rdd的前n个元素","返回rdd的第一个元素","返回一个list，list中包含","返回一个pythonrdd类型","返回一个pythonrdd类型，此时还没开始计算","返回一个对象","返回值","返回值：","返回函数计算结果。","返回列表。","返回字段pid_value是一个稀疏向量类型数据","返回模型中关于用户的所有属性","返回的是一个dataframe，这里的count计算的是每一个分组的个数，但当前还没有进行计算","返回结果","返回结果为连接参数产生的字符串。如有任何一个参数为null","返回迭代器。","还原预测值","这个命令将student.txt文件复制到hive的warehouse目录中，这个目录由hive.metastore.warehouse.dir配置项设置，默认值为/user/hive/warehouse。overwrite选项将导致hive事先删除student目录下所有的文件,","这个用户或物品普遍高于或低于平均值的差值，我们称为偏置(bias)","这个类提供了一个与hbase交互的入口,","这个问题不明显","这些api用于检索和操作hbase中的数据。","这些内存直接受操作系统管理（而不是jvm）。","这样做保留了特征的多样性，但是也要注意如果数据过于稀疏(样本较少、维度过高)，其效果反而会变差","这样如果有多个资源位，那么每个资源位都会对应相应的一个推荐列表","这种设计使spark运行效率更高","这里为了保证用户数量保持不变，将每个用户的评分数据按比例进行拆分","这里主要是利用我们前面训练的als模型进行协同过滤召回，但是注意，我们als模型召回的是用户最感兴趣的类别，而我们需要的是用户可能感兴趣的广告的集合，因此我们还需要根据召回的类别匹配出对应的广告。","这里以user","这里先介绍稠密评分矩阵的处理，稀疏矩阵的处理相对会复杂一些，我们到后面再来介绍。","这里利用物品相似度预测的计算同上，同样考虑了用户自身的平均打分因素，结合预测物品与相似物品的加权平均相似度打分进行来进行预测","这里我们只需要adgroupid、和cateid","这里数据量比较小，直接转换为panda","这里注意predict参数，如果是预测多个，那么参数必须是直接有列表构成的rdd参数，而不能是dataframe.rdd类型","这里由于类型只有四个，所以直接使用collect，把数据全部加载出来","进入pyspark环境","进入到","进入到$spark_home/sbin目录","进入到解压后的hadoop目录","进程和","进程通信，根据集群资源，为用户程序分配第一个container(容器)，并将","进行行间隔,","连接池","连接集群","迭代更新bu","迭代次数","追加型数据库","追求指标增长,","适合大规模海量数据，pb级数据；","适合存储大文件","适合用二维表来展示的数据","适合运行在通用硬件(commod","适合非结构化数据存储","适用于廉价设备；","选出new_user_class_level全部的","选出所有func返回值为true的元素，生成一个新的rdd返回","选择合适的算法","透视表，将电影id转换为列名称，转换成为一个us","逐个预测","通信很耗费性能。","通常svd矩阵分解指的是svd（奇异值）分解技术，在这我们姑且将其命名为tradit","通常如果user","通常数据存储数据，都是以物品的id作为索引，去提取物品的其他信息数据","通常计算相似度的结果希望是[","通常，可以根据cpu核心数量指定分区数量（每个cpu有2","通用资源管理系统","通知当前worker上所有的task,","通知相应datanode,","通过column","通过connection找到user表","通过datafram","通过docker","通过hdfs向hive中add","通过jps命令查看当前运行的进程","通过sc直接使用","通过spark","通过spark实现点击流日志分析","通过timerang","通过信息过滤实现目标提升","通过前面两个demo，相信大家应该已经对协同过滤推荐算法的设计与实现有了比较清晰的认识。","通过可视化界面查看hdfs的运行情况","通过外部数据创建rdd","通过对比可得，该篇影评的关键词排序应为：“自由”、“船长”、“海盗”。把这些词语的tf","通过对用户观影标签的次数进行统计，计算用户的每个初始标签的权重值，排序后选取top","通过心跳和namenode保持通讯","通过指定时间戳获取不同版本的数据","通过数据分析指标监控企业运营状态,","通过时间戳查询","通过时间戳过滤器","通过最小二乘推导，我们最终分别得到了b_u和b_i​的表达式，但他们的表达式中却又各自包含对方，因此这里我们将利用一种叫交替最小二乘的方法来计算他们的值：","通过浏览器查看","通过物品的uid","通过用户的id","通过计算两两的相似度来进行排序，即可找出top","速度快,","逻辑运算符","遍历als_model","遍历召回集","遍历所有用户","遍历每一行数据","避免了去写","那么欧式距离就是衡量这两个点之间的距离.","都可以视为'相似'","都在同一个空间下表示为两个点,","都需要这一个ip表,","配合使用,将一行数据拆分成多行数据，在此基础上可以对拆分的数据进行聚合","配置","配置hbase","配置java环境变量","配置mapreduc","配置master的地址","配置master的端口","配置python环境","配置spark","配置spark环境变量","配置yarn","配置伪分布式环境","配置文件作用","配置环境变量","采样数据","重写覆盖","重新计算读取出来的数据校验和,","重要的方法","长尾效应","长期的⽬标","问卷调查:","问题：物品的标签来自哪儿？","限制起始的rowkey","限制输出两行","除了前面处理的pvalue_level和new_user_class_level需要作为特征以外，(能体现出用户的购买力特征)，还有：","随机丢弃用户行为历史","随机扰动模型参数","随机梯度下降法优化","随机梯度下降：","随机森林中","随机森林模型：pyspark.mllib.tree.randomforestmodel","随机森林：pyspark.mllib.tree.randomforest","随着大数据技术的发展，spark","随着机器学习技术的逐渐发展与完善，推荐系统也逐渐运用机器学习的思想来进行推荐。将机器学习应用到推荐系统中的方案真是不胜枚举。以下对model","随着表的不断增大，对于新纪录的增加，查找，删除等(dml)的维护也更加困难。对于数据库中的超大型表，可以通过把它的数据分成若干个小表，从而简化数据库的管理活动，对于每一个简化后的小表，我们称为一个单个的分区。","隐含因子个数是10个","隐含特征的矩阵，funk","隐含特征，项目","隐式反馈","隐式类别数量","隐形数据","集合函数","集群)","集群可以使用廉价机器，成本低","集群相关概念","需求","需求：想要将一个大时间段（1天），即多个小时间段的数据内的数据持续进行累积操作","需求：监听某个端口上的网络数据，实时统计出现的不同单词个数。","需求：监听网络端口的数据，获取到每个批次的出现的单词数量，并且需要把每个批次的信息保留下来","需要一种灵活的框架可同时进行批处理、流式计算、交互式计算","需要与nm通信：启动/停止task，task是运行在container里面，am也是运行在container里面","需要先将缺失值全部替换为数值，与原有特征一起处理","需要先将缺失值全部替换为数值，便于处理，否则会抛出异常","需要在非常短的时间内返回结果","需要用户的点击数据","需要进行缓存的特征值","需要进行资源调度管理","需要通过hql添加分区","需要通过大数据实现","静态json数据的读取和操作","非常适用于布尔向量表示","非搜索广告（例如展示广告，信息流广告）的点击率的计算很多就来源于用户的兴趣和广告自身的特征，以及上下文环境。通常好位置能达到百分之几的点击率。对于很多底部的广告，点击率非常低，常常是千分之几，甚至更低","非结构化数据","非结构化数据是数据结构不规则或不完整","面向列的数据库","页面浏览","页面跳转都记一次pv","项目实现分析","项目效果展示","预处理behavior_log数据集","预测任意用户对任意电影的评分","预测值实际应该为2","预测值总数","预测全部的pvalue_level值:","预测全部评分","预测单个数据","预测用户对物品的评分","预测用户对电影的评分：","预测电影评分","预测的评分值","预测结果","预测结果，类型为容器，每个元素是一个包含uid,iid,real_rating,pred_rating的序列","预测给定用户对给定物品的评分值","预测评分","频繁的创建和销毁对象造成大量的gc","首先执行逻辑执行计划，然后转换为物理执行计划(选择成本最小的)，通过code","首先计算出整个评分数据集的平均评分\\mu​是3.5分","首先，要定义一个state，可以是任意的数据类型","马太效应","高","高于10%：往往会考虑舍弃该特征","高可用","高可靠","高度容错性的系统，适合部署在廉价的机器上","高延迟","高扩展性","高效连接用户和物品","高级源","鲁棒性","默认为批处理的滑动间隔来确定计算结果的频率","默认情况下,","默认是按列进行计算，因此如果计算用户间的相似度，当前需要进行转置","默认没有这个","（","（以用户1对电影1评分为例）","（注：wal，writ","（注：发送完成信号的时机取决于集群是强一致性还是最终一致性，强一致性则需要所有datanode写完后才向namenode汇报。最终一致性则其中任意一个datanode写完后就能单独向namenode汇报，hdfs一般情况下都是强调强一致性）","（注：并不是写好一个块或一整个文件后才向后分发）","（注：并不是每写完一个packet后就返回确认信息，个人觉得因为packet中的每个chunk都携带校验信息，没必要每写一个就汇报一下，这样效率太慢。正确的做法是写完一个block块后，对校验信息进行汇总分析，就能得出是否有块写错的情况发生）","（精选）","（默认：/user/hive/warehouse）","，1:是,0:否","，则返回值为","，形成一个user","，是concat()的特殊形式。第一个参数是其它参数的分隔符。分隔符的位置放在要连接的两个字符串之间。分隔符可以是一个字符串，也可以是其它参数。如果分隔符为","：基于用户的协同过滤推荐（user"],"pipeline":["stopWordFilter","stemmer"]},"store":{"./":{"url":"./","title":"简介","keywords":"","body":"推荐系统基础\n推荐系统简介\n\n了解推荐相关常用概念\n知道推荐系统的工程架构和算法架构\n知道推荐系统的常用算法\n知道协同过滤推荐的相关原理\n了解推荐系统的评估\n了解推荐系统的冷启动问题\n\n推荐系统算法\n\n知道常用的基于模型的推荐算法\n知道基于回归模型的协同过滤推荐原理\n知道基于矩阵分解的协同过滤推荐原理\n了解基于内容推荐算法概念\n了解物品画像，用户画像概念\n了解物品冷启动的推荐方法\n\nHadoop\n\nHadoop概述\n知道Hadoop的概念及发展历史\n说出hadoop的核心组件\n知道hadoop的优势\n\n\n分布式文件系统 HDFS\n知道什么是hdfs\n说出hdfs的架构\n能够掌握hdfs的环境搭建\n能够掌握hdfs shell的基本使用\n知道hdfs shell的优缺点\n\n\nYARN&MapReduce\n\n了解YARN概念和产生背景\n了解MapReduce概念\n说出YARN执行流程\n说出MapReduce原理\n独立完成Mrjob实现wordcount\n完成提交作业到YARN上执行\n\n\nHadoop概念扩展\n\n知道hadoop生态组成\n了解hdfs读写流程\n说出Hadoop发行版本的选择\n\n\n\nHive\n\n了解Hive原理和架构\n知道HQL和SQL的区别\n知道Hive的内部表、外部表、分区表\n知道Hive的UDF（自定义函数）\n\nHBase\n\n了解HBase的基本架构\n知道列式数据库与行数据库的区别\n知道HBase和关系型数据库的区别\n掌握HBase的shell操作\n掌握HappyBase的常用API\n\nSpark Core\n\n了解spark概念\n知道spark的特点（与hadoop对比）\n知道RDD的概念\n掌握transformation和action算子的基本使用\n独立实现spark standalone模式的启动\n说出广播变量的概念\n了解spark的安装部署\n知道spark作业提交集群的过程\n\nSpark SQL\n\n说出Spark Sql的相关概念\n说出DataFrame与RDD的联系\n独立实现Spark Sql对JSON数据的处理\n独立实现Spark Sql进行数据清洗\n\nSpark Streaming\n\n说出Spark Streaming的特点\n说出DStreaming的常见操作api\n能够应用Spark Streaming实现实时数据处理\n能够应用Spark Streaming的状态操作解决实际问题\n\n推荐系统案例\n\n知道CTR预估概念\n说出SparkML 和 Spark MLlib的区别\n能够应用SparkML训练ALS模型\n能够应用SparkMLlib训练LR模型\n说出处理缺失值的常件办法\n\n"},"day01_推荐系统介绍/01_推荐系统简介.html":{"url":"day01_推荐系统介绍/01_推荐系统简介.html","title":"1.1_推荐系统简介","keywords":"","body":"1.1 推荐系统简介\n学习目标\n\n了解推荐系统概念及产生背景\n记忆推荐系统工作原理及作用\n了解推荐系统与web项目区别\n\n1 推荐系统概念及产生背景\n个性化推荐(推荐系统)经历了多年的发展，已经成为互联网产品的标配，也是AI成功落地的分支之一，在电商(淘宝/京东)、资讯(今日头条/微博)、音乐(网易云音乐/QQ音乐)、短视频(抖音/快手)等热门应用中,推荐系统都是核心组件之一。\n\n什么是推荐系统\n没有明确需求的用户访问了我们的服务, 且服务的物品对用户构成了信息过载, 系统通过一定的规则对物品进行排序,并将排在前面的物品展示给用户,这样的系统就是推荐系统\n\n信息过载 & 用户需求不明确\n\n分类⽬录（1990s）：覆盖少量热门⽹站。典型应用：Hao123 Yahoo\n搜索引擎（2000s）：通过搜索词明确需求。典型应用：Google Baidu\n推荐系统（2010s）：不需要⽤户提供明确的需求，通过分析⽤\n户的历史⾏为给⽤户的兴趣进⾏建模，从⽽主动给⽤户推荐能\n够满⾜他们兴趣和需求的信息。\n\n\n推荐系统 V.S. 搜索引擎\n\n  \n    \n    搜索\n    推荐\n  \n  \n     行为方式 \n     主动 \n     被动 \n  \n  \n     意图 \n     明确 \n     模糊 \n  \n  \n     个性化 \n     弱 \n     强 \n  \n  \n     流量分布 \n     马太效应 \n     长尾效应 \n  \n  \n     目标 \n     快速满足  \n     持续服务 \n  \n  \n     评估指标 \n     简明 \n     复杂 \n  \n\n\n\n\n2 推荐系统的工作原理及作用\n\n推荐系统的工作原理\n\n社会化推荐 向朋友咨询, 社会化推荐, 让好友给自己推荐物品\n基于内容的推荐 打开搜索引擎, 输入自己喜欢的演员的名字, 然后看看返回结果中还有什么电影是自己没看过的\n基于流行度的推荐 查看票房排行榜, \n基于协同过滤的推荐 找到和自己历史兴趣相似的用户, 看看他们最近在看什么电影\n\n\n推荐系统的作用\n\n高效连接用户和物品\n提高用户停留时间和用户活跃程度\n有效的帮助产品实现其商业价值\n\n\n推荐系统的应用场景\n\n\n\n3 推荐系统和Web项目的区别\n\n通过信息过滤实现目标提升 V.S. 稳定的信息流通系统\nweb项目: 处理复杂业务逻辑，处理高并发，为用户构建一个稳定的信息流通服务\n推荐系统: 追求指标增长, 留存率/阅读时间/GMV (Gross Merchandise Volume电商网站成交金额)/视频网站VV (Video View)\n\n\n确定 V.S. 不确定思维\nweb项目: 对结果有确定预期\n推荐系统: 结果是概率问题\n\n\n\n"},"day01_推荐系统介绍/02_推荐系统架构设计.html":{"url":"day01_推荐系统介绍/02_推荐系统架构设计.html","title":"1.2_推荐系统架构设计","keywords":"","body":"1.2 推荐系统设计\n学习目标\n\n了解推荐系统要素\n记忆推荐系统架构\n\n1 推荐系统要素\n\nUI 和 UE(前端界面)\n数据 (Lambda架构)\n业务知识\n算法\n\n2 推荐系统架构\n\n推荐系统整体架构\n\n\n大数据Lambda架构\n\nLambda架构是由实时大数据处理框架Storm的作者Nathan Marz提出的一个实时大数据处理框架。\n\nLambda架构的将离线计算和实时计算整合，设计出一个能满足实时大数据系统关键特性的架构，包括有：高容错、低延时和可扩展等。\n\n分层架构\n\n批处理层\n数据不可变, 可进行任何计算, 可水平扩展\n高延迟  几分钟~几小时(计算量和数据量不同)\n日志收集： Flume\n分布式存储： Hadoop\n分布式计算： Hadoop、Spark\n视图存储数据库\nnosql(HBase/Cassandra)\nRedis/memcache\nMySQL\n\n\n\n\n实时处理层\n流式处理, 持续计算\n存储和分析某个窗口期内的数据（一段时间的热销排行，实时热搜等）\n实时数据收集 flume & kafka\n实时数据分析  spark streaming/storm/flink\n\n\n服务层\n支持随机读\n需要在非常短的时间内返回结果\n读取批处理层和实时处理层结果并对其归并\n\n\n\n\nLambda架构图\n\n\n\n\n推荐算法架构\n\n召回阶段 (海选)\n召回决定了最终推荐结果的天花板\n常用算法:\n协同过滤\n基于内容\n\n\n\n\n排序阶段 （精选）\n召回决定了最终推荐结果的天花板, 排序逼近这个极限, 决定了最终的推荐效果\nCTR预估 (点击率预估 使用LR算法)  估计用户是否会点击某个商品 需要用户的点击数据\n\n\n策略调整\n\n\n\n\n\n推荐系统的整体架构\n\n\n\n\n"},"day01_推荐系统介绍/03_推荐算法.html":{"url":"day01_推荐系统介绍/03_推荐算法.html","title":"1.3_推荐算法","keywords":"","body":"1.3 推荐算法\n学习目标\n\n了解推荐模型构建流程\n理解协同过滤原理\n\n记忆相似度计算方法\n\n应用杰卡德相似度实现简单协同过滤推荐案例\n\n1 推荐模型构建流程\nData(数据)->Features(特征)->ML Algorithm(选择算法训练模型)->Prediction Output(预测输出)\n\n数据清洗/数据处理\n\n数据来源\n显性数据\nRating 打分\nComments 评论/评价\n\n\n隐形数据\n Order history 历史订单\n Cart events    加购物车\n Page views    页面浏览\n Click-thru      点击\n Search log     搜索记录\n\n\n\n\n数据量/数据能否满足要求\n\n\n特征工程\n\n从数据中筛选特征\n\n一个给定的商品，可能被拥有类似品味或需求的用户购买\n\n使用用户行为数据描述商品\n\n\n\n\n用数据表示特征\n\n将所有用户行为合并在一起 ，形成一个user-item 矩阵\n\n\n\n\n\n\n\n\n选择合适的算法\n\n协同过滤\n基于内容\n\n\n产生推荐结果\n\n对推荐结果进行评估（评估方法后面章节介绍），评估通过后上线\n\n\n\n2 最经典的推荐算法：协同过滤推荐算法（Collaborative Filtering）\n算法思想：物以类聚，人以群分\n基本的协同过滤推荐算法基于以下假设：\n\n“跟你喜好相似的人喜欢的东西你也很有可能喜欢” ：基于用户的协同过滤推荐（User-based CF）\n“跟你喜欢的东西相似的东西你也很有可能喜欢 ”：基于物品的协同过滤推荐（Item-based CF）\n\n实现协同过滤推荐有以下几个步骤：\n\n找出最相似的人或物品：TOP-N相似的人或物品\n通过计算两两的相似度来进行排序，即可找出TOP-N相似的人或物品\n\n根据相似的人或物品产生推荐结果\n利用TOP-N结果生成初始推荐结果，然后过滤掉用户已经有过记录的物品或明确表示不感兴趣的物品\n\n\n以下是一个简单的示例，数据集相当于一个用户对物品的购买记录表：打勾表示用户对物品的有购买记录\n\n关于相似度计算这里先用一个简单的思想：如有两个同学X和Y，X同学爱好[足球、篮球、乒乓球]，Y同学爱好[网球、足球、篮球、羽毛球]，可见他们的共同爱好有2个，那么他们的相似度可以用：2/3 * 2/4 = 1/3 ≈ 0.33 来表示。\nUser-Based CF\n\nItem-Based CF\n\n\n\n  通过前面两个demo，相信大家应该已经对协同过滤推荐算法的设计与实现有了比较清晰的认识。\n3 相似度计算(Similarity Calculation)\n\n\n相似度的计算方法\n\n欧氏距离, 是一个欧式空间下度量距离的方法. 两个物体, 都在同一个空间下表示为两个点, 假如叫做p,q, 分别都是n个坐标, 那么欧式距离就是衡量这两个点之间的距离. 欧氏距离不适用于布尔向量之间\n\n\n​    欧氏距离的值是一个非负数, 最大值正无穷, 通常计算相似度的结果希望是[-1,1]或[0,1]之间,一般可以使用\n​    如下转化公式:\n\n余弦相似度\n\n度量的是两个向量之间的夹角, 用夹角的余弦值来度量相似的情况\n两个向量的夹角为0是,余弦值为1, 当夹角为90度是余弦值为0,为180度是余弦值为-1\n余弦相似度在度量文本相似度, 用户相似度 物品相似度的时候较为常用\n余弦相似度的特点, 与向量长度无关,余弦相似度计算要对向量长度归一化, 两个向量只要方向一致,无论程度强弱, 都可以视为'相似'\n\n\n\n\n\n\n\n\n皮尔逊相关系数Pearson\n\n实际上也是余弦相似度, 不过先对向量做了中心化, 向量a b各自减去向量的均值后, 再计算余弦相似度\n皮尔逊相似度计算结果在-1,1之间 -1表示负相关, 1表示正相关\n度量两个变量是不是同增同减\n皮尔逊相关系数度量的是两个变量的变化趋势是否一致, 不适合计算布尔值向量之间的相关度\n\n\n\n杰卡德相似度 Jaccard\n\n两个集合的交集元素个数在并集中所占的比例, 非常适用于布尔向量表示\n分子是两个布尔向量做点积计算, 得到的就是交集元素的个数\n分母是两个布尔向量做或运算, 再求元素和\n\n\n\n如何选择余弦相似度\n\n余弦相似度/皮尔逊相关系数适合用户评分数据(实数值),\n杰卡德相似度适用于隐式反馈数据(0,1布尔值 是否收藏,是否点击,是否加购物车)\n\n\n\n4 协同过滤推荐算法代码实现：\n\n构建数据集：\nusers = [\"User1\", \"User2\", \"User3\", \"User4\", \"User5\"]\nitems = [\"Item A\", \"Item B\", \"Item C\", \"Item D\", \"Item E\"]\n# 构建数据集\ndatasets = [\n    [\"buy\",None,\"buy\",\"buy\",None],\n    [\"buy\",None,None,\"buy\",\"buy\"],\n    [\"buy\",None,\"buy\",None,None],\n    [None,\"buy\",None,\"buy\",\"buy\"],\n    [\"buy\",\"buy\",\"buy\",None,\"buy\"],\n]\n\n\n计算时我们数据通常都需要对数据进行处理，或者编码，目的是为了便于我们对数据进行运算处理，比如这里是比较简单的情形，我们用1、0分别来表示用户的是否购买过该物品，则我们的数据集其实应该是这样的：\nusers = [\"User1\", \"User2\", \"User3\", \"User4\", \"User5\"]\nitems = [\"Item A\", \"Item B\", \"Item C\", \"Item D\", \"Item E\"]\n# 用户购买记录数据集\ndatasets = [\n    [1,0,1,1,0],\n    [1,0,0,1,1],\n    [1,0,1,0,0],\n    [0,1,0,1,1],\n    [1,1,1,0,1],\n]\nimport pandas as pd\n\ndf = pd.DataFrame(datasets,\n                  columns=items,\n                  index=users)\nprint(df)\n\n\n有了数据集，接下来我们就可以进行相似度的计算，不过对于相似度的计算其实是有很多专门的相似度计算方法的，比如余弦相似度、皮尔逊相关系数、杰卡德相似度等等。这里我们选择使用杰卡德相似系数[0,1]\nfrom sklearn.metrics import jaccard_similarity_score\n# 直接计算某两项的杰卡德相似系数\n# 计算Item A 和Item B的相似度\nprint(jaccard_similarity_score(df[\"Item A\"], df[\"Item B\"]))\n\n# 计算所有的数据两两的杰卡德相似系数\nfrom sklearn.metrics.pairwise import pairwise_distances\n# 计算用户间相似度\nuser_similar = 1 - pairwise_distances(df, metric=\"jaccard\")\nuser_similar = pd.DataFrame(user_similar, columns=users, index=users)\nprint(\"用户之间的两两相似度：\")\nprint(user_similar)\n\n# 计算物品间相似度\nitem_similar = 1 - pairwise_distances(df.T, metric=\"jaccard\")\nitem_similar = pd.DataFrame(item_similar, columns=items, index=items)\nprint(\"物品之间的两两相似度：\")\nprint(item_similar)\n\n有了两两的相似度，接下来就可以筛选TOP-N相似结果，并进行推荐了\n\nUser-Based CF\nimport pandas as pd\nimport numpy as np\nfrom pprint import pprint\n\nusers = [\"User1\", \"User2\", \"User3\", \"User4\", \"User5\"]\nitems = [\"Item A\", \"Item B\", \"Item C\", \"Item D\", \"Item E\"]\n# 用户购买记录数据集\ndatasets = [\n    [1,0,1,1,0],\n    [1,0,0,1,1],\n    [1,0,1,0,0],\n    [0,1,0,1,1],\n    [1,1,1,0,1],\n]\n\ndf = pd.DataFrame(datasets,\n                  columns=items,\n                  index=users)\n\n# 计算所有的数据两两的杰卡德相似系数\nfrom sklearn.metrics.pairwise import pairwise_distances\n# 计算用户间相似度  1-杰卡德距离=杰卡德相似度\nuser_similar = 1 - pairwise_distances(df, metric=\"jaccard\")\nuser_similar = pd.DataFrame(user_similar, columns=users, index=users)\nprint(\"用户之间的两两相似度：\")\nprint(user_similar)\n\ntopN_users = {}\n# 遍历每一行数据\nfor i in user_similar.index:\n    # 取出每一列数据，并删除自身，然后排序数据\n    _df = user_similar.loc[i].drop([i])\n    #sort_values 排序 按照相似度降序排列\n    _df_sorted = _df.sort_values(ascending=False)\n    # 从排序之后的结果中切片 取出前两条（相似度最高的两个）\n    top2 = list(_df_sorted.index[:2])\n    topN_users[i] = top2\n\nprint(\"Top2相似用户：\")\npprint(topN_users)\n\n# 准备空白dict用来保存推荐结果\nrs_results = {}\n#遍历所有的最相似用户\nfor user, sim_users in topN_users.items():\n    rs_result = set()    # 存储推荐结果\n    for sim_user in sim_users:\n        # 构建初始的推荐结果\n        rs_result = rs_result.union(set(df.ix[sim_user].replace(0,np.nan).dropna().index))\n    # 过滤掉已经购买过的物品\n    rs_result -= set(df.ix[user].replace(0,np.nan).dropna().index)\n    rs_results[user] = rs_result\nprint(\"最终推荐结果：\")\npprint(rs_results)\n\n\nItem-Based CF\nimport pandas as pd\nimport numpy as np\nfrom pprint import pprint\n\nusers = [\"User1\", \"User2\", \"User3\", \"User4\", \"User5\"]\nitems = [\"Item A\", \"Item B\", \"Item C\", \"Item D\", \"Item E\"]\n# 用户购买记录数据集\ndatasets = [\n    [1,0,1,1,0],\n    [1,0,0,1,1],\n    [1,0,1,0,0],\n    [0,1,0,1,1],\n    [1,1,1,0,1],\n]\n\ndf = pd.DataFrame(datasets,\n                  columns=items,\n                  index=users)\n\n# 计算所有的数据两两的杰卡德相似系数\nfrom sklearn.metrics.pairwise import pairwise_distances\n# 计算物品间相似度\nitem_similar = 1 - pairwise_distances(df.T, metric=\"jaccard\")\nitem_similar = pd.DataFrame(item_similar, columns=items, index=items)\nprint(\"物品之间的两两相似度：\")\nprint(item_similar)\n\ntopN_items = {}\n# 遍历每一行数据\nfor i in item_similar.index:\n    # 取出每一列数据，并删除自身，然后排序数据\n    _df = item_similar.loc[i].drop([i])\n    _df_sorted = _df.sort_values(ascending=False)\n\n    top2 = list(_df_sorted.index[:2])\n    topN_items[i] = top2\n\nprint(\"Top2相似物品：\")\npprint(topN_items)\n\nrs_results = {}\n# 构建推荐结果\nfor user in df.index:    # 遍历所有用户\n    rs_result = set()\n    for item in df.ix[user].replace(0,np.nan).dropna().index:   # 取出每个用户当前已购物品列表\n        # 根据每个物品找出最相似的TOP-N物品，构建初始推荐结果\n        rs_result = rs_result.union(topN_items[item])\n    # 过滤掉用户已购的物品\n    rs_result -= set(df.ix[user].replace(0,np.nan).dropna().index)\n    # 添加到结果中\n    rs_results[user] = rs_result\n\nprint(\"最终推荐结果：\")\npprint(rs_results)\n\n\n\n关于协同过滤推荐算法使用的数据集\n在前面的demo中，我们只是使用用户对物品的一个购买记录，类似也可以是比如浏览点击记录、收听记录等等。这样数据我们预测的结果其实相当于是在预测用户是否对某物品感兴趣，对于喜好程度不能很好的预测。\n因此在协同过滤推荐算法中其实会更多的利用用户对物品的“评分”数据来进行预测，通过评分数据集，我们可以预测用户对于他没有评分过的物品的评分。其实现原理和思想和都是一样的，只是使用的数据集是用户-物品的评分数据。\n关于用户-物品评分矩阵\n用户-物品的评分矩阵，根据评分矩阵的稀疏程度会有不同的解决方案\n\n稠密评分矩阵\n\n\n稀疏评分矩阵\n\n\n\n这里先介绍稠密评分矩阵的处理，稀疏矩阵的处理相对会复杂一些，我们到后面再来介绍。\n使用协同过滤推荐算法对用户进行评分预测\n\n数据集：\n目的：预测用户1对物品E的评分\n\n构建数据集：注意这里构建评分数据时，对于缺失的部分我们需要保留为None，如果设置为0那么会被当作评分值为0去对待\nusers = [\"User1\", \"User2\", \"User3\", \"User4\", \"User5\"]\nitems = [\"Item A\", \"Item B\", \"Item C\", \"Item D\", \"Item E\"]\n# 用户购买记录数据集\ndatasets = [\n    [5,3,4,4,None],\n    [3,1,2,3,3],\n    [4,3,4,3,5],\n    [3,3,1,5,4],\n    [1,5,5,2,1],\n]\n\n\n计算相似度：对于评分数据这里我们采用皮尔逊相关系数[-1,1]来计算，-1表示强负相关，+1表示强正相关\n\npandas中corr方法可直接用于计算皮尔逊相关系数\n\ndf = pd.DataFrame(datasets,\n                  columns=items,\n                  index=users)\n\nprint(\"用户之间的两两相似度：\")\n# 直接计算皮尔逊相关系数\n# 默认是按列进行计算，因此如果计算用户间的相似度，当前需要进行转置\nuser_similar = df.T.corr()\nprint(user_similar.round(4))\n\nprint(\"物品之间的两两相似度：\")\nitem_similar = df.corr()\nprint(item_similar.round(4))\n\n# 运行结果：\n用户之间的两两相似度：\n        User1   User2   User3   User4   User5\nUser1  1.0000  0.8528  0.7071  0.0000 -0.7921\nUser2  0.8528  1.0000  0.4677  0.4900 -0.9001\nUser3  0.7071  0.4677  1.0000 -0.1612 -0.4666\nUser4  0.0000  0.4900 -0.1612  1.0000 -0.6415\nUser5 -0.7921 -0.9001 -0.4666 -0.6415  1.0000\n物品之间的两两相似度：\n        Item A  Item B  Item C  Item D  Item E\nItem A  1.0000 -0.4767 -0.1231  0.5322  0.9695\nItem B -0.4767  1.0000  0.6455 -0.3101 -0.4781\nItem C -0.1231  0.6455  1.0000 -0.7206 -0.4276\nItem D  0.5322 -0.3101 -0.7206  1.0000  0.5817\nItem E  0.9695 -0.4781 -0.4276  0.5817  1.0000\n可以看到与用户1最相似的是用户2和用户3；与物品A最相似的物品分别是物品E和物品D。\n注意：我们在预测评分时，往往是通过与其有正相关的用户或物品进行预测，如果不存在正相关的情况，那么将无法做出预测。这一点尤其是在稀疏评分矩阵中尤为常见，因为稀疏评分矩阵中很难得出正相关系数。\n\n评分预测：\nUser-Based CF 评分预测：使用用户间的相似度进行预测\n关于评分预测的方法也有比较多的方案，下面介绍一种效果比较好的方案，该方案考虑了用户本身的评分评分以及近邻用户的加权平均相似度打分来进行预测：\n\r\n  pred(u,i)=\\hat{r}_{ui}=\\cfrac{\\sum_{v\\in U}sim(u,v)*r_{vi}}{\\sum_{v\\in U}|sim(u,v)|}\r\n  \n我们要预测用户1对物品E的评分，那么可以根据与用户1最近邻的用户2和用户3进行预测，计算如下：\n​\r\n  pred(u_1, i_5) =\\cfrac{0.85*3+0.71*5}{0.85+0.71} = 3.91\r\n  \n最终预测出用户1对物品5的评分为3.91\nItem-Based CF 评分预测：使用物品间的相似度进行预测\n这里利用物品相似度预测的计算同上，同样考虑了用户自身的平均打分因素，结合预测物品与相似物品的加权平均相似度打分进行来进行预测\n\r\n  pred(u,i)=\\hat{r}_{ui}=\\cfrac{\\sum_{j\\in I_{rated}}sim(i,j)*r_{uj}}{\\sum_{j\\in I_{rated}}sim(i,j)}\r\n  \n我们要预测用户1对物品E的评分，那么可以根据与物品E最近邻的物品A和物品D进行预测，计算如下：\n\r\n  pred(u_1, i_5) = \\cfrac {0.97*5+0.58*4}{0.97+0.58} = 4.63\r\n  \n对比可见，User-Based CF预测评分和Item-Based CF的评分结果也是存在差异的，因为严格意义上他们其实应当属于两种不同的推荐算法，各自在不同的领域不同场景下，都会比另一种的效果更佳，但具体哪一种更佳，必须经过合理的效果评估，因此在实现推荐系统时这两种算法往往都是需要去实现的，然后对产生的推荐效果进行评估分析选出更优方案。\n\n\n"},"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html":{"url":"day01_推荐系统介绍/04_案例--基于协同过滤的电影推荐.html","title":"1.4_案例--基于协同过滤的电影推荐","keywords":"","body":"1.4 案例--基于协同过滤的电影推荐\n学习目标\n\n应用基于用户的协同过滤实现电影评分预测\n应用基于物品的协同过滤实现电影评分预测\n\n1 User-Based CF 预测电影评分\n\n数据集下载\n\n下载地址：MovieLens Latest Datasets Small\n建议下载ml-latest-small.zip，数据量小，便于我们单机使用和运行\n\n\n加载ratings.csv，转换为用户-电影评分矩阵并计算用户之间相似度\nimport os\n\nimport pandas as pd\nimport numpy as np\n\nDATA_PATH = \"./datasets/ml-latest-small/ratings.csv\"\n\ndtype = {\"userId\": np.int32, \"movieId\": np.int32, \"rating\": np.float32}\n# 加载数据，我们只用前三列数据，分别是用户ID，电影ID，已经用户对电影的对应评分\nratings = pd.read_csv(data_path, dtype=dtype, usecols=range(3))\n# 透视表，将电影ID转换为列名称，转换成为一个User-Movie的评分矩阵\nratings_matrix = ratings.pivot_table(index=[\"userId\"], columns=[\"movieId\"],values=\"rating\")\n#计算用户之间相似度\nuser_similar = ratings_matrix.T.corr()\n\n\n预测用户对物品的评分 （以用户1对电影1评分为例）\n评分公式\n\r\n  pred(u,i)=\\hat{r}_{ui}=\\cfrac{\\sum_{v\\in U}sim(u,v)*r_{vi}}{\\sum_{v\\in U}|sim(u,v)|}\r\n  \n# 1. 找出uid用户的相似用户\nsimilar_users = user_similar[1].drop([1]).dropna()\n# 相似用户筛选规则：正相关的用户\nsimilar_users = similar_users.where(similar_users>0).dropna()\n# 2. 从用户1的近邻相似用户中筛选出对物品1有评分记录的近邻用户\nids = set(ratings_matrix[1].dropna().index)&set(similar_users.index)\nfinally_similar_users = similar_users.ix[list(1)]\n# 3. 结合uid用户与其近邻用户的相似度预测uid用户对iid物品的评分\nnumerator = 0    # 评分预测公式的分子部分的值\ndenominator = 0    # 评分预测公式的分母部分的值\nfor sim_uid, similarity in finally_similar_users.iteritems():\n    # 近邻用户的评分数据\n    sim_user_rated_movies = ratings_matrix.ix[sim_uid].dropna()\n    # 近邻用户对iid物品的评分\n    sim_user_rating_for_item = sim_user_rated_movies[1]\n    # 计算分子的值\n    numerator += similarity * sim_user_rating_for_item\n    # 计算分母的值\n    denominator += similarity\n# 4 计算预测的评分值\npredict_rating = numerator/denominator\nprint(\"预测出用户对电影的评分：%0.2f\" % (1, 1, predict_rating))\n\n\n封装成方法 预测任意用户对任意电影的评分\ndef predict(uid, iid, ratings_matrix, user_similar):\n    '''\n    预测给定用户对给定物品的评分值\n    :param uid: 用户ID\n    :param iid: 物品ID\n    :param ratings_matrix: 用户-物品评分矩阵\n    :param user_similar: 用户两两相似度矩阵\n    :return: 预测的评分值\n    '''\n    print(\"开始预测用户对电影的评分...\"%(uid, iid))\n    # 1. 找出uid用户的相似用户\n    similar_users = user_similar[uid].drop([uid]).dropna()\n    # 相似用户筛选规则：正相关的用户\n    similar_users = similar_users.where(similar_users>0).dropna()\n    if similar_users.empty is True:\n        raise Exception(\"用户没有相似的用户\" % uid)\n\n    # 2. 从uid用户的近邻相似用户中筛选出对iid物品有评分记录的近邻用户\n    ids = set(ratings_matrix[iid].dropna().index)&set(similar_users.index)\n    finally_similar_users = similar_users.ix[list(ids)]\n\n    # 3. 结合uid用户与其近邻用户的相似度预测uid用户对iid物品的评分\n    numerator = 0    # 评分预测公式的分子部分的值\n    denominator = 0    # 评分预测公式的分母部分的值\n    for sim_uid, similarity in finally_similar_users.iteritems():\n        # 近邻用户的评分数据\n        sim_user_rated_movies = ratings_matrix.ix[sim_uid].dropna()\n        # 近邻用户对iid物品的评分\n        sim_user_rating_for_item = sim_user_rated_movies[iid]\n        # 计算分子的值\n        numerator += similarity * sim_user_rating_for_item\n        # 计算分母的值\n        denominator += similarity\n\n    # 计算预测的评分值并返回\n    predict_rating = numerator/denominator\n    print(\"预测出用户对电影的评分：%0.2f\" % (uid, iid, predict_rating))\n    return round(predict_rating, 2)\n\n\n为某一用户预测所有电影评分\ndef predict_all(uid, ratings_matrix, user_similar):\n    '''\n    预测全部评分\n    :param uid: 用户id\n    :param ratings_matrix: 用户-物品打分矩阵\n    :param user_similar: 用户两两间的相似度\n    :return: 生成器，逐个返回预测评分\n    '''\n    # 准备要预测的物品的id列表\n    item_ids = ratings_matrix.columns\n    # 逐个预测\n    for iid in item_ids:\n        try:\n            rating = predict(uid, iid, ratings_matrix, user_similar)\n        except Exception as e:\n            print(e)\n        else:\n            yield uid, iid, rating\nif __name__ == '__main__':\n    for i in predict_all(1, ratings_matrix, user_similar):\n        pass\n\n\n根据评分为指定用户推荐topN个电影\ndef top_k_rs_result(k):\n    results = predict_all(1, ratings_matrix, user_similar)\n    return sorted(results, key=lambda x: x[2], reverse=True)[:k]\nif __name__ == '__main__':\n    from pprint import pprint\n    result = top_k_rs_result(20)\n    pprint(result)\n\n\n\n2 Item-Based CF 预测电影评分\n\n加载ratings.csv，转换为用户-电影评分矩阵并计算用户之间相似度\nimport os\n\nimport pandas as pd\nimport numpy as np\n\nDATA_PATH = \"./datasets/ml-latest-small/ratings.csv\"\n\ndtype = {\"userId\": np.int32, \"movieId\": np.int32, \"rating\": np.float32}\n# 加载数据，我们只用前三列数据，分别是用户ID，电影ID，已经用户对电影的对应评分\nratings = pd.read_csv(data_path, dtype=dtype, usecols=range(3))\n# 透视表，将电影ID转换为列名称，转换成为一个User-Movie的评分矩阵\nratings_matrix = ratings.pivot_table(index=[\"userId\"], columns=[\"movieId\"],values=\"rating\")\n#计算用户之间相似度\nitem_similar = ratings_matrix.corr()\n\n\n预测用户对物品的评分 （以用户1对电影1评分为例）\n评分公式\n\r\n  pred(u,i)=\\hat{r}_{ui}=\\cfrac{\\sum_{v\\in U}sim(u,v)*r_{vi}}{\\sum_{v\\in U}|sim(u,v)|}\r\n  \n# 1. 找出iid物品的相似物品\nsimilar_items = item_similar[1].drop([1]).dropna()\n# 相似物品筛选规则：正相关的物品\nsimilar_items = similar_items.where(similar_items>0).dropna()\n# 2. 从iid物品的近邻相似物品中筛选出uid用户评分过的物品\nids = set(ratings_matrix.ix[1].dropna().index)&set(similar_items.index)\nfinally_similar_items = similar_items.ix[list(ids)]\n\n# 3. 结合iid物品与其相似物品的相似度和uid用户对其相似物品的评分，预测uid对iid的评分\nnumerator = 0    # 评分预测公式的分子部分的值\ndenominator = 0    # 评分预测公式的分母部分的值\nfor sim_iid, similarity in finally_similar_items.iteritems():\n    # 近邻物品的评分数据\n    sim_item_rated_movies = ratings_matrix[sim_iid].dropna()\n    # 1用户对相似物品物品的评分\n    sim_item_rating_from_user = sim_item_rated_movies[1]\n    # 计算分子的值\n    numerator += similarity * sim_item_rating_from_user\n    # 计算分母的值\n    denominator += similarity\n\n# 计算预测的评分值并返回\npredict_rating = sum_up/sum_down\nprint(\"预测出用户对电影的评分：%0.2f\" % (uid, iid, predict_rating))\n\n\n封装成方法 预测任意用户对任意电影的评分\ndef predict(uid, iid, ratings_matrix, user_similar):\n    '''\n    预测给定用户对给定物品的评分值\n    :param uid: 用户ID\n    :param iid: 物品ID\n    :param ratings_matrix: 用户-物品评分矩阵\n    :param user_similar: 用户两两相似度矩阵\n    :return: 预测的评分值\n    '''\n    print(\"开始预测用户对电影的评分...\"%(uid, iid))\n    # 1. 找出uid用户的相似用户\n    similar_users = user_similar[uid].drop([uid]).dropna()\n    # 相似用户筛选规则：正相关的用户\n    similar_users = similar_users.where(similar_users>0).dropna()\n    if similar_users.empty is True:\n        raise Exception(\"用户没有相似的用户\" % uid)\n\n    # 2. 从uid用户的近邻相似用户中筛选出对iid物品有评分记录的近邻用户\n    ids = set(ratings_matrix[iid].dropna().index)&set(similar_users.index)\n    finally_similar_users = similar_users.ix[list(ids)]\n\n    # 3. 结合uid用户与其近邻用户的相似度预测uid用户对iid物品的评分\n    numerator = 0    # 评分预测公式的分子部分的值\n    denominator = 0    # 评分预测公式的分母部分的值\n    for sim_uid, similarity in finally_similar_users.iteritems():\n        # 近邻用户的评分数据\n        sim_user_rated_movies = ratings_matrix.ix[sim_uid].dropna()\n        # 近邻用户对iid物品的评分\n        sim_user_rating_for_item = sim_user_rated_movies[iid]\n        # 计算分子的值\n        numerator += similarity * sim_user_rating_for_item\n        # 计算分母的值\n        denominator += similarity\n\n    # 计算预测的评分值并返回\n    predict_rating = numerator/denominator\n    print(\"预测出用户对电影的评分：%0.2f\" % (uid, iid, predict_rating))\n    return round(predict_rating, 2)\n\n\n为某一用户预测所有电影评分\ndef predict_all(uid, ratings_matrix, item_similar):\n    '''\n    预测全部评分\n    :param uid: 用户id\n    :param ratings_matrix: 用户-物品打分矩阵\n    :param item_similar: 物品两两间的相似度\n    :return: 生成器，逐个返回预测评分\n    '''\n    # 准备要预测的物品的id列表\n    item_ids = ratings_matrix.columns\n    # 逐个预测\n    for iid in item_ids:\n        try:\n            rating = predict(uid, iid, ratings_matrix, item_similar)\n        except Exception as e:\n            print(e)\n        else:\n            yield uid, iid, rating\n\nif __name__ == '__main__':\n    for i in predict_all(1, ratings_matrix, item_similar):\n        pass\n\n\n根据评分为指定用户推荐topN个电影\ndef top_k_rs_result(k):\n    results = predict_all(1, ratings_matrix, item_similar)\n    return sorted(results, key=lambda x: x[2], reverse=True)[:k]\nif __name__ == '__main__':\n    from pprint import pprint\n    result = top_k_rs_result(20)\n    pprint(result)\n\n\n\n3\n"},"day01_推荐系统介绍/07_ 推荐系统评估.html":{"url":"day01_推荐系统介绍/07_ 推荐系统评估.html","title":"1.5_推荐系统评估","keywords":"","body":"1.5 推荐系统评估\n学习目标\n\n了解推荐系统的常用评估指标\n了解推荐系统的评估方法\n\n1 推荐系统的评估指标\n\n好的推荐系统可以实现用户, 服务提供方, 内容提供方的共赢\n\n\n\n评估数据来源显示反馈和隐式反馈\n\n  \n    \n    显式反馈\n    隐式反馈\n  \n  \n  例子 \n  电影/书籍评分 \n 是否喜欢这个推荐 \n  播放/点击 评论 下载 购买 \n  \n  \n     准确性 \n     高 \n     低 \n  \n  \n     数量 \n     少 \n     多 \n  \n  \n     获取成本 \n     高 \n     低 \n  \n\n\n常用评估指标\n• 准确性  • 信任度\n• 满意度  • 实时性\n• 覆盖率  • 鲁棒性\n• 多样性  • 可扩展性\n• 新颖性  • 商业⽬标\n• 惊喜度  • ⽤户留存\n\n准确性 (理论角度) Netflix 美国录像带租赁\n评分预测\nRMSE   MAE\n\n\ntopN推荐\n召回率 精准率\n\n\n\n\n准确性 (业务角度)\n\n\n\n覆盖度\n信息熵 对于推荐越大越好\n覆盖率\n\n\n多样性&新颖性&惊喜性\n多样性：推荐列表中两两物品的不相似性。（相似性如何度量？\n新颖性：未曾关注的类别、作者；推荐结果的平均流⾏度\n惊喜性：历史不相似（惊）但很满意（喜）\n往往需要牺牲准确性\n使⽤历史⾏为预测⽤户对某个物品的喜爱程度\n系统过度强调实时性\n\n\nExploitation & Exploration 探索与利用问题\nExploitation(开发 利用)：选择现在可能最佳的⽅案\nExploration(探测 搜索)：选择现在不确定的⼀些⽅案，但未来可能会有⾼收益的⽅案\n在做两类决策的过程中，不断更新对所有决策的不确定性的认知，优化\n长期的⽬标\n\n\nEE问题实践\n兴趣扩展: 相似话题, 搭配推荐\n人群算法: userCF 用户聚类\n平衡个性化推荐和热门推荐比例\n随机丢弃用户行为历史\n随机扰动模型参数\n\n\nEE可能带来的问题\n探索伤害用户体验, 可能导致用户流失\n探索带来的长期收益(留存率)评估周期长, KPI压力大\n如何平衡实时兴趣和长期兴趣\n如何平衡短期产品体验和长期系统生态\n如何平衡大众口味和小众需求\n\n\n\n\n\n2 推荐系统评估方法\n\n评估方法\n问卷调查: 成本高\n离线评估:\n只能在用户看到过的候选集上做评估, 且跟线上真实效果存在偏差\n只能评估少数指标\n速度快, 不损害用户体验\n\n\n在线评估: 灰度发布 & A/B测试 50% 全量上线\n实践: 离线评估和在线评估结合, 定期做问卷调查\n\n\n\n"},"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html":{"url":"day01_推荐系统介绍/08_ 推荐系统冷启动问题.html","title":"1.6_推荐系统的冷启动问题","keywords":"","body":"1.6 推荐系统的冷启动问题\n学习目标\n\n记忆推荐系统冷启动概念\n了解处理推荐系统冷启动的常用方法\n\n1 推荐系统冷启动概念\n\n⽤户冷启动：如何为新⽤户做个性化推荐\n物品冷启动：如何将新物品推荐给⽤户（协同过滤）\n系统冷启动：⽤户冷启动+物品冷启动\n本质是推荐系统依赖历史数据，没有历史数据⽆法预测⽤户偏好\n\n2 处理推荐系统冷启动问题的常用方法\n\n用户冷启动\n\n1.收集⽤户特征\n\n⽤户注册信息：性别、年龄、地域\n\n设备信息：定位、⼿机型号、app列表\n\n社交信息、推⼴素材、安装来源\n\n\n\n\n2 引导用户填写兴趣\n\n\n3 使用其它站点的行为数据, 例如腾讯视频&QQ音乐 今日头条&抖音\n\n4 新老用户推荐策略的差异\n\n新⽤户在冷启动阶段更倾向于热门排⾏榜，⽼⽤户会更加需要长尾推荐\nExplore Exploit⼒度\n使⽤单独的特征和模型预估\n\n\n举例 性别与电视剧的关系\n\n\n\n\n\n物品冷启动\n\n给物品打标签\n利用物品的内容信息，将新物品先投放给曾经喜欢过和它内容相似的其他物品的用户。\n\n\n\n系统冷启动\n\n基于内容的推荐 系统早期\n基于内容的推荐逐渐过渡到协同过滤\n基于内容的推荐和协同过滤的推荐结果都计算出来 加权求和得到最终推荐结果\n\n\n\n"},"day02_推荐算法/01_基于模型的协同过滤推荐.html":{"url":"day02_推荐算法/01_基于模型的协同过滤推荐.html","title":"2.1_基于模型的协同过滤推荐","keywords":"","body":"Model-Based 协同过滤算法\n随着机器学习技术的逐渐发展与完善，推荐系统也逐渐运用机器学习的思想来进行推荐。将机器学习应用到推荐系统中的方案真是不胜枚举。以下对Model-Based CF算法做一个大致的分类：\n\n基于分类算法、回归算法、聚类算法\n基于矩阵分解的推荐\n基于神经网络算法\n基于图模型算法\n\n接下来我们重点学习以下几种应用较多的方案：\n\n基于回归模型的协同过滤推荐\n基于矩阵分解的协同过滤推荐\n\n"},"day02_推荐算法/03_基于回归模型的协同过滤推荐.html":{"url":"day02_推荐算法/03_基于回归模型的协同过滤推荐.html","title":"2.2_基于回归模型的协同过滤推荐","keywords":"","body":"基于回归模型的协同过滤推荐\n如果我们将评分看作是一个连续的值而不是离散的值，那么就可以借助线性回归思想来预测目标用户对某物品的评分。其中一种实现策略被称为Baseline（基准预测）。\nBaseline：基准预测\nBaseline设计思想基于以下的假设：\n\n有些用户的评分普遍高于其他用户，有些用户的评分普遍低于其他用户。比如有些用户天生愿意给别人好评，心慈手软，比较好说话，而有的人就比较苛刻，总是评分不超过3分（5分满分）\n一些物品的评分普遍高于其他物品，一些物品的评分普遍低于其他物品。比如一些物品一被生产便决定了它的地位，有的比较受人们欢迎，有的则被人嫌弃。\n\n这个用户或物品普遍高于或低于平均值的差值，我们称为偏置(bias)\nBaseline目标：\n\n找出每个用户普遍高于或低于他人的偏置值b_u\n找出每件物品普遍高于或低于其他物品的偏置值b_i​\n我们的目标也就转化为寻找最优的b_u和 b_i\n\n使用Baseline的算法思想预测评分的步骤如下：\n\n计算所有电影的平均评分\\mu​（即全局平均评分）\n\n计算每个用户评分与平均评分\\mu的偏置值b_u​\n\n计算每部电影所接受的评分与平均评分\\mu的偏置值b_i\n\n预测用户对电影的评分：\n\r\n  \\hat{r}_{ui} = b_{ui} = \\mu + b_u + b_i\r\n  \n\n举例：通过Baseline来预测用户A对电影“阿甘正传”的评分\n\n首先计算出整个评分数据集的平均评分\\mu​是3.5分\n用户A比较苛刻，普遍比平均评分低0.5分，即用户A的偏置值b_i​是-0.5；\n“阿甘正传”比较热门且备受好评，评分普遍比平均评分要高1.2分，“阿甘正传”的偏置是+1.2\n因此就可以预测出用户A对电影“阿甘正传”的评分为：3.5+(-0.5)+1.2​，也就是4.2分。\n\n\n\n对于所有电影的平均评分是直接能计算出的，因此问题在于要测出每个用户的评分偏置和每部电影的得分偏置。对于线性回归问题，我们可以利用平方差构建损失函数如下：\n\r\n\\begin{split}\r\nCost &= \\sum_{u,i\\in R}(r_{ui}-\\hat{r}_{ui})^2\r\n\\\\&=\\sum_{u,i\\in R}(r_{ui}-\\mu-b_u-b_i)^2\r\n\\end{split}\r\n\n\n加入L2正则化：\n\r\nCost=\\sum_{u,i\\in R}(r_{ui}-\\mu-b_u-b_i)^2 + \\lambda*(\\sum_u {b_u}^2 + \\sum_i {b_i}^2)\r\n\n公式解析：\n\n公式第一部分 \\sum_{u,i\\in R}(r_{ui}-\\mu-b_u-b_i)^2是用来寻找与已知评分数据拟合最好的b_u和b_i​\n公式第二部分\\lambda*(\\sum_u {b_u}^2 + \\sum_i {b_i}^2)​是正则化项，用于避免过拟合现象\n\n对于最小过程的求解，我们一般采用随机梯度下降法或者交替最小二乘法来优化实现。\n方法一：随机梯度下降法优化\n使用随机梯度下降优化算法预测Baseline偏置值\nstep 1：梯度下降法推导\n损失函数： （ λ 为正则化系数）\n\r\n\\begin{split}\r\n&J(\\theta)=Cost=f(b_u, b_i)\\\\\r\n\\\\\r\n&J(\\theta)=\\sum_{u,i\\in R}(r_{ui}-\\mu-b_u-b_i)^2 + \\lambda*(\\sum_u {b_u}^2 + \\sum_i {b_i}^2)\r\n\\end{split}\r\n\n梯度下降参数更新原始公式：（公式中α为学习率）\n\r\n\\theta_j:=\\theta_j-\\alpha\\cfrac{\\partial }{\\partial \\theta_j}J(\\theta)\r\n\n梯度下降更新b_u:\n​    损失函数偏导推导：\n\r\n\\begin{split}\r\n\\cfrac{\\partial}{\\partial b_u} J(\\theta)&=\\cfrac{\\partial}{\\partial b_u} f(b_u, b_i)\r\n\\\\&=2\\sum_{u,i\\in R}(r_{ui}-\\mu-b_u-b_i)(-1) + 2\\lambda{b_u}\r\n\\\\&=-2\\sum_{u,i\\in R}(r_{ui}-\\mu-b_u-b_i) + 2\\lambda*b_u\r\n\\end{split}\r\n\n​    b_u​更新(因为alpha可以人为控制，所以2可以省略掉)：\n\r\n\\begin{split}\r\nb_u&:=b_u - \\alpha*(-\\sum_{u,i\\in R}(r_{ui}-\\mu-b_u-b_i) + \\lambda * b_u)\\\\\r\n&:=b_u + \\alpha*(\\sum_{u,i\\in R}(r_{ui}-\\mu-b_u-b_i) - \\lambda* b_u)\r\n\\end{split}\r\n\n同理可得，梯度下降更新b_i​:\n\r\nb_i:=b_i + \\alpha*(\\sum_{u,i\\in R}(r_{ui}-\\mu-b_u-b_i) -\\lambda*b_i)\r\n\nstep 2：随机梯度下降\n由于随机梯度下降法本质上利用每个样本的损失来更新参数，而不用每次求出全部的损失和，因此使用SGD时：\n单样本损失值：\n\r\n\\begin{split}\r\nerror &=r_{ui}-\\hat{r}_{ui}\r\n\\\\&= r_{ui}-(\\mu+b_u+b_i)\r\n\\\\&= r_{ui}-\\mu-b_u-b_i\r\n\\end{split}\r\n\n参数更新：\n\r\n\\begin{split}\r\nb_u&:=b_u + \\alpha*((r_{ui}-\\mu-b_u-b_i) -\\lambda*b_u)  \\\\\r\n&:=b_u + \\alpha*(error - \\lambda*b_u) \\\\\r\n\\\\\r\nb_i&:=b_i + \\alpha*((r_{ui}-\\mu-b_u-b_i) -\\lambda*b_i)\\\\\r\n&:=b_i + \\alpha*(error -\\lambda*b_i)\r\n\\end{split}\r\n\nstep 3：算法实现\n\ntips pandas 版本不要过低 pandas  0.24.2\n\n数据加载\n\n\nimport pandas as pd\nimport numpy as np\ndtype = [(\"userId\", np.int32), (\"movieId\", np.int32), (\"rating\", np.float32)]\ndataset = pd.read_csv(\"ml-latest-small/ratings.csv\", usecols=range(3), dtype=dict(dtype))\n\n\n数据初始化\ntips 更多关于groupby的 API 详见 http://pandas.pydata.org/pandas-docs/stable/reference/groupby.html\n\n\n# 用户评分数据  groupby 分组  groupby('userId') 根据用户id分组 agg（aggregation聚合）\nusers_ratings = dataset.groupby('userId').agg([list])\n# 物品评分数据\nitems_ratings = dataset.groupby('movieId').agg([list])\n# 计算全局平均分\nglobal_mean = dataset['rating'].mean()\n# 初始化bu bi\nbu = dict(zip(users_ratings.index, np.zeros(len(users_ratings))))\nbi = dict(zip(items_ratings.index, np.zeros(len(items_ratings))))\n\n\n关于zip\n\nzip() 函数用于将可迭代的对象作为参数，将对象中对应的元素打包成一个个元组，然后返回由这些元组组成的对象，这样做的好处是节约了不少的内存。\n我们可以使用 list() 转换来输出列表。\n如果各个迭代器的元素个数不一致，则返回列表长度与最短的对象相同，利用 * 号操作符，可以将元组解压为列表。\n\n语法 zip([iterable, ...])\n\n示例：\n\n\na = [1,2,3]\nb = [4,5,6]\nc = [4,5,6,7,8]\nzipped = zip(a,b)     # 返回一个对象\n>>> zipped\n\n>>> list(zipped)  # list() 转换为列表\n[(1, 4), (2, 5), (3, 6)]\n>>> list(zip(a,c))              # 元素个数与最短的列表一致\n[(1, 4), (2, 5), (3, 6)]\n\na1, a2 = zip(*zip(a,b))          # 与 zip 相反，zip(*) 可理解为解压，返回二维矩阵式\n>>> list(a1)\n[1, 2, 3]\n>>> list(a2)\n[4, 5, 6]\n\n\n\n\n更新bu bi\n\n#number_epochs 迭代次数 alpha学习率  reg 正则化系数\nfor i in range(number_epochs):\n    print(\"iter%d\" % i)\n    for uid, iid, real_rating in dataset.itertuples(index=False):\n        error = real_rating - (global_mean + bu[uid] + bi[iid])\n        bu[uid] += alpha * (error - reg * bu[uid])\n        bi[iid] += alpha * (error - reg * bi[iid])\n\n\n预测评分\n\ndef predict(uid, iid):\n    predict_rating = global_mean + bu[uid] + bi[iid]\n    return predict_rating\n\n\n整体封装\n\nimport pandas as pd\nimport numpy as np\n\n\nclass BaselineCFBySGD(object):\n\n    def __init__(self, number_epochs, alpha, reg, columns=[\"uid\", \"iid\", \"rating\"]):\n        # 梯度下降最高迭代次数\n        self.number_epochs = number_epochs\n        # 学习率\n        self.alpha = alpha\n        # 正则参数\n        self.reg = reg\n        # 数据集中user-item-rating字段的名称\n        self.columns = columns\n\n    def fit(self, dataset):\n        '''\n        :param dataset: uid, iid, rating\n        :return:\n        '''\n        self.dataset = dataset\n        # 用户评分数据\n        self.users_ratings = dataset.groupby(self.columns[0]).agg([list])[[self.columns[1], self.columns[2]]]\n        # 物品评分数据\n        self.items_ratings = dataset.groupby(self.columns[1]).agg([list])[[self.columns[0], self.columns[2]]]\n        # 计算全局平均分\n        self.global_mean = self.dataset[self.columns[2]].mean()\n        # 调用sgd方法训练模型参数\n        self.bu, self.bi = self.sgd()\n\n    def sgd(self):\n        '''\n        利用随机梯度下降，优化bu，bi的值\n        :return: bu, bi\n        '''\n        # 初始化bu、bi的值，全部设为0\n        bu = dict(zip(self.users_ratings.index, np.zeros(len(self.users_ratings))))\n        bi = dict(zip(self.items_ratings.index, np.zeros(len(self.items_ratings))))\n\n        for i in range(self.number_epochs):\n            print(\"iter%d\" % i)\n            for uid, iid, real_rating in self.dataset.itertuples(index=False):\n                error = real_rating - (self.global_mean + bu[uid] + bi[iid])\n\n                bu[uid] += self.alpha * (error - self.reg * bu[uid])\n                bi[iid] += self.alpha * (error - self.reg * bi[iid])\n\n        return bu, bi\n\n    def predict(self, uid, iid):\n        predict_rating = self.global_mean + self.bu[uid] + self.bi[iid]\n        return predict_rating\n\n\nif __name__ == '__main__':\n    dtype = [(\"userId\", np.int32), (\"movieId\", np.int32), (\"rating\", np.float32)]\n    dataset = pd.read_csv(\"datasets/ml-latest-small/ratings.csv\", usecols=range(3), dtype=dict(dtype))\n\n    bcf = BaselineCFBySGD(20, 0.1, 0.1, [\"userId\", \"movieId\", \"rating\"])\n    bcf.fit(dataset)\n\n    while True:\n        uid = int(input(\"uid: \"))\n        iid = int(input(\"iid: \"))\n        print(bcf.predict(uid, iid))\n\nStep 4: 准确性指标评估\n\n添加test方法，然后使用之前实现accuary方法计算准确性指标\n\nimport pandas as pd\nimport numpy as np\n\ndef data_split(data_path, x=0.8, random=False):\n    '''\n    切分数据集， 这里为了保证用户数量保持不变，将每个用户的评分数据按比例进行拆分\n    :param data_path: 数据集路径\n    :param x: 训练集的比例，如x=0.8，则0.2是测试集\n    :param random: 是否随机切分，默认False\n    :return: 用户-物品评分矩阵\n    '''\n    print(\"开始切分数据集...\")\n    # 设置要加载的数据字段的类型\n    dtype = {\"userId\": np.int32, \"movieId\": np.int32, \"rating\": np.float32}\n    # 加载数据，我们只用前三列数据，分别是用户ID，电影ID，已经用户对电影的对应评分\n    ratings = pd.read_csv(data_path, dtype=dtype, usecols=range(3))\n\n    testset_index = []\n    # 为了保证每个用户在测试集和训练集都有数据，因此按userId聚合\n    for uid in ratings.groupby(\"userId\").any().index:\n        user_rating_data = ratings.where(ratings[\"userId\"]==uid).dropna()\n        if random:\n            # 因为不可变类型不能被 shuffle方法作用，所以需要强行转换为列表\n            index = list(user_rating_data.index)\n            np.random.shuffle(index)    # 打乱列表\n            _index = round(len(user_rating_data) * x)\n            testset_index += list(index[_index:])\n        else:\n            # 将每个用户的x比例的数据作为训练集，剩余的作为测试集\n            index = round(len(user_rating_data) * x)\n            testset_index += list(user_rating_data.index.values[index:])\n\n    testset = ratings.loc[testset_index]\n    trainset = ratings.drop(testset_index)\n    print(\"完成数据集切分...\")\n    return trainset, testset\n\ndef accuray(predict_results, method=\"all\"):\n    '''\n    准确性指标计算方法\n    :param predict_results: 预测结果，类型为容器，每个元素是一个包含uid,iid,real_rating,pred_rating的序列\n    :param method: 指标方法，类型为字符串，rmse或mae，否则返回两者rmse和mae\n    :return:\n    '''\n\n    def rmse(predict_results):\n        '''\n        rmse评估指标\n        :param predict_results:\n        :return: rmse\n        '''\n        length = 0\n        _rmse_sum = 0\n        for uid, iid, real_rating, pred_rating in predict_results:\n            length += 1\n            _rmse_sum += (pred_rating - real_rating) ** 2\n        return round(np.sqrt(_rmse_sum / length), 4)\n\n    def mae(predict_results):\n        '''\n        mae评估指标\n        :param predict_results:\n        :return: mae\n        '''\n        length = 0\n        _mae_sum = 0\n        for uid, iid, real_rating, pred_rating in predict_results:\n            length += 1\n            _mae_sum += abs(pred_rating - real_rating)\n        return round(_mae_sum / length, 4)\n\n    def rmse_mae(predict_results):\n        '''\n        rmse和mae评估指标\n        :param predict_results:\n        :return: rmse, mae\n        '''\n        length = 0\n        _rmse_sum = 0\n        _mae_sum = 0\n        for uid, iid, real_rating, pred_rating in predict_results:\n            length += 1\n            _rmse_sum += (pred_rating - real_rating) ** 2\n            _mae_sum += abs(pred_rating - real_rating)\n        return round(np.sqrt(_rmse_sum / length), 4), round(_mae_sum / length, 4)\n\n    if method.lower() == \"rmse\":\n        rmse(predict_results)\n    elif method.lower() == \"mae\":\n        mae(predict_results)\n    else:\n        return rmse_mae(predict_results)\n\nclass BaselineCFBySGD(object):\n\n    def __init__(self, number_epochs, alpha, reg, columns=[\"uid\", \"iid\", \"rating\"]):\n        # 梯度下降最高迭代次数\n        self.number_epochs = number_epochs\n        # 学习率\n        self.alpha = alpha\n        # 正则参数\n        self.reg = reg\n        # 数据集中user-item-rating字段的名称\n        self.columns = columns\n\n    def fit(self, dataset):\n        '''\n        :param dataset: uid, iid, rating\n        :return:\n        '''\n        self.dataset = dataset\n        # 用户评分数据\n        self.users_ratings = dataset.groupby(self.columns[0]).agg([list])[[self.columns[1], self.columns[2]]]\n        # 物品评分数据\n        self.items_ratings = dataset.groupby(self.columns[1]).agg([list])[[self.columns[0], self.columns[2]]]\n        # 计算全局平均分\n        self.global_mean = self.dataset[self.columns[2]].mean()\n        # 调用sgd方法训练模型参数\n        self.bu, self.bi = self.sgd()\n\n    def sgd(self):\n        '''\n        利用随机梯度下降，优化bu，bi的值\n        :return: bu, bi\n        '''\n        # 初始化bu、bi的值，全部设为0\n        bu = dict(zip(self.users_ratings.index, np.zeros(len(self.users_ratings))))\n        bi = dict(zip(self.items_ratings.index, np.zeros(len(self.items_ratings))))\n\n        for i in range(self.number_epochs):\n            print(\"iter%d\" % i)\n            for uid, iid, real_rating in self.dataset.itertuples(index=False):\n                error = real_rating - (self.global_mean + bu[uid] + bi[iid])\n\n                bu[uid] += self.alpha * (error - self.reg * bu[uid])\n                bi[iid] += self.alpha * (error - self.reg * bi[iid])\n\n        return bu, bi\n\n    def predict(self, uid, iid):\n        '''评分预测'''\n        if iid not in self.items_ratings.index:\n            raise Exception(\"无法预测用户对电影的评分，因为训练集中缺失的数据\".format(uid=uid, iid=iid))\n\n        predict_rating = self.global_mean + self.bu[uid] + self.bi[iid]\n        return predict_rating\n\n    def test(self,testset):\n        '''预测测试集数据'''\n        for uid, iid, real_rating in testset.itertuples(index=False):\n            try:\n                pred_rating = self.predict(uid, iid)\n            except Exception as e:\n                print(e)\n            else:\n                yield uid, iid, real_rating, pred_rating\n\nif __name__ == '__main__':\n\n    trainset, testset = data_split(\"datasets/ml-latest-small/ratings.csv\", random=True)\n\n    bcf = BaselineCFBySGD(20, 0.1, 0.1, [\"userId\", \"movieId\", \"rating\"])\n    bcf.fit(trainset)\n\n    pred_results = bcf.test(testset)\n\n    rmse, mae = accuray(pred_results)\n\n    print(\"rmse: \", rmse, \"mae: \", mae)\n\n方法二：交替最小二乘法优化\n使用交替最小二乘法优化算法预测Baseline偏置值\nstep 1: 交替最小二乘法推导\n最小二乘法和梯度下降法一样，可以用于求极值。\n最小二乘法思想：对损失函数求偏导，然后再使偏导为0\n同样，损失函数：\n\r\nJ(\\theta)=\\sum_{u,i\\in R}(r_{ui}-\\mu-b_u-b_i)^2 + \\lambda*(\\sum_u {b_u}^2 + \\sum_i {b_i}^2)\r\n\n经过交替最小二乘\n\r\nb_u := \\cfrac {\\sum_{u,i\\in R}(r_{ui}-\\mu-b_i)}{\\lambda_1 + |R(u)|}\r\n\n其中|R(u)|表示用户u的有过评分数量\n同理可得：\n\r\nb_i := \\cfrac {\\sum_{u,i\\in R}(r_{ui}-\\mu-b_u)}{\\lambda_2 + |R(i)|}\r\n\n其中|R(i)|表示物品i​收到的评分数量\nb_u和b_i​分别属于用户和物品的偏置，因此他们的正则参数可以分别设置两个独立的参数\nstep 2: 交替最小二乘法应用\n通过最小二乘推导，我们最终分别得到了b_u和b_i​的表达式，但他们的表达式中却又各自包含对方，因此这里我们将利用一种叫交替最小二乘的方法来计算他们的值：    \n\n计算其中一项，先固定其他未知参数，即看作其他未知参数为已知\n如求b_u时，将b_i看作是已知；求b_i时，将b_u​看作是已知；如此反复交替，不断更新二者的值，求得最终的结果。这就是交替最小二乘法（ALS）\n\nstep 3: 算法实现\n\n数据加载初始化与之前完全相同\n迭代更新bu bi\n\nfor i in range(number_epochs):\n    print(\"iter%d\" % i)\n    for iid, uids, ratings in items_ratings.itertuples(index=True):\n        _sum = 0\n        for uid, rating in zip(uids, ratings):\n            _sum += rating - global_mean - bu[uid]\n        bi[iid] = _sum / (reg_bi + len(uids))\n\n    for uid, iids, ratings in users_ratings.itertuples(index=True):\n        _sum = 0\n        for iid, rating in zip(iids, ratings):\n            _sum += rating - global_mean - bi[iid]\n        bu[uid] = _sum / (reg_bu + len(iids))\n\nimport pandas as pd\nimport numpy as np\n\n\nclass BaselineCFByALS(object):\n\n    def __init__(self, number_epochs, reg_bu, reg_bi, columns=[\"uid\", \"iid\", \"rating\"]):\n        # 梯度下降最高迭代次数\n        self.number_epochs = number_epochs\n        # bu的正则参数\n        self.reg_bu = reg_bu\n        # bi的正则参数\n        self.reg_bi = reg_bi\n        # 数据集中user-item-rating字段的名称\n        self.columns = columns\n\n    def fit(self, dataset):\n        '''\n        :param dataset: uid, iid, rating\n        :return:\n        '''\n        self.dataset = dataset\n        # 用户评分数据\n        self.users_ratings = dataset.groupby(self.columns[0]).agg([list])[[self.columns[1], self.columns[2]]]\n        # 物品评分数据\n        self.items_ratings = dataset.groupby(self.columns[1]).agg([list])[[self.columns[0], self.columns[2]]]\n        # 计算全局平均分\n        self.global_mean = self.dataset[self.columns[2]].mean()\n        # 调用sgd方法训练模型参数\n        self.bu, self.bi = self.als()\n\n    def als(self):\n        '''\n        利用随机梯度下降，优化bu，bi的值\n        :return: bu, bi\n        '''\n        # 初始化bu、bi的值，全部设为0\n        bu = dict(zip(self.users_ratings.index, np.zeros(len(self.users_ratings))))\n        bi = dict(zip(self.items_ratings.index, np.zeros(len(self.items_ratings))))\n\n        for i in range(self.number_epochs):\n            print(\"iter%d\" % i)\n            for iid, uids, ratings in self.items_ratings.itertuples(index=True):\n                _sum = 0\n                for uid, rating in zip(uids, ratings):\n                    _sum += rating - self.global_mean - bu[uid]\n                bi[iid] = _sum / (self.reg_bi + len(uids))\n\n            for uid, iids, ratings in self.users_ratings.itertuples(index=True):\n                _sum = 0\n                for iid, rating in zip(iids, ratings):\n                    _sum += rating - self.global_mean - bi[iid]\n                bu[uid] = _sum / (self.reg_bu + len(iids))\n        return bu, bi\n\n    def predict(self, uid, iid):\n        predict_rating = self.global_mean + self.bu[uid] + self.bi[iid]\n        return predict_rating\n\n\nif __name__ == '__main__':\n    dtype = [(\"userId\", np.int32), (\"movieId\", np.int32), (\"rating\", np.float32)]\n    dataset = pd.read_csv(\"datasets/ml-latest-small/ratings.csv\", usecols=range(3), dtype=dict(dtype))\n\n    bcf = BaselineCFByALS(20, 25, 15, [\"userId\", \"movieId\", \"rating\"])\n    bcf.fit(dataset)\n\n    while True:\n        uid = int(input(\"uid: \"))\n        iid = int(input(\"iid: \"))\n        print(bcf.predict(uid, iid))\n\nStep 4: 准确性指标评估\nimport pandas as pd\nimport numpy as np\n\ndef data_split(data_path, x=0.8, random=False):\n    '''\n    切分数据集， 这里为了保证用户数量保持不变，将每个用户的评分数据按比例进行拆分\n    :param data_path: 数据集路径\n    :param x: 训练集的比例，如x=0.8，则0.2是测试集\n    :param random: 是否随机切分，默认False\n    :return: 用户-物品评分矩阵\n    '''\n    print(\"开始切分数据集...\")\n    # 设置要加载的数据字段的类型\n    dtype = {\"userId\": np.int32, \"movieId\": np.int32, \"rating\": np.float32}\n    # 加载数据，我们只用前三列数据，分别是用户ID，电影ID，已经用户对电影的对应评分\n    ratings = pd.read_csv(data_path, dtype=dtype, usecols=range(3))\n\n    testset_index = []\n    # 为了保证每个用户在测试集和训练集都有数据，因此按userId聚合\n    for uid in ratings.groupby(\"userId\").any().index:\n        user_rating_data = ratings.where(ratings[\"userId\"]==uid).dropna()\n        if random:\n            # 因为不可变类型不能被 shuffle方法作用，所以需要强行转换为列表\n            index = list(user_rating_data.index)\n            np.random.shuffle(index)    # 打乱列表\n            _index = round(len(user_rating_data) * x)\n            testset_index += list(index[_index:])\n        else:\n            # 将每个用户的x比例的数据作为训练集，剩余的作为测试集\n            index = round(len(user_rating_data) * x)\n            testset_index += list(user_rating_data.index.values[index:])\n\n    testset = ratings.loc[testset_index]\n    trainset = ratings.drop(testset_index)\n    print(\"完成数据集切分...\")\n    return trainset, testset\n\ndef accuray(predict_results, method=\"all\"):\n    '''\n    准确性指标计算方法\n    :param predict_results: 预测结果，类型为容器，每个元素是一个包含uid,iid,real_rating,pred_rating的序列\n    :param method: 指标方法，类型为字符串，rmse或mae，否则返回两者rmse和mae\n    :return:\n    '''\n\n    def rmse(predict_results):\n        '''\n        rmse评估指标\n        :param predict_results:\n        :return: rmse\n        '''\n        length = 0\n        _rmse_sum = 0\n        for uid, iid, real_rating, pred_rating in predict_results:\n            length += 1\n            _rmse_sum += (pred_rating - real_rating) ** 2\n        return round(np.sqrt(_rmse_sum / length), 4)\n\n    def mae(predict_results):\n        '''\n        mae评估指标\n        :param predict_results:\n        :return: mae\n        '''\n        length = 0\n        _mae_sum = 0\n        for uid, iid, real_rating, pred_rating in predict_results:\n            length += 1\n            _mae_sum += abs(pred_rating - real_rating)\n        return round(_mae_sum / length, 4)\n\n    def rmse_mae(predict_results):\n        '''\n        rmse和mae评估指标\n        :param predict_results:\n        :return: rmse, mae\n        '''\n        length = 0\n        _rmse_sum = 0\n        _mae_sum = 0\n        for uid, iid, real_rating, pred_rating in predict_results:\n            length += 1\n            _rmse_sum += (pred_rating - real_rating) ** 2\n            _mae_sum += abs(pred_rating - real_rating)\n        return round(np.sqrt(_rmse_sum / length), 4), round(_mae_sum / length, 4)\n\n    if method.lower() == \"rmse\":\n        rmse(predict_results)\n    elif method.lower() == \"mae\":\n        mae(predict_results)\n    else:\n        return rmse_mae(predict_results)\n\nclass BaselineCFByALS(object):\n\n    def __init__(self, number_epochs, reg_bu, reg_bi, columns=[\"uid\", \"iid\", \"rating\"]):\n        # 梯度下降最高迭代次数\n        self.number_epochs = number_epochs\n        # bu的正则参数\n        self.reg_bu = reg_bu\n        # bi的正则参数\n        self.reg_bi = reg_bi\n        # 数据集中user-item-rating字段的名称\n        self.columns = columns\n\n    def fit(self, dataset):\n        '''\n        :param dataset: uid, iid, rating\n        :return:\n        '''\n        self.dataset = dataset\n        # 用户评分数据\n        self.users_ratings = dataset.groupby(self.columns[0]).agg([list])[[self.columns[1], self.columns[2]]]\n        # 物品评分数据\n        self.items_ratings = dataset.groupby(self.columns[1]).agg([list])[[self.columns[0], self.columns[2]]]\n        # 计算全局平均分\n        self.global_mean = self.dataset[self.columns[2]].mean()\n        # 调用sgd方法训练模型参数\n        self.bu, self.bi = self.als()\n\n    def als(self):\n        '''\n        利用随机梯度下降，优化bu，bi的值\n        :return: bu, bi\n        '''\n        # 初始化bu、bi的值，全部设为0\n        bu = dict(zip(self.users_ratings.index, np.zeros(len(self.users_ratings))))\n        bi = dict(zip(self.items_ratings.index, np.zeros(len(self.items_ratings))))\n\n        for i in range(self.number_epochs):\n            print(\"iter%d\" % i)\n            for iid, uids, ratings in self.items_ratings.itertuples(index=True):\n                _sum = 0\n                for uid, rating in zip(uids, ratings):\n                    _sum += rating - self.global_mean - bu[uid]\n                bi[iid] = _sum / (self.reg_bi + len(uids))\n\n            for uid, iids, ratings in self.users_ratings.itertuples(index=True):\n                _sum = 0\n                for iid, rating in zip(iids, ratings):\n                    _sum += rating - self.global_mean - bi[iid]\n                bu[uid] = _sum / (self.reg_bu + len(iids))\n        return bu, bi\n\n    def predict(self, uid, iid):\n        '''评分预测'''\n        if iid not in self.items_ratings.index:\n            raise Exception(\"无法预测用户对电影的评分，因为训练集中缺失的数据\".format(uid=uid, iid=iid))\n\n        predict_rating = self.global_mean + self.bu[uid] + self.bi[iid]\n        return predict_rating\n\n    def test(self,testset):\n        '''预测测试集数据'''\n        for uid, iid, real_rating in testset.itertuples(index=False):\n            try:\n                pred_rating = self.predict(uid, iid)\n            except Exception as e:\n                print(e)\n            else:\n                yield uid, iid, real_rating, pred_rating\n\n\nif __name__ == '__main__':\n    trainset, testset = data_split(\"datasets/ml-latest-small/ratings.csv\", random=True)\n\n    bcf = BaselineCFByALS(20, 25, 15, [\"userId\", \"movieId\", \"rating\"])\n    bcf.fit(trainset)\n\n    pred_results = bcf.test(testset)\n\n    rmse, mae = accuray(pred_results)\n\n    print(\"rmse: \", rmse, \"mae: \", mae)\n\n函数求导：\n\n\n"},"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html":{"url":"day02_推荐算法/04_基于矩阵分解的协同过滤推荐.html","title":"2.3_基于矩阵分解的协同过滤推荐","keywords":"","body":"基于矩阵分解的CF算法\n矩阵分解发展史\nTraditional SVD:\n通常SVD矩阵分解指的是SVD（奇异值）分解技术，在这我们姑且将其命名为Traditional SVD（传统并经典着）其公式如下：\n\nTraditional SVD分解的形式为3个矩阵相乘，中间矩阵为奇异值矩阵。如果想运用SVD分解的话，有一个前提是要求矩阵是稠密的，即矩阵里的元素要非空，否则就不能运用SVD分解。\n很显然我们的数据其实绝大多数情况下都是稀疏的，因此如果要使用Traditional SVD，一般的做法是先用均值或者其他统计学方法来填充矩阵，然后再运用Traditional SVD分解降维，但这样做明显对数据的原始性造成一定影响。\nFunkSVD（LFM）\n刚才提到的Traditional SVD首先需要填充矩阵，然后再进行分解降维，同时存在计算复杂度高的问题，因为要分解成3个矩阵，所以后来提出了Funk SVD的方法，它不在将矩阵分解为3个矩阵，而是分解为2个用户-隐含特征，项目-隐含特征的矩阵，Funk SVD也被称为最原始的LFM模型\n\n借鉴线性回归的思想，通过最小化观察数据的平方来寻求最优的用户和项目的隐含向量表示。同时为了避免过度拟合（Overfitting）观测数据，又提出了带有L2正则项的FunkSVD，上公式：\n\n以上两种最优化函数都可以通过梯度下降或者随机梯度下降法来寻求最优解。\nBiasSVD:\n在FunkSVD提出来之后，出现了很多变形版本，其中一个相对成功的方法是BiasSVD，顾名思义，即带有偏置项的SVD分解：\n\n它基于的假设和Baseline基准预测是一样的，但这里将Baseline的偏置引入到了矩阵分解中\nSVD++:\n人们后来又提出了改进的BiasSVD，被称为SVD++，该算法是在BiasSVD的基础上添加了用户的隐式反馈信息：\n\n显示反馈指的用户的评分这样的行为，隐式反馈指用户的浏览记录、购买记录、收听记录等。\nSVD++是基于这样的假设：在BiasSVD基础上，认为用户对于项目的历史浏览记录、购买记录、收听记录等可以从侧面反映用户的偏好。\n"},"day02_推荐算法/05_LFM算法实现.html":{"url":"day02_推荐算法/05_LFM算法实现.html","title":"2.4_LFM算法实现","keywords":"","body":"基于矩阵分解的CF算法实现（一）：LFM\nLFM也就是前面提到的Funk SVD矩阵分解\nLFM原理解析\nLFM(latent factor model)隐语义模型核心思想是通过隐含特征联系用户和物品，如下图：\n\n\nP矩阵是User-LF矩阵，即用户和隐含特征矩阵。LF有三个，表示共总有三个隐含特征。\nQ矩阵是LF-Item矩阵，即隐含特征和物品的矩阵\nR矩阵是User-Item矩阵，有P*Q得来\n能处理稀疏评分矩阵\n\n利用矩阵分解技术，将原始User-Item的评分矩阵（稠密/稀疏）分解为P和Q矩阵，然后利用P*Q​还原出User-Item评分矩阵R​。整个过程相当于降维处理，其中：\n\n矩阵值P_{11}​表示用户1对隐含特征1的权重值\n\n矩阵值Q_{11}​表示隐含特征1在物品1上的权重值\n\n矩阵值R_{11}就表示预测的用户1对物品1的评分，且R_{11}=\\vec{P_{1,k}}\\cdot \\vec{Q_{k,1}}\n\n\n\n利用LFM预测用户对物品的评分，$k​$表示隐含特征数量：\n\r\n\\begin{split}\r\n\\hat {r}_{ui} &=\\vec {p_{uk}}\\cdot \\vec {q_{ik}}\r\n\\\\&={\\sum_{k=1}}^k p_{uk}q_{ik}\r\n\\end{split}\r\n\n因此最终，我们的目标也就是要求出P矩阵和Q矩阵及其当中的每一个值，然后再对用户-物品的评分进行预测。\n损失函数\n同样对于评分预测我们利用平方差来构建损失函数：\n\r\n\\begin{split}\r\nCost &= \\sum_{u,i\\in R} (r_{ui}-\\hat{r}_{ui})^2\r\n\\\\&=\\sum_{u,i\\in R} (r_{ui}-{\\sum_{k=1}}^k p_{uk}q_{ik})^2\r\n\\end{split}\r\n\n加入L2正则化：\n\r\nCost = \\sum_{u,i\\in R} (r_{ui}-{\\sum_{k=1}}^k p_{uk}q_{ik})^2 + \\lambda(\\sum_U{p_{uk}}^2+\\sum_I{q_{ik}}^2)\r\n\n随机梯度下降法优化\n梯度下降更新参数p_{uk}和q_{ik}​：（α学习率  λ正则化系数）\n\r\n\\begin{split}\r\np_{uk}&:=p_{uk}+\\alpha [\\sum_{u,i\\in R} (r_{ui}-{\\sum_{k=1}}^k p_{uk}q_{ik})q_{ik} - \\lambda p_{uk}]\r\n\\end{split}\r\n\n\r\n\\begin{split}\r\nq_{ik}&:=q_{ik} + \\alpha[\\sum_{u,i\\in R} (r_{ui}-{\\sum_{k=1}}^k p_{uk}q_{ik})p_{uk} - \\lambda q_{ik}]\r\n\\end{split}\r\n\n随机梯度下降： 向量乘法 每一个分量相乘 求和\n\r\n\\begin{split}\r\n&p_{uk}:=p_{uk}+\\alpha [(r_{ui}-{\\sum_{k=1}}^k p_{uk}q_{ik})q_{ik} - \\lambda_1 p_{uk}]\r\n\\\\&q_{ik}:=q_{ik} + \\alpha[(r_{ui}-{\\sum_{k=1}}^k p_{uk}q_{ik})p_{uk} - \\lambda_2 q_{ik}]\r\n\\end{split}\r\n\n由于P矩阵和Q矩阵是两个不同的矩阵，通常分别采取不同的正则参数，如\\lambda_1和\\lambda_2\n算法实现\n\n数据加载\n\nimport pandas as pd\nimport numpy as np\ndtype = [(\"userId\", np.int32), (\"movieId\", np.int32), (\"rating\", np.float32)]\ndataset = pd.read_csv(\"ml-latest-small/ratings.csv\", usecols=range(3), dtype=dict(dtype))\n\n\n数据初始化\ntips 更多关于groupby的 API 详见 http://pandas.pydata.org/pandas-docs/stable/reference/groupby.html\n\n\n# 用户评分数据  groupby 分组  groupby('userId') 根据用户id分组 agg（aggregation聚合）\nusers_ratings = dataset.groupby('userId').agg([list])\n# 物品评分数据\nitems_ratings = dataset.groupby('movieId').agg([list])\n# 计算全局平均分\nglobal_mean = dataset['rating'].mean()\n# 初始化P Q  610  9700   K值  610*K    9700*K\n# User-LF  10 代表 隐含因子个数是10个\nP = dict(zip(users_ratings.index,np.random.rand(len(users_ratings),10).astype(np.float32)\n        ))\n# Item-LF\nQ = dict(zip(items_ratings.index,np.random.rand(len(items_ratings),10).astype(np.float32)\n        ))\n\n\n梯度下降优化损失函数\n\n#梯度下降优化损失函数\nfor i in range(15):\n    print('*'*10,i)\n    for uid,iid,real_rating in dataset.itertuples(index = False):\n        #遍历 用户 物品的评分数据 通过用户的id 到用户矩阵中获取用户向量\n        v_puk = P[uid]\n        # 通过物品的uid 到物品矩阵里获取物品向量\n        v_qik = Q[iid]\n        #计算损失\n        error = real_rating-np.dot(v_puk,v_qik)\n        # 0.02学习率 0.01正则化系数\n        v_puk += 0.02*(error*v_qik-0.01*v_puk)\n        v_qik += 0.02*(error*v_puk-0.01*v_qik)\n\n        P[uid] = v_puk\n        Q[iid] = v_qik\n\n\n评分预测\n\ndef predict(self, uid, iid):\n    # 如果uid或iid不在，我们使用全剧平均分作为预测结果返回\n    if uid not in self.users_ratings.index or iid not in self.items_ratings.index:\n        return self.globalMean\n    p_u = self.P[uid]\n    q_i = self.Q[iid]\n\n    return np.dot(p_u, q_i)\n\n'''\nLFM Model\n'''\nimport pandas as pd\nimport numpy as np\n\n# 评分预测    1-5\nclass LFM(object):\n\n    def __init__(self, alpha, reg_p, reg_q, number_LatentFactors=10, number_epochs=10, columns=[\"uid\", \"iid\", \"rating\"]):\n        self.alpha = alpha # 学习率\n        self.reg_p = reg_p    # P矩阵正则\n        self.reg_q = reg_q    # Q矩阵正则\n        self.number_LatentFactors = number_LatentFactors  # 隐式类别数量\n        self.number_epochs = number_epochs    # 最大迭代次数\n        self.columns = columns\n\n    def fit(self, dataset):\n        '''\n        fit dataset\n        :param dataset: uid, iid, rating\n        :return:\n        '''\n\n        self.dataset = pd.DataFrame(dataset)\n\n        self.users_ratings = dataset.groupby(self.columns[0]).agg([list])[[self.columns[1], self.columns[2]]]\n        self.items_ratings = dataset.groupby(self.columns[1]).agg([list])[[self.columns[0], self.columns[2]]]\n\n        self.globalMean = self.dataset[self.columns[2]].mean()\n\n        self.P, self.Q = self.sgd()\n\n    def _init_matrix(self):\n        '''\n        初始化P和Q矩阵，同时为设置0，1之间的随机值作为初始值\n        :return:\n        '''\n        # User-LF\n        P = dict(zip(\n            self.users_ratings.index,\n            np.random.rand(len(self.users_ratings), self.number_LatentFactors).astype(np.float32)\n        ))\n        # Item-LF\n        Q = dict(zip(\n            self.items_ratings.index,\n            np.random.rand(len(self.items_ratings), self.number_LatentFactors).astype(np.float32)\n        ))\n        return P, Q\n\n    def sgd(self):\n        '''\n        使用随机梯度下降，优化结果\n        :return:\n        '''\n        P, Q = self._init_matrix()\n\n        for i in range(self.number_epochs):\n            print(\"iter%d\"%i)\n            error_list = []\n            for uid, iid, r_ui in self.dataset.itertuples(index=False):\n                # User-LF P\n                ## Item-LF Q\n                v_pu = P[uid] #用户向量\n                v_qi = Q[iid] #物品向量\n                err = np.float32(r_ui - np.dot(v_pu, v_qi))\n\n                v_pu += self.alpha * (err * v_qi - self.reg_p * v_pu)\n                v_qi += self.alpha * (err * v_pu - self.reg_q * v_qi)\n\n                P[uid] = v_pu \n                Q[iid] = v_qi\n\n                # for k in range(self.number_of_LatentFactors):\n                #     v_pu[k] += self.alpha*(err*v_qi[k] - self.reg_p*v_pu[k])\n                #     v_qi[k] += self.alpha*(err*v_pu[k] - self.reg_q*v_qi[k])\n\n                error_list.append(err ** 2)\n            print(np.sqrt(np.mean(error_list)))\n        return P, Q\n\n    def predict(self, uid, iid):\n        # 如果uid或iid不在，我们使用全剧平均分作为预测结果返回\n        if uid not in self.users_ratings.index or iid not in self.items_ratings.index:\n            return self.globalMean\n\n        p_u = self.P[uid]\n        q_i = self.Q[iid]\n\n        return np.dot(p_u, q_i)\n\n    def test(self,testset):\n        '''预测测试集数据'''\n        for uid, iid, real_rating in testset.itertuples(index=False):\n            try:\n                pred_rating = self.predict(uid, iid)\n            except Exception as e:\n                print(e)\n            else:\n                yield uid, iid, real_rating, pred_rating\n\nif __name__ == '__main__':\n    dtype = [(\"userId\", np.int32), (\"movieId\", np.int32), (\"rating\", np.float32)]\n    dataset = pd.read_csv(\"datasets/ml-latest-small/ratings.csv\", usecols=range(3), dtype=dict(dtype))\n\n    lfm = LFM(0.02, 0.01, 0.01, 10, 100, [\"userId\", \"movieId\", \"rating\"])\n    lfm.fit(dataset)\n\n    while True:\n        uid = input(\"uid: \")\n        iid = input(\"iid: \")\n        print(lfm.predict(int(uid), int(iid)))\n\n"},"day02_推荐算法/06_BiasSVD算法实现.html":{"url":"day02_推荐算法/06_BiasSVD算法实现.html","title":"2.5_BiasSVD算法实现","keywords":"","body":"基于矩阵分解的CF算法实现（二）：BiasSvd\nBiasSvd其实就是前面提到的Funk SVD矩阵分解基础上加上了偏置项。\nBiasSvd\n利用BiasSvd预测用户对物品的评分，k表示隐含特征数量：\n\r\n\\begin{split}\r\n\\hat {r}_{ui} &=\\mu + b_u + b_i + \\vec {p_{uk}}\\cdot \\vec {q_{ki}}\r\n\\\\&=\\mu + b_u + b_i + {\\sum_{k=1}}^k p_{uk}q_{ik}\r\n\\end{split}\r\n\n损失函数\n同样对于评分预测我们利用平方差来构建损失函数：\n\r\n\\begin{split}\r\nCost &= \\sum_{u,i\\in R} (r_{ui}-\\hat{r}_{ui})^2\r\n\\\\&=\\sum_{u,i\\in R} (r_{ui}-\\mu - b_u - b_i -{\\sum_{k=1}}^k p_{uk}q_{ik})^2\r\n\\end{split}\r\n\n加入L2正则化：\n\r\nCost = \\sum_{u,i\\in R} (r_{ui}-\\mu - b_u - b_i-{\\sum_{k=1}}^k p_{uk}q_{ik})^2 \r\n\\\\+ \\lambda(\\sum_U{b_u}^2+\\sum_I{b_i}^2+\\sum_U{p_{uk}}^2+\\sum_I{q_{ik}}^2)\r\n\n随机梯度下降法优化\n梯度下降更新参数p_{uk}：\n\r\n\\begin{split}\r\np_{uk}&:=p_{uk}+\\alpha [\\sum_{u,i\\in R} (r_{ui}-\\mu - b_u - b_i-{\\sum_{k=1}}^k p_{uk}q_{ik})q_{ik} - \\lambda p_{uk}]\r\n\\end{split}\r\n\n同理：\n\r\n\\begin{split}\r\nq_{ik}&:=q_{ik} + \\alpha[\\sum_{u,i\\in R} (r_{ui}-\\mu - b_u - b_i-{\\sum_{k=1}}^k p_{uk}q_{ik})p_{uk} - \\lambda q_{ik}]\r\n\\end{split}\r\n\n\r\nb_u:=b_u + \\alpha[\\sum_{u,i\\in R} (r_{ui}-\\mu - b_u - b_i-{\\sum_{k=1}}^k p_{uk}q_{ik}) - \\lambda b_u]\r\n\n\r\nb_i:=b_i + \\alpha[\\sum_{u,i\\in R} (r_{ui}-\\mu - b_u - b_i-{\\sum_{k=1}}^k p_{uk}q_{ik}) - \\lambda b_i]\r\n\n随机梯度下降：\n\r\n\\begin{split}\r\n&p_{uk}:=p_{uk}+\\alpha [(r_{ui}-\\mu - b_u - b_i-{\\sum_{k=1}}^k p_{uk}q_{ik})q_{ik} - \\lambda_1 p_{uk}]\r\n\\\\&q_{ik}:=q_{ik} + \\alpha[(r_{ui}-\\mu - b_u - b_i-{\\sum_{k=1}}^k p_{uk}q_{ik})p_{uk} - \\lambda_2 q_{ik}]\r\n\\end{split}\r\n\n\r\nb_u:=b_u + \\alpha[(r_{ui}-\\mu - b_u - b_i-{\\sum_{k=1}}^k p_{uk}q_{ik}) - \\lambda_3 b_u]\r\n\n\r\nb_i:=b_i + \\alpha[(r_{ui}-\\mu - b_u - b_i-{\\sum_{k=1}}^k p_{uk}q_{ik}) - \\lambda_4 b_i]\r\n\n由于P矩阵和Q矩阵是两个不同的矩阵，通常分别采取不同的正则参数，如\\lambda_1和\\lambda_2\n算法实现\n'''\nBiasSvd Model\n'''\nimport math\nimport random\nimport pandas as pd\nimport numpy as np\n\nclass BiasSvd(object):\n\n    def __init__(self, alpha, reg_p, reg_q, reg_bu, reg_bi, number_LatentFactors=10, number_epochs=10, columns=[\"uid\", \"iid\", \"rating\"]):\n        self.alpha = alpha # 学习率\n        self.reg_p = reg_p\n        self.reg_q = reg_q\n        self.reg_bu = reg_bu\n        self.reg_bi = reg_bi\n        self.number_LatentFactors = number_LatentFactors  # 隐式类别数量\n        self.number_epochs = number_epochs\n        self.columns = columns\n\n    def fit(self, dataset):\n        '''\n        fit dataset\n        :param dataset: uid, iid, rating\n        :return:\n        '''\n\n        self.dataset = pd.DataFrame(dataset)\n\n        self.users_ratings = dataset.groupby(self.columns[0]).agg([list])[[self.columns[1], self.columns[2]]]\n        self.items_ratings = dataset.groupby(self.columns[1]).agg([list])[[self.columns[0], self.columns[2]]]\n        self.globalMean = self.dataset[self.columns[2]].mean()\n\n        self.P, self.Q, self.bu, self.bi = self.sgd()\n\n    def _init_matrix(self):\n        '''\n        初始化P和Q矩阵，同时为设置0，1之间的随机值作为初始值\n        :return:\n        '''\n        # User-LF\n        P = dict(zip(\n            self.users_ratings.index,\n            np.random.rand(len(self.users_ratings), self.number_LatentFactors).astype(np.float32)\n        ))\n        # Item-LF\n        Q = dict(zip(\n            self.items_ratings.index,\n            np.random.rand(len(self.items_ratings), self.number_LatentFactors).astype(np.float32)\n        ))\n        return P, Q\n\n    def sgd(self):\n        '''\n        使用随机梯度下降，优化结果\n        :return:\n        '''\n        P, Q = self._init_matrix()\n\n        # 初始化bu、bi的值，全部设为0\n        bu = dict(zip(self.users_ratings.index, np.zeros(len(self.users_ratings))))\n        bi = dict(zip(self.items_ratings.index, np.zeros(len(self.items_ratings))))\n\n        for i in range(self.number_epochs):\n            print(\"iter%d\"%i)\n            error_list = []\n            for uid, iid, r_ui in self.dataset.itertuples(index=False):\n                v_pu = P[uid]\n                v_qi = Q[iid]\n                err = np.float32(r_ui - self.globalMean - bu[uid] - bi[iid] - np.dot(v_pu, v_qi))\n\n                v_pu += self.alpha * (err * v_qi - self.reg_p * v_pu)\n                v_qi += self.alpha * (err * v_pu - self.reg_q * v_qi)\n\n                P[uid] = v_pu \n                Q[iid] = v_qi\n\n                bu[uid] += self.alpha * (err - self.reg_bu * bu[uid])\n                bi[iid] += self.alpha * (err - self.reg_bi * bi[iid])\n\n                error_list.append(err ** 2)\n            print(np.sqrt(np.mean(error_list)))\n\n        return P, Q, bu, bi\n\n    def predict(self, uid, iid):\n\n        if uid not in self.users_ratings.index or iid not in self.items_ratings.index:\n            return self.globalMean\n\n        p_u = self.P[uid]\n        q_i = self.Q[iid]\n\n        return self.globalMean + self.bu[uid] + self.bi[iid] + np.dot(p_u, q_i)\n\n\nif __name__ == '__main__':\n    dtype = [(\"userId\", np.int32), (\"movieId\", np.int32), (\"rating\", np.float32)]\n    dataset = pd.read_csv(\"datasets/ml-latest-small/ratings.csv\", usecols=range(3), dtype=dict(dtype))\n\n    bsvd = BiasSvd(0.02, 0.01, 0.01, 0.01, 0.01, 10, 20)\n    bsvd.fit(dataset)\n\n    while True:\n        uid = input(\"uid: \")\n        iid = input(\"iid: \")\n        print(bsvd.predict(int(uid), int(iid)))\n\n"},"day02_推荐算法/07_基于内容的推荐算法.html":{"url":"day02_推荐算法/07_基于内容的推荐算法.html","title":"2.6_基于内容的推荐算法","keywords":"","body":"基于内容的推荐算法（Content-Based）\n简介\n基于内容的推荐方法是非常直接的，它以物品的内容描述信息为依据来做出的推荐，本质上是基于对物品和用户自身的特征或属性的直接分析和计算。\n例如，假设已知电影A是一部喜剧，而恰巧我们得知某个用户喜欢看喜剧电影，那么我们基于这样的已知信息，就可以将电影A推荐给该用户。\n基于内容的推荐实现步骤\n\n画像构建。顾名思义，画像就是刻画物品或用户的特征。本质上就是给用户或物品贴标签。\n\n物品画像：例如给电影《战狼2》贴标签，可以有哪些？\n\n\"动作\"、\"吴京\"、\"吴刚\"、\"张翰\"、\"大陆电影\"、\"国产\"、\"爱国\"、\"军事\"等等一系列标签是不是都可以贴上\n\n用户画像：例如已知用户的观影历史是：\"《战狼1》\"、\"《战狼2》\"、\"《建党伟业》\"、\"《建军大业》\"、\"《建国大业》\"、\"《红海行动》\"、\"《速度与激情1-8》\"等，我们是不是就可以分析出该用户的一些兴趣特征如：\"爱国\"、\"战争\"、\"赛车\"、\"动作\"、\"军事\"、\"吴京\"、\"韩三平\"等标签。\n\n\n\n\n问题：物品的标签来自哪儿？\n\nPGC    物品画像--冷启动\n物品自带的属性（物品一产生就具备的）：如电影的标题、导演、演员、类型等等\n服务提供方设定的属性（服务提供方为物品附加的属性）：如短视频话题、微博话题（平台拟定）\n其他渠道：如爬虫\n\n\nUGC    冷启动问题\n用户在享受服务过程中提供的物品的属性：如用户评论内容，微博话题（用户拟定）\n\n\n\n根据PGC内容构建的物品画像的可以解决物品的冷启动问题\n基于内容推荐的算法流程：\n\n根据PGC/UGC内容构建物品画像\n根据用户行为记录生成用户画像\n根据用户画像从物品中寻找最匹配的TOP-N物品进行推荐\n\n物品冷启动处理：\n\n根据PGC内容构建物品画像\n利用物品画像计算物品间两两相似情况\n为每个物品产生TOP-N最相似的物品进行相关推荐：如与该商品相似的商品有哪些？与该文章相似文章有哪些？\n\n"},"day02_推荐算法/08_物品画像.html":{"url":"day02_推荐算法/08_物品画像.html","title":"2.7_电影推荐(ContentBased)物品画像","keywords":"","body":"基于内容的电影推荐：物品画像\n物品画像构建步骤：\n\n利用tags.csv中每部电影的标签作为电影的候选关键词\n利用TF·IDF计算每部电影的标签的tfidf值，选取TOP-N个关键词作为电影画像标签\n将电影的分类词直接作为每部电影的画像标签\n\n基于TF-IDF的特征提取技术\n前面提到，物品画像的特征标签主要都是指的如电影的导演、演员、图书的作者、出版社等结构话的数据，也就是他们的特征提取，尤其是体征向量的计算是比较简单的，如直接给作品的分类定义0或者1的状态。\n但另外一些特征，比如电影的内容简介、电影的影评、图书的摘要等文本数据，这些被称为非结构化数据，首先他们本应该也属于物品的一个特征标签，但是这样的特征标签进行量化时，也就是计算它的特征向量时是很难去定义的。\n因此这时就需要借助一些自然语言处理、信息检索等技术，将如用户的文本评论或其他文本内容信息的非结构化数据进行量化处理，从而实现更加完善的物品画像/用户画像。\nTF-IDF算法便是其中一种在自然语言处理领域中应用比较广泛的一种算法。可用来提取目标文档中，并得到关键词用于计算对于目标文档的权重，并将这些权重组合到一起得到特征向量。\n算法原理\nTF-IDF自然语言处理领域中计算文档中词或短语的权值的方法，是词频（Term Frequency，TF）和逆转文档频率（Inverse Document Frequency，IDF）的乘积。TF指的是某一个给定的词语在该文件中出现的次数。这个数字通常会被正规化，以防止它偏向长的文件（同一个词语在长文件里可能会比短文件有更高的词频，而不管该词语重要与否）。IDF是一个词语普遍重要性的度量，某一特定词语的IDF，可以由总文件数目除以包含该词语之文件的数目，再将得到的商取对数得到。\nTF-IDF算法基于一个这样的假设：若一个词语在目标文档中出现的频率高而在其他文档中出现的频率低，那么这个词语就可以用来区分出目标文档。这个假设需要掌握的有两点：\n\n在本文档出现的频率高；\n在其他文档出现的频率低。\n\n因此，TF-IDF算法的计算可以分为词频（Term Frequency，TF）和逆转文档频率（Inverse Document Frequency，IDF）两部分，由TF和IDF的乘积来设置文档词语的权重。\nTF指的是一个词语在文档中的出现频率。假设文档集包含的文档数为N，文档集中包含关键词k_i的文档数为n_i，f_{ij}表示关键词k_i在文档d_j中出现的次数，f_{dj}表示文档d_j中出现的词语总数，k_i在文档dj中的词频TF_{ij}定义为：TF_{ij}=\\frac {f_{ij}}{f_{dj}}。并且注意，这个数字通常会被正规化，以防止它偏向长的文件（指同一个词语在长文件里可能会比短文件有更高的词频，而不管该词语重要与否）。\nIDF是一个词语普遍重要性的度量。表示某一词语在整个文档集中出现的频率，由它计算的结果取对数得到关键词k_i的逆文档频率IDF_i：IDF_i=log\\frac {N}{n_i}\n由TF和IDF计算词语的权重为：w_{ij}=TF_{ij}·IDF_{i}=\\frac {f_{ij}}{f_{dj}}·log\\frac {N}{n_i}\n结论：TF-IDF与词语在文档中的出现次数成正比，与该词在整个文档集中的出现次数成反比。\n用途：在目标文档中，提取关键词(特征标签)的方法就是将该文档所有词语的TF-IDF计算出来并进行对比，取其中TF-IDF值最大的k个数组成目标文档的特征向量用以表示文档。\n注意：文档中存在的停用词（Stop Words），如“是”、“的”之类的，对于文档的中心思想表达没有意义的词，在分词时需要先过滤掉再计算其他词语的TF-IDF值。\n算法举例\n对于计算影评的TF-IDF，以电影“加勒比海盗：黑珍珠号的诅咒”为例，假设它总共有1000篇影评，其中一篇影评的总词语数为200，其中出现最频繁的词语为“海盗”、“船长”、“自由”，分别是20、15、10次，并且这3个词在所有影评中被提及的次数分别为1000、500、100，就这3个词语作为关键词的顺序计算如下。\n\n将影评中出现的停用词过滤掉，计算其他词语的词频。以出现最多的三个词为例进行计算如下：\n\n“海盗”出现的词频为20/200＝0.1\n“船长”出现的词频为15/200=0.075\n“自由”出现的词频为10/200=0.05；\n\n\n计算词语的逆文档频率如下：\n\n“海盗”的IDF为：log(1000/1000)=0\n“船长”的IDF为：log(1000/500)=0.3\n“自由”的IDF为：log(1000/100)=1\n\n\n由1和2计算的结果求出词语的TF-IDF结果，“海盗”为0，“船长”为0.0225，“自由”为0.05。\n\n通过对比可得，该篇影评的关键词排序应为：“自由”、“船长”、“海盗”。把这些词语的TF-IDF值作为它们的权重按照对应的顺序依次排列，就得到这篇影评的特征向量，我们就用这个向量来代表这篇影评，向量中每一个维度的分量大小对应这个属性的重要性。\n将总的影评集中所有的影评向量与特定的系数相乘求和，得到这部电影的综合影评向量，与电影的基本属性结合构建视频的物品画像，同理构建用户画像，可采用多种方法计算物品画像和用户画像之间的相似度，为用户做出推荐。\n加载数据集\nimport pandas as pd\nimport numpy as np\n'''\n- 利用tags.csv中每部电影的标签作为电影的候选关键词\n- 利用TF·IDF计算每部电影的标签的tfidf值，选取TOP-N个关键词作为电影画像标签\n- 并将电影的分类词直接作为每部电影的画像标签\n'''\n\ndef get_movie_dataset():\n    # 加载基于所有电影的标签\n    # all-tags.csv来自ml-latest数据集中\n    # 由于ml-latest-small中标签数据太多，因此借助其来扩充\n    _tags = pd.read_csv(\"datasets/ml-latest-small/all-tags.csv\", usecols=range(1, 3)).dropna()\n    tags = _tags.groupby(\"movieId\").agg(list)\n\n    # 加载电影列表数据集\n    movies = pd.read_csv(\"datasets/ml-latest-small/movies.csv\", index_col=\"movieId\")\n    # 将类别词分开\n    movies[\"genres\"] = movies[\"genres\"].apply(lambda x: x.split(\"|\"))\n    # 为每部电影匹配对应的标签数据，如果没有将会是NAN\n    movies_index = set(movies.index) & set(tags.index)\n    new_tags = tags.loc[list(movies_index)]\n    ret = movies.join(new_tags)\n\n    # 构建电影数据集，包含电影Id、电影名称、类别、标签四个字段\n    # 如果电影没有标签数据，那么就替换为空列表\n    # map(fun,可迭代对象)\n    movie_dataset = pd.DataFrame(\n        map(\n            lambda x: (x[0], x[1], x[2], x[2]+x[3]) if x[3] is not np.nan else (x[0], x[1], x[2], []), ret.itertuples())\n        , columns=[\"movieId\", \"title\", \"genres\",\"tags\"]\n    )\n\n    movie_dataset.set_index(\"movieId\", inplace=True)\n    return movie_dataset\n\nmovie_dataset = get_movie_dataset()\nprint(movie_dataset)\n\n\nmap函数\n\n描述\nmap() 会根据提供的函数对指定序列做映射。\n第一个参数 function 以参数序列中的每一个元素调用 function 函数，返回包含每次 function 函数返回值的新列表。\n\n语法\nmap() 函数语法：\nmap(function, iterable, ...)\n\n\n参数\n\nfunction -- 函数\niterable -- 一个或多个序列\n\n\n返回值\nPython 2.x 返回列表。\nPython 3.x 返回迭代器。\n\n示例\n>>>def square(x) :            # 计算平方数\n...     return x ** 2\n... \n>>> map(square, [1,2,3,4,5])   # 计算列表各个元素的平方\n[1, 4, 9, 16, 25]\n>>> map(lambda x: x ** 2, [1, 2, 3, 4, 5])  # 使用 lambda 匿名函数\n[1, 4, 9, 16, 25]\n\n# 提供了两个列表，对相同位置的列表数据进行相加\n>>> map(lambda x, y: x + y, [1, 3, 5, 7, 9], [2, 4, 6, 8, 10])\n[3, 7, 11, 15, 19]\n\n\n\n\n\n基于TF·IDF提取TOP-N关键词，构建电影画像\n\ngensim介绍\n\npython 三方库 自然语言处理利器\n支持包括TF-IDF，word2vec在内的多种主题模型算法\n安装 pip install gensim\n\n\ngensim基本概念\n\n语料（Corpus）：一组原始文本的集合，在Gensim中，Corpus通常是一个可迭代的对象（比如列表）。每一次迭代返回一个可用于表达文本对象的（稀疏）向量。\n向量（Vector）：由一组文本特征构成的列表。是一段文本在Gensim中的内部表达。\n模型（Model）\n\n\n词袋模型（BOW bag of words)\n文本特征提取有两个非常重要的模型：\n\n词集模型：单词构成的集合，集合自然每个元素都只有一个，也即词集中的每个单词都只有一个。\n词袋模型：在词集的基础上如果一个单词在文档中出现不止一次，统计其出现的次数（频数）。\n\n两者本质上的区别，词袋是在词集的基础上增加了频率的维度，词集只关注有和没有，词袋还要关注有几个。\n\n\nfrom gensim.models import TfidfModel\n\nimport pandas as pd\nimport numpy as np\n\nfrom pprint import pprint\n\n# ......\n\ndef create_movie_profile(movie_dataset):\n    '''\n    使用tfidf，分析提取topn关键词\n    :param movie_dataset: \n    :return: \n    '''\n    dataset = movie_dataset[\"tags\"].values\n\n    from gensim.corpora import Dictionary\n    # 根据数据集建立词袋，并统计词频，将所有词放入一个词典，使用索引进行获取\n    dct = Dictionary(dataset)\n    # 根据将每条数据，返回对应的词索引和词频\n    corpus = [dct.doc2bow(line) for line in dataset]\n    # 训练TF-IDF模型，即计算TF-IDF值\n    model = TfidfModel(corpus)\n\n    movie_profile = {}\n    for i, mid in enumerate(movie_dataset.index):\n        # 根据每条数据返回，向量\n        vector = model[corpus[i]]\n        # 按照TF-IDF值得到top-n的关键词\n        movie_tags = sorted(vector, key=lambda x: x[1], reverse=True)[:30]\n        # 根据关键词提取对应的名称\n        movie_profile[mid] = dict(map(lambda x:(dct[x[0]], x[1]), movie_tags))\n\n    return movie_profile\n\nmovie_dataset = get_movie_dataset()\npprint(create_movie_profile(movie_dataset))\n\n完善画像关键词\nfrom gensim.models import TfidfModel\n\nimport pandas as pd\nimport numpy as np\n\nfrom pprint import pprint\n\n# ......\n\ndef create_movie_profile(movie_dataset):\n    '''\n    使用tfidf，分析提取topn关键词\n    :param movie_dataset:\n    :return:\n    '''\n    dataset = movie_dataset[\"tags\"].values\n\n    from gensim.corpora import Dictionary\n    # 根据数据集建立词袋，并统计词频，将所有词放入一个词典，使用索引进行获取\n    dct = Dictionary(dataset)\n    # 根据将每条数据，返回对应的词索引和词频\n    corpus = [dct.doc2bow(line) for line in dataset]\n    # 训练TF-IDF模型，即计算TF-IDF值\n    model = TfidfModel(corpus)\n\n    _movie_profile = []\n    for i, data in enumerate(movie_dataset.itertuples()):\n        mid = data[0]\n        title = data[1]\n        genres = data[2]\n        vector = model[corpus[i]]\n        movie_tags = sorted(vector, key=lambda x: x[1], reverse=True)[:30]\n        topN_tags_weights = dict(map(lambda x: (dct[x[0]], x[1]), movie_tags))\n        # 将类别词的添加进去，并设置权重值为1.0\n        for g in genres:\n            topN_tags_weights[g] = 1.0\n        topN_tags = [i[0] for i in topN_tags_weights.items()]\n        _movie_profile.append((mid, title, topN_tags, topN_tags_weights))\n\n    movie_profile = pd.DataFrame(_movie_profile, columns=[\"movieId\", \"title\", \"profile\", \"weights\"])\n    movie_profile.set_index(\"movieId\", inplace=True)\n    return movie_profile\n\nmovie_dataset = get_movie_dataset()\npprint(create_movie_profile(movie_dataset))\n\n为了根据指定关键词迅速匹配到对应的电影，因此需要对物品画像的标签词，建立倒排索引\n倒排索引介绍\n通常数据存储数据，都是以物品的ID作为索引，去提取物品的其他信息数据\n而倒排索引就是用物品的其他数据作为索引，去提取它们对应的物品的ID列表\n# ......\n\n'''\n建立tag-物品的倒排索引\n'''\n\ndef create_inverted_table(movie_profile):\n    inverted_table = {}\n    for mid, weights in movie_profile[\"weights\"].iteritems():\n        for tag, weight in weights.items():\n            #到inverted_table dict 用tag作为Key去取值 如果取不到就返回[]\n            _ = inverted_table.get(tag, [])\n            #将电影的id 和 权重 放到一个tuple中 添加到list中\n            _.append((mid, weight))\n            #将修改后的值设置回去 \n            inverted_table.setdefault(tag, _)\n    return inverted_table\n\ninverted_table = create_inverted_table(movie_profile)\npprint(inverted_table)\n\n"},"day02_推荐算法/09_用户画像.html":{"url":"day02_推荐算法/09_用户画像.html","title":"2.8_电影推荐(ContentBased)用户画像","keywords":"","body":"基于内容的电影推荐：用户画像\n用户画像构建步骤：\n\n根据用户的评分历史，结合物品画像，将有观影记录的电影的画像标签作为初始标签反打到用户身上\n通过对用户观影标签的次数进行统计，计算用户的每个初始标签的权重值，排序后选取TOP-N作为用户最终的画像标签\n\n用户画像建立\nimport pandas as pd\nimport numpy as np\nfrom gensim.models import TfidfModel\n\nfrom functools import reduce\nimport collections\n\nfrom pprint import pprint\n\n# ......\n\n'''\nuser profile画像建立：\n1. 提取用户观看列表\n2. 根据观看列表和物品画像为用户匹配关键词，并统计词频\n3. 根据词频排序，最多保留TOP-k个词，这里K设为100，作为用户的标签\n'''\n\ndef create_user_profile():\n    watch_record = pd.read_csv(\"datasets/ml-latest-small/ratings.csv\", usecols=range(2), dtype={\"userId\":np.int32, \"movieId\": np.int32})\n\n    watch_record = watch_record.groupby(\"userId\").agg(list)\n    # print(watch_record)\n\n    movie_dataset = get_movie_dataset()\n    movie_profile = create_movie_profile(movie_dataset)\n\n    user_profile = {}\n    for uid, mids in watch_record.itertuples():\n        record_movie_prifole = movie_profile.loc[list(mids)]\n        counter = collections.Counter(reduce(lambda x, y: list(x)+list(y), record_movie_prifole[\"profile\"].values))\n        # 取出出现次数最多的前50个词\n        interest_words = counter.most_common(50)\n        # 取出出现次数最多的词 出现的次数\n        maxcount = interest_words[0][1]\n        # 利用次数计算权重 出现次数最多的词权重为1\n        interest_words = [(w,round(c/maxcount, 4)) for w,c in interest_words]\n        user_profile[uid] = interest_words\n\n    return user_profile\n\nuser_profile = create_user_profile()\npprint(user_profile)\n\n\nreduce函数\n\n描述\nreduce() 函数会对参数序列中元素进行累积。\n函数将一个数据集合（链表，元组等）中的所有数据进行下列操作：用传给 reduce 中的函数 function（有两个参数）先对集合中的第 1、2 个元素进行操作，得到的结果再与第三个数据用 function 函数运算，最后得到一个结果。\n\n语法\nreduce() 函数语法：\nreduce(function, iterable[, initializer])\n\n\n参数\n\nfunction -- 函数，有两个参数\niterable -- 可迭代对象\ninitializer -- 可选，初始参数\n\n\n返回值\n返回函数计算结果。\n\n示例\n>>>def add(x, y) :            # 两数相加\n...     return x + y\n... \n>>> reduce(add, [1,2,3,4,5])   # 计算列表和：1+2+3+4+5\n15\n>>> reduce(lambda x, y: x+y, [1,2,3,4,5])  # 使用 lambda 匿名函数\n15\n\n\n\n\n使用collections.Counter类统计列表元素出现次数\nfrom collections import Counter\nnames = [\"Stanley\", \"Lily\", \"Bob\", \"Well\", \"Peter\", \"Bob\", \"Well\", \"Peter\", \"Well\", \"Peter\", \"Bob\",\"Stanley\", \"Lily\", \"Bob\", \"Well\", \"Peter\", \"Bob\", \"Bob\", \"Well\", \"Peter\", \"Bob\", \"Well\"]\nnames_counts = Counter(names)\n\n\n\n"},"day02_推荐算法/10_TOPN用户推荐.html":{"url":"day02_推荐算法/10_TOPN用户推荐.html","title":"2.9_电影推荐(ContentBased)TOP-N用户推荐","keywords":"","body":"基于内容的电影推荐：为用户产生TOP-N推荐结果\n# ......\n\nuser_profile = create_user_profile()\n\nwatch_record = pd.read_csv(\"datasets/ml-latest-small/ratings.csv\", usecols=range(2),dtype={\"userId\": np.int32, \"movieId\": np.int32})\n\nwatch_record = watch_record.groupby(\"userId\").agg(list)\n\nfor uid, interest_words in user_profile.items():\n    result_table = {} # 电影id:[0.2,0.5,0.7]\n    for interest_word, interest_weight in interest_words:\n        related_movies = inverted_table[interest_word]\n        for mid, related_weight in related_movies:\n            _ = result_table.get(mid, [])\n            _.append(interest_weight)    # 只考虑用户的兴趣程度\n            # _.append(related_weight)    # 只考虑兴趣词与电影的关联程度\n            # _.append(interest_weight*related_weight)    # 二者都考虑\n            result_table.setdefault(mid, _)\n\n    rs_result = map(lambda x: (x[0], sum(x[1])), result_table.items())\n    rs_result = sorted(rs_result, key=lambda x:x[1], reverse=True)[:100]\n    print(uid)\n    pprint(rs_result)\n    break\n\n    # 历史数据  ==>  历史兴趣程度 ==>  历史推荐结果       离线推荐    离线计算\n    # 在线推荐 ===>    娱乐(王思聪)   ===>   我 ==>  王思聪 100%  \n    # 近线：最近1天、3天、7天           实时计算\n\n"},"day03_Hadoop/ha1.1.html":{"url":"day03_Hadoop/ha1.1.html","title":"01_什么是Hadoop","keywords":"","body":"1.1 什么是Hadoop\n\nHadoop名字的由来\n\n作者：Doug cutting\nHadoop项目作者的孩子给一个棕黄色的大象样子的填充玩具的命名\n\n\n\nHadoop的概念:\n\nApache™ Hadoop®  是一个开源的, 可靠的(reliable), 可扩展的(scalable)分布式计算框架\n允许使用简单的编程模型跨计算机集群分布式处理大型数据集\n可扩展: 从单个服务器扩展到数千台计算机，每台计算机都提供本地计算和存储\n可靠的: 不依靠硬件来提供高可用性(high-availability)，而是在应用层检测和处理故障，从而在计算机集群之上提供高可用服务\n\n\n\n\nHadoop能做什么?\n\n搭建大型数据仓库\nPB级数据的存储 处理 分析 统计等业务\n\n搜索引擎\n\n日志分析\n\n数据挖掘\n\n商业智能(Business Intelligence，简称：BI)\n商业智能通常被理解为将企业中现有的数据(订单、库存、交易账目、客户和供应商等数据)转化为知识，帮助企业做出明智的业务经营决策的工具。从技术层面上讲，是数据仓库、数据挖掘等技术的综合运用。\n\n\n\n\n\n\nHadoop发展史\n\n2003-2004年 Google发表了三篇论文\n\nGFS：Google的分布式文件系统Google File System \nMapReduce: Simplified Data Processing on Large Clusters \nBigTable：一个大型的分布式数据库\n\n\n2006年2月Hadoop成为Apache的独立开源项目( Doug Cutting等人实现了DFS和MapReduce机制)。\n2006年4月— 标准排序(10 GB每个节点)在188个节点上运行47.9个小时。 \n2008年4月— 赢得世界最快1TB数据排序在900个节点上用时209秒。 \n2008年— 淘宝开始投入研究基于Hadoop的系统–云梯。云梯总容量约9.3PB，共有1100台机器，每天处理18000道作业，扫描500TB数据。 \n2009年3月— Cloudera推出CDH（Cloudera’s Dsitribution Including Apache Hadoop）\n2009年5月— Yahoo的团队使用Hadoop对1 TB的数据进行排序只花了62秒时间。 \n2009年7月— Hadoop Core项目更名为Hadoop Common; \n2009年7月— MapReduce和Hadoop Distributed File System (HDFS)成为Hadoop项目的独立子项目。\n2012年11月— Apache Hadoop 1.0 Available\n2018年4月— Apache Hadoop 3.1 Available\n搜索引擎时代\n有保存大量网页的需求(单机  集群)\n词频统计 word count  PageRank\n\n\n数据仓库时代\nFaceBook推出Hive\n曾经进行数分析与统计时, 仅限于数据库,受数据量和计算能力的限制, 我们只能对最重要的数据进行统计和分析(决策数据,财务相关)\nHive可以在Hadoop上运行SQL操作, 可以把运行日志, 应用采集数据,数据库数据放到一起分析\n\n\n数据挖掘时代\n啤酒尿不湿\n关联分析\n用户画像/物品画像\n\n\n机器学习时代  广义大数据\n大数据提高数据存储能力, 为机器学习提供燃料\nalpha go\nsiri 小爱 天猫精灵\n\n\n\n\n\n"},"day03_Hadoop/ha1.2.html":{"url":"day03_Hadoop/ha1.2.html","title":"02_Hadoop核心组件","keywords":"","body":"1.2 Hadoop核心组件\n\nHadoop是所有搜索引擎的共性问题的廉价解决方案\n\n如何存储持续增长的海量网页:  单节点 V.S. 分布式存储\n如何对持续增长的海量网页进行排序: 超算 V.S. 分布式计算\nHDFS 解决分布式存储问题\nMapReduce 解决分布式计算问题\n\n\nHadoop Common: The common utilities that support the other Hadoop modules.(hadoop的核心组件)\n\nHadoop Distributed File System (HDFS™): A distributed file system that provides high-throughput access to application data.(分布式文件系统)\n源自于Google的GFS论文, 论文发表于2003年10月\nHDFS是GFS的开源实现\nHDFS的特点:扩展性&容错性&海量数量存储\n将文件切分成指定大小的数据块, 并在多台机器上保存多个副本\n数据切分、多副本、容错等操作对用户是透明的\n\n\n下面这张图是数据块多份复制存储的示意\n图中对于文件 /users/sameerp/data/part-0，其复制备份数设置为2, 存储的BlockID分别为1、3。\nBlock1的两个备份存储在DataNode0和DataNode2两个服务器上\nBlock3的两个备份存储在DataNode4和DataNode6两个服务器上\n\n\n\n\n\nHadoop MapReduce: A YARN-based system for parallel processing of large data sets.\n\n分布式计算框架\n源于Google的MapReduce论文，论文发表于2004年12月\nMapReduce是GoogleMapReduce的开源实现\nMapReduce特点:扩展性&容错性&海量数据离线处理\n\n\n\nHadoop YARN: A framework for job scheduling and cluster resource management.(资源调度系统)\n\nYARN: Yet Another Resource Negotiator\n\n负责整个集群资源的管理和调度\n\nYARN特点:扩展性&容错性&多框架资源统一调度\n\n\n\n\n\n"},"day03_Hadoop/ha1.3.html":{"url":"day03_Hadoop/ha1.3.html","title":"03_Hadoop优势","keywords":"","body":"1.3 Hadoop优势\n\n高可靠\n数据存储: 数据块多副本\n数据计算: 某个节点崩溃, 会自动重新调度作业计算\n\n\n高扩展性\n存储/计算资源不够时，可以横向的线性扩展机器\n一个集群中可以包含数以千计的节点\n集群可以使用廉价机器，成本低\n\n\nHadoop生态系统成熟\n\n"},"day03_Hadoop/ha2.1.html":{"url":"day03_Hadoop/ha2.1.html","title":"01_HDFS的使用","keywords":"","body":"2.1 HDFS的使用\n\n启动HDFS\n\n来到$HADOOP_HOME/sbin目录下\n执行start-dfs.sh\n\n[hadoop@hadoop00 sbin]$ ./start-dfs.sh\n\n\n可以看到 namenode和 datanode启动的日志信息\n\nStarting namenodes on [hadoop00]\nhadoop00: starting namenode, logging to /home/hadoop/app/hadoop-2.6.0-cdh5.7.0/logs/hadoop-hadoop-namenode-hadoop00.out\nlocalhost: starting datanode, logging to /home/hadoop/app/hadoop-2.6.0-cdh5.7.0/logs/hadoop-hadoop-datanode-hadoop00.out\nStarting secondary namenodes [0.0.0.0]\n0.0.0.0: starting secondarynamenode, logging to /home/hadoop/app/hadoop-2.6.0-cdh5.7.0/logs/hadoop-hadoop-secondarynamenode-hadoop00.out\n\n\n通过jps命令查看当前运行的进程\n\n[hadoop@hadoop00 sbin]$ jps\n4416 DataNode\n4770 Jps\n4631 SecondaryNameNode\n4251 NameNode\n\n\n可以看到 NameNode DataNode 以及 SecondaryNameNode 说明启动成功\n\n\n通过可视化界面查看HDFS的运行情况\n\n通过浏览器查看 192.168.19.137:50070 \n\n\n\nOverview界面查看整体情况\n\n\n\nDatanodes界面查看datanode的情况\n\n\n\n\n\n"},"day03_Hadoop/ha2.2.html":{"url":"day03_Hadoop/ha2.2.html","title":"02_HDFS Shell操作","keywords":"","body":"2.2 HDFS shell操作\n\n调用文件系统(FS)Shell命令应使用 bin/hadoop fs 的形式\n\nls\n使用方法：hadoop fs -ls \n如果是文件，则按照如下格式返回文件信息：\n文件名  文件大小 修改日期 修改时间 权限 用户ID 组ID \n如果是目录，则返回它直接子文件的一个列表，就像在Unix中一样。目录返回列表的信息如下：\n目录名  修改日期 修改时间 权限 用户ID 组ID \n示例：\nhadoop fs -ls /user/hadoop/file1 /user/hadoop/file2 hdfs://host:port/user/hadoop/dir1 /nonexistentfile \n返回值：\n成功返回0，失败返回-1。 \n\ntext\n使用方法：hadoop fs -text  \n将源文件输出为文本格式。允许的格式是zip和TextRecordInputStream。\n\nmv\n使用方法：hadoop fs -mv URI [URI …] \n将文件从源路径移动到目标路径。这个命令允许有多个源路径，此时目标路径必须是一个目录。不允许在不同的文件系统间移动文件。 \n示例：\n\nhadoop fs -mv /user/hadoop/file1 /user/hadoop/file2\nhadoop fs -mv hdfs://host:port/file1 hdfs://host:port/file2 hdfs://host:port/file3 hdfs://host:port/dir1\n\n返回值：\n成功返回0，失败返回-1。\n\nput\n使用方法：hadoop fs -put  ... \n从本地文件系统中复制单个或多个源路径到目标文件系统。也支持从标准输入中读取输入写入目标文件系统。\n\nhadoop fs -put localfile /user/hadoop/hadoopfile\nhadoop fs -put localfile1 localfile2 /user/hadoop/hadoopdir\nhadoop fs -put localfile hdfs://host:port/hadoop/hadoopfile\nhadoop fs -put - hdfs://host:port/hadoop/hadoopfile \n从标准输入中读取输入。\n\n返回值：\n成功返回0，失败返回-1。\n\nrm\n使用方法：hadoop fs -rm URI [URI …]\n删除指定的文件。只删除非空目录和文件。请参考rmr命令了解递归删除。\n示例：\n\nhadoop fs -rm hdfs://host:port/file /user/hadoop/emptydir\n\n返回值：\n成功返回0，失败返回-1。\n\n\n\nhttp://hadoop.apache.org/docs/r1.0.4/cn/hdfs_shell.html\n\n\n2.4.1 HDFS shell操作练习\n\n在centos 中创建 test.txt  \ntouch test.txt\n\n\n在centos中为test.txt 添加文本内容\nvi test.txt\n\n\n在HDFS中创建 hadoop001/test 文件夹\nhadoop fs -mkdir -p /hadoop001/test\n\n\n把text.txt文件上传到HDFS中\nhadoop fs -put test.txt /hadoop001/test/\n\n\n查看hdfs中 hadoop001/test/test.txt 文件内容\nhadoop fs -cat /hadoop001/test/test.txt\n\n\n将hdfs中 hadoop001/test/test.txt文件下载到centos\n hadoop fs -get /hadoop001/test/test.txt test.txt\n\n\n删除HDFS中 hadoop001/test/\n hadoop fs -rm -r /hadoop001\n\n\n"},"day03_Hadoop/ha2.3.html":{"url":"day03_Hadoop/ha2.3.html","title":"03_HDFS设计思路","keywords":"","body":"2.3 HDFS设计思路\n\n分布式文件系统的设计思路：\n\n\n\n\nHDFS的设计目标\n适合运行在通用硬件(commodity hardware)上的分布式文件系统\n高度容错性的系统，适合部署在廉价的机器上\nHDFS能提供高吞吐量的数据访问，非常适合大规模数据集上的应用\n容易扩展，为用户提供性能不错的文件存储服务\n\n\n\n"},"day03_Hadoop/ha2.4.html":{"url":"day03_Hadoop/ha2.4.html","title":"04_HDFS架构","keywords":"","body":"2.4 HDFS架构\n\n1个NameNode/NN(Master)  带 DataNode/DN(Slaves) (Master-Slave结构)\n1个文件会被拆分成多个Block\nNameNode(NN)\n负责客户端请求的响应\n负责元数据（文件的名称、副本系数、Block存放的DN）的管理\n元数据 MetaData 描述数据的数据\n\n\n监控DataNode健康状况 10分钟没有收到DataNode报告认为Datanode死掉了\n\n\nDataNode(DN)\n存储用户的文件对应的数据块(Block)\n要定期向NN发送心跳信息，汇报本身及其所有的block信息，健康状况\n\n\n分布式集群NameNode和DataNode部署在不同机器上\n\n\n\nHDFS优缺点\n优点\n数据冗余 硬件容错\n适合存储大文件\n处理流式数据\n可构建在廉价机器上\n\n\n缺点\n低延迟的数据访问\n小文件存储\n\n\n\n\n\n"},"day03_Hadoop/ha2.5.html":{"url":"day03_Hadoop/ha2.5.html","title":"05_HDFS环境搭建","keywords":"","body":"2.5 HDFS环境搭建\n\n下载jdk 和 hadoop 放到 ~/software目录下 然后解压到 ~/app目录下\ntar -zxvf 压缩包名字 -C ~/app/\n\n\n配置环境变量\nvi ~/.bash_profile\nexport JAVA_HOME=/root/bigdata/jdk\nexport PATH=$JAVA_HOME/bin:$PATH\nexport HADOOP_HOME=/root/bigdata/hadoop\nexport PATH=$HADOOP_HOME/bin:$PATH\n\n#保存退出后\nsource ~/.bash_profile\n\n\n进入到解压后的hadoop目录 修改配置文件\n\n配置文件作用\n\ncore-site.xml  指定hdfs的访问方式\nhdfs-site.xml  指定namenode 和 datanode 的数据存储位置\nmapred-site.xml 配置mapreduce\nyarn-site.xml  配置yarn\n\n\n修改hadoop-env.sh\n\n\ncd etc/hadoop\nvi hadoop-env.sh\n#找到下面内容添加java home\nexport_JAVA_HOME=/root/bigdata/jdk\n\n\n修改 core-site.xml 在 节点中添加\n\n\n        \n                hadoop.tmp.dir\n                file:/root/bigdata/hadoop/tmp\n        \n        \n                fs.defaultFS\n                hdfs://hadoop-master:9000\n        \n\n\n\n修改hdfs-site.xml 在 configuration节点中添加\n\n\n    dfs.namenode.name.dir\n    /root/bigdata/hadoop/hdfs/name\n\n\n    dfs.datanode.data.dir\n    /root/bigdata/hadoop/hdfs/data\n\n\n    dfs.replication\n    1\n\n\n\n修改 mapred-site.xml \n默认没有这个 从模板文件复制 \n\ncp mapred-site.xml.template mapred-site.xml\n\n​    在mapred-site.xml  的configuration 节点中添加\n\n    mapreduce.framework.name\n    yarn\n\n\n\n修改yarn-site.xml configuration 节点中添加\n\n\n    yarn.nodemanager.aux-services\n    mapreduce_shuffle\n\n\n\n来到hadoop的bin目录\n./hadoop namenode -format (这个命令只运行一次)\n\n\n启动hdfs 进入到  sbin\n./start-dfs.sh\n\n\n启动启动yarn 在sbin中\n\n\n"},"day03_Hadoop/ha3.1.html":{"url":"day03_Hadoop/ha3.1.html","title":"01_资源调度框架YARN","keywords":"","body":"资源调度框架 YARN\n3.1.1 什么是YARN\n\nYet Another Resource Negotiator, 另一种资源协调者\n通用资源管理系统\n为上层应用提供统一的资源管理和调度，为集群在利用率、资源统一管理和数据共享等方面带来了巨大好处\n\n3.1.2 YARN产生背景\n\n通用资源管理系统\n\nHadoop数据分布式存储（数据分块，冗余存储）\n当多个MapReduce任务要用到相同的hdfs数据， 需要进行资源调度管理\nHadoop1.x时并没有YARN，MapReduce 既负责进行计算作业又处理服务器集群资源调度管理\n\n\n服务器集群资源调度管理和MapReduce执行过程耦合在一起带来的问题\n\nHadoop早期, 技术只有Hadoop, 这个问题不明显\n\n随着大数据技术的发展，Spark Storm ... 计算框架都要用到服务器集群资源 \n\n如果没有通用资源管理系统，只能为多个集群分别提供数据\n\n资源利用率低 运维成本高\n\n\n\nYarn (Yet Another Resource Negotiator) 另一种资源调度器\n\nMesos 大数据资源管理产品\n\n\n\n\n不同计算框架可以共享同一个HDFS集群上的数据，享受整体的资源调度\n\n\n\n3.1.3 YARN的架构和执行流程\n\nResourceManager: RM 资源管理器\n​    整个集群同一时间提供服务的RM只有一个，负责集群资源的统一管理和调度\n​    处理客户端的请求： submit, kill\n​    监控我们的NM，一旦某个NM挂了，那么该NM上运行的任务需要告诉我们的AM来如何进行处理\nNodeManager: NM 节点管理器\n​    整个集群中有多个，负责自己本身节点资源管理和使用\n​    定时向RM汇报本节点的资源使用情况\n​    接收并处理来自RM的各种命令：启动Container\n​    处理来自AM的命令\nApplicationMaster: AM\n​    每个应用程序对应一个：MR、Spark，负责应用程序的管理\n​    为应用程序向RM申请资源（core、memory），分配给内部task\n​    需要与NM通信：启动/停止task，task是运行在container里面，AM也是运行在container里面\nContainer 容器: 封装了CPU、Memory等资源的一个容器,是一个任务运行环境的抽象\nClient: 提交作业 查询作业的运行进度,杀死作业\n\n\n1，Client提交作业请求\n2，ResourceManager 进程和 NodeManager 进程通信，根据集群资源，为用户程序分配第一个Container(容器)，并将 ApplicationMaster 分发到这个容器上面\n3，在启动的Container中创建ApplicationMaster\n4，ApplicationMaster启动后向ResourceManager注册进程,申请资源\n5，ApplicationMaster申请到资源后，向对应的NodeManager申请启动Container,将要执行的程序分发到NodeManager上\n6，Container启动后，执行对应的任务\n7，Tast执行完毕之后，向ApplicationMaster返回结果\n8，ApplicationMaster向ResourceManager 请求kill\n3.1.5 YARN环境搭建\n1）mapred-site.xml\n\n    mapreduce.framework.name\n    yarn\n\n2）yarn-site.xml\n\n    yarn.nodemanager.aux-services\n    mapreduce_shuffle\n\n3) 启动YARN相关的进程\nsbin/start-yarn.sh\n4）验证\n​    jps\n​        ResourceManager\n​        NodeManager\n​    http://192,168.19.137:8088\n5）停止YARN相关的进程\n​    sbin/stop-yarn.sh\n"},"day03_Hadoop/ha3.2.html":{"url":"day03_Hadoop/ha3.2.html","title":"02_分布式计算框架MapReduce","keywords":"","body":"分布式处理框架 MapReduce\n3.2.1 什么是MapReduce\n\n源于Google的MapReduce论文(2004年12月)\nHadoop的MapReduce是Google论文的开源实现\nMapReduce优点: 海量数据离线处理&易开发\nMapReduce缺点: 实时流式计算\n\n3.2.2 MapReduce编程模型\n\nMapReduce分而治之的思想\n数钱实例：一堆钞票，各种面值分别是多少\n单点策略\n一个人数所有的钞票，数出各种面值有多少张\n\n\n分治策略\n每个人分得一堆钞票，数出各种面值有多少张\n汇总，每个人负责统计一种面值\n\n\n解决数据可以切割进行计算的应用\n\n\n\n\nMapReduce编程分Map和Reduce阶段\n将作业拆分成Map阶段和Reduce阶段\nMap阶段 Map Tasks 分：把复杂的问题分解为若干\"简单的任务\"\nReduce阶段: Reduce Tasks 合：reduce\n\n\nMapReduce编程执行步骤\n\n准备MapReduce的输入数据\n准备Mapper数据\nShuffle\nReduce处理\n结果输出\n\n\n编程模型\n\n借鉴函数式编程方式\n\n用户只需要实现两个函数接口：\n\nMap(in_key,in_value)\n--->(out_key,intermediate_value) list\n\nReduce(out_key,intermediate_value) list\n--->out_value list\n\n\n\nWord Count 词频统计案例\n\n\n\n\n\n"},"day03_Hadoop/ha3.3.html":{"url":"day03_Hadoop/ha3.3.html","title":"03_MapReduce实战","keywords":"","body":"MapReduce实战\n3.3.1 利用MRJob编写和运行MapReduce代码\nmrjob 简介\n\n使用python开发在Hadoop上运行的程序, mrjob是最简单的方式\nmrjob程序可以在本地测试运行也可以部署到Hadoop集群上运行\n如果不想成为hadoop专家, 但是需要利用Hadoop写MapReduce代码,mrJob是很好的选择\n\nmrjob 安装\n\n使用pip安装\npip install mrjob\n\n\n\nmrjob实现WordCount\nfrom mrjob.job import MRJob\n\nclass MRWordCount(MRJob):\n\n    #每一行从line中输入\n    def mapper(self, _, line):\n        for word in line.split():\n            yield word,1\n\n    # word相同的 会走到同一个reduce\n    def reducer(self, word, counts):\n        yield word, sum(counts)\n\nif __name__ == '__main__':\n    MRWordCount.run()\n\n运行WordCount代码\n打开命令行, 找到一篇文本文档, 敲如下命令:\npython mr_word_count.py my_file.txt\n\n3.3.2 运行MRJOB的不同方式\n1、内嵌(-r inline)方式\n特点是调试方便，启动单一进程模拟任务执行状态和结果，默认(-r inline)可以省略，输出文件使用 > output-file 或-o output-file，比如下面两种运行方式是等价的\npython word_count.py -r inline input.txt > output.txt\npython word_count.py input.txt > output.txt\n2、本地(-r local)方式\n用于本地模拟Hadoop调试，与内嵌(inline)方式的区别是启动了多进程执行每一个任务。如：\npython word_count.py -r local input.txt > output1.txt\n3、Hadoop(-r hadoop)方式\n用于hadoop环境，支持Hadoop运行调度控制参数，如：\n1)指定Hadoop任务调度优先级(VERY_HIGH|HIGH),如：--jobconf mapreduce.job.priority=VERY_HIGH。\n2)Map及Reduce任务个数限制，如：--jobconf mapreduce.map.tasks=2  --jobconf mapreduce.reduce.tasks=5\npython word_count.py -r hadoop hdfs:///test.txt -o  hdfs:///output\n3.3.3 mrjob 实现 topN统计（实验）\n统计数据中出现次数最多的前n个数据\nimport sys\nfrom mrjob.job import MRJob,MRStep\nimport heapq\n\nclass TopNWords(MRJob):\n    def mapper(self, _, line):\n        if line.strip() != \"\":\n            for word in line.strip().split():\n                yield word,1\n\n    #介于mapper和reducer之间，用于临时的将mapper输出的数据进行统计\n    def combiner(self, word, counts):\n        yield word,sum(counts)\n\n    def reducer_sum(self, word, counts):\n        yield None,(sum(counts),word)\n\n    #利用heapq将数据进行排序，将最大的2个取出\n    def top_n_reducer(self,_,word_cnts):\n        for cnt,word in heapq.nlargest(2,word_cnts):\n            yield word,cnt\n\n    #实现steps方法用于指定自定义的mapper，comnbiner和reducer方法\n    def steps(self):\n        #传入两个step 定义了执行的顺序\n        return [\n            MRStep(mapper=self.mapper,\n                   combiner=self.combiner,\n                   reducer=self.reducer_sum),\n            MRStep(reducer=self.top_n_reducer)\n        ]\n\ndef main():\n    TopNWords.run()\n\nif __name__=='__main__':\n    main()\n\n"},"day03_Hadoop/ha3.5.html":{"url":"day03_Hadoop/ha3.5.html","title":"04_MapReduce原理","keywords":"","body":"3.4 MapReduce原理详解\n单机程序计算流程\n输入数据--->读取数据--->处理数据--->写入数据--->输出数据\nHadoop计算流程\ninput data：输入数据\nInputFormat：对数据进行切分，格式化处理\nmap：将前面切分的数据做map处理(将数据进行分类，输出(k,v)键值对数据)\nshuffle&sort:将相同的数据放在一起，并对数据进行排序处理\nreduce：将map输出的数据进行hash计算，对每个map数据进行统计计算\nOutputFormat：格式化输出数据\n\n\n\n\n\nmap：将数据进行处理\nbuffer in memory：达到80%数据时，将数据锁在内存上，将这部分输出到磁盘上\npartitions：在磁盘上有很多\"小的数据\"，将这些数据进行归并排序。\nmerge on disk：将所有的\"小的数据\"进行合并。\nreduce：不同的reduce任务，会从map中对应的任务中copy数据\n​        在reduce中同样要进行merge操作\nMapReduce架构\n\nMapReduce架构 1.X\nJobTracker:负责接收客户作业提交，负责任务到作业节点上运行，检查作业的状态\nTaskTracker：由JobTracker指派任务，定期向JobTracker汇报状态，在每一个工作节点上永远只会有一个TaskTracker\n\n\n\n\n\nMapReduce2.X架构\n\nResourceManager：负责资源的管理，负责提交任务到NodeManager所在的节点运行，检查节点的状态\nNodeManager：由ResourceManager指派任务，定期向ResourceManager汇报状态\n\n\n\n\n"},"day03_Hadoop/ha4.1.html":{"url":"day03_Hadoop/ha4.1.html","title":"01_Hadoop生态系统","keywords":"","body":"4.1 Hadoop生态系统\n狭义的Hadoop VS 广义的Hadoop\n\n广义的Hadoop：指的是Hadoop生态系统，Hadoop生态系统是一个很庞大的概念，hadoop是其中最重要最基础的一个部分，生态系统中每一子系统只解决某一个特定的问题域（甚至可能更窄），不搞统一型的全能系统，而是小而精的多个小系统；\n\n\nHive:数据仓库\nR:数据分析\nMahout:机器学习库\npig：脚本语言，跟Hive类似\nOozie:工作流引擎，管理作业执行顺序\nZookeeper:用户无感知，主节点挂掉选择从节点作为主的\nFlume:日志收集框架\nSqoop:数据交换框架，例如：关系型数据库与HDFS之间的数据交换\nHbase : 海量数据中的查询，相当于分布式文件系统中的数据库\nSpark: 分布式的计算框架基于内存\n\nspark core\nspark sql\nspark streaming 准实时 不算是一个标准的流式计算\nspark ML spark MLlib\n\nKafka: 消息队列\nStorm: 分布式的流式计算框架  python操作storm \nFlink: 分布式的流式计算框架\nHadoop生态系统的特点\n\n开源、社区活跃\n\n囊括了大数据处理的方方面面\n\n成熟的生态圈\n\n"},"day03_Hadoop/ha4.2.html":{"url":"day03_Hadoop/ha4.2.html","title":"02_HDFS读写流程&高可用","keywords":"","body":"4.2HDFS 读写流程& 高可用\n\nHDFS读写流程\n\n\n\n\n\n客户端向NameNode发出写文件请求。\n\n检查是否已存在文件、检查权限。若通过检查，直接先将操作写入EditLog，并返回输出流对象。 \n（注：WAL，write ahead log，先写Log，再写内存，因为EditLog记录的是最新的HDFS客户端执行所有的写操作。如果后续真实写操作失败了，由于在真实写操作之前，操作就被写入EditLog中了，故EditLog中仍会有记录，我们不用担心后续client读不到相应的数据块，因为在第5步中DataNode收到块后会有一返回确认信息，若没写成功，发送端没收到确认信息，会一直重试，直到成功）\n\nclient端按128MB的块切分文件。\n\nclient将NameNode返回的分配的可写的DataNode列表和Data数据一同发送给最近的第一个DataNode节点，此后client端和NameNode分配的多个DataNode构成pipeline管道，client端向输出流对象中写数据。client每向第一个DataNode写入一个packet，这个packet便会直接在pipeline里传给第二个、第三个…DataNode。 \n（注：并不是写好一个块或一整个文件后才向后分发）\n\n每个DataNode写完一个块后，会返回确认信息。 \n（注：并不是每写完一个packet后就返回确认信息，个人觉得因为packet中的每个chunk都携带校验信息，没必要每写一个就汇报一下，这样效率太慢。正确的做法是写完一个block块后，对校验信息进行汇总分析，就能得出是否有块写错的情况发生）\n\n写完数据，关闭输输出流。\n\n发送完成信号给NameNode。\n（注：发送完成信号的时机取决于集群是强一致性还是最终一致性，强一致性则需要所有DataNode写完后才向NameNode汇报。最终一致性则其中任意一个DataNode写完后就能单独向NameNode汇报，HDFS一般情况下都是强调强一致性） \n\n\n\nHDFS如何实现高可用(HA)\n\n数据存储故障容错\n磁盘介质在存储过程中受环境或者老化影响,数据可能错乱\n对于存储在 DataNode 上的数据块，计算并存储校验和（CheckSum)\n读取数据的时候, 重新计算读取出来的数据校验和, 校验不正确抛出异常, 从其它DataNode上读取备份数据\n\n\n磁盘故障容错\nDataNode 监测到本机的某块磁盘损坏\n将该块磁盘上存储的所有 BlockID 报告给 NameNode\nNameNode 检查这些数据块在哪些DataNode上有备份,\n通知相应DataNode, 将数据复制到其他服务器上\n\n\nDataNode故障容错\n通过心跳和NameNode保持通讯\n超时未发送心跳, NameNode会认为这个DataNode已经宕机\nNameNode查找这个DataNode上有哪些数据块, 以及这些数据在其它DataNode服务器上的存储情况\n从其它DataNode服务器上复制数据\n\n\nNameNode故障容错\n主从热备 secondary namenode\nzookeeper配合 master节点选举\n\n\n\n\n\n"},"day03_Hadoop/ha4.3.html":{"url":"day03_Hadoop/ha4.3.html","title":"03_Hadoop发行版选择","keywords":"","body":"4.3 Hadoop发行版的选择\n\nApache Hadoop\n开源社区版\n最新的Hadoop版本都是从Apache Hadoop发布的\nHadoop Hive Flume  版本不兼容的问题 jar包  spark scala  Java->.class->.jar ->JVM\n\n\nCDH: Cloudera Distributed Hadoop\n\nCloudera 在社区版的基础上做了一些修改\n\nhttp://archive.cloudera.com/cdh5/cdh/5/\n\n\nhadoop-2.6.0-cdh-5.7.0 和 Flume*-cdh5.7.0 cdh版本一致 的各个组件配合是有不会有兼容性问题\n\nCDH版本的这些组件 没有全部开源\n\n\nHDP: Hortonworks Data Platform\n\n4.4 大数据产品与互联网产品结合\n\n分布式系统执行任务瓶颈: 延迟高 MapReduce 几分钟 Spark几秒钟\n互联网产品要求\n毫秒级响应(1秒以内完成)\n需要通过大数据实现 统计分析 数据挖掘 关联推荐 用户画像\n\n\n大数据平台\n整合网站应用和大数据系统之间的差异, 将应用产生的数据导入到大数据系统, 经过处理计算后再导出给应用程序使用\n\n\n互联网大数据平台架构:\n\n\n\n数据采集\nApp/Web 产生的数据&日志同步到大数据系统\n数据库同步:Sqoop  日志同步:Flume 打点: Kafka\n不同数据源产生的数据质量可能差别很大\n数据库 也许可以直接用\n日志 爬虫 大量的清洗,转化处理 \n\n\n\n\n数据处理\n\n大数据存储与计算的核心\n数据同步后导入HDFS\nMapReduce Hive Spark 读取数据进行计算 结果再保存到HDFS\nMapReduce Hive Spark 离线计算, HDFS 离线存储\n离线计算通常针对(某一类别)全体数据, 比如 历史上所有订单\n离线计算特点: 数据规模大, 运行时间长\n\n\n流式计算\n淘宝双11 每秒产生订单数 监控宣传\nStorm(毫秒) SparkStreaming(秒)\n\n\n\n\n数据输出与展示\n\nHDFS需要把数据导出交给应用程序, 让用户实时展示  ECharts\n淘宝卖家量子魔方\n\n\n给运营和决策层提供各种统计报告, 数据需要写入数据库\n很多运营管理人员, 上班后就会登陆后台数据系统\n\n\n\n\n任务调度系统\n将上面三个部分整合起来\n\n\n\n4.5 大数据应用--数据分析\n\n通过数据分析指标监控企业运营状态, 及时调整运营和产品策略,是大数据技术的关键价值之一\n\n大数据平台(互联网企业)运行的绝大多数大数据计算都是关于数据分析的\n\n统计指标\n关联分析,\n汇总报告,\n\n\n运营数据是公司管理的基础\n\n了解公司目前发展的状况\n数据驱动运营: 调节指标对公司进行管理\n\n\n运营数据的获取需要大数据平台的支持\n\n埋点采集数据\n数据库,日志 三方采集数据\n对数据清洗 转换 存储 \n利用SQL进行数据统计 汇总 分析\n得到需要的运营数据报告\n\n\n运营常用数据指标\n\n新增用户数  UG  user growth 用户增长\n\n产品增长性的关键指标\n新增访问网站(新下载APP)的用户数\n\n\n用户留存率\n\n用户留存率 = 留存用户数 / 当期新增用户数\n3日留存  5日留存 7日留存\n\n\n活跃用户数\n\n打开使用产品的用户\n日活\n月活\n提升活跃是网站运营的重要目标\n\n\nPV Page View\n\n打开产品就算活跃\n打开以后是否频繁操作就用PV衡量, 每次点击, 页面跳转都记一次PV\n\n\nGMV\n\n成交总金额(Gross Merchandise Volume) 电商网站统计营业额, 反应网站应收能力的重要指标\nGMV相关的指标: 订单量 客单价\n\n\n转化率\n转化率 = 有购买行为的用户数 / 总访问用户数\n\n\n\n\n\n4.6 数据分析案例\n\n背景: 某电商网站, 垂直领域领头羊, 各项指标相对稳定\n\n运营人员发现从 8 月 15 日开始，网站的订单量连续四天明显下跌\n\n8 月 18 号早晨发现 8 月 17 号的订单量没有恢复正常，运营人员开始尝试寻找原因\n\n是否有负面报道被扩散\n是否竞争对手在做活动\n是否某类商品缺货\n价格异常\n\n\n没有找到原因, 将问题交给数据分析团队\n\n\n数据分析师分析可能性\n\n新增用户出现问题\n查看日活数据, 发现日活没有明显下降\n基本判断, 用户在访问网站的过程中,转化出了问题\n\n\n\n\n\n转化过程:\n\n打开APP\n搜索关键词 浏览搜索结果列表\n点击商品访问详情\n有购买意向开始咨询\n放入购物车\n支付\n\n\n\n订单活跃转化率 = 日订单量 / 打开用户数\n\n搜索打开转化率 = 搜索用户数 / 打开用户数\n\n有明显降幅的是咨询详情转化率\n\n\n对咨询信息分类统计后发现，新用户的咨询量几乎为 0\n于是将问题提交给技术部门调查，工程师查看 8 月 15 日当天发布记录,发现有消息队列SDK更新\n\n\n\nHadoop企业应用案例之消费大数据\n亚马逊提前发货系统\nHadoop企业案例之商业零售大数据\n智能推荐\n"},"Hive&HBase/01_hive介绍.html":{"url":"Hive&HBase/01_hive介绍.html","title":"01_Hive基本概念","keywords":"","body":"4.1 Hive基本概念\n1 Hive简介 \n学习目标\n- 了解什么是Hive\n- 了解为什么使用Hive\n什么是 Hive\n\nHive 由 Facebook 实现并开源，是基于 Hadoop 的一个数据仓库工具，可以将结构化的数据映射为一张数据库表，并提供 HQL(Hive SQL)查询功能，底层数据是存储在 HDFS 上。\nHive 本质: 将 SQL 语句转换为 MapReduce 任务运行，使不熟悉 MapReduce 的用户很方便地利用 HQL 处理和计算 HDFS 上的结构化的数据,是一款基于 HDFS 的 MapReduce 计算框架\n主要用途：用来做离线数据分析，比直接用 MapReduce 开发效率更高。\n\n为什么使用 Hive\n\n直接使用 Hadoop MapReduce 处理数据所面临的问题：\n人员学习成本太高\nMapReduce 实现复杂查询逻辑开发难度太大\n\n\n使用 Hive\n操作接口采用类 SQL 语法，提供快速开发的能力\n避免了去写 MapReduce，减少开发人员的学习成本\n功能扩展很方便\n\n\n\n2 Hive 架构\nHive 架构图\n\nHive 组件\n\n用户接口：包括 CLI、JDBC/ODBC、WebGUI。\nCLI(command line interface)为 shell 命令行\nJDBC/ODBC 是 Hive 的 JAVA 实现，与传统数据库JDBC 类似\nWebGUI 是通过浏览器访问 Hive。\nHiveServer2基于Thrift, 允许远程客户端使用多种编程语言如Java、Python向Hive提交请求\n\n\n元数据存储：通常是存储在关系数据库如 mysql/derby 中。\nHive 将元数据存储在数据库中。\nHive 中的元数据包括\n表的名字\n表的列\n分区及其属性\n表的属性（是否为外部表等）\n表的数据所在目录等。\n\n\n\n\n解释器、编译器、优化器、执行器:完成 HQL 查询语句从词法分析、语法分析、编译、优化以及查询计划的生成。生成的查询计划存储在 HDFS 中，并在随后由 MapReduce 调用执行\n\nHive 与 Hadoop 的关系\nHive 利用 HDFS 存储数据，利用 MapReduce 查询分析数据。\nHive是数据仓库工具，没有集群的概念，如果想提交Hive作业只需要在hadoop集群 Master节点上装Hive就可以了\n3 Hive 与传统数据库对比\n\nhive 用于海量数据的离线数据分析。\n\n\n  \n    \n    Hive\n    关系型数据库\n  \n  \n     ANSI SQL \n     不完全支持 \n     支持 \n  \n  \n     更新 \n     INSERT OVERWRITE\\INTO TABLE(默认) \n     UPDATE\\INSERT\\DELETE \n  \n  \n     事务 \n     不支持(默认) \n     支持 \n  \n  \n     模式 \n     读模式 \n     写模式 \n  \n  \n     查询语言 \n     HQL  \n     SQL\n  \n  \n     数据存储 \n     HDFS \n     Raw Device or Local FS \n  \n  \n     执行 \n     MapReduce \n     Executor\n  \n  \n     执行延迟 \n     高 \n     低 \n  \n  \n     子查询 \n     只能用在From子句中 \n     完全支持 \n  \n  \n     处理数据规模 \n     大 \n     小 \n  \n  \n     可扩展性 \n     高 \n     低 \n  \n  \n     索引 \n     0.8版本后加入位图索引 \n     有复杂的索引 \n  \n\n\n\nhive支持的数据类型\n原子数据类型  \nTINYINT SMALLINT INT BIGINT BOOLEAN FLOAT DOUBLE STRING BINARY TIMESTAMP DECIMAL CHAR VARCHAR DATE\n\n\n复杂数据类型\nARRAY\nMAP\nSTRUCT\n\n\n\n\nhive中表的类型\n托管表 (managed table) (内部表)\n外部表\n\n\n\n4 Hive 数据模型\n\nHive 中所有的数据都存储在 HDFS 中，没有专门的数据存储格式\n在创建表时指定数据中的分隔符，Hive 就可以映射成功，解析数据。\nHive 中包含以下数据模型：\ndb：在 hdfs 中表现为 hive.metastore.warehouse.dir 目录下一个文件夹\ntable：在 hdfs 中表现所属 db 目录下一个文件夹\nexternal table：数据存放位置可以在 HDFS 任意指定路径\npartition：在 hdfs 中表现为 table 目录下的子目录\nbucket：在 hdfs 中表现为同一个表目录下根据 hash 散列之后的多个文件\n\n\n\n5 Hive 安装部署\n\nHive 安装前需要安装好 JDK 和 Hadoop。配置好环境变量。\n\n下载Hive的安装包 http://archive.cloudera.com/cdh5/cdh/5/ 并解压\n tar -zxvf hive-1.1.0-cdh5.7.0.tar.gz  -C ~/app/\n\n\n进入到 解压后的hive目录 找到 conf目录, 修改配置文件\ncp hive-env.sh.template hive-env.sh\nvi hive-env.sh\n\n在hive-env.sh中指定hadoop的路径\nHADOOP_HOME=/root/bigdata/hadoop\n\n\n配置环境变量\n\nvi ~/.bash_profile\n\n\nexport HIVE_HOME=/root/bigdata/hive\nexport PATH=$HIVE_HOME/bin:$PATH\n\n\nsource ~/.bash_profile\n\n\n\n\n根据元数据存储的介质不同，分为下面两个版本，其中 derby 属于内嵌模式。实际生产环境中则使用 mysql 来进行元数据的存储。\n\n内置 derby 版： \nbin/hive 启动即可使用\n缺点：不同路径启动 hive，每一个 hive 拥有一套自己的元数据，无法共享\n\nmysql 版： \n\n上传 mysql驱动到 hive安装目录的lib目录下\nmysql-connector-java-5.*.jar\n\nvi conf/hive-site.xml 配置 Mysql 元数据库信息(MySql安装见文档)\n\n\n\n\n    \n        javax.jdo.option.ConnectionUserName\n        root\n    \n    \n        javax.jdo.option.ConnectionPassword\n        password\n    \n   \n        javax.jdo.option.ConnectionURLmysql\n        jdbc:mysql://127.0.0.1:3306/hive\n    \n    \n        javax.jdo.option.ConnectionDriverName\n        com.mysql.jdbc.Driver\n    \n        \n  \n    hive.exec.script.wrapper\n    \n    \n  \n\n\n\n\n\n\n\nhive启动\n\n启动docker \nservice docker start\n\n通过docker 启动mysql\ndocker start mysql\n\n启动 hive的metastore元数据服务\nhive --service metastore\n\n启动hive\nhive\n\nMySQL\n\n用户名：root\n密码：password\n\n\n\n\n\n"},"Hive&HBase/02_hive的shell操作.html":{"url":"Hive&HBase/02_hive的shell操作.html","title":"02_Hive的shell操作","keywords":"","body":"4.2 Hive 基本操作\n1 Hive HQL操作初体验\n\n创建数据库\nCREATE DATABASE test;\n\n\n显示所有数据库\nSHOW DATABASES;\n\n\n创建表\nCREATE TABLE student(classNo string, stuNo string, score int) row format delimited fields terminated by ',';\n\n\nrow format delimited fields terminated by ','  指定了字段的分隔符为逗号，所以load数据的时候，load的文本也要为逗号，否则加载后为NULL。hive只支持单个字符的分隔符，hive默认的分隔符是\\001\n\n\n将数据load到表中\n\n在本地文件系统创建一个如下的文本文件：/home/hadoop/tmp/student.txt\nC01,N0101,82\nC01,N0102,59\nC01,N0103,65\nC02,N0201,81\nC02,N0202,82\nC02,N0203,79\nC03,N0301,56\nC03,N0302,92\nC03,N0306,72\n\nload data local inpath '/home/hadoop/tmp/student.txt'overwrite into table student;\n\n\n这个命令将student.txt文件复制到hive的warehouse目录中，这个目录由hive.metastore.warehouse.dir配置项设置，默认值为/user/hive/warehouse。Overwrite选项将导致Hive事先删除student目录下所有的文件, 并将文件内容映射到表中。\nHive不会对student.txt做任何格式处理，因为Hive本身并不强调数据的存储格式。\n\n\n\n查询表中的数据 跟SQL类似\nhive>select * from student;\n\n\n分组查询group by和统计 count\nhive>select classNo,count(score) from student where score>=60 group by classNo;\n\n从执行结果可以看出 hive把查询的结果变成了MapReduce作业通过hadoop执行\n\n\n2 Hive的内部表和外部表\n\n  \n    \n    内部表(managed table)\n    外部表(external table)\n  \n  \n     概念 \n     创建表时无external修饰 \n     创建表时被external修饰 \n  \n  \n     数据管理 \n     由Hive自身管理 \n     由HDFS管理 \n  \n  \n     数据保存位置 \n     hive.metastore.warehouse.dir  （默认：/user/hive/warehouse） \n     hdfs中任意位置 \n  \n  \n     删除时影响 \n     直接删除元数据（metadata）及存储数据 \n     仅会删除元数据，HDFS上的文件并不会被删除 \n  \n  \n     表结构修改时影响 \n     修改会将修改直接同步给元数据  \n     表结构和分区进行修改，则需要修复（MSCK REPAIR TABLE table_name;）\n  \n\n\n\n案例\n\n创建一个外部表student2\n\nCREATE EXTERNAL TABLE student2 (classNo string, stuNo string, score int) row format delimited fields terminated by ',' location '/tmp/student';\n\n\n装载数据\nload data local inpath '/root/tmp/student.txt' overwrite into table student2;\n\n\n\n\n显示表信息\ndesc formatted table_name;\n\n\n删除表查看结果\ndrop table student;\n\n\n再次创建外部表 student2\n\n不插入数据直接查询查看结果\nselect * from student2;\n\n\n\n3 分区表\n\n什么是分区表\n\n随着表的不断增大，对于新纪录的增加，查找，删除等(DML)的维护也更加困难。对于数据库中的超大型表，可以通过把它的数据分成若干个小表，从而简化数据库的管理活动，对于每一个简化后的小表，我们称为一个单个的分区。\nhive中分区表实际就是对应hdfs文件系统上独立的文件夹，该文件夹内的文件是该分区所有数据文件。\n分区可以理解为分类，通过分类把不同类型的数据放到不同的目录下。\n分类的标准就是分区字段，可以一个，也可以多个。\n分区表的意义在于优化查询。查询时尽量利用分区字段。如果不使用分区字段，就会全部扫描。\n\n\n创建分区表\ntom,4300\njerry,12000\nmike,13000\njake,11000\nrob,10000\n\n\n\n  create table employee (name string,salary bigint) partitioned by (date1 string) row format delimited fields terminated by ',' lines terminated by '\\n' stored as textfile;\n\n\n查看表的分区\nshow partitions employee;\n\n\n添加分区\nalter table employee add if not exists partition(date1='2018-12-01');\n\n加载数据到分区\nload data local inpath '/root/tmp/employee.txt' into table employee partition(date1='2018-12-01');\n\n如果重复加载同名文件，不会报错，会自动创建一个*_copy_1.txt\n\n外部分区表即使有分区的目录结构, 也必须要通过hql添加分区, 才能看到相应的数据\nhadoop fs -mkdir /user/hive/warehouse/employee/date1=2018-12-04\nhadoop fs -copyFromLocal /tmp/employee.txt /user/hive/warehouse/test.db/employee/date1=2018-12-04/employee.txt\n\n\n此时查看表中数据发现数据并没有变化, 需要通过hql添加分区\nalter table employee add if not exists partition(date1='2018-12-04');\n\n此时再次查看才能看到新加入的数据\n\n\n\n总结\n\n利用分区表方式减少查询时需要扫描的数据量\n分区字段不是表中的列, 数据文件中没有对应的列\n分区仅仅是一个目录名\n查看数据时, hive会自动添加分区列\n支持多级分区, 多级子目录\n\n\n\n\n\n4 动态分区\n\n在写入数据时自动创建分区(包括目录结构)\n\n创建表\ncreate table employee2 (name string,salary bigint) partitioned by (date1 string) row format delimited fields terminated by ',' lines terminated by '\\n' stored as textfile;\n\n导入数据\ninsert into table employee2 partition(date1) select name,salary,date1 from employee;\n\n\n使用动态分区需要设置参数\nset hive.exec.dynamic.partition.mode=nonstrict;\n\n\n\n"},"Hive&HBase/03_Hive的函数和自定义函数.html":{"url":"Hive&HBase/03_Hive的函数和自定义函数.html","title":"03_Hive的函数和自定义函数","keywords":"","body":"4.3 Hive 函数\n1 内置运算符\n在 Hive 有四种类型的运算符：\n\n关系运算符\n\n算术运算符\n\n逻辑运算符\n\n复杂运算\n(内容较多，见《Hive 官方文档》》)\n\n\n2 内置函数\nhttps://cwiki.apache.org/confluence/display/Hive/LanguageManual+UDF\n\n简单函数: 日期函数 字符串函数 类型转换 \n统计函数: sum avg distinct\n集合函数\n分析函数\nshow functions;  显示所有函数\ndesc function 函数名;\ndesc function extended 函数名;\n\n3 Hive 自定义函数和 Transform\n\nUDF\n\n当 Hive 提供的内置函数无法满足你的业务处理需要时，此时就可以考虑使用用户自定义函数（UDF：user-defined function）。\n\nTRANSFORM,and UDF and UDAF\nit is possible to plug in your own custom mappers and reducers\n A UDF is basically only a transformation done by a mapper meaning that each row should be mapped to exactly one row. A UDAF on the other hand allows us to transform a group of rows into one or more rows, meaning that we can reduce the number of input rows to a single output row by some custom aggregation.\nUDF：就是做一个mapper，对每一条输入数据，映射为一条输出数据。\nUDAF:就是一个reducer，把一组输入数据映射为一条(或多条)输出数据。\n一个脚本至于是做mapper还是做reducer，又或者是做udf还是做udaf，取决于我们把它放在什么样的hive操作符中。放在select中的基本就是udf，放在distribute by和cluster by中的就是reducer。\nWe can control if the script is run in a mapper or reducer step by the way we formulate our HiveQL query.\nThe statements DISTRIBUTE BY and CLUSTER BY allow us to indicate that we want to actually perform an aggregation.\nUser-Defined Functions (UDFs) for transformations and even aggregations which are therefore called User-Defined Aggregation Functions (UDAFs)\n\n\n\nUDF示例(运行java已经编写好的UDF)\n\n在hdfs中创建 /user/hive/lib目录\nhadoop fs -mkdir /user/hive/lib\n\n\n把 hive目录下 lib/hive-contrib-hive-contrib-1.1.0-cdh5.7.0.jar 放到hdfs中\nhadoop fs -put hive-contrib-1.1.0-cdh5.7.0.jar /user/hive/lib/\n\n\n把集群中jar包的位置添加到hive中\nhive> add jar hdfs:///user/hive/lib/hive-contrib-1.1.0-cdh5.7.0.jar;\n\n\n在hive中创建临时UDF\nhive> CREATE TEMPORARY FUNCTION row_sequence as 'org.apache.hadoop.hive.contrib.udf.UDFRowSequence'\n\n\n在之前的案例中使用临时自定义函数(函数功能: 添加自增长的行号)\nSelect row_sequence(),* from employee;\n\n\n创建非临时自定义函数\nCREATE FUNCTION row_sequence as 'org.apache.hadoop.hive.contrib.udf.UDFRowSequence' using jar 'hdfs:///user/hive/lib/hive-contrib-1.1.0-cdh5.7.0.jar';\n\n\n\nPython UDF\n\n准备案例环境\n\n创建表\nCREATE table u(fname STRING,lname STRING);\n\n\n向表中插入数据\ninsert into table u2 values('George','washington');\ninsert into table u2 values('George','bush');\ninsert into table u2 values('Bill','clinton');\ninsert into table u2 values('Bill','gates');\n\n\n\n\n编写map风格脚本\nimport sys\nfor line in sys.stdin:\n    line = line.strip()\n    fname , lname = line.split('\\t')\n    l_name = lname.upper()\n    print '\\t'.join([fname, str(l_name)])\n\n\n通过hdfs向hive中ADD file\n\n加载文件到hdfs\nhadoop fs -put udf.py /user/hive/lib/\n\n\nhive从hdfs中加载python脚本\nADD FILE hdfs:///user/hive/lib/udf.py;\nADD FILE /root/tmp/udf1.py;\n\n\n\n\nTransform\nSELECT TRANSFORM(fname, lname) USING 'python udf1.py' AS (fname, l_name) FROM u;\n\n\n\n\n\n"},"Hive&HBase/04_hive综合案例.html":{"url":"Hive&HBase/04_hive综合案例.html","title":"04_Hive综合案例","keywords":"","body":"4.4 hive综合案例\n\n内容推荐数据处理\n\n\n需求\n根据用户行为以及文章标签筛选出用户最感兴趣(阅读最多)的标签\n\n\n\n\n相关数据\n​    user_id article_id event_time\n11,101,2018-12-01 06:01:10\n22,102,2018-12-01 07:28:12\n33,103,2018-12-01 07:50:14\n11,104,2018-12-01 09:08:12\n22,103,2018-12-01 13:37:12\n33,102,2018-12-02 07:09:12\n11,101,2018-12-02 18:42:12\n35,105,2018-12-03 09:21:12\n22,104,2018-12-03 16:42:12\n77,103,2018-12-03 18:31:12\n99,102,2018-12-04 00:04:12\n33,101,2018-12-04 19:10:12\n11,101,2018-12-05 09:07:12\n35,102,2018-12-05 11:00:12\n22,103,2018-12-05 12:11:12\n77,104,2018-12-05 18:02:02\n99,105,2018-12-05 20:09:11\n\n文章数据\n\nartical_id,artical_url,artical_keywords\n101,http://www.itcast.cn/1.html,kw8|kw1\n102,http://www.itcast.cn/2.html,kw6|kw3\n103,http://www.itcast.cn/3.html,kw7\n104,http://www.itcast.cn/4.html,kw5|kw1|kw4|kw9\n105,http://www.itcast.cn/5.html,\n\n数据上传hdfs\nhadoop fs -mkdir /tmp/demo\nhadoop fs -mkdir /tmp/demo/user_action\n\n\n创建外部表\n\n用户行为表\n\ndrop table if exists user_actions;\nCREATE EXTERNAL TABLE user_actions(\n    user_id STRING,\n    article_id STRING,\n    time_stamp STRING\n)\nROW FORMAT delimited fields terminated by ','\nLOCATION '/tmp/demo/user_action';\n\n\n文章表\n\ndrop table if exists articles;\nCREATE EXTERNAL TABLE articles(\n    article_id STRING,\n    url STRING,\n    key_words array\n)\nROW FORMAT delimited fields terminated by ',' \nCOLLECTION ITEMS terminated BY '|' \nLOCATION '/tmp/demo/article_keywords';\n/*\nkey_words array  数组的数据类型\nCOLLECTION ITEMS terminated BY '|'  数组的元素之间用'|'分割\n*/\n\n\n查看数据\n\nselect * from user_actions;\nselect * from articles;\n\n\n分组查询每个用户的浏览记录\n\ncollect_set/collect_list作用:\n将group by中的某列转为一个数组返回\ncollect_list不去重而collect_set去重\n\n\ncollect_set\n\nselect user_id,collect_set(article_id) \nfrom user_actions group by user_id;\n\n11      [\"101\",\"104\"]\n22      [\"102\",\"103\",\"104\"]\n33      [\"103\",\"102\",\"101\"]\n35      [\"105\",\"102\"]\n77      [\"103\",\"104\"]\n99      [\"102\",\"105\"]\n\n\ncollect_list\n\nselect user_id,collect_list(article_id) \nfrom user_actions group by user_id;\n\n11      [\"101\",\"104\",\"101\",\"101\"]\n22      [\"102\",\"103\",\"104\",\"103\"]\n33      [\"103\",\"102\",\"101\"]\n35      [\"105\",\"102\"]\n77      [\"103\",\"104\"]\n99      [\"102\",\"105\"]\n\n\nsort_array: 对数组排序\n\nselect user_id,sort_array(collect_list(article_id)) as contents \nfrom user_actions group by user_id;\n\n11      [\"101\",\"101\",\"101\",\"104\"]\n22      [\"102\",\"103\",\"103\",\"104\"]\n33      [\"101\",\"102\",\"103\"]\n35      [\"102\",\"105\"]\n77      [\"103\",\"104\"]\n99      [\"102\",\"105\"]\n\n\n查看每一篇文章的关键字 lateral view explode\n\nexplode函数 将array 拆分\n\nselect explode(key_words) from articles;\n\n\nlateral view 和 explode 配合使用,将一行数据拆分成多行数据，在此基础上可以对拆分的数据进行聚合\n\nselect article_id,kw from articles lateral view explode(key_words) t as kw;\n\n101     kw8\n101     kw1\n102     kw6\n102     kw3\n103     kw7\n104     kw5\n104     kw1\n104     kw4\n104     kw9\n\nselect article_id,kw from articles lateral view outer explode(key_words) t as kw;\n\n101     kw8\n101     kw1\n102     kw6\n102     kw3\n103     kw7\n104     kw5\n104     kw1\n104     kw4\n104     kw9\n105     NULL\n#含有outer\n\n\n\n\n\n\n根据文章id找到用户查看文章的关键字\n\n原始数据\n\n101     http://www.itcast.cn/1.html     [\"kw8\",\"kw1\"]\n102     http://www.itcast.cn/2.html     [\"kw6\",\"kw3\"]\n103     http://www.itcast.cn/3.html     [\"kw7\"]\n104     http://www.itcast.cn/4.html     [\"kw5\",\"kw1\",\"kw4\",\"kw9\"]\n105     http://www.itcast.cn/5.html     []\n\nselect a.user_id, b.kw from user_actions \nas a left outer JOIN (select article_id,kw from articles\nlateral view outer explode(key_words) t as kw) b\non (a.article_id = b.article_id)\norder by a.user_id;\n\n11      kw1\n11      kw8\n11      kw5\n11      kw1\n11      kw4\n11      kw1\n11      kw9\n11      kw8\n11      kw1\n11      kw8\n22      kw1\n22      kw7\n22      kw9\n22      kw4\n22      kw5\n22      kw7\n22      kw3\n22      kw6\n33      kw8\n33      kw1\n33      kw3\n33      kw6\n33      kw7\n35      NULL\n35      kw6\n35      kw3\n77      kw9\n77      kw1\n77      kw7\n77      kw4\n77      kw5\n99      kw3\n99      kw6\n99      NULL\n\n\n根据文章id找到用户查看文章的关键字并统计频率\nselect a.user_id, b.kw,count(1) as weight \nfrom user_actions as a \nleft outer JOIN (select article_id,kw from articles\nlateral view outer explode(key_words) t as kw) b\non (a.article_id = b.article_id)\ngroup by a.user_id,b.kw \norder by a.user_id,weight desc;\n\n11      kw1     4\n11      kw8     3\n11      kw5     1\n11      kw9     1\n11      kw4     1\n22      kw7     2\n22      kw9     1\n22      kw1     1\n22      kw3     1\n22      kw4     1\n22      kw5     1\n22      kw6     1\n33      kw3     1\n33      kw8     1\n33      kw7     1\n33      kw6     1\n33      kw1     1\n35      NULL    1\n35      kw3     1\n35      kw6     1\n77      kw1     1\n77      kw4     1\n77      kw5     1\n77      kw7     1\n77      kw9     1\n99      NULL    1\n99      kw3     1\n99      kw6     1\n\n\nCONCAT：\nCONCAT(str1,str2,…)  \n返回结果为连接参数产生的字符串。如有任何一个参数为NULL ，则返回值为 NULL。\nselect concat(user_id,article_id) from user_actions;\n\nCONCAT_WS:\n使用语法为：CONCAT_WS(separator,str1,str2,…)\nCONCAT_WS() 代表 CONCAT With Separator ，是CONCAT()的特殊形式。第一个参数是其它参数的分隔符。分隔符的位置放在要连接的两个字符串之间。分隔符可以是一个字符串，也可以是其它参数。如果分隔符为 NULL，则结果为 NULL。\nselect concat_ws(':',user_id,article_id) from user_actions;\n\n\n将用户查看的关键字和频率合并成 key:value形式\nselect a.user_id, concat_ws(':',b.kw,cast (count(1) as string)) as kw_w \nfrom user_actions as a \nleft outer JOIN (select article_id,kw from articles\nlateral view outer explode(key_words) t as kw) b\non (a.article_id = b.article_id)\ngroup by a.user_id,b.kw;\n\n11      kw1:4\n11      kw4:1\n11      kw5:1\n11      kw8:3\n11      kw9:1\n22      kw1:1\n22      kw3:1\n22      kw4:1\n22      kw5:1\n22      kw6:1\n22      kw7:2\n22      kw9:1\n33      kw1:1\n33      kw3:1\n33      kw6:1\n33      kw7:1\n33      kw8:1\n35      1\n35      kw3:1\n35      kw6:1\n77      kw1:1\n77      kw4:1\n77      kw5:1\n77      kw7:1\n77      kw9:1\n99      1\n99      kw3:1\n99      kw6:1\n\n\n将用户查看的关键字和频率合并成 key:value形式并按用户聚合\nselect cc.user_id,concat_ws(',',collect_set(cc.kw_w))\nfrom(\nselect a.user_id, concat_ws(':',b.kw,cast (count(1) as string)) as kw_w \nfrom user_actions as a \nleft outer JOIN (select article_id,kw from articles\nlateral view outer explode(key_words) t as kw) b\non (a.article_id = b.article_id)\ngroup by a.user_id,b.kw\n) as cc \ngroup by cc.user_id;\n\n11      kw1:4,kw4:1,kw5:1,kw8:3,kw9:1\n22      kw1:1,kw3:1,kw4:1,kw5:1,kw6:1,kw7:2,kw9:1\n33      kw1:1,kw3:1,kw6:1,kw7:1,kw8:1\n35      1,kw3:1,kw6:1\n77      kw1:1,kw4:1,kw5:1,kw7:1,kw9:1\n99      1,kw3:1,kw6:1\n\n\n将上面聚合结果转换成map\nselect cc.user_id,str_to_map(concat_ws(',',collect_set(cc.kw_w))) as wm\nfrom(\nselect a.user_id, concat_ws(':',b.kw,cast (count(1) as string)) as kw_w \nfrom user_actions as a \nleft outer JOIN (select article_id,kw from articles\nlateral view outer explode(key_words) t as kw) b\non (a.article_id = b.article_id)\ngroup by a.user_id,b.kw\n) as cc \ngroup by cc.user_id;\n\n11      {\"kw1\":\"4\",\"kw4\":\"1\",\"kw5\":\"1\",\"kw8\":\"3\",\"kw9\":\"1\"}\n22      {\"kw1\":\"1\",\"kw3\":\"1\",\"kw4\":\"1\",\"kw5\":\"1\",\"kw6\":\"1\",\"kw7\":\"2\",\"kw9\":\"1\"}\n33      {\"kw1\":\"1\",\"kw3\":\"1\",\"kw6\":\"1\",\"kw7\":\"1\",\"kw8\":\"1\"}\n35      {\"1\":null,\"kw3\":\"1\",\"kw6\":\"1\"}\n77      {\"kw1\":\"1\",\"kw4\":\"1\",\"kw5\":\"1\",\"kw7\":\"1\",\"kw9\":\"1\"}\n99      {\"1\":null,\"kw3\":\"1\",\"kw6\":\"1\"}\n\n\n将用户的阅读偏好结果保存到表中\ncreate table user_kws as \nselect cc.user_id,str_to_map(concat_ws(',',collect_set(cc.kw_w))) as wm\nfrom(\nselect a.user_id, concat_ws(':',b.kw,cast (count(1) as string)) as kw_w \nfrom user_actions as a \nleft outer JOIN (select article_id,kw from articles\nlateral view outer explode(key_words) t as kw) b\non (a.article_id = b.article_id)\ngroup by a.user_id,b.kw\n) as cc \ngroup by cc.user_id;\n\n\n从表中通过key查询map中的值\nselect user_id, wm['kw1'] from user_kws;\n\n11      4\n22      1\n33      1\n35      NULL\n77      1\n99      NULL\n\n\n从表中获取map中所有的key 和 所有的value\nselect user_id,map_keys(wm),map_values(wm) from user_kws;\n\n11      [\"kw1\",\"kw4\",\"kw5\",\"kw8\",\"kw9\"] [\"4\",\"1\",\"1\",\"3\",\"1\"]\n22      [\"kw1\",\"kw3\",\"kw4\",\"kw5\",\"kw6\",\"kw7\",\"kw9\"]     [\"1\",\"1\",\"1\",\"1\",\"1\",\"2\",\"1\"]\n33      [\"kw1\",\"kw3\",\"kw6\",\"kw7\",\"kw8\"] [\"1\",\"1\",\"1\",\"1\",\"1\"]\n35      [\"1\",\"kw3\",\"kw6\"]       [null,\"1\",\"1\"]\n77      [\"kw1\",\"kw4\",\"kw5\",\"kw7\",\"kw9\"] [\"1\",\"1\",\"1\",\"1\",\"1\"]\n99      [\"1\",\"kw3\",\"kw6\"]       [null,\"1\",\"1\"]\n\n\n用lateral view explode把map中的数据转换成多列\nselect user_id,keyword,weight from user_kws lateral view explode(wm) t as keyword,weight;\n\n11      kw1     4\n11      kw4     1\n11      kw5     1\n11      kw8     3\n11      kw9     1\n22      kw1     1\n22      kw3     1\n22      kw4     1\n22      kw5     1\n22      kw6     1\n22      kw7     2\n22      kw9     1\n33      kw1     1\n33      kw3     1\n33      kw6     1\n33      kw7     1\n33      kw8     1\n35      1       NULL\n35      kw3     1\n35      kw6     1\n77      kw1     1\n77      kw4     1\n77      kw5     1\n77      kw7     1\n77      kw9     1\n99      1       NULL\n99      kw3     1\n99      kw6     1\n\n\n\n"},"Hive&HBase/05_hBase简介与环境部署.html":{"url":"Hive&HBase/05_hBase简介与环境部署.html","title":"01_HBase简介与环境部署","keywords":"","body":"5.1 HBase简介\n1 什么是HBase\n\nHBase是一个分布式的、面向列的开源数据库\nHBase是Google BigTable的开源实现\nHBase不同于一般的关系数据库, 适合非结构化数据存储\n\n2 BigTable\n\nBigTable是Google设计的分布式数据存储系统，用来处理海量的数据的一种非关系型的数据库。\n适合大规模海量数据，PB级数据；\n分布式、并发数据处理，效率极高；\n易于扩展，支持动态伸缩\n适用于廉价设备；\n不适用于传统关系型数据的存储；\n\n\n\n3 面向列的数据库\nHBase 与 传统关系数据库的区别\n\n  \n    \n    HBase\n    关系型数据库\n  \n  \n     数据库大小 \n     PB级别  \n    GB TB\n  \n  \n     数据类型 \n     Bytes \n     丰富的数据类型 \n  \n    \n     事务支持 \n     ACID只支持单个Row级别 \n     全面的ACID支持, 对Row和表\n  \n  \n     索引 \n     只支持Row-key \n     支持 \n  \n    \n     吞吐量 \n     百万写入/秒 \n     数千写入/秒\n  \n\n\n\n关系型数据库中数据示例\n\n\n  \n    ID\n    FILE NAME\n    FILE PATH\n    FILE TYPE\n    FILE SIZE\n    CREATOR\n  \n  \n     1 \n     file1.txt  \n    /home\n     txt \n     1024 \n     tom \n  \n  \n     2 \n     file2.txt  \n    /home/pics\n     jpg \n     5032 \n     jerry \n  \n\n\n\n同样数据保存到列式数据库中\n\n\n\nRowKey\nFILE INFO\nSAVE INFO\n\n\n 1 \n name:file1.txt\ntype:txt\nsize:1024\npath:/home/pics\ncreator:Jerry\n\n\n\n 2 \nname:file2.jpg\ntype:jpg\nsize:5032\n path:/home\ncreator:Tom\n\n\n\n\n\n\n行数据库&列数据库存储方式比较\n\n\n4 什么是非结构化数据存储\n\n结构化数据\n适合用二维表来展示的数据\n\n\n非结构化数据\n非结构化数据是数据结构不规则或不完整\n没有预定义的数据模型\n不方便用数据库二维逻辑表来表现\n办公文档、文本、图片、XML, HTML、各类报表、图像和音频/视频信息等\n\n\n\n5 HBase在Hadoop生态中的地位\n\nHBase是Apache基金会顶级项目\n\nHBase基于HDFS进行数据存储\n\nHBase可以存储超大数据并适合用来进行大数据的实时查询\n\n\n\n6 HBase与HDFS\n\nHBase建立在Hadoop文件系统上, 利用了HDFS的容错能力\nHBase提供对数据的随机实时读/写访问功能\nHBase内部使用哈希表, 并存储索引, 可以快速查找HDFS中数据\n\n7 HBase使用场景\n\n瞬间写入量很大\n大量数据需要长期保存, 且数量会持续增长\nHBase不适合有join, 多级索引, 表关系复杂的数据模型\n\n"},"Hive&HBase/06_hbase数据模型.html":{"url":"Hive&HBase/06_hbase数据模型.html","title":"02_Hbase数据模型","keywords":"","body":"5.2 HBase的数据模型\n\nNameSpace: 关系型数据库的\"数据库\"(database)\n\n表(table)：用于存储管理数据，具有稀疏的、面向列的特点。HBase中的每一张表，就是所谓的大表(Bigtable)，可以有上亿行，上百万列。对于为值为空的列，并不占用存储空间，因此表可以设计的非常稀疏。\n\n行(Row)：在表里面,每一行代表着一个数据对象,每一行都是以一个行键(Row Key)来进行唯一标识的, 行键并没有什么特定的数据类型, 以二进制的字节来存储\n\n列(Column): HBase的列由 Column family 和 Column qualifier 组成, 由冒号: 进行行间隔, 如 family: qualifier\n\n行键(RowKey)：类似于MySQL中的主键，HBase根据行键来快速检索数据，一个行键对应一条记录。与MySQL主键不同的是，HBase的行键是天然固有的，每一行数据都存在行键。\n\n列族(ColumnFamily)：是列的集合。列族在表定义时需要指定，而列在插入数据时动态指定。列中的数据都是以二进制形式存在，没有数据类型。在物理存储结构上，每个表中的每个列族单独以一个文件存储。一个表可以有多个列簇。\n\n列修饰符(Column Qualifier) : 列族中的数据通过列标识来进行映射, 可以理解为一个键值对(key-value), 列修饰符(Column Qualifier) 就是key 对应关系型数据库的列\n\n时间戳(TimeStamp)：是列的一个属性，是一个64位整数。由行键和列确定的单元格，可以存储多个数据，每个数据含有时间戳属性，数据具有版本特性。可根据版本(VERSIONS)或时间戳来指定查询历史版本数据，如果都不指定，则默认返回最新版本的数据。\n\n区域(Region)：HBase自动把表水平划分成的多个区域，划分的区域随着数据的增大而增多。\n\nHBase 支持特定场景下的 ACID，即对行级别的 操作保证完全的 ACID\n\ncap定理\n\n分布式系统的最大难点，就是各个节点的状态如何同步。CAP 定理是这方面的基本定理，也是理解分布式系统的起点。\n\n一致性(所有节点在同一时间具有相同的数据)\n\n\n可用性(保证每个请求不管成功或失败都有响应,但不保证获取的数据的正确性)\n\n分区容错性(系统中任意信息的丢失或失败不会影响系统的运行,系统如果不能在某个时限内达成数据一致性,就必须在上面两个操作之间做出选择)\n\n\n\nhbase是CAP中的CP系统,即hbase是强一致性的\n\n\n\n\n"},"Hive&HBase/07_hbase的安装与shell操作.html":{"url":"Hive&HBase/07_hbase的安装与shell操作.html","title":"03_Hbase的安装与shell操作","keywords":"","body":"5.3 HBase 的安装与Shell操作\n1 HBase的安装\n\n下载安装包 http://archive.cloudera.com/cdh5/cdh/5/hbase-1.2.0-cdh5.7.0.tar.gz\n\n配置伪分布式环境\n\n环境变量配置\nexport HBASE_HOME=/root/bigdata/hbase\nexport PATH=$HBASE_HOME/bin:$PATH\n\n\n配置hbase-env.sh\nexport JAVA_HOME=/root/bigdata/jdk\nexport HBASE_MANAGES_ZK=false  --如果你是使用hbase自带的zk就是true，如果使用自己的zk就是false\n\n\n配置hbase-site.xml\n\n        \n                hbase.rootdir\n                hdfs://hadoop-master:9000/hbase\n        \n        \n                hbase.cluster.distributed\n                true\n        \n        \n                hbase.master\n                hadoop-master:60000\n        \n        \n                hbase.zookeeper.quorum\n                hadoop-master:2181\n        \n        \n                hbase.zookeeper.property.clientPort\n                2181\n        \n        \n                hbase.zookeeper.property.dataDir\n                /root/bigdata/zookeeper-3.4.14/dataDir\n        \n        \n                hbase.unsafe.stream.capability.enforce\n            false\n        \n\n\n\n启动hbase（启动的hbase的时候要保证hadoop集群已经启动）\n/hbase/bin/start-hbase.sh\n\n\n输入hbase shell（进入shell命令行）\n\n\n\n\n2 HBase shell\n\nHBase DDL 和 DML 命令\n\n\n  \n    名称\n    命令表达式\n  \n  \n     创建表 \n    create '表名', '列族名1','列族名2','列族名n' \n  \n  \n     添加记录 \n     put '表名','行名','列名:','值 \n  \n    \n     查看记录 \n     get '表名','行名' \n  \n  \n     查看表中的记录总数 \n     count '表名' \n  \n    \n     删除记录 \n     delete '表名', '行名','列名' \n  \n  \n     删除一张表 \n     第一步 disable '表名' 第二步 drop '表名' \n  \n  \n     查看所有记录 \n     scan \"表名称\" \n  \n  \n     查看指定表指定列所有数据 \n     scan '表名' ,{COLUMNS=>'列族名:列名'} \n  \n   \n     更新记录 \n     重写覆盖 \n  \n\n\n\n连接集群\n\nhbase shell\n\n创建表\n\ncreate 'user','base_info'\n\n\n删除表\n\ndisable 'user'\ndrop 'user'\n\n\n创建名称空间\n\ncreate_namespace 'test'\n\n\n展示现有名称空间\n\nlist_namespace\n\n\n创建表的时候添加namespace\n\ncreate 'test:user','base_info'\n\n\n显示某个名称空间下有哪些表\n\nlist_namespace_tables 'test'\n\n插入数据\nput  ‘表名’，‘rowkey的值’，’列族：列标识符‘，’值‘\n\n\nput 'user','rowkey_10','base_info:username','Tom'\nput 'user','rowkey_10','base_info:birthday','2014-07-10'\nput 'user','rowkey_10','base_info:sex','1'\nput 'user','rowkey_10','base_info:address','Tokyo'\n\nput 'user','rowkey_16','base_info:username','Mike'\nput 'user','rowkey_16','base_info:birthday','2014-07-10'\nput 'user','rowkey_16','base_info:sex','1'\nput 'user','rowkey_16','base_info:address','beijing'\n\nput 'user','rowkey_22','base_info:username','Jerry'\nput 'user','rowkey_22','base_info:birthday','2014-07-10'\nput 'user','rowkey_22','base_info:sex','1'\nput 'user','rowkey_22','base_info:address','Newyork'\n\nput 'user','rowkey_24','base_info:username','Nico'\nput 'user','rowkey_24','base_info:birthday','2014-07-10'\nput 'user','rowkey_24','base_info:sex','1'\nput 'user','rowkey_24','base_info:address','shanghai'\n\nput 'user','rowkey_25','base_info:username','Rose'\nput 'user','rowkey_25','base_info:birthday','2014-07-10'\nput 'user','rowkey_25','base_info:sex','1'\nput 'user','rowkey_25','base_info:address','Soul'\n\n\n查询表中的所有数据\n\nscan 'user'\n#HBase中一般存储数据量都很大 很少使用全表查询 scan会加上一些条件限制\n\n\nScan查询中添加限制条件\n\nscan '名称空间:表名', {COLUMNS => ['列族名1', '列族名2'], LIMIT => 10, STARTROW => '起始的rowkey'}  # 通过COLUMNS  LIMIT STARTROW 等条件缩小查询范围\n\n#LIMIT=>2 限制输出两行\nscan 'user' ,{COLUMNS =>['base_info'],LIMIT=>2}\n## 返回结果\nROW                        COLUMN+CELL\n rowkey_10                 column=base_info:address, timestamp=1558323139732, value=Tokyo\n rowkey_10                 column=base_info:birthday, timestamp=1558323139636, value=2014-07-10\n rowkey_10                 column=base_info:sex, timestamp=1558323139678, value=1\n rowkey_10                 column=base_info:username, timestamp=1558323918953, value=Tom4\n rowkey_16                 column=base_info:address, timestamp=1558323139963, value=beijing\n rowkey_16                 column=base_info:birthday, timestamp=1558323139866, value=2014-07-10\n rowkey_16                 column=base_info:sex, timestamp=1558323139907, value=1\n\n#STARTROW 限制起始的Rowkey\nscan 'user' ,{COLUMNS =>['base_info'],LIMIT=>2,STARTROW=>'rowkey_16'}\n#返回结果：\nROW                        COLUMN+CELL\n rowkey_16                 column=base_info:address, timestamp=1558323139963, value=beijing\n rowkey_16                 column=base_info:birthday, timestamp=1558323139866, value=2014-07-10\n rowkey_16                 column=base_info:sex, timestamp=1558323139907, value=1\n rowkey_22                 column=base_info:address, timestamp=1558323140188, value=Newyork\n rowkey_22                 column=base_info:birthday, timestamp=1558323140107, value=2014-07-10\n rowkey_22                 column=base_info:sex, timestamp=1558323140143, value=1\n rowkey_22                 column=base_info:username, timestamp=1558323140036, value=Jerry\n\n\nscan查询添加过滤器\n\nROWPREFIXFILTER rowkey 前缀过滤器\n\nscan 'user', {ROWPREFIXFILTER=>'rowkey_22'}\n#显示结果\nROW                        COLUMN+CELL\n rowkey_22                 column=base_info:address, timestamp=1558323140188, value=Newyork\n rowkey_22                 column=base_info:birthday, timestamp=1558323140107, value=2014-07-10\n rowkey_22                 column=base_info:sex, timestamp=1558323140143, value=1\n rowkey_22                 column=base_info:username, timestamp=1558323140036, value=Jerry\n1 row(s)\nTook 0.0120 seconds\n\n\n查询某个rowkey的数据\n\n\nget 'user','rowkey_16'\n\n查询某个列簇的数据\n\nget 'user','rowkey_16','base_info'\nget 'user','rowkey_16','base_info:username'\nget 'user', 'rowkey_16', {COLUMN => ['base_info:username','base_info:sex']}\n\n\n删除表中的数据\n\ndelete 'user', 'rowkey_16', 'base_info:username'\n\n清空数据\n\ntruncate 'user'\n\n操作列簇\n\nalter 'user', NAME => 'f2'\nalter 'user', 'delete' => 'f2'\n\nHBase 追加型数据库 会保留多个版本数据\ndesc 'user'\nTable user is ENABLED\nuser\nCOLUMN FAMILIES DESCRIPTION\n{NAME => 'base_info', VERSIONS => '1', EVICT_BLOCKS_ON_CLOSE => 'false', NEW_VERSION_B\nHE_DATA_ON_WRITE => 'false', DATA_BLOCK_ENCODING => 'NONE', TTL => 'FOREVER', MI\nER => 'NONE', CACHE_INDEX_ON_WRITE => 'false', IN_MEMORY => 'false', CACHE_BLOOM\nse', COMPRESSION => 'NONE', BLOCKCACHE => 'false', BLOCKSIZE => '65536'}\n\n\nVERSIONS=>'1'说明最多可以显示一个版本 修改数据\n\nput 'user','rowkey_10','base_info:username','Tom'\n\n\n指定显示多个版本\n\nget 'user','rowkey_10',{COLUMN=>'base_info:username',VERSIONS=>2}\n\n\n修改可以显示的版本数量\n\nalter 'user',NAME=>'base_info',VERSIONS=>10\n\n\n通过时间戳查询\n\n通过TIMERANGE 指定时间范围\n\nscan 'user',{COLUMNS => 'base_info', TIMERANGE => [1558323139732, 1558323139866]}\nget 'user','rowkey_10',{COLUMN=>'base_info:username',VERSIONS=>2,TIMERANGE => [1558323904130, 1558323918954]}\n\n\n通过时间戳过滤器 指定具体时间戳的值\n\nscan 'user',{FILTER => 'TimestampsFilter (1558323139732, 1558323139866)'}\nget 'user','rowkey_10',{COLUMN=>'base_info:username',VERSIONS=>2,FILTER => 'TimestampsFilter (1558323904130, 1558323918954)'}\n\n\n获取最近多个版本的数据\n\nget 'user','rowkey_10',{COLUMN=>'base_info:username',VERSIONS=>10}\n\nCOLUMN                           CELL\n base_info:username              timestamp=1558323918953, value=Tom4\n base_info:username              timestamp=1558323904133, value=Tom3\n base_info:username              timestamp=1558323758696, value=Tom2\n base_info:username              timestamp=1558323139575, value=Tom\n\n\n通过指定时间戳获取不同版本的数据\n\nget 'user','rowkey_10',{COLUMN=>'base_info:username',TIMESTAMP=>1558323904133}\n\n#返回结果如下\nCOLUMN                           CELL\n base_info:username              timestamp=1558323904133, value=Tom3\n\nget 'user','rowkey_10',{COLUMN=>'base_info:username',TIMESTAMP=>1558323918953}\n#返回结果如下\nCOLUMN                           CELL\n base_info:username              timestamp=1558323918953, value=Tom4\n\n\n\n\n命令表\n\n\n"},"Hive&HBase/08_HappyBase操作HBase.html":{"url":"Hive&HBase/08_HappyBase操作HBase.html","title":"04_HappyBase操作HBase","keywords":"","body":"5.4 HappyBase操作Hbase\n\n什么是HappyBase\n\nHappyBase is a developer-friendly Python library to interact with Apache HBase. HappyBase is designed for use in standard HBase setups, and offers application developers a Pythonic API to interact with HBase. Below the surface, HappyBase uses the Python Thrift library to connect to HBase using its Thrift gateway, which is included in the standard HBase 0.9x releases.\n\n\nHappyBase 是FaceBook员工开发的操作HBase的python库, 其基于Python Thrift, 但使用方式比Thrift简单, 已被广泛应用\n\n启动hbase thrift server : hbase-daemon.sh start thrift\n\n安装happy base\n\npip install happybase\n\n\n如何使用HappyBase\n\n建立连接\n\nimport happybase\nconnection = happybase.Connection('somehost')\n\n\n当连接建立时, 会自动创建一个与 HBase Thrift server的socket链接. 可以通过参数禁止自动链接, 然后再需要连接是调用 Connection.open():\n\nconnection = happybase.Connection('somehost', autoconnect=False)\n# before first use:\nconnection.open()\n\n\nConnection  这个类提供了一个与HBase交互的入口, 比如获取HBase中所有的表:  Connection.tables():\n\nprint(connection.tables())\n\n\n操作表\nTable类提供了大量API, 这些API用于检索和操作HBase中的数据。 在上面的示例中，我们已经使用Connection.tables（）方法查询HBase中的表。 如果还没有任何表，可使用Connection.create_table（）创建一个新表：\n\n\n\nconnection.create_table('users',{'cf1': dict()})\n\n\n创建表之后可以传入表名获取到Table类的实例:\ntable = connection.table('mytable')\n\n查询操作\n\n\n# api\ntable.scan() #全表查询\ntable.row('row_key') # 查询一行\ntable.rows([row_keys]) # 查询多行\n#封装函数\ndef scanQuery():\n    # 创建和hbase的连接\n    connection = happybase.Connection('192.168.19.137')\n    #通过connection找到user表 获得table对象\n    table = connection.table('user')\n    filter = \"ColumnPrefixFilter('username')\"\n    #row_start 指定起始rowkey 缩小查询范围\n    #filter 添加过滤器\n    for key,value in table.scan(row_start='rowkey_10',filter=filter):\n        print(key,value)\n    # 关闭连接\n    connection.close()\ndef getQuery():\n    connection = happybase.Connection('192.168.19.137')\n    # 通过connection找到user表 获得table对象\n    table = connection.table('user')\n    result = table.row('rowkey_22',columns=['base_info:username'])\n    #result = table.row('rowkey_22',columns=['base_info:username'])\n    result = table.rows(['rowkey_22','rowkey_16'],columns=['base_info:username'])\n    print(result)\n    # 关闭连接\n    connection.close()\n\n\n插入数据\n\n#api\ntable.put(row_key, {'cf:cq':'value'})\ndef insertData():\n    connection = happybase.Connection('192.168.19.137')\n    # 通过connection找到user表 获得table对象\n    table = connection.table('users')\n    table.put('rk_01',{'cf1:address':'beijing'})\n    # 关闭连接\n    for key,value in table.scan():\n        print(key,value)\n    connection.close()\n\n\n删除数据\n\n#api\ntable.delete(row_key, cf_list)\n\ndef deleteData():\n    connection = happybase.Connection('192.168.19.137')\n    # 通过connection找到user表 获得table对象\n    table = connection.table('users')\n    table.delete('rk_01',['cf1:username'])\n    # 关闭连接\n    for key,value in table.scan():\n        print(key,value)\n    connection.close()\n\n\n删除表\n\n#api\nconn.delete_table(table_name, True)\n#函数封装\ndef delete_table(table_name):\n    pretty_print('delete table %s now.' % table_name)\n    conn.delete_table(table_name, True)\n\n\n\n\n完整代码\n\nimport happybase\n\ndef connectHBase():\n    #创建和hbase的连接\n    connection = happybase.Connection('192.168.19.137')\n    #获取hbase中的所有表\n    print(connection.tables())\n    #关闭连接\n    connection.close()\n\ndef createTable():\n    connection = happybase.Connection('192.168.19.137')\n    connection.create_table('users',{'cf1': dict()})\n    print(connection.tables())\n    connection.close()\n\ndef scanQuery():\n    # 创建和hbase的连接\n    connection = happybase.Connection('192.168.19.137')\n    #通过connection找到user表 获得table对象\n    table = connection.table('user')\n    filter = \"ColumnPrefixFilter('username')\"\n    #row_start 指定起始rowkey 缩小查询范围\n    #filter 添加过滤器\n    for key,value in table.scan(row_start='rowkey_10',filter=filter):\n        print(key,value)\n    # 关闭连接\n    connection.close()\ndef getQuery():\n    connection = happybase.Connection('192.168.19.137')\n    # 通过connection找到user表 获得table对象\n    table = connection.table('user')\n    result = table.row('rowkey_22',columns=['base_info:username'])\n    #result = table.row('rowkey_22',columns=['base_info:username'])\n    result = table.rows(['rowkey_22','rowkey_16'],columns=['base_info:username'])\n    print(result)\n    # 关闭连接\n    connection.close()\ndef insertData():\n    connection = happybase.Connection('192.168.19.137')\n    # 通过connection找到user表 获得table对象\n    table = connection.table('users')\n    table.put('rk_01',{'cf1:address':'beijing'})\n    # 关闭连接\n    for key,value in table.scan():\n        print(key,value)\n    connection.close()\ndef deleteData():\n    connection = happybase.Connection('192.168.19.137')\n    # 通过connection找到user表 获得table对象\n    table = connection.table('users')\n    table.delete('rk_01',['cf1:username'])\n    # 关闭连接\n    for key,value in table.scan():\n        print(key,value)\n    connection.close()\n\ndef deletetable():\n    #创建和hbase的连接\n    connection = happybase.Connection('192.168.19.137')\n    #获取hbase中的所有表\n    connection.delete_table('users',disable=True)\n    print(connection.tables())\n    #关闭连接\n    connection.close()\ndef main():\n    #connectHBase()\n    #createTable()\n    scanQuery()\n    #getQuery()\n    #insertData()\n    #deleteData()\n    #deletetable()\n\n  if __name__ == \"__main__\":\n      main()\n\n"},"Hive&HBase/10_HBase组件.html":{"url":"Hive&HBase/10_HBase组件.html","title":"05_HBase组件","keywords":"","body":"5.6 HBase组件\n1 HBase 基础架构\n\nClient\n\n①与zookeeper通信, 找到数据入口地址\n②使用HBase RPC机制与HMaster和HRegionServer进行通信；\n③Client与HMaster进行通信进行管理类操作；\n④Client与HRegionServer进行数据读写类操作。\n\nZookeeper\n\n①保证任何时候，集群中只有一个running master，避免单点问题；\n②存贮所有Region的寻址入口，包括-ROOT-表地址、HMaster地址；\n③实时监控Region Server的状态，将Region server的上线和下线信息，实时通知给Master；\n④存储Hbase的schema，包括有哪些table，每个table有哪些column family。\n\nHMaster\n可以启动多个HMaster，通过Zookeeper的Master Election机制保证总有一个Master运行。\n角色功能：\n\n①为Region server分配region；\n②负责region server的负载均衡；\n③发现失效的region serve并重新分配其上的region；\n④HDFS上的垃圾文件回收；\n⑤处理用户对表的增删改查操作。\n\nHRegionServer\nHBase中最核心的模块，主要负责响应用户I/O请求，向HDFS文件系统中读写数据。\n作用：\n\n①维护Master分配给它的region，处理对这些region的IO请求；\n②负责切分在运行过程中变得过大的region。\n此外，HRegionServer管理一系列HRegion对象，每个HRegion对应Table中一个Region，HRegion由多个HStore组成，每个HStore对应Table中一个Column Family的存储，Column Family就是一个集中的存储单元，故将具有相同IO特性的Column放在一个Column Family会更高效。\n\nHStore\n\nHBase存储的核心，由MemStore和StoreFile组成。\n\n\n\n用户写入数据的流程为：client访问ZK, ZK返回RegionServer地址-> client访问RegionServer写入数据 -> 数据存入MemStore，一直到MemStore满 -> Flush成StoreFile\n\nHRegion\n\n一个表最开始存储的时候，是一个region。\n一个Region中会有个多个store，每个store用来存储一个列簇。如果只有一个column family，就只有一个store。\nregion会随着插入的数据越来越多，会进行拆分。默认大小是10G一个。\n\nHLog\n\n在分布式系统环境中，无法避免系统出错或者宕机，一旦HRegionServer意外退出，MemStore中的内存数据就会丢失，引入HLog就是防止这种情况。\n\n2 HBase模块协作\n\nHBase启动\nHMaster启动, 注册到Zookeeper, 等待RegionServer汇报\nRegionServer注册到Zookeeper, 并向HMaster汇报\n对各个RegionServer(包括失效的)的数据进行整理, 分配Region和meta信息\n\n\nRegionServer失效\nHMaster将失效RegionServer上的Region分配到其他节点\nHMaster更新hbase: meta 表以保证数据正常访问\n\n\nHMaster失效\n处于Backup状态的其他HMaster节点推选出一个转为Active状态\n数据能正常读写, 但是不能创建删除表, 也不能更改表结构\n\n\n\n"},"day05_Spark_core/spark_core_1.html":{"url":"day05_Spark_core/spark_core_1.html","title":"01_Spark入门","keywords":"","body":"spark 入门\n课程目标：\n\n了解spark概念\n知道spark的特点（与hadoop对比）\n独立实现spark local模式的启动\n\n1.1 spark概述\n\n1、什么是spark\n\n基于内存的计算引擎，它的计算速度非常快。但是仅仅只涉及到数据的计算，并没有涉及到数据的存储。\n\n\n2、为什么要学习spark\nMapReduce框架局限性\n\n1，Map结果写磁盘，Reduce写HDFS，多个MR之间通过HDFS交换数据\n2，任务调度和启动开销大\n3，无法充分利用内存\n4，不适合迭代计算（如机器学习、图计算等等），交互式处理（数据挖掘）\n5，不适合流式处理（点击日志分析）\n6，MapReduce编程不够灵活，仅支持Map和Reduce两种操作\n\nHadoop生态圈\n\n批处理：MapReduce、Hive、Pig\n流式计算：Storm\n交互式计算：Impala、presto\n\n需要一种灵活的框架可同时进行批处理、流式计算、交互式计算\n\n内存计算引擎，提供cache机制来支持需要反复迭代计算或者多次数据共享，减少数据读取的IO开销\nDAG引擎，较少多次计算之间中间结果写到HDFS的开销\n使用多线程模型来减少task启动开销，shuffle过程中避免不必要的sort操作以及减少磁盘IO\n\nspark的缺点是：吃内存，不太稳定\n\n3、spark特点\n\n1、速度快（比mapreduce在内存中快100倍，在磁盘中快10倍）\nspark中的job中间结果可以不落地，可以存放在内存中。\nmapreduce中map和reduce任务都是以进程的方式运行着，而spark中的job是以线程方式运行在进程中。\n\n\n2、易用性（可以通过java/scala/python/R开发spark应用程序）\n3、通用性（可以使用spark sql/spark streaming/mlib/Graphx）\n4、兼容性（spark程序可以运行在standalone/yarn/mesos）\n\n\n\n1.2 spark启动（local模式）和WordCount(演示)\n\n启动pyspark\n\n在$SPARK_HOME/sbin目录下执行\n\n./pyspark\n\n\n\n\nsc = spark.sparkContext\nwords = sc.textFile('file:///home/hadoop/tmp/word.txt') \\\n            .flatMap(lambda line: line.split(\" \")) \\\n            .map(lambda x: (x, 1)) \\\n            .reduceByKey(lambda a, b: a + b).collect()\n\n\n输出结果：\n[('python', 2), ('hadoop', 1), ('bc', 1), ('foo', 4), ('test', 2), ('bar', 2), ('quux', 2), ('abc', 2), ('ab', 1), ('you', 1), ('ac', 1), ('bec', 1), ('by', 1), ('see', 1), ('labs', 2), ('me', 1), ('welcome', 1)]\n\n\n\n\n\n"},"day05_Spark_core/spark_core_2.html":{"url":"day05_Spark_core/spark_core_2.html","title":"02_RDD概念介绍","keywords":"","body":"RDD概述\n课程目标：\n\n知道RDD的概念\n独立实现RDD的创建\n\n2.1 什么是RDD\n\nRDD（Resilient Distributed Dataset）叫做弹性分布式数据集，是Spark中最基本的数据抽象，它代表一个不可变、可分区、里面的元素可并行计算的集合.\nDataset:一个数据集，简单的理解为集合，用于存放数据的\nDistributed：它的数据是分布式存储，并且可以做分布式的计算\nResilient：弹性的\n它表示的是数据可以保存在磁盘，也可以保存在内存中\n数据分布式也是弹性的\n弹性:并不是指他可以动态扩展，而是容错机制。\nRDD会在多个节点上存储，就和hdfs的分布式道理是一样的。hdfs文件被切分为多个block存储在各个节点上，而RDD是被切分为多个partition。不同的partition可能在不同的节点上\nspark读取hdfs的场景下，spark把hdfs的block读到内存就会抽象为spark的partition。\nspark计算结束，一般会把数据做持久化到hive，hbase，hdfs等等。我们就拿hdfs举例，将RDD持久化到hdfs上，RDD的每个partition就会存成一个文件，如果文件小于128M，就可以理解为一个partition对应hdfs的一个block。反之，如果大于128M，就会被且分为多个block，这样，一个partition就会对应多个block。\n\n\n\n\n不可变  Rdd1->rdd2 \n可分区 partition\n并行计算\n\n\n\n2.2 RDD的创建\n\n第一步 创建sparkContext\n\nSparkContext, Spark程序的入口. SparkContext代表了和Spark集群的链接, 在Spark集群中通过SparkContext来创建RDD\nSparkConf  创建SparkContext的时候需要一个SparkConf， 用来传递Spark应用的基本信息\n\nconf = SparkConf().setAppName(appName).setMaster(master)\nsc = SparkContext(conf=conf)\n\n\n创建RDD\n\n进入pyspark环境\n\n[hadoop@hadoop000 ~]$ pyspark\nPython 3.5.0 (default, Nov 13 2018, 15:43:53)\n[GCC 4.8.5 20150623 (Red Hat 4.8.5-28)] on linux\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\n19/03/08 12:19:55 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable\nSetting default log level to \"WARN\".\nTo adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).\nWelcome to\n      ____              __\n     / __/__  ___ _____/ /__\n    _\\ \\/ _ \\/ _ `/ __/  '_/\n   /__ / .__/\\_,_/_/ /_/\\_\\   version 2.3.0\n      /_/\n\nUsing Python version 3.5.0 (default, Nov 13 2018 15:43:53)\nSparkSession available as 'spark'.\n>>> sc\n\n\n\n在spark shell中 已经为我们创建好了 SparkContext 通过sc直接使用\n可以在spark UI中看到当前的Spark作业 在浏览器访问当前centos的4040端口 192.168.19.137:4040\n\n\n\nParallelized Collections方式创建RDD\n\n调用SparkContext的 parallelize 方法并且传入已有的可迭代对象或者集合\n\ndata = [1, 2, 3, 4, 5]\ndistData = sc.parallelize(data)\n\n>>> data = [1, 2, 3, 4, 5]\n>>> distData = sc.parallelize(data)\n>>> data\n[1, 2, 3, 4, 5]\n>>> distData\nParallelCollectionRDD[0] at parallelize at PythonRDD.scala:175\n\n\n在spark ui中观察执行情况\n\n\n\n在通过parallelize方法创建RDD 的时候可以指定分区数量\n\n>>> distData = sc.parallelize(data,5)\n>>> distData.reduce(lambda a, b: a + b)\n15\n\n\n在spark ui中观察执行情况\n\n\n\nSpark将为群集的每个分区（partition）运行一个任务（task）。 通常，可以根据CPU核心数量指定分区数量（每个CPU有2-4个分区）如未指定分区数量，Spark会自动设置分区数。\n\n\n通过外部数据创建RDD\n\nPySpark可以从Hadoop支持的任何存储源创建RDD，包括本地文件系统，HDFS，Cassandra，HBase，Amazon S3等\n支持整个目录、多文件、通配符\n支持压缩文件\n\n>>> rdd1 = sc.textFile('file:///root/tmp/word.txt')\n>>> rdd1.collect()\n['foo foo quux labs foo bar quux abc bar see you by test welcome test', 'abc labs foo me python hadoop ab ac bc bec python']\n\n\n\n\n\n"},"day05_Spark_core/spark_core_3.html":{"url":"day05_Spark_core/spark_core_3.html","title":"03_RDD常用算子练习","keywords":"","body":"spark-core RDD常用算子练习\n课程目标\n\n说出RDD的三类算子\n掌握transformation和action算子的基本使用\n\n3.1 RDD 常用操作\n\nRDD 支持两种类型的操作：\n\ntransformation\n从一个已经存在的数据集创建一个新的数据集\nrdd a ----->transformation ----> rdd b\n\n\n比如， map就是一个transformation 操作，把数据集中的每一个元素传给一个函数并返回一个新的RDD，代表transformation操作的结果 \n\n\naction\n获取对数据进行运算操作之后的结果\n比如， reduce 就是一个action操作，使用某个函数聚合RDD所有元素的操作，并返回最终计算结果\n\n\n\n\n所有的transformation操作都是惰性的（lazy）\n\n不会立即计算结果\n只记下应用于数据集的transformation操作\n只有调用action一类的操作之后才会计算所有transformation\n这种设计使Spark运行效率更高\n例如map reduce 操作，map创建的数据集将用于reduce，map阶段的结果不会返回，仅会返回reduce结果。\n\n\npersist 操作\npersist操作用于将数据缓存 可以缓存在内存中 也可以缓存到磁盘上， 也可以复制到磁盘的其它节点上\n\n\n\n3.2 RDD Transformation算子\n\nmap: map(func)\n\n将func函数作用到数据集的每一个元素上，生成一个新的RDD返回\n\n>>> rdd1 = sc.parallelize([1,2,3,4,5,6,7,8,9],3)\n>>> rdd2 = rdd1.map(lambda x: x+1)\n>>> rdd2.collect()\n[2, 3, 4, 5, 6, 7, 8, 9, 10]\n\n>>> rdd1 = sc.parallelize([1,2,3,4,5,6,7,8,9],3)\n>>> def add(x):\n...     return x+1\n...\n>>> rdd2 = rdd1.map(add)\n>>> rdd2.collect()\n[2, 3, 4, 5, 6, 7, 8, 9, 10]\n\n\n\n  \n\nfilter\n\nfilter(func) 选出所有func返回值为true的元素，生成一个新的RDD返回\n\n>>> rdd1 = sc.parallelize([1,2,3,4,5,6,7,8,9],3)\n>>> rdd2 = rdd1.map(lambda x:x*2)\n>>> rdd3 = rdd2.filter(lambda x:x>4)\n>>> rdd3.collect()\n[6, 8, 10, 12, 14, 16, 18]\n\n\nflatmap\n\nflatMap会先执行map的操作，再将所有对象合并为一个对象\n\n>>> rdd1 = sc.parallelize([\"a b c\",\"d e f\",\"h i j\"])\n>>> rdd2 = rdd1.flatMap(lambda x:x.split(\" \"))\n>>> rdd2.collect()\n['a', 'b', 'c', 'd', 'e', 'f', 'h', 'i', 'j']\n\n\nflatMap和map的区别：flatMap在map的基础上将结果合并到一个list中\n\n>>> rdd1 = sc.parallelize([\"a b c\",\"d e f\",\"h i j\"])\n>>> rdd2 = rdd1.map(lambda x:x.split(\" \"))\n>>> rdd2.collect()\n[['a', 'b', 'c'], ['d', 'e', 'f'], ['h', 'i', 'j']]\n\n\nunion\n\n对两个RDD求并集\n\n>>> rdd1 = sc.parallelize([(\"a\",1),(\"b\",2)])\n>>> rdd2 = sc.parallelize([(\"c\",1),(\"b\",3)])\n>>> rdd3 = rdd1.union(rdd2)\n>>> rdd3.collect()\n[('a', 1), ('b', 2), ('c', 1), ('b', 3)]\n\n\nintersection\n\n对两个RDD求交集\n\n>>> rdd1 = sc.parallelize([(\"a\",1),(\"b\",2)])\n>>> rdd2 = sc.parallelize([(\"c\",1),(\"b\",3)])\n>>> rdd3 = rdd1.union(rdd2)\n>>> rdd4 = rdd3.intersection(rdd2)\n>>> rdd4.collect()\n[('c', 1), ('b', 3)]\n\n\ngroupByKey\n\n以元组中的第0个元素作为key，进行分组，返回一个新的RDD\n\n>>> rdd1 = sc.parallelize([(\"a\",1),(\"b\",2)])\n>>> rdd2 = sc.parallelize([(\"c\",1),(\"b\",3)])\n>>> rdd3 = rdd1.union(rdd2)\n>>> rdd4 = rdd3.groupByKey()\n>>> rdd4.collect()\n[('a', ), ('c', ), ('b', )]\n\n\ngroupByKey之后的结果中 value是一个Iterable\n\n>>> result[2]\n('b', )\n>>> result[2][1]\n\n>>> list(result[2][1])\n[2, 3]\n\n\nreduceByKey\n\n将key相同的键值对，按照Function进行计算\n\n>>> rdd = sc.parallelize([(\"a\", 1), (\"b\", 1), (\"a\", 1)])\n>>> rdd.reduceByKey(lambda x,y:x+y).collect()\n[('b', 1), ('a', 2)]\n\n\nsortByKey\n\nsortByKey(ascending=True, numPartitions=None, keyfunc=>)\nSorts this RDD, which is assumed to consist of (key, value) pairs.\n\n\n>>> tmp = [('a', 1), ('b', 2), ('1', 3), ('d', 4), ('2', 5)]\n>>> sc.parallelize(tmp).sortByKey().first()\n('1', 3)\n>>> sc.parallelize(tmp).sortByKey(True, 1).collect()\n[('1', 3), ('2', 5), ('a', 1), ('b', 2), ('d', 4)]\n>>> sc.parallelize(tmp).sortByKey(True, 2).collect()\n[('1', 3), ('2', 5), ('a', 1), ('b', 2), ('d', 4)]\n>>> tmp2 = [('Mary', 1), ('had', 2), ('a', 3), ('little', 4), ('lamb', 5)]\n>>> tmp2.extend([('whose', 6), ('fleece', 7), ('was', 8), ('white', 9)])\n>>> sc.parallelize(tmp2).sortByKey(True, 3, keyfunc=lambda k: k.lower()).collect()\n[('a', 3), ('fleece', 7), ('had', 2), ('lamb', 5),...('white', 9), ('whose', 6)]\n\n\n\n\n\n3.3 RDD Action算子\n\ncollect\n\n返回一个list，list中包含 RDD中的所有元素\n只有当数据量较小的时候使用Collect 因为所有的结果都会加载到内存中\n\n\nreduce\n\nreduce将RDD中元素两两传递给输入函数，同时产生一个新的值，新产生的值与RDD中下一个元素再被传递给输入函数直到最后只有一个值为止。\n\n>>> rdd1 = sc.parallelize([1,2,3,4,5])\n>>> rdd1.reduce(lambda x,y : x+y)\n15\n\n\nfirst\n\n返回RDD的第一个元素\n\n>>> sc.parallelize([2, 3, 4]).first()\n2\n\n\ntake\n\n返回RDD的前N个元素\ntake(num)\n\n>>> sc.parallelize([2, 3, 4, 5, 6]).take(2)\n[2, 3]\n>>> sc.parallelize([2, 3, 4, 5, 6]).take(10)\n[2, 3, 4, 5, 6]\n>>> sc.parallelize(range(100), 100).filter(lambda x: x > 90).take(3)\n[91, 92, 93]\n\n\ncount\n返回RDD中元素的个数\n>>> sc.parallelize([2, 3, 4]).count()\n3\n\n\n3.4 Spark RDD两类算子执行示意\n\n\n"},"day05_Spark_core/spark_core_4.html":{"url":"day05_Spark_core/spark_core_4.html","title":"04_Spark-Core实战案例_pv&uv统计","keywords":"","body":"spark-core 实战案例\n课程目标：\n\n独立实现Spark RDD的word count案例\n独立实现spark RDD的PV UV统计案例\n\n4.1利用PyCharm编写spark wordcount程序\n\n环境配置\n将spark目录下的python目录下的pyspark整体拷贝到pycharm使用的python环境下\n将下图中的pyspark\n\n拷贝到pycharm使用的：xxx\\Python\\Python36\\Lib\\site-packages目录下\n\n代码\n\n\nimport sys\n\n\nfrom pyspark.sql import SparkSession\n\nif __name__ == '__main__':\n\n    if len(sys.argv) != 2:\n        print(\"Usage: avg \", file=sys.stderr)\n        sys.exit(-1)\n\n    spark = SparkSession.builder.appName(\"test\").getOrCreate()\n    sc = spark.sparkContext\n\n    counts = sc.textFile(sys.argv[1]) \\\n            .flatMap(lambda line: line.split(\" \")) \\\n            .map(lambda x: (x, 1)) \\\n            .reduceByKey(lambda a, b: a + b)\n\n    output = counts.collect()\n\n    for (word, count) in output:\n        print(\"%s: %i\" % (word, count))\n\n    sc.stop()\n\n\n将代码上传到远程cent-os系统上\n\n在系统上执行指令\nspark-submit --master local wc.py file:///root/bigdata/data/spark_test.log\n\n\n4.2 通过spark实现点击流日志分析\n在新闻类网站中，经常要衡量一条网络新闻的页面访问量，最常见的就是uv和pv，如果在所有新闻中找到访问最多的前几条新闻，topN是最常见的指标。\n\n数据示例\n\n#每条数据代表一次访问记录 包含了ip 访问时间 访问的请求方式 访问的地址...信息\n194.237.142.21 - - [18/Sep/2013:06:49:18 +0000] \"GET /wp-content/uploads/2013/07/rstudio-git3.png HTTP/1.1\" 304 0 \"-\" \"Mozilla/4.0 (compatible;)\"\n183.49.46.228 - - [18/Sep/2013:06:49:23 +0000] \"-\" 400 0 \"-\" \"-\"\n163.177.71.12 - - [18/Sep/2013:06:49:33 +0000] \"HEAD / HTTP/1.1\" 200 20 \"-\" \"DNSPod-Monitor/1.0\"\n163.177.71.12 - - [18/Sep/2013:06:49:36 +0000] \"HEAD / HTTP/1.1\" 200 20 \"-\" \"DNSPod-Monitor/1.0\"\n101.226.68.137 - - [18/Sep/2013:06:49:42 +0000] \"HEAD / HTTP/1.1\" 200 20 \"-\" \"DNSPod-Monitor/1.0\"\n101.226.68.137 - - [18/Sep/2013:06:49:45 +0000] \"HEAD / HTTP/1.1\" 200 20 \"-\" \"DNSPod-Monitor/1.0\"\n60.208.6.156 - - [18/Sep/2013:06:49:48 +0000] \"GET /wp-content/uploads/2013/07/rcassandra.png HTTP/1.0\" 200 185524 \"http://cos.name/category/software/packages/\" \"Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/29.0.1547.66 Safari/537.36\"\n222.68.172.190 - - [18/Sep/2013:06:49:57 +0000] \"GET /images/my.jpg HTTP/1.1\" 200 19939 \"http://www.angularjs.cn/A00n\" \"Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/29.0.1547.66 Safari/537.36\"\n222.68.172.190 - - [18/Sep/2013:06:50:08 +0000] \"-\" 400 0 \"-\" \"-\"\n\n\n访问的pv\npv：网站的总访问量\nfrom pyspark.sql import SparkSession\n\nspark = SparkSession.builder.appName(\"pv\").getOrCreate()\nsc = spark.sparkContext\nrdd1 = sc.textFile(\"file:///root/bigdata/data/access.log\")\n#把每一行数据记为(\"pv\",1)\nrdd2 = rdd1.map(lambda x:(\"pv\",1)).reduceByKey(lambda a,b:a+b)\nrdd2.collect()\nsc.stop()\n\n\n访问的uv\nuv：网站的独立用户访问量\nfrom pyspark.sql import SparkSession\n\nspark = SparkSession.builder.appName(\"pv\").getOrCreate()\nsc = spark.sparkContext\nrdd1 = sc.textFile(\"file:///root/bigdata/data/access.log\")\n#对每一行按照空格拆分，将ip地址取出\nrdd2 = rdd1.map(lambda x:x.split(\" \")).map(lambda x:x[0])\n#把每个ur记为1\nrdd3 = rdd2.distinct().map(lambda x:(\"uv\",1))\nrdd4 = rdd3.reduceByKey(lambda a,b:a+b)\nrdd4.saveAsTextFile(\"hdfs:///uv/result\")\nsc.stop()\n\n\n访问的topN\nfrom pyspark.sql import SparkSession\n\nspark = SparkSession.builder.appName(\"topN\").getOrCreate()\nsc = spark.sparkContext\nrdd1 = sc.textFile(\"file:///root/bigdata/data/access.log\")\n#对每一行按照空格拆分，将url数据取出，把每个url记为1\nrdd2 = rdd1.map(lambda x:x.split(\" \")).filter(lambda x:len(x)>10).map(lambda x:(x[10],1))\n#对数据进行累加，按照url出现次数的降序排列\nrdd3 = rdd2.reduceByKey(lambda a,b:a+b).sortBy(lambda x:x[1],ascending=False)\n#取出序列数据中的前n个\nrdd4 = rdd3.take(5)\nrdd4.collect()\nsc.stop()\n\n\n\n"},"day05_Spark_core/spark_core_5.html":{"url":"day05_Spark_core/spark_core_5.html","title":"05_Spark-Core实战_ip统计","keywords":"","body":"spark-core实战\n课程目标\n\n独立实现ip地址查询\n说出广播变量的概念\n\n5.1通过spark实现ip地址查询\n需求\n在互联网中，我们经常会见到城市热点图这样的报表数据，例如在百度统计中，会统计今年的热门旅游城市、热门报考学校等，会将这样的信息显示在热点图中。\n因此，我们需要通过日志信息（运行商或者网站自己生成）和城市ip段信息来判断用户的ip段，统计热点经纬度。\nip日志信息\n在ip日志信息中，我们只需要关心ip这一个维度就可以了，其他的不做介绍\n思路\n1、 加载城市ip段信息，获取ip起始数字和结束数字，经度，纬度\n2、 加载日志数据，获取ip信息，然后转换为数字，和ip段比较\n3、 比较的时候采用二分法查找，找到对应的经度和纬度\n4，对相同的经度和纬度做累计求和\n\n\n代码\nfrom pyspark.sql import SparkSession\n# 255.255.255.255 0~255 256  2^8 8位2进制数 32位2进制数\n#将ip转换为特殊的数字形式  223.243.0.0|223.243.191.255|  255 2^8\n#‭11011111‬\n#00000000\n#1101111100000000\n#‭        11110011‬\n#11011111111100110000000000000000\ndef ip_transform(ip):     \n    ips = ip.split(\".\")#[223,243,0,0] 32位二进制数\n    ip_num = 0\n    for i in ips:\n        ip_num = int(i) | ip_num = int(broadcast_value[mid][0]) and ip_num  int(broadcast_value[mid][1]):\n            start = mid\n\ndef main():\n    spark = SparkSession.builder.appName(\"test\").getOrCreate()\n    sc = spark.sparkContext\n    city_id_rdd = sc.textFile(\"file:///root/tmp/ip.txt\").map(lambda x:x.split(\"|\")).map(lambda x: (x[2], x[3], x[13], x[14]))\n    #创建一个广播变量\n    city_broadcast = sc.broadcast(city_id_rdd.collect())\n    dest_data = sc.textFile(\"file:///root/tmp/20090121000132.394251.http.format\").map(\n        lambda x: x.split(\"|\")[1])\n    #根据取出对应的位置信息\n    def get_pos(x):\n        city_broadcast_value = city_broadcast.value\n        #根据单个ip获取对应经纬度信息\n        def get_result(ip):\n            ip_num = ip_transform(ip)\n            index = binary_search(ip_num, city_broadcast_value)\n            #((纬度,精度),1)\n            return ((city_broadcast_value[index][2], city_broadcast_value[index][3]), 1)\n\n        x = map(tuple,[get_result(ip) for ip in x])\n        return x\n\n    dest_rdd = dest_data.mapPartitions(lambda x: get_pos(x)) #((纬度,精度),1)\n    result_rdd = dest_rdd.reduceByKey(lambda a, b: a + b)\n    print(result_rdd.collect())\n    sc.stop()\n\nif __name__ == '__main__':\n    main()\n\n\n广播变量的使用\n\n要统计Ip所对应的经纬度, 每一条数据都会去查询ip表\n每一个task 都需要这一个ip表, 默认情况下, 所有task都会去复制ip表\n实际上 每一个Worker上会有多个task, 数据也是只需要进行查询操作的, 所以这份数据可以共享,没必要每个task复制一份\n可以通过广播变量, 通知当前worker上所有的task, 来共享这个数据,避免数据的多次复制,可以大大降低内存的开销\nsparkContext.broadcast(要共享的数据)\n\n\n\n"},"day05_Spark_core/spark_core_6.html":{"url":"day05_Spark_core/spark_core_6.html","title":"06_Spark安装部署&standalone模式介绍","keywords":"","body":"spark 安装部署及standalone模式介绍\n学习目标\n\n知道Spark的安装过程，知道standalone启动模式\n\n知道spark作业提交集群的过程\n\n\n1 spark 安装部署\n\n修改配置文件\nspark-env.sh(需要将spark-env.sh.template重命名)\n配置java环境变量\nexport JAVA_HOME=java_home_path\n\n\n配置PYTHON环境\nexport PYSPARK_PYTHON=/xx/pythonx_home/bin/pythonx\n\n\n配置master的地址\nexport SPARK_MASTER_HOST=node-teach\n\n\n配置master的端口\nexport SPARK_MASTER_PORT=7077\n\n\n\n\n\n\n配置spark环境变量\nexport SPARK_HOME=/xxx/spark2.x\nexport PATH=$PATH:$SPARK_HOME/bin\n\n\n\n2 Spark Standalone模式启动\n启动Spark集群\n\n进入到$SPARK_HOME/sbin目录\n\n启动Master    \n\n./start-master.sh -h 192.168.19.137\n\n\n启动Slave\n\n ./start-slave.sh spark://192.168.19.137:7077\n\n\njps查看进程\n\n27073 Master\n27151 Worker\n\n\n关闭防火墙\n\nsystemctl stop firewalld\n\n\n通过SPARK WEB UI查看Spark集群及Spark\nhttp://192.168.19.137:8080/  监控Spark集群\nhttp://192.168.19.137:4040/  监控Spark Job\n\n\n\n\n\n3 spark 集群相关概念\n\nspark集群架构(Standalone模式)\n\n\nApplication\n用户自己写的Spark应用程序，批处理作业的集合。Application的main方法为应用程序的入口，用户通过Spark的API，定义了RDD和对RDD的操作。\n\nMaster和Worker\n整个集群分为 Master 节点和 Worker 节点，相当于 Hadoop 的 Master 和 Slave 节点。\n\nMaster：Standalone模式中主控节点，负责接收Client提交的作业，管理Worker，并命令Worker启动Driver和Executor。\nWorker：Standalone模式中slave节点上的守护进程，负责管理本节点的资源，定期向Master汇报心跳，接收Master的命令，启动Driver和Executor。\n\n\nClient：客户端进程，负责提交作业到Master。\n\nDriver： 一个Spark作业运行时包括一个Driver进程，也是作业的主进程，负责作业的解析、生成Stage并调度Task到Executor上。包括DAGScheduler，TaskScheduler。\n\nExecutor：即真正执行作业的地方，一个集群一般包含多个Executor，每个Executor接收Driver的命令Launch Task，一个Executor可以执行一到多个Task。\n\n\n\nSpark作业相关概念\n\nStage：一个Spark作业一般包含一到多个Stage。\n\nTask：一个Stage包含一到多个Task，通过多个Task实现并行运行的功能。\n\nDAGScheduler： 实现将Spark作业分解成一到多个Stage，每个Stage根据RDD的Partition个数决定Task的个数，然后生成相应的Task 放到TaskScheduler中。\n\nTaskScheduler：实现Task分配到Executor上执行。\n\n\n\n\n\n\n"},"day06_Spark_sql&Spark_streaming/s1.1.html":{"url":"day06_Spark_sql&Spark_streaming/s1.1.html","title":"01_Spark SQL简介","keywords":"","body":"1、Spark SQL 概述\nSpark SQL概念\n\nSpark SQL is Apache Spark's module for working with structured data.\n它是spark中用于处理结构化数据的一个模块\n\n\n\nSpark SQL历史\n\nHive是目前大数据领域，事实上的数据仓库标准。\n\n\n\nShark：shark底层使用spark的基于内存的计算模型，从而让性能比Hive提升了数倍到上百倍。\n底层很多东西还是依赖于Hive，修改了内存管理、物理计划、执行三个模块\n2014年6月1日的时候，Spark宣布了不再开发Shark，全面转向Spark SQL的开发\n\nSpark SQL优势\n\nWrite Less Code\n\n\n\nPerformance\n\n\npython操作RDD，转换为可执行代码，运行在java虚拟机，涉及两个不同语言引擎之间的切换，进行进程间        通信很耗费性能。\nDataFrame\n\n是RDD为基础的分布式数据集，类似于传统关系型数据库的二维表，dataframe记录了对应列的名称和类型\ndataFrame引入schema和off-heap(使用操作系统层面上的内存)\n1、解决了RDD的缺点\n序列化和反序列化开销大\n频繁的创建和销毁对象造成大量的GC\n2、丢失了RDD的优点\nRDD编译时进行类型检查\nRDD具有面向对象编程的特性\n\n\n\n用scala/python编写的RDD比Spark SQL编写转换的RDD慢，涉及到执行计划\n\nCatalystOptimizer：Catalyst优化器\nProjectTungsten：钨丝计划，为了提高RDD的效率而制定的计划\nCode gen：代码生成器\n\n\n直接编写RDD也可以自实现优化代码，但是远不及SparkSQL前面的优化操作后转换的RDD效率高，快1倍左右\n优化引擎：类似mysql等关系型数据库基于成本的优化器\n首先执行逻辑执行计划，然后转换为物理执行计划(选择成本最小的)，通过Code Generation最终生成为RDD\n\nLanguage-independent API\n用任何语言编写生成的RDD都一样，而使用spark-core编写的RDD，不同的语言生成不同的RDD\n\nSchema\n结构化数据，可以直接看出数据的详情\n在RDD中无法看出，解释性不强，无法告诉引擎信息，没法详细优化。\n\n\n为什么要学习sparksql \nsparksql特性\n\n1、易整合\n2、统一的数据源访问\n3、兼容hive\n4、提供了标准的数据库连接（jdbc/odbc）\n\n"},"day06_Spark_sql&Spark_streaming/s1.2.html":{"url":"day06_Spark_sql&Spark_streaming/s1.2.html","title":"02_DataFrame介绍","keywords":"","body":"2、DataFrame\n2.1 介绍\n在Spark语义中，DataFrame是一个分布式的行集合，可以想象为一个关系型数据库的表，或者一个带有列名的Excel表格。它和RDD一样，有这样一些特点：\n\nImmuatable：一旦RDD、DataFrame被创建，就不能更改，只能通过transformation生成新的RDD、DataFrame\nLazy Evaluations：只有action才会触发Transformation的执行\nDistributed：DataFrame和RDD一样都是分布式的\ndataframe和dataset统一，dataframe只是dataset[ROW]的类型别名。由于Python是弱类型语言，只能使用DataFrame\n\nDataFrame vs RDD\n\nRDD：分布式的对象的集合，Spark并不知道对象的详细模式信息\nDataFrame：分布式的Row对象的集合，其提供了由列组成的详细模式信息，使得Spark SQL可以进行某些形式的执行优化。\nDataFrame和普通的RDD的逻辑框架区别如下所示：\n\n\n\n左侧的RDD Spark框架本身不了解 Person类的内部结构。\n\n右侧的DataFrame提供了详细的结构信息（schema——每列的名称，类型）\n\nDataFrame还配套了新的操作数据的方法，DataFrame API（如df.select())和SQL(select id, name from xx_table where ...)。\nDataFrame还引入了off-heap,意味着JVM堆以外的内存, 这些内存直接受操作系统管理（而不是JVM）。\n\nRDD是分布式的Java对象的集合。DataFrame是分布式的Row对象的集合。DataFrame除了提供了比RDD更丰富的算子以外，更重要的特点是提升执行效率、减少数据读取以及执行计划的优化。\n\nDataFrame的抽象后，我们处理数据更加简单了，甚至可以用SQL来处理数据了\n通过DataFrame API或SQL处理数据，会自动经过Spark 优化器（Catalyst）的优化，即使你写的程序或SQL不仅高效，也可以运行的很快。\nDataFrame相当于是一个带着schema的RDD\n\nPandas DataFrame vs Spark DataFrame\n\nCluster Parallel：集群并行执行\nLazy Evaluations: 只有action才会触发Transformation的执行\nImmutable：不可更改\nPandas rich API：比Spark SQL api丰富\n\n2.2 创建DataFrame\n1，创建dataFrame的步骤\n​    调用方法例如：spark.read.xxx方法\n2，其他方式创建dataframe\n\ncreateDataFrame：pandas dataframe、list、RDD\n\n数据源：RDD、csv、json、parquet、orc、jdbc\njsonDF = spark.read.json(\"xxx.json\")\n\njsonDF = spark.read.format('json').load('xxx.json')\n\nparquetDF = spark.read.parquet(\"xxx.parquet\")\n\njdbcDF = spark.read.format(\"jdbc\").option(\"url\",\"jdbc:mysql://localhost:3306/db_name\").option(\"dbtable\",\"table_name\").option(\"user\",\"xxx\").option(\"password\",\"xxx\").load()\n\n\nTransformation:延迟性操作\n\naction：立即操作\n\n\n\n2.3 DataFrame API实现\n基于RDD创建\nfrom pyspark.sql import SparkSession\nfrom pyspark.sql import Row\n\nspark = SparkSession.builder.appName('test').getOrCreate()\nsc = spark.sparkContext\n# spark.conf.set(\"spark.sql.shuffle.partitions\", 6)\n# ================直接创建==========================\nl = [('Ankit',25),('Jalfaizy',22),('saurabh',20),('Bala',26)]\nrdd = sc.parallelize(l)\n#为数据添加列名\npeople = rdd.map(lambda x: Row(name=x[0], age=int(x[1])))\n#创建DataFrame\nschemaPeople = spark.createDataFrame(people)\n\n从csv中读取数据\n# ==================从csv读取======================\n#加载csv类型的数据并转换为DataFrame\ndf = spark.read.format(\"csv\"). \\\n    option(\"header\", \"true\") \\\n    .load(\"iris.csv\")\n#显示数据结构\ndf.printSchema()\n#显示前10条数据\ndf.show(10)\n#统计总量\ndf.count()\n#列名\ndf.columns\n\n增加一列\n# ===============增加一列(或者替换) withColumn===========\n#定义一个新的列，数据为其他某列数据的两倍\n#如果操作的是原有列，可以替换原有列的数据\ndf.withColumn('newWidth',df.SepalWidth * 2).show()\n\n删除一列\n# ==========删除一列  drop=========================\n#删除一列\ndf.drop('cls').show()\n\n统计信息\n#================ 统计信息 describe================\ndf.describe().show()\n#计算某一列的描述信息\ndf.describe('cls').show()\n\n提取部分列\n# ===============提取部分列 select==============\ndf.select('SepalLength','SepalWidth').show()\n\n基本统计功能\n# ==================基本统计功能 distinct count=====\ndf.select('cls').distinct().count()\n\n分组统计\n# 分组统计 groupby(colname).agg({'col':'fun','col2':'fun2'})\ndf.groupby('cls').agg({'SepalWidth':'mean','SepalLength':'max'}).show()\n\n# avg(), count(), countDistinct(), first(), kurtosis(),\n# max(), mean(), min(), skewness(), stddev(), stddev_pop(),\n# stddev_samp(), sum(), sumDistinct(), var_pop(), var_samp() and variance()\n\n自定义的汇总方法\n# 自定义的汇总方法\nimport pyspark.sql.functions as fn\n#调用函数并起一个别名\ndf.agg(fn.count('SepalWidth').alias('width_count'),fn.countDistinct('cls').alias('distinct_cls_count')).show()\n\n拆分数据集\n#====================数据集拆成两部分 randomSplit ===========\n#设置数据比例将数据划分为两部分\ntrainDF, testDF = df.randomSplit([0.6, 0.4])\n\n采样数据\n# ================采样数据 sample===========\n#withReplacement：是否有放回的采样\n#fraction：采样比例\n#seed：随机种子\nsdf = df.sample(False,0.2,100)\n\n查看两个数据集在类别上的差异\n#查看两个数据集在类别上的差异 subtract，确保训练数据集覆盖了所有分类\ndiff_in_train_test = testDF.select('cls').subtract(trainDF.select('cls'))\ndiff_in_train_test.distinct().count()\n\n交叉表\n# ================交叉表 crosstab=============\ndf.crosstab('cls','SepalLength').show()\n\nudf\nudf：自定义函数\n#================== 综合案例 + udf================\n# 测试数据集中有些类别在训练集中是不存在的，找到这些数据集做后续处理\ntrainDF,testDF = df.randomSplit([0.99,0.01])\n\ndiff_in_train_test = trainDF.select('cls').subtract(testDF.select('cls')).distinct().show()\n\n#首先找到这些类，整理到一个列表\nnot_exist_cls = trainDF.select('cls').subtract(testDF.select('cls')).distinct().rdd.map(lambda x :x[0]).collect()\n\n#定义一个方法，用于检测\ndef should_remove(x):\n    if x in not_exist_cls:\n        return -1\n    else :\n        return x\n\n#创建udf，udf函数需要两个参数：\n# Function\n# Return type (in my case StringType())\n\n#在RDD中可以直接定义函数，交给rdd的transformatioins方法进行执行\n#在DataFrame中需要通过udf将自定义函数封装成udf函数再交给DataFrame进行调用执行\n\nfrom pyspark.sql.types import StringType\nfrom pyspark.sql.functions import udf\n\n\ncheck = udf(should_remove,StringType())\n\nresultDF = trainDF.withColumn('New_cls',check(trainDF['cls'])).filter('New_cls <> -1')\n\nresultDF.show()\n\n"},"day06_Spark_sql&Spark_streaming/s1.3.html":{"url":"day06_Spark_sql&Spark_streaming/s1.3.html","title":"03_Spark SQL 处理JSON数据","keywords":"","body":"3、JSON数据的处理\n3.1 介绍\nJSON数据\n\nSpark SQL can automatically infer the schema of a JSON dataset and load it as a DataFrame\nSpark SQL能够自动将JSON数据集以结构化的形式加载为一个DataFrame\n\nThis conversion can be done using SparkSession.read.json on a JSON file\n读取一个JSON文件可以用SparkSession.read.json方法\n\n\n从JSON到DataFrame\n\n指定DataFrame的schema\n1，通过反射自动推断，适合静态数据\n2，程序指定，适合程序运行中动态生成的数据\n\n\n加载json数据\n#使用内部的schema\njsonDF = spark.read.json(\"xxx.json\")\njsonDF = spark.read.format('json').load('xxx.json')\n\n#指定schema\njsonDF = spark.read.schema(jsonSchema).json('xxx.json')\n\n嵌套结构的JSON\n\n重要的方法\n1，get_json_object\n2，get_json\n3，explode\n\n\n3.2 实践\n3.1 静态json数据的读取和操作\n无嵌套结构的json数据\nfrom pyspark.sql import SparkSession\nspark =  SparkSession.builder.appName('json_demo').getOrCreate()\nsc = spark.sparkContext\n\n# ==========================================\n#                无嵌套结构的json\n# ==========================================\njsonString = [\n\"\"\"{ \"id\" : \"01001\", \"city\" : \"AGAWAM\",  \"pop\" : 15338, \"state\" : \"MA\" }\"\"\",\n\"\"\"{ \"id\" : \"01002\", \"city\" : \"CUSHMAN\", \"pop\" : 36963, \"state\" : \"MA\" }\"\"\"\n]\n\n从json字符串数组得到DataFrame\n# 从json字符串数组得到rdd有两种方法\n# 1. 转换为rdd，再从rdd到DataFrame\n# 2. 直接利用spark.createDataFrame()，见后面例子\n\njsonRDD = sc.parallelize(jsonString)   # stringJSONRDD\njsonDF =  spark.read.json(jsonRDD)  # convert RDD into DataFrame\njsonDF.printSchema()\njsonDF.show()\n\n直接从文件生成DataFrame\n# -- 直接从文件生成DataFrame\n#只有被压缩后的json文件内容，才能被spark-sql正确读取，否则格式化后的数据读取会出现问题\njsonDF = spark.read.json(\"xxx.json\")\n# or\n# jsonDF = spark.read.format('json').load('xxx.json')\n\njsonDF.printSchema()\njsonDF.show()\n\njsonDF.filter(jsonDF.pop>4000).show(10)\n#依照已有的DataFrame，创建一个临时的表(相当于mysql数据库中的一个表)，这样就可以用纯sql语句进行数据操作\njsonDF.createOrReplaceTempView(\"tmp_table\")\n\nresultDF = spark.sql(\"select * from tmp_table where pop>4000\")\nresultDF.show(10)\n\n3.2 动态json数据的读取和操作\n指定DataFrame的Schema\n3.1节中的例子为通过反射自动推断schema，适合静态数据\n下面我们来讲解如何进行程序指定schema\n没有嵌套结构的json\njsonString = [\n\"\"\"{ \"id\" : \"01001\", \"city\" : \"AGAWAM\",  \"pop\" : 15338, \"state\" : \"MA\" }\"\"\",\n\"\"\"{ \"id\" : \"01002\", \"city\" : \"CUSHMAN\", \"pop\" : 36963, \"state\" : \"MA\" }\"\"\"\n]\n\njsonRDD = sc.parallelize(jsonString)\n\nfrom pyspark.sql.types import *\n\n#定义结构类型\n#StructType：schema的整体结构，表示JSON的对象结构\n#XXXStype:指的是某一列的数据类型\njsonSchema = StructType() \\\n  .add(\"id\", StringType(),True) \\\n  .add(\"city\", StringType()) \\\n  .add(\"pop\" , LongType()) \\\n  .add(\"state\",StringType())\n\njsonSchema = StructType() \\\n  .add(\"id\", LongType(),True) \\\n  .add(\"city\", StringType()) \\\n  .add(\"pop\" , DoubleType()) \\\n  .add(\"state\",StringType())\n\nreader = spark.read.schema(jsonSchema)\n\njsonDF = reader.json(jsonRDD)\njsonDF.printSchema()\njsonDF.show()\n\n带有嵌套结构的json\nfrom pyspark.sql.types import *\njsonSchema = StructType([\n    StructField(\"id\", StringType(), True),\n    StructField(\"city\", StringType(), True),\n    StructField(\"loc\" , ArrayType(DoubleType())),\n    StructField(\"pop\", LongType(), True),\n    StructField(\"state\", StringType(), True)\n])\n\nreader = spark.read.schema(jsonSchema)\njsonDF = reader.json('data/nest.json')\njsonDF.printSchema()\njsonDF.show(2)\njsonDF.filter(jsonDF.pop>4000).show(10)\n\n"},"day06_Spark_sql&Spark_streaming/s1.4.html":{"url":"day06_Spark_sql&Spark_streaming/s1.4.html","title":"04Spark SQL案例数据清洗","keywords":"","body":"4、数据清洗\n前面我们处理的数据实际上都是已经被处理好的规整数据，但是在大数据整个生产过程中，需要先对数据进行数据清洗，将杂乱无章的数据整理为符合后面处理要求的规整数据。\n数据去重\n'''\n1.删除重复数据\n\ngroupby().count()：可以看到数据的重复情况\n'''\ndf = spark.createDataFrame([\n  (1, 144.5, 5.9, 33, 'M'),\n  (2, 167.2, 5.4, 45, 'M'),\n  (3, 124.1, 5.2, 23, 'F'),\n  (4, 144.5, 5.9, 33, 'M'),\n  (5, 133.2, 5.7, 54, 'F'),\n  (3, 124.1, 5.2, 23, 'F'),\n  (5, 129.2, 5.3, 42, 'M'),\n], ['id', 'weight', 'height', 'age', 'gender'])\n\n# 查看重复记录\n#无意义重复数据去重：数据中行与行完全重复\n# 1.首先删除完全一样的记录\ndf2 = df.dropDuplicates()\n\n#有意义去重：删除除去无意义字段之外的完全重复的行数据\n# 2.其次，关键字段值完全一模一样的记录（在这个例子中，是指除了id之外的列一模一样）\n# 删除某些字段值完全一样的重复记录，subset参数定义这些字段\ndf3 = df2.dropDuplicates(subset = [c for c in df2.columns if c!='id'])\n# 3.有意义的重复记录去重之后，再看某个无意义字段的值是否有重复（在这个例子中，是看id是否重复）\n# 查看某一列是否有重复值\nimport pyspark.sql.functions as fn\ndf3.agg(fn.count('id').alias('id_count'),fn.countDistinct('id').alias('distinct_id_count')).collect()\n# 4.对于id这种无意义的列重复，添加另外一列自增id\n\ndf3.withColumn('new_id',fn.monotonically_increasing_id()).show()\n\n缺失值处理\n'''\n2.处理缺失值\n2.1 对缺失值进行删除操作(行，列)\n2.2 对缺失值进行填充操作(列的均值)\n2.3 对缺失值对应的行或列进行标记\n'''\ndf_miss = spark.createDataFrame([\n(1, 143.5, 5.6, 28,'M', 100000),\n(2, 167.2, 5.4, 45,'M', None),\n(3, None , 5.2, None, None, None),\n(4, 144.5, 5.9, 33, 'M', None),\n(5, 133.2, 5.7, 54, 'F', None),\n(6, 124.1, 5.2, None, 'F', None),\n(7, 129.2, 5.3, 42, 'M', 76000),],\n ['id', 'weight', 'height', 'age', 'gender', 'income'])\n\n# 1.计算每条记录的缺失值情况\n\ndf_miss.rdd.map(lambda row:(row['id'],sum([c==None for c in row]))).collect()\n[(1, 0), (2, 1), (3, 4), (4, 1), (5, 1), (6, 2), (7, 0)]\n\n# 2.计算各列的缺失情况百分比\ndf_miss.agg(*[(1 - (fn.count(c) / fn.count('*'))).alias(c + '_missing') for c in df_miss.columns]).show()\n\n# 3、删除缺失值过于严重的列\n# 其实是先建一个DF，不要缺失值的列\ndf_miss_no_income = df_miss.select([\nc for c in df_miss.columns if c != 'income'\n])\n\n# 4、按照缺失值删除行（threshold是根据一行记录中，缺失字段的百分比的定义）\ndf_miss_no_income.dropna(thresh=3).show()\n\n# 5、填充缺失值，可以用fillna来填充缺失值，\n# 对于bool类型、或者分类类型，可以为缺失值单独设置一个类型，missing\n# 对于数值类型，可以用均值或者中位数等填充\n\n# fillna可以接收两种类型的参数：\n# 一个数字、字符串，这时整个DataSet中所有的缺失值都会被填充为相同的值。\n# 也可以接收一个字典｛列名：值｝这样\n\n# 先计算均值，并组织成一个字典\nmeans = df_miss_no_income.agg( *[fn.mean(c).alias(c) for c in df_miss_no_income.columns if c != 'gender']).toPandas().to_dict('records')[0]\n# 然后添加其它的列\nmeans['gender'] = 'missing'\n\ndf_miss_no_income.fillna(means).show()\n\n异常值处理\n'''\n3、异常值处理\n异常值：不属于正常的值 包含：缺失值，超过正常范围内的较大值或较小值\n分位数去极值\n中位数绝对偏差去极值\n正态分布去极值\n上述三种操作的核心都是：通过原始数据设定一个正常的范围，超过此范围的就是一个异常值\n'''\ndf_outliers = spark.createDataFrame([\n(1, 143.5, 5.3, 28),\n(2, 154.2, 5.5, 45),\n(3, 342.3, 5.1, 99),\n(4, 144.5, 5.5, 33),\n(5, 133.2, 5.4, 54),\n(6, 124.1, 5.1, 21),\n(7, 129.2, 5.3, 42),\n], ['id', 'weight', 'height', 'age'])\n# 设定范围 超出这个范围的 用边界值替换\n\n# approxQuantile方法接收三个参数：参数1，列名；参数2：想要计算的分位点，可以是一个点，也可以是一个列表（0和1之间的小数），第三个参数是能容忍的误差，如果是0，代表百分百精确计算。\n\ncols = ['weight', 'height', 'age']\n\nbounds = {}\nfor col in cols:\n    quantiles = df_outliers.approxQuantile(col, [0.25, 0.75], 0.05)\n    IQR = quantiles[1] - quantiles[0]\n    bounds[col] = [\n        quantiles[0] - 1.5 * IQR,\n        quantiles[1] + 1.5 * IQR\n        ]\n\n>>> bounds\n{'age': [-11.0, 93.0], 'height': [4.499999999999999, 6.1000000000000005], 'weight': [91.69999999999999, 191.7]}\n\n# 为异常值字段打标志\noutliers = df_outliers.select(*['id'] + [( (df_outliers[c]  bounds[c][1]) ).alias(c + '_o') for c in cols ])\noutliers.show()\n#\n# +---+--------+--------+-----+\n# | id|weight_o|height_o|age_o|\n# +---+--------+--------+-----+\n# |  1|   false|   false|false|\n# |  2|   false|   false|false|\n# |  3|    true|   false| true|\n# |  4|   false|   false|false|\n# |  5|   false|   false|false|\n# |  6|   false|   false|false|\n# |  7|   false|   false|false|\n# +---+--------+--------+-----+\n\n# 再回头看看这些异常值的值，重新和原始数据关联\n\ndf_outliers = df_outliers.join(outliers, on='id')\ndf_outliers.filter('weight_o').select('id', 'weight').show()\n# +---+------+\n# | id|weight|\n# +---+------+\n# |  3| 342.3|\n# +---+------+\n\ndf_outliers.filter('age_o').select('id', 'age').show()\n# +---+---+\n# | id|age|\n# +---+---+\n# |  3| 99|\n# +---+---+\n\n"},"day06_Spark_sql&Spark_streaming/ss1.1.html":{"url":"day06_Spark_sql&Spark_streaming/ss1.1.html","title":"05_Spark Streaming简介","keywords":"","body":"1、sparkStreaming概述\n1.1 SparkStreaming是什么\n\n它是一个可扩展，高吞吐具有容错性的流式计算框架\n吞吐量：单位时间内成功传输数据的数量\n\n\n之前我们接触的spark-core和spark-sql都是处理属于离线批处理任务，数据一般都是在固定位置上，通常我们写好一个脚本，每天定时去处理数据，计算，保存数据结果。这类任务通常是T+1(一天一个任务)，对实时性要求不高。\n\n但在企业中存在很多实时性处理的需求，例如：双十一的京东阿里，通常会做一个实时的数据大屏，显示实时订单。这种情况下，对数据实时性要求较高，仅仅能够容忍到延迟1分钟或几秒钟。\n\n实时计算框架对比\nStorm\n\n流式计算框架\n以record为单位处理数据\n也支持micro-batch方式（Trident）\n\nSpark\n\n批处理计算框架\n以RDD为单位处理数据\n支持micro-batch流式处理数据（Spark Streaming）\n\n对比：\n\n吞吐量：Spark Streaming优于Storm\n延迟：Spark Streaming差于Storm\n\n1.2 SparkStreaming的组件\n\nStreaming Context\n一旦一个Context已经启动(调用了Streaming Context的start()),就不能有新的流算子(Dstream)建立或者是添加到context中\n一旦一个context已经停止,不能重新启动(Streaming Context调用了stop方法之后 就不能再次调 start())\n在JVM(java虚拟机)中, 同一时间只能有一个Streaming Context处于活跃状态, 一个SparkContext创建一个Streaming Context\n在Streaming Context上调用Stop方法, 也会关闭SparkContext对象, 如果只想仅关闭Streaming Context对象,设置stop()的可选参数为false\n一个SparkContext对象可以重复利用去创建多个Streaming Context对象(不关闭SparkContext前提下), 但是需要关一个再开下一个\n\n\nDStream (离散流)\n代表一个连续的数据流\n在内部, DStream由一系列连续的RDD组成\nDStreams中的每个RDD都包含确定时间间隔内的数据\n任何对DStreams的操作都转换成了对DStreams隐含的RDD的操作\n数据源\n基本源\nTCP/IP Socket\nFileSystem\n\n\n高级源\nKafka\nFlume\n\n\n\n\n\n\n\n"},"day06_Spark_sql&Spark_streaming/ss1.2.html":{"url":"day06_Spark_sql&Spark_streaming/ss1.2.html","title":"06_Spark Streaming实现WordCount","keywords":"","body":"2、Spark Streaming编码实践\nSpark Streaming编码步骤：\n\n1，创建一个StreamingContext\n2，从StreamingContext中创建一个数据对象\n3，对数据对象进行Transformations操作\n4，输出结果\n5，开始和停止\n\n利用Spark Streaming实现WordCount\n需求：监听某个端口上的网络数据，实时统计出现的不同单词个数。\n1，需要安装一个nc工具：sudo yum install -y nc\n2，执行指令：nc -lk 9999 -v\nimport os\n# 配置spark driver和pyspark运行时，所使用的python解释器路径\nPYSPARK_PYTHON = \"/miniconda2/envs/py365/bin/python\"\nJAVA_HOME='/root/bigdata/jdk'\nSPARK_HOME = \"/root/bigdata/spark\"\n# 当存在多个版本时，不指定很可能会导致出错\nos.environ[\"PYSPARK_PYTHON\"] = PYSPARK_PYTHON\nos.environ[\"PYSPARK_DRIVER_PYTHON\"] = PYSPARK_PYTHON\nos.environ['JAVA_HOME']=JAVA_HOME\nos.environ[\"SPARK_HOME\"] = SPARK_HOME\n\nfrom pyspark import SparkContext\nfrom pyspark.streaming import StreamingContext\n\nif __name__ == \"__main__\":\n\n    sc = SparkContext(\"local[2]\",appName=\"NetworkWordCount\")\n    #参数2：指定执行计算的时间间隔\n    ssc = StreamingContext(sc, 1)\n    #监听ip，端口上的上的数据\n    lines = ssc.socketTextStream('localhost',9999)\n    #将数据按空格进行拆分为多个单词\n    words = lines.flatMap(lambda line: line.split(\" \"))\n    #将单词转换为(单词，1)的形式\n    pairs = words.map(lambda word:(word,1))\n    #统计单词个数\n    wordCounts = pairs.reduceByKey(lambda x,y:x+y)\n    #打印结果信息，会使得前面的transformation操作执行\n    wordCounts.pprint()\n    #启动StreamingContext\n    ssc.start()\n    #等待计算结束\n    ssc.awaitTermination()\n\n可视化查看效果：http://192.168.19.137:4040\n点击streaming，查看效果\n"},"day06_Spark_sql&Spark_streaming/ss1.3.html":{"url":"day06_Spark_sql&Spark_streaming/ss1.3.html","title":"07_Spark Steaming的状态操作","keywords":"","body":"3、Spark Streaming的状态操作\n在Spark Streaming中存在两种状态操作\n\nUpdateStateByKey\nWindows操作\n\n使用有状态的transformation，需要开启Checkpoint\n\nspark streaming 的容错机制\n它将足够多的信息checkpoint到某些具备容错性的存储系统如hdfs上，以便出错时能够迅速恢复\n\n3.1 updateStateByKey\nSpark Streaming实现的是一个实时批处理操作，每隔一段时间将数据进行打包，封装成RDD，是无状态的。\n无状态：指的是每个时间片段的数据之间是没有关联的。\n需求：想要将一个大时间段（1天），即多个小时间段的数据内的数据持续进行累积操作\n一般超过一天都是用RDD或Spark SQL来进行离线批处理\n如果没有UpdateStateByKey，我们需要将每一秒的数据计算好放入mysql中取，再用mysql来进行统计计算\nSpark Streaming中提供这种状态保护机制，即updateStateByKey\n步骤：\n\n首先，要定义一个state，可以是任意的数据类型\n其次，要定义state更新函数--指定一个函数如何使用之前的state和新值来更新state\n对于每个batch，Spark都会为每个之前已经存在的key去应用一次state更新函数，无论这个key在batch中是否有新的数据。如果state更新函数返回none，那么key对应的state就会被删除\n对于每个新出现的key，也会执行state更新函数\n\n举例：词统计。\n案例：updateStateByKey\n需求：监听网络端口的数据，获取到每个批次的出现的单词数量，并且需要把每个批次的信息保留下来\n代码\nimport os\n# 配置spark driver和pyspark运行时，所使用的python解释器路径\nPYSPARK_PYTHON = \"/miniconda2/envs/py365/bin/python\"\nJAVA_HOME='/root/bigdata/jdk'\nSPARK_HOME = \"/root/bigdata/spark\"\n# 当存在多个版本时，不指定很可能会导致出错\nos.environ[\"PYSPARK_PYTHON\"] = PYSPARK_PYTHON\nos.environ[\"PYSPARK_DRIVER_PYTHON\"] = PYSPARK_PYTHON\nos.environ['JAVA_HOME']=JAVA_HOME\nos.environ[\"SPARK_HOME\"] = SPARK_HOME\nfrom pyspark.streaming import StreamingContext\nfrom pyspark.sql.session import SparkSession\n\n# 创建SparkContext\nspark = SparkSession.builder.master(\"local[2]\").getOrCreate()\nsc = spark.sparkContext\n\nssc = StreamingContext(sc, 3)\n#开启检查点\nssc.checkpoint(\"checkpoint\")\n\n#定义state更新函数\ndef updateFunc(new_values, last_sum):\n    return sum(new_values) + (last_sum or 0)\n\nlines = ssc.socketTextStream(\"localhost\", 9999)\n# 对数据以空格进行拆分，分为多个单词\ncounts = lines.flatMap(lambda line: line.split(\" \")) \\\n    .map(lambda word: (word, 1)) \\\n    .updateStateByKey(updateFunc=updateFunc)#应用updateStateByKey函数\n\ncounts.pprint()\n\nssc.start()\nssc.awaitTermination()\n\n3.2 Windows\n\n窗口长度L：运算的数据量\n滑动间隔G：控制每隔多长时间做一次运算\n\n每隔G秒，统计最近L秒的数据\n\n操作细节\n\nWindow操作是基于窗口长度和滑动间隔来工作的\n窗口的长度控制考虑前几批次数据量\n默认为批处理的滑动间隔来确定计算结果的频率\n\n相关函数\n\n\nSmart computation\ninvAddFunc\n\nreduceByKeyAndWindow(func,invFunc,windowLength,slideInterval,[num,Tasks])\nfunc:正向操作，类似于updateStateByKey\ninvFunc：反向操作\n\n例如在热词时，在上一个窗口中可能是热词，这个一个窗口中可能不是热词，就需要在这个窗口中把该次剔除掉\n典型案例：热点搜索词滑动统计，每隔10秒，统计最近60秒钟的搜索词的搜索频次，并打印出最靠前的3个搜索词出现次数。\n\n案例\n监听网络端口的数据，每隔3秒统计前6秒出现的单词数量\nimport os\n# 配置spark driver和pyspark运行时，所使用的python解释器路径\nPYSPARK_PYTHON = \"/miniconda2/envs/py365/bin/python\"\nJAVA_HOME='/root/bigdata/jdk'\nSPARK_HOME = \"/root/bigdata/spark\"\n# 当存在多个版本时，不指定很可能会导致出错\nos.environ[\"PYSPARK_PYTHON\"] = PYSPARK_PYTHON\nos.environ[\"PYSPARK_DRIVER_PYTHON\"] = PYSPARK_PYTHON\nos.environ['JAVA_HOME']=JAVA_HOME\nos.environ[\"SPARK_HOME\"] = SPARK_HOME\nfrom pyspark import SparkContext\nfrom pyspark.streaming import StreamingContext\nfrom pyspark.sql.session import SparkSession\n\ndef get_countryname(line):\n    country_name = line.strip()\n\n    if country_name == 'usa':\n        output = 'USA'\n    elif country_name == 'ind':\n        output = 'India'\n    elif country_name == 'aus':\n        output = 'Australia'\n    else:\n        output = 'Unknown'\n\n    return (output, 1)\n\nif __name__ == \"__main__\":\n    #定义处理的时间间隔\n    batch_interval = 1 # base time unit (in seconds)\n    #定义窗口长度\n    window_length = 6 * batch_interval\n    #定义滑动时间间隔\n    frequency = 3 * batch_interval\n\n    #获取StreamingContext\n    spark = SparkSession.builder.master(\"local[2]\").getOrCreate()\n    sc = spark.sparkContext\n    ssc = StreamingContext(sc, batch_interval)\n\n    #需要设置检查点\n    ssc.checkpoint(\"checkpoint\")\n\n    lines = ssc.socketTextStream('localhost', 9999)\n    addFunc = lambda x, y: x + y\n    invAddFunc = lambda x, y: x - y\n    #调用reduceByKeyAndWindow，来进行窗口函数的调用\n    window_counts = lines.map(get_countryname) \\\n        .reduceByKeyAndWindow(addFunc, invAddFunc, window_length, frequency)\n    #输出处理结果信息\n    window_counts.pprint()\n\n    ssc.start()\n    ssc.awaitTermination()\n\n"},"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html":{"url":"day07_推荐系统案例/01_个性化电商广告推荐系统介绍.html","title":"01_个性化电商广告推荐系统介绍","keywords":"","body":"一 个性化电商广告推荐系统介绍\n1.1 数据集介绍\n\nAli_Display_Ad_Click是阿里巴巴提供的一个淘宝展示广告点击率预估数据集\n数据集来源：天池竞赛\n\n原始样本骨架raw_sample\n淘宝网站中随机抽样了114万用户8天内的广告展示/点击日志（2600万条记录），构成原始的样本骨架。 字段说明如下：\n\nuser_id：脱敏过的用户ID；\nadgroup_id：脱敏过的广告单元ID；\ntime_stamp：时间戳；\npid：资源位；\nnoclk：为1代表没有点击；为0代表点击；\nclk：为0代表没有点击；为1代表点击；\n\n用前面7天的做训练样本（20170506-20170512），用第8天的做测试样本（20170513）\n\n广告基本信息表ad_feature\n本数据集涵盖了raw_sample中全部广告的基本信息(约80万条目)。字段说明如下：\n\nadgroup_id：脱敏过的广告ID；\ncate_id：脱敏过的商品类目ID；\ncampaign_id：脱敏过的广告计划ID；\ncustomer_id: 脱敏过的广告主ID；\nbrand_id：脱敏过的品牌ID；\nprice: 宝贝的价格\n\n其中一个广告ID对应一个商品（宝贝），一个宝贝属于一个类目，一个宝贝属于一个品牌。\n\n用户基本信息表user_profile\n本数据集涵盖了raw_sample中全部用户的基本信息(约100多万用户)。字段说明如下：\n\nuserid：脱敏过的用户ID；\ncms_segid：微群ID；\ncms_group_id：cms_group_id；\nfinal_gender_code：性别 1:男,2:女；\nage_level：年龄层次； 1234\npvalue_level：消费档次，1:低档，2:中档，3:高档；\nshopping_level：购物深度，1:浅层用户,2:中度用户,3:深度用户\noccupation：是否大学生 ，1:是,0:否\nnew_user_class_level：城市层级\n\n\n用户的行为日志behavior_log\n本数据集涵盖了raw_sample中全部用户22天内的购物行为(共七亿条记录)。字段说明如下：\nuser：脱敏过的用户ID；\ntime_stamp：时间戳；\nbtag：行为类型, 包括以下四种：\n​    类型 | 说明\n​    pv | 浏览\n​    cart | 加入购物车\n​    fav | 喜欢\n​    buy | 购买\ncate_id：脱敏过的商品类目id；\nbrand_id: 脱敏过的品牌id；\n这里以user + time_stamp为key，会有很多重复的记录；这是因为我们的不同的类型的行为数据是不同部门记录的，在打包到一起的时候，实际上会有小的偏差（即两个一样的time_stamp实际上是差异比较小的两个时间）\n\n\n1.2 项目效果展示\n\n1.3 项目实现分析\n\n主要包括\n\n一份广告点击的样本数据raw_sample.csv：体现的是用户对不同位置广告点击、没点击的情况\n一份广告基本信息数据ad_feature.csv：体现的是每个广告的类目(id)、品牌(id)、价格特征\n一份用户基本信息数据user_profile.csv：体现的是用户群组、性别、年龄、消费购物档次、所在城市级别等特征\n一份用户行为日志数据behavior_log.csv：体现用户对商品类目(id)、品牌(id)的浏览、加购物车、收藏、购买等信息\n\n我们是在对非搜索类型的广告进行点击率预测和推荐(没有搜索词、没有广告的内容特征信息)\n\n推荐业务处理主要流程： 召回 ===> 排序 ===> 过滤\n离线处理业务流\nraw_sample.csv ==> 历史样本数据\nad_feature.csv ==> 广告特征数据\nuser_profile.csv ==> 用户特征数据\nraw_sample.csv + ad_feature.csv + user_profile.csv ==> CTR点击率预测模型\nbehavior_log.csv ==> 评分数据 ==> user-cate/brand评分数据 ==> 协同过滤 ==> top-N cate/brand ==> 关联广告\n协同过滤召回 ==> top-N cate/brand ==> 关联对应的广告完成召回\n\n\n在线处理业务流\n数据处理部分：\n实时行为日志 ==> 实时特征 ==> 缓存\n实时行为日志 ==> 实时商品类别/品牌 ==> 实时广告召回集 ==> 缓存\n\n\n推荐任务部分：\nCTR点击率预测模型 + 广告/用户特征(缓存) + 对应的召回集(缓存) ==> 点击率排序 ==> top-N 广告推荐结果\n\n\n\n\n\n\n涉及技术：Flume、Kafka、Spark-streming\\HDFS、Spark SQL、Spark ML、Redis\nFlume：日志数据收集\nKafka：实时日志数据处理队列\nHDFS：存储数据\nSpark SQL：离线处理\nSpark ML：模型训练\nRedis：缓存\n\n\n\n\n\n1.4 点击率预测(CTR--Click-Through-Rate)概念\n\n电商广告推荐通常使用广告点击率(CTR--Click-Through-Rate)预测来实现\n点击率预测 VS 推荐算法\n点击率预测需要给出精准的点击概率，比如广告A点击率0.5%、广告B的点击率0.12%等；而推荐算法很多时候只需要得出一个最优的次序A>B>C即可。\n点击率预测使用的算法通常是如逻辑回归(Logic Regression)这样的机器学习算法，而推荐算法则是一些基于协同过滤推荐、基于内容的推荐等思想实现的算法\n点击率 VS 转化率\n点击率预测是对每次广告的点击情况做出预测，可以判定这次为点击或不点击，也可以给出点击或不点击的概率\n转化率指的是从状态A进入到状态B的概率，电商的转化率通常是指到达网站后，进而有成交记录的用户比率，如用户成交量/用户访问量\n搜索和非搜索广告点击率预测的区别\n搜索中有很强的搜索信号-“查询词(Query)”，查询词和广告内容的匹配程度很大程度影响了点击概率，搜索广告的点击率普遍较高\n非搜索广告（例如展示广告，信息流广告）的点击率的计算很多就来源于用户的兴趣和广告自身的特征，以及上下文环境。通常好位置能达到百分之几的点击率。对于很多底部的广告，点击率非常低，常常是千分之几，甚至更低\n\n\n"},"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html":{"url":"day07_推荐系统案例/02_根据用户行为数据创建ALS模型并召回商品.html","title":"02_根据用户行为数据创建ALS模型并召回商品","keywords":"","body":"二 根据用户行为数据创建ALS模型并召回商品\n2.0 用户行为数据拆分\n\n方便练习可以对数据做拆分处理\n\npandas的数据分批读取  chunk 厚厚的一块 相当大的数量或部分\n\nimport pandas as pd\nreader = pd.read_csv('behavior_log.csv',chunksize=100,iterator=True)\ncount = 0;\nfor chunk in reader:\n    count += 1\n    if count ==1:\n        chunk.to_csv('test4.csv',index = False)\n    elif count>1 and count\n\n\n2.1 预处理behavior_log数据集\n\n创建spark session\n\nimport os\n# 配置spark driver和pyspark运行时，所使用的python解释器路径\nPYSPARK_PYTHON = \"/miniconda2/envs/py365/bin/python\"\nJAVA_HOME='/root/bigdata/jdk'\nSPARK_HOME = \"/root/bigdata/spark\"\n# 当存在多个版本时，不指定很可能会导致出错\nos.environ[\"PYSPARK_PYTHON\"] = PYSPARK_PYTHON\nos.environ[\"PYSPARK_DRIVER_PYTHON\"] = PYSPARK_PYTHON\nos.environ['JAVA_HOME']=JAVA_HOME\nos.environ[\"SPARK_HOME\"] = SPARK_HOME\n# spark配置信息\nfrom pyspark import SparkConf\nfrom pyspark.sql import SparkSession\n\nSPARK_APP_NAME = \"preprocessingBehaviorLog\"\nSPARK_URL = \"spark://192.168.19.137:7077\"\n\nconf = SparkConf()    # 创建spark config对象\nconfig = (\n    (\"spark.app.name\", SPARK_APP_NAME),    # 设置启动的spark的app名称，没有提供，将随机产生一个名称\n    (\"spark.executor.memory\", \"6g\"),    # 设置该app启动时占用的内存用量，默认1g\n    (\"spark.master\", SPARK_URL),    # spark master的地址\n    (\"spark.executor.cores\", \"4\"),    # 设置spark executor使用的CPU核心数\n    # 以下三项配置，可以控制执行器数量\n#     (\"spark.dynamicAllocation.enabled\", True),\n#     (\"spark.dynamicAllocation.initialExecutors\", 1),    # 1个执行器\n#     (\"spark.shuffle.service.enabled\", True)\n#     ('spark.sql.pivotMaxValues', '99999'),  # 当需要pivot DF，且值很多时，需要修改，默认是10000\n)\n# 查看更详细配置及说明：https://spark.apache.org/docs/latest/configuration.html\n\nconf.setAll(config)\n\n# 利用config对象，创建spark session\nspark = SparkSession.builder.config(conf=conf).getOrCreate()\n\n\n从hdfs中加载csv文件为DataFrame\n\n# 从hdfs加载CSV文件为DataFrame\ndf = spark.read.csv(\"hdfs://localhost:9000/data/behavior_log.csv\", header=True)\ndf.show()    # 查看dataframe，默认显示前20条\n# 大致查看一下数据类型\ndf.printSchema()    # 打印当前dataframe的结构\n\n显示结果:\n+------+----------+----+-----+------+\n|  user|time_stamp|btag| cate| brand|\n+------+----------+----+-----+------+\n|558157|1493741625|  pv| 6250| 91286|\n|558157|1493741626|  pv| 6250| 91286|\n|558157|1493741627|  pv| 6250| 91286|\n|728690|1493776998|  pv|11800| 62353|\n|332634|1493809895|  pv| 1101|365477|\n|857237|1493816945|  pv| 1043|110616|\n|619381|1493774638|  pv|  385|428950|\n|467042|1493772641|  pv| 8237|301299|\n|467042|1493772644|  pv| 8237|301299|\n|991528|1493780710|  pv| 7270|274795|\n|991528|1493780712|  pv| 7270|274795|\n|991528|1493780712|  pv| 7270|274795|\n|991528|1493780712|  pv| 7270|274795|\n|991528|1493780714|  pv| 7270|274795|\n|991528|1493780765|  pv| 7270|274795|\n|991528|1493780714|  pv| 7270|274795|\n|991528|1493780765|  pv| 7270|274795|\n|991528|1493780764|  pv| 7270|274795|\n|991528|1493780633|  pv| 7270|274795|\n|991528|1493780764|  pv| 7270|274795|\n+------+----------+----+-----+------+\nonly showing top 20 rows\n\nroot\n |-- user: string (nullable = true)\n |-- time_stamp: string (nullable = true)\n |-- btag: string (nullable = true)\n |-- cate: string (nullable = true)\n |-- brand: string (nullable = true)\n\n\n从hdfs加载数据为dataframe，并设置结构\n\nfrom pyspark.sql.types import StructType, StructField, StringType, IntegerType, LongType\n# 构建结构对象\nschema = StructType([\n    StructField(\"userId\", IntegerType()),\n    StructField(\"timestamp\", LongType()),\n    StructField(\"btag\", StringType()),\n    StructField(\"cateId\", IntegerType()),\n    StructField(\"brandId\", IntegerType())\n])\n# 从hdfs加载数据为dataframe，并设置结构\nbehavior_log_df = spark.read.csv(\"hdfs://localhost:9000/data/behavior_log.csv\", header=True, schema=schema)\nbehavior_log_df.show()\nbehavior_log_df.count()\n\n显示结果:\n+------+----------+----+------+-------+\n|userId| timestamp|btag|cateId|brandId|\n+------+----------+----+------+-------+\n|558157|1493741625|  pv|  6250|  91286|\n|558157|1493741626|  pv|  6250|  91286|\n|558157|1493741627|  pv|  6250|  91286|\n|728690|1493776998|  pv| 11800|  62353|\n|332634|1493809895|  pv|  1101| 365477|\n|857237|1493816945|  pv|  1043| 110616|\n|619381|1493774638|  pv|   385| 428950|\n|467042|1493772641|  pv|  8237| 301299|\n|467042|1493772644|  pv|  8237| 301299|\n|991528|1493780710|  pv|  7270| 274795|\n|991528|1493780712|  pv|  7270| 274795|\n|991528|1493780712|  pv|  7270| 274795|\n|991528|1493780712|  pv|  7270| 274795|\n|991528|1493780714|  pv|  7270| 274795|\n|991528|1493780765|  pv|  7270| 274795|\n|991528|1493780714|  pv|  7270| 274795|\n|991528|1493780765|  pv|  7270| 274795|\n|991528|1493780764|  pv|  7270| 274795|\n|991528|1493780633|  pv|  7270| 274795|\n|991528|1493780764|  pv|  7270| 274795|\n+------+----------+----+------+-------+\nonly showing top 20 rows\n\nroot\n |-- userId: integer (nullable = true)\n |-- timestamp: long (nullable = true)\n |-- btag: string (nullable = true)\n |-- cateId: integer (nullable = true)\n |-- brandId: integer (nullable = true)\n\n\n分析数据集字段的类型和格式\n查看是否有空值\n查看每列数据的类型\n查看每列数据的类别情况\n\n\n\nprint(\"查看userId的数据情况：\", behavior_log_df.groupBy(\"userId\").count().count())\n# 约113w用户\n#注意：behavior_log_df.groupBy(\"userId\").count()  返回的是一个dataframe，这里的count计算的是每一个分组的个数，但当前还没有进行计算\n# 当调用df.count()时才开始进行计算，这里的count计算的是dataframe的条目数，也就是共有多少个分组\n\n查看user的数据情况： 1136340\n\nprint(\"查看btag的数据情况：\", behavior_log_df.groupBy(\"btag\").count().collect())    # collect会把计算结果全部加载到内存，谨慎使用\n# 只有四种类型数据：pv、fav、cart、buy\n# 这里由于类型只有四个，所以直接使用collect，把数据全部加载出来\n\n查看btag的数据情况： [Row(btag='buy', count=9115919), Row(btag='fav', count=9301837), Row(btag='cart', count=15946033), Row(btag='pv', count=688904345)]\n\nprint(\"查看cateId的数据情况：\", behavior_log_df.groupBy(\"cateId\").count().count())\n# 约12968类别id\n\n查看cateId的数据情况： 12968\n\nprint(\"查看brandId的数据情况：\", behavior_log_df.groupBy(\"brandId\").count().count())\n# 约460561品牌id\n\n查看brandId的数据情况： 460561\n\nprint(\"判断数据是否有空值：\", behavior_log_df.count(), behavior_log_df.dropna().count())\n# 约7亿条目723268134 723268134\n# 本数据集无空值条目，可放心处理\n\n判断数据是否有空值： 723268134 723268134\n\n\npivot透视操作，把某列里的字段值转换成行并进行聚合运算(pyspark.sql.GroupedData.pivot)\n如果透视的字段中的不同属性值超过10000个，则需要设置spark.sql.pivotMaxValues，否则计算过程中会出现错误。文档介绍。\n\n\n\n# 统计每个用户对各类商品的pv、fav、cart、buy数量\ncate_count_df = behavior_log_df.groupBy(behavior_log_df.userId, behavior_log_df.cateId).pivot(\"btag\",[\"pv\",\"fav\",\"cart\",\"buy\"]).count()\ncate_count_df.printSchema()    # 此时还没有开始计算\n\n显示效果:\nroot\n |-- userId: integer (nullable = true)\n |-- cateId: integer (nullable = true)\n |-- pv: long (nullable = true)\n |-- fav: long (nullable = true)\n |-- cart: long (nullable = true)\n |-- buy: long (nullable = true)\n\n\n统计每个用户对各个品牌的pv、fav、cart、buy数量并保存结果\n\n# 统计每个用户对各个品牌的pv、fav、cart、buy数量\nbrand_count_df = behavior_log_df.groupBy(behavior_log_df.userId, behavior_log_df.brandId).pivot(\"btag\",[\"pv\",\"fav\",\"cart\",\"buy\"]).count()\n# brand_count_df.show()    # 同上\n# 113w * 46w\n# 由于运算时间比较长，所以这里先将结果存储起来，供后续其他操作使用\n# 写入数据时才开始计算\ncate_count_df.write.csv(\"hdfs://localhost:9000/preprocessing_dataset/cate_count.csv\", header=True)\nbrand_count_df.write.csv(\"hdfs://localhost:9000/preprocessing_dataset/brand_count.csv\", header=True)\n\n2.2 根据用户对类目偏好打分训练ALS模型\n\n根据您统计的次数 + 打分规则 ==> 偏好打分数据集 ==> ALS模型\n\n# spark ml的模型训练是基于内存的，如果数据过大，内存空间小，迭代次数过多的化，可能会造成内存溢出，报错\n# 设置Checkpoint的话，会把所有数据落盘，这样如果异常退出，下次重启后，可以接着上次的训练节点继续运行\n# 但该方法其实指标不治本，因为无法防止内存溢出，所以还是会报错\n# 如果数据量大，应考虑的是增加内存、或限制迭代次数和训练数据量级等\nspark.sparkContext.setCheckpointDir(\"hdfs://localhost:9000/checkPoint/\")\nfrom pyspark.sql.types import StructType, StructField, StringType, IntegerType, LongType, FloatType\n\n# 构建结构对象\nschema = StructType([\n    StructField(\"userId\", IntegerType()),\n    StructField(\"cateId\", IntegerType()),\n    StructField(\"pv\", IntegerType()),\n    StructField(\"fav\", IntegerType()),\n    StructField(\"cart\", IntegerType()),\n    StructField(\"buy\", IntegerType())\n])\n\n# 从hdfs加载CSV文件\ncate_count_df = spark.read.csv(\"hdfs://localhost:9000/preprocessing_dataset/cate_count.csv\", header=True, schema=schema)\ncate_count_df.printSchema()\ncate_count_df.first()    # 第一行数据\n\n显示结果:\nroot\n |-- userId: integer (nullable = true)\n |-- cateId: integer (nullable = true)\n |-- pv: integer (nullable = true)\n |-- fav: integer (nullable = true)\n |-- cart: integer (nullable = true)\n |-- buy: integer (nullable = true)\n\nRow(userId=1061650, cateId=4520, pv=2326, fav=None, cart=53, buy=None)\n\n\n处理每一行数据：r表示row对象\n\ndef process_row(r):\n    # 处理每一行数据：r表示row对象\n\n    # 偏好评分规则：\n    #     m: 用户对应的行为次数\n    #     该偏好权重比例，次数上限仅供参考，具体数值应根据产品业务场景权衡\n    #     pv: if m\n\n返回一个PythonRDD类型\n\n# 返回一个PythonRDD类型，此时还没开始计算\ncate_count_df.rdd.map(process_row).toDF([\"userId\", \"cateId\", \"rating\"])\n\n显示结果:\nDataFrame[userId: bigint, cateId: bigint, rating: double]\n\n\n用户对商品类别的打分数据\n\n# 用户对商品类别的打分数据\n# map返回的结果是rdd类型，需要调用toDF方法转换为Dataframe\ncate_rating_df = cate_count_df.rdd.map(process_row).toDF([\"userId\", \"cateId\", \"rating\"])\n# 注意：toDF不是每个rdd都有的方法，仅局限于此处的rdd\n# 可通过该方法获得 user-cate-matrix\n# 但由于cateId字段过多，这里运算量比很大，机器内存要求很高才能执行，否则无法完成任务\n# 请谨慎使用\n\n# 但好在我们训练ALS模型时，不需要转换为user-cate-matrix，所以这里可以不用运行\n# cate_rating_df.groupBy(\"userId\").povit(\"cateId\").min(\"rating\")\n# 用户对类别的偏好打分数据\ncate_rating_df\n\n显示结果:\nDataFrame[userId: bigint, cateId: bigint, rating: double]\n\n通常如果USER-ITEM打分数据应该是通过一下方式进行处理转换为USER-ITEM-MATRIX\n\n\n但这里我们将使用的Spark的ALS模型进行CF推荐，因此注意这里数据输入不需要提前转换为矩阵，直接是 USER-ITEM-RATE的数据\n\n基于Spark的ALS隐因子模型进行CF评分预测\n\nALS的意思是交替最小二乘法（Alternating Least Squares），是Spark2.*中加入的进行基于模型的协同过滤（model-based CF）的推荐系统算法。\n同SVD，它也是一种矩阵分解技术，对数据进行降维处理。\n\n详细使用方法：pyspark.ml.recommendation.ALS\n\n注意：由于数据量巨大，因此这里也不考虑基于内存的CF算法\n参考：为什么Spark中只有ALS\n\n\n\n\n# 使用pyspark中的ALS矩阵分解方法实现CF评分预测\n# 文档地址：https://spark.apache.org/docs/2.2.2/api/python/pyspark.ml.html?highlight=vectors#module-pyspark.ml.recommendation\nfrom pyspark.ml.recommendation import ALS   # ml：dataframe， mllib：rdd\n\n# 利用打分数据，训练ALS模型\nals = ALS(userCol='userId', itemCol='cateId', ratingCol='rating', checkpointInterval=5)\n\n# 此处训练时间较长\nmodel = als.fit(cate_rating_df)\n\n\n模型训练好后，调用方法进行使用，具体API查看\n\n# model.recommendForAllUsers(N) 给所有用户推荐TOP-N个物品\nret = model.recommendForAllUsers(3)\n# 由于是给所有用户进行推荐，此处运算时间也较长\nret.show()\n# 推荐结果存放在recommendations列中，\nret.select(\"recommendations\").show()\n\n显示结果:\n+------+--------------------+\n|userId|     recommendations|\n+------+--------------------+\n|   148|[[3347, 12.547271...|\n|   463|[[1610, 9.250818]...|\n|   471|[[1610, 10.246621...|\n|   496|[[1610, 5.162216]...|\n|   833|[[5607, 9.065482]...|\n|  1088|[[104, 6.886987],...|\n|  1238|[[5631, 14.51981]...|\n|  1342|[[5720, 10.89842]...|\n|  1580|[[5731, 8.466453]...|\n|  1591|[[1610, 12.835257...|\n|  1645|[[1610, 11.968531...|\n|  1829|[[1610, 17.576496...|\n|  1959|[[1610, 8.353473]...|\n|  2122|[[1610, 12.652732...|\n|  2142|[[1610, 12.48068]...|\n|  2366|[[1610, 11.904813...|\n|  2659|[[5607, 11.699315...|\n|  2866|[[1610, 7.752719]...|\n|  3175|[[3347, 2.3429515...|\n|  3749|[[1610, 3.641833]...|\n+------+--------------------+\nonly showing top 20 rows\n\n+--------------------+\n|     recommendations|\n+--------------------+\n|[[3347, 12.547271...|\n|[[1610, 9.250818]...|\n|[[1610, 10.246621...|\n|[[1610, 5.162216]...|\n|[[5607, 9.065482]...|\n|[[104, 6.886987],...|\n|[[5631, 14.51981]...|\n|[[5720, 10.89842]...|\n|[[5731, 8.466453]...|\n|[[1610, 12.835257...|\n|[[1610, 11.968531...|\n|[[1610, 17.576496...|\n|[[1610, 8.353473]...|\n|[[1610, 12.652732...|\n|[[1610, 12.48068]...|\n|[[1610, 11.904813...|\n|[[5607, 11.699315...|\n|[[1610, 7.752719]...|\n|[[3347, 2.3429515...|\n|[[1610, 3.641833]...|\n+--------------------+\nonly showing top 20 rows\n\n\nmodel.recommendForUserSubset 给部分用户推荐TOP-N个物品\n\n# 注意：recommendForUserSubset API，2.2.2版本中无法使用\ndataset = spark.createDataFrame([[1],[2],[3]])\ndataset = dataset.withColumnRenamed(\"_1\", \"userId\")\nret = model.recommendForUserSubset(dataset, 3)\n\n# 只给部分用推荐，运算时间短\nret.show()\nret.collect()    # 注意： collect会将所有数据加载到内存，慎用\n\n显示结果:\n+------+--------------------+\n|userId|     recommendations|\n+------+--------------------+\n|     1|[[1610, 25.4989],...|\n|     3|[[5607, 13.665942...|\n|     2|[[5579, 5.9051886...|\n+------+--------------------+\n\n[Row(userId=1, recommendations=[Row(cateId=1610, rating=25.498899459838867), Row(cateId=5737, rating=24.901548385620117), Row(cateId=3347, rating=20.736785888671875)]),\n Row(userId=3, recommendations=[Row(cateId=5607, rating=13.665942192077637), Row(cateId=1610, rating=11.770171165466309), Row(cateId=3347, rating=10.35690689086914)]),\n Row(userId=2, recommendations=[Row(cateId=5579, rating=5.90518856048584), Row(cateId=2447, rating=5.624575138092041), Row(cateId=5690, rating=5.2555742263793945)])]\n\n\ntransform中提供userId和cateId可以对打分进行预测，利用打分结果排序后\n\n# transform中提供userId和cateId可以对打分进行预测，利用打分结果排序后，同样可以实现TOP-N的推荐\nmodel.transform\n# 将模型进行存储\nmodel.save(\"hdfs://localhost:9000/models/userCateRatingALSModel.obj\")\n# 测试存储的模型\nfrom pyspark.ml.recommendation import ALSModel\n# 从hdfs加载之前存储的模型\nals_model = ALSModel.load(\"hdfs://localhost:9000/models/userCateRatingALSModel.obj\")\n# model.recommendForAllUsers(N) 给用户推荐TOP-N个物品\nresult = als_model.recommendForAllUsers(3)\nresult.show()\n\n显示结果:\n+------+--------------------+\n|userId|     recommendations|\n+------+--------------------+\n|   148|[[3347, 12.547271...|\n|   463|[[1610, 9.250818]...|\n|   471|[[1610, 10.246621...|\n|   496|[[1610, 5.162216]...|\n|   833|[[5607, 9.065482]...|\n|  1088|[[104, 6.886987],...|\n|  1238|[[5631, 14.51981]...|\n|  1342|[[5720, 10.89842]...|\n|  1580|[[5731, 8.466453]...|\n|  1591|[[1610, 12.835257...|\n|  1645|[[1610, 11.968531...|\n|  1829|[[1610, 17.576496...|\n|  1959|[[1610, 8.353473]...|\n|  2122|[[1610, 12.652732...|\n|  2142|[[1610, 12.48068]...|\n|  2366|[[1610, 11.904813...|\n|  2659|[[5607, 11.699315...|\n|  2866|[[1610, 7.752719]...|\n|  3175|[[3347, 2.3429515...|\n|  3749|[[1610, 3.641833]...|\n+------+--------------------+\nonly showing top 20 rows\n\n\n召回到redis\n\nimport redis\nhost = \"192.168.19.137\"\nport = 6379    \n# 召回到redis\ndef recall_cate_by_cf(partition):\n    # 建立redis 连接池\n    pool = redis.ConnectionPool(host=host, port=port)\n    # 建立redis客户端\n    client = redis.Redis(connection_pool=pool)\n    for row in partition:\n        client.hset(\"recall_cate\", row.userId, [i.cateId for i in row.recommendations])\n# 对每个分片的数据进行处理 #mapPartitions Transformation   map\n# foreachPartition Action操作             foreachRDD\nresult.foreachPartition(recall_cate_by_cf)\n\n# 注意：这里这是召回的是用户最感兴趣的n个类别\n# 总的条目数，查看redis中总的条目数是否一致\nresult.count()\n\n显示结果:\n1136340\n\n2.3 根据用户对品牌偏好打分训练ALS模型\nfrom pyspark.sql.types import StructType, StructField, StringType, IntegerType\n\nschema = StructType([\n    StructField(\"userId\", IntegerType()),\n    StructField(\"brandId\", IntegerType()),\n    StructField(\"pv\", IntegerType()),\n    StructField(\"fav\", IntegerType()),\n    StructField(\"cart\", IntegerType()),\n    StructField(\"buy\", IntegerType())\n])\n# 从hdfs加载预处理好的品牌的统计数据\nbrand_count_df = spark.read.csv(\"hdfs://localhost:9000/preprocessing_dataset/brand_count.csv\", header=True, schema=schema)\n# brand_count_df.show()\ndef process_row(r):\n    # 处理每一行数据：r表示row对象\n\n    # 偏好评分规则：\n    #     m: 用户对应的行为次数\n    #     该偏好权重比例，次数上限仅供参考，具体数值应根据产品业务场景权衡\n    #     pv: if m\n\n基于Spark的ALS隐因子模型进行CF评分预测\n\nALS的意思是交替最小二乘法（Alternating Least Squares），是Spark中进行基于模型的协同过滤（model-based CF）的推荐系统算法，也是目前Spark内唯一一个推荐算法。\n同SVD，它也是一种矩阵分解技术，但理论上，ALS在海量数据的处理上要优于SVD。\n更多了解：pyspark.ml.recommendation.ALS\n注意：由于数据量巨大，因此这里不考虑基于内存的CF算法\n参考：为什么Spark中只有ALS\n\n\n\n使用pyspark中的ALS矩阵分解方法实现CF评分预测\n\n\n# 使用pyspark中的ALS矩阵分解方法实现CF评分预测\n# 文档地址：https://spark.apache.org/docs/latest/api/python/pyspark.ml.html?highlight=vectors#module-pyspark.ml.recommendation\nfrom pyspark.ml.recommendation import ALS\n\nals = ALS(userCol='userId', itemCol='brandId', ratingCol='rating', checkpointInterval=2)\n# 利用打分数据，训练ALS模型\n# 此处训练时间较长\nmodel = als.fit(brand_rating_df)\n# model.recommendForAllUsers(N) 给用户推荐TOP-N个物品\nmodel.recommendForAllUsers(3).show()\n# 将模型进行存储\nmodel.save(\"hdfs://localhost:9000/models/userBrandRatingModel.obj\")\n# 测试存储的模型\nfrom pyspark.ml.recommendation import ALSModel\n# 从hdfs加载模型\nmy_model = ALSModel.load(\"hdfs://localhost:9000/models/userBrandRatingModel.obj\")\nmy_model\n# model.recommendForAllUsers(N) 给用户推荐TOP-N个物品\nmy_model.recommendForAllUsers(3).first()\n\n"},"day07_推荐系统案例/03_CTR预估数据准备.html":{"url":"day07_推荐系统案例/03_CTR预估数据准备.html","title":"03_CTR预估数据准备","keywords":"","body":"三 CTR预估数据准备\n3.1 分析并预处理raw_sample数据集\n# 从HDFS中加载样本数据信息\ndf = spark.read.csv(\"hdfs://localhost:9000/data/raw_sample.csv\", header=True)\ndf.show()    # 展示数据，默认前20条\ndf.printSchema()\n\n显示结果:\n+------+----------+----------+-----------+------+---+\n|  user|time_stamp|adgroup_id|        pid|nonclk|clk|\n+------+----------+----------+-----------+------+---+\n|581738|1494137644|         1|430548_1007|     1|  0|\n|449818|1494638778|         3|430548_1007|     1|  0|\n|914836|1494650879|         4|430548_1007|     1|  0|\n|914836|1494651029|         5|430548_1007|     1|  0|\n|399907|1494302958|         8|430548_1007|     1|  0|\n|628137|1494524935|         9|430548_1007|     1|  0|\n|298139|1494462593|         9|430539_1007|     1|  0|\n|775475|1494561036|         9|430548_1007|     1|  0|\n|555266|1494307136|        11|430539_1007|     1|  0|\n|117840|1494036743|        11|430548_1007|     1|  0|\n|739815|1494115387|        11|430539_1007|     1|  0|\n|623911|1494625301|        11|430548_1007|     1|  0|\n|623911|1494451608|        11|430548_1007|     1|  0|\n|421590|1494034144|        11|430548_1007|     1|  0|\n|976358|1494156949|        13|430548_1007|     1|  0|\n|286630|1494218579|        13|430539_1007|     1|  0|\n|286630|1494289247|        13|430539_1007|     1|  0|\n|771431|1494153867|        13|430548_1007|     1|  0|\n|707120|1494220810|        13|430548_1007|     1|  0|\n|530454|1494293746|        13|430548_1007|     1|  0|\n+------+----------+----------+-----------+------+---+\nonly showing top 20 rows\n\nroot\n |-- user: string (nullable = true)\n |-- time_stamp: string (nullable = true)\n |-- adgroup_id: string (nullable = true)\n |-- pid: string (nullable = true)\n |-- nonclk: string (nullable = true)\n |-- clk: string (nullable = true)\n\n\n分析数据集字段的类型和格式\n查看是否有空值\n查看每列数据的类型\n查看每列数据的类别情况\n\n\n\nprint(\"样本数据集总条目数：\", df.count())\n# 约2600w\nprint(\"用户user总数：\", df.groupBy(\"user\").count().count())\n# 约 114w，略多余日志数据中用户数\nprint(\"广告id adgroup_id总数：\", df.groupBy(\"adgroup_id\").count().count())\n# 约85w\nprint(\"广告展示位pid情况：\", df.groupBy(\"pid\").count().collect())\n# 只有两种广告展示位，占比约为六比四\nprint(\"广告点击数据情况clk：\", df.groupBy(\"clk\").count().collect())\n# 点和不点比率约： 1:20\n\n显示结果:\n样本数据集总条目数： 26557961\n用户user总数： 1141729\n广告id adgroup_id总数： 846811\n广告展示位pid情况： [Row(pid='430548_1007', count=16472898), Row(pid='430539_1007', count=10085063)]\n广告点击数据情况clk： [Row(clk='0', count=25191905), Row(clk='1', count=1366056)]\n\n\n使用dataframe.withColumn更改df列数据结构；使用dataframe.withColumnRenamed更改列名称\n\n# 更改表结构，转换为对应的数据类型\nfrom pyspark.sql.types import StructType, StructField, IntegerType, FloatType, LongType, StringType\n\n# 打印df结构信息\ndf.printSchema()   \n# 更改df表结构：更改列类型和列名称\nraw_sample_df = df.\\\n    withColumn(\"user\", df.user.cast(IntegerType())).withColumnRenamed(\"user\", \"userId\").\\\n    withColumn(\"time_stamp\", df.time_stamp.cast(LongType())).withColumnRenamed(\"time_stamp\", \"timestamp\").\\\n    withColumn(\"adgroup_id\", df.adgroup_id.cast(IntegerType())).withColumnRenamed(\"adgroup_id\", \"adgroupId\").\\\n    withColumn(\"pid\", df.pid.cast(StringType())).\\\n    withColumn(\"nonclk\", df.nonclk.cast(IntegerType())).\\\n    withColumn(\"clk\", df.clk.cast(IntegerType()))\nraw_sample_df.printSchema()\nraw_sample_df.show()\n\n显示结果:\nroot\n |-- user: string (nullable = true)\n |-- time_stamp: string (nullable = true)\n |-- adgroup_id: string (nullable = true)\n |-- pid: string (nullable = true)\n |-- nonclk: string (nullable = true)\n |-- clk: string (nullable = true)\n\nroot\n |-- userId: integer (nullable = true)\n |-- timestamp: long (nullable = true)\n |-- adgroupId: integer (nullable = true)\n |-- pid: string (nullable = true)\n |-- nonclk: integer (nullable = true)\n |-- clk: integer (nullable = true)\n\n+------+----------+---------+-----------+------+---+\n|userId| timestamp|adgroupId|        pid|nonclk|clk|\n+------+----------+---------+-----------+------+---+\n|581738|1494137644|        1|430548_1007|     1|  0|\n|449818|1494638778|        3|430548_1007|     1|  0|\n|914836|1494650879|        4|430548_1007|     1|  0|\n|914836|1494651029|        5|430548_1007|     1|  0|\n|399907|1494302958|        8|430548_1007|     1|  0|\n|628137|1494524935|        9|430548_1007|     1|  0|\n|298139|1494462593|        9|430539_1007|     1|  0|\n|775475|1494561036|        9|430548_1007|     1|  0|\n|555266|1494307136|       11|430539_1007|     1|  0|\n|117840|1494036743|       11|430548_1007|     1|  0|\n|739815|1494115387|       11|430539_1007|     1|  0|\n|623911|1494625301|       11|430548_1007|     1|  0|\n|623911|1494451608|       11|430548_1007|     1|  0|\n|421590|1494034144|       11|430548_1007|     1|  0|\n|976358|1494156949|       13|430548_1007|     1|  0|\n|286630|1494218579|       13|430539_1007|     1|  0|\n|286630|1494289247|       13|430539_1007|     1|  0|\n|771431|1494153867|       13|430548_1007|     1|  0|\n|707120|1494220810|       13|430548_1007|     1|  0|\n|530454|1494293746|       13|430548_1007|     1|  0|\n+------+----------+---------+-----------+------+---+\nonly showing top 20 rows\n\n\n特征选取（Feature Selection）\n\n特征选择就是选择那些靠谱的Feature，去掉冗余的Feature，对于搜索广告，Query关键词和广告的匹配程度很重要；但对于展示广告，广告本身的历史表现，往往是最重要的Feature。\n根据经验，该数据集中，只有广告展示位pid对比较重要，且数据不同数据之间的占比约为6:4，因此pid可以作为一个关键特征\nnonclk和clk在这里是作为目标值，不做为特征\n\n\n\n热独编码 OneHotEncode\n\n热独编码是一种经典编码，是使用N位状态寄存器(如0和1)来对N个状态进行编码，每个状态都由他独立的寄存器位，并且在任意时候，其中只有一位有效。\n假设有三组特征，分别表示年龄，城市，设备；\n[\"男\", \"女\"][0,1]\n[\"北京\", \"上海\", \"广州\"][0,1,2]\n[\"苹果\", \"小米\", \"华为\", \"微软\"][0,1,2,3]\n传统变化： 对每一组特征，使用枚举类型，从0开始；\n[\"男“，”上海“，”小米“]=[ 0,1,1]\n[\"女“，”北京“，”苹果“] =[1,0,0]\n传统变化后的数据不是连续的，而是随机分配的，不容易应用在分类器中\n而经过热独编码，数据会变成稀疏的，方便分类器处理：\n[\"男“，”上海“，”小米“]=[ 1,0,0,1,0,0,1,0,0]\n[\"女“，”北京“，”苹果“] =[0,1,1,0,0,1,0,0,0]\n这样做保留了特征的多样性，但是也要注意如果数据过于稀疏(样本较少、维度过高)，其效果反而会变差\n\n\n\nSpark中使用热独编码\n\n注意：热编码只能对字符串类型的列数据进行处理\nStringIndexer：对指定字符串列数据进行特征处理，如将性别数据“男”、“女”转化为0和1\nOneHotEncoder：对特征列数据，进行热编码，通常需结合StringIndexer一起使用\nPipeline：让数据按顺序依次被处理，将前一次的处理结果作为下一次的输入\n\n\n\n特征处理\n\n\n'''特征处理'''\n'''\npid 资源位。该特征属于分类特征，只有两类取值，因此考虑进行热编码处理即可，分为是否在资源位1、是否在资源位2 两个特征\n'''\nfrom pyspark.ml.feature import OneHotEncoder\nfrom pyspark.ml.feature import StringIndexer\nfrom pyspark.ml import Pipeline\n\n# StringIndexer对指定字符串列进行特征处理\nstringindexer = StringIndexer(inputCol='pid', outputCol='pid_feature')\n\n# 对处理出来的特征处理列进行，热独编码\nencoder = OneHotEncoder(dropLast=False, inputCol='pid_feature', outputCol='pid_value')\n# 利用管道对每一个数据进行热独编码处理\npipeline = Pipeline(stages=[stringindexer, encoder])\npipeline_model = pipeline.fit(raw_sample_df)\nnew_df = pipeline_model.transform(raw_sample_df)\nnew_df.show()\n\n显示结果:\n+------+----------+---------+-----------+------+---+-----------+-------------+\n|userId| timestamp|adgroupId|        pid|nonclk|clk|pid_feature|    pid_value|\n+------+----------+---------+-----------+------+---+-----------+-------------+\n|581738|1494137644|        1|430548_1007|     1|  0|        0.0|(2,[0],[1.0])|\n|449818|1494638778|        3|430548_1007|     1|  0|        0.0|(2,[0],[1.0])|\n|914836|1494650879|        4|430548_1007|     1|  0|        0.0|(2,[0],[1.0])|\n|914836|1494651029|        5|430548_1007|     1|  0|        0.0|(2,[0],[1.0])|\n|399907|1494302958|        8|430548_1007|     1|  0|        0.0|(2,[0],[1.0])|\n|628137|1494524935|        9|430548_1007|     1|  0|        0.0|(2,[0],[1.0])|\n|298139|1494462593|        9|430539_1007|     1|  0|        1.0|(2,[1],[1.0])|\n|775475|1494561036|        9|430548_1007|     1|  0|        0.0|(2,[0],[1.0])|\n|555266|1494307136|       11|430539_1007|     1|  0|        1.0|(2,[1],[1.0])|\n|117840|1494036743|       11|430548_1007|     1|  0|        0.0|(2,[0],[1.0])|\n|739815|1494115387|       11|430539_1007|     1|  0|        1.0|(2,[1],[1.0])|\n|623911|1494625301|       11|430548_1007|     1|  0|        0.0|(2,[0],[1.0])|\n|623911|1494451608|       11|430548_1007|     1|  0|        0.0|(2,[0],[1.0])|\n|421590|1494034144|       11|430548_1007|     1|  0|        0.0|(2,[0],[1.0])|\n|976358|1494156949|       13|430548_1007|     1|  0|        0.0|(2,[0],[1.0])|\n|286630|1494218579|       13|430539_1007|     1|  0|        1.0|(2,[1],[1.0])|\n|286630|1494289247|       13|430539_1007|     1|  0|        1.0|(2,[1],[1.0])|\n|771431|1494153867|       13|430548_1007|     1|  0|        0.0|(2,[0],[1.0])|\n|707120|1494220810|       13|430548_1007|     1|  0|        0.0|(2,[0],[1.0])|\n|530454|1494293746|       13|430548_1007|     1|  0|        0.0|(2,[0],[1.0])|\n+------+----------+---------+-----------+------+---+-----------+-------------+\nonly showing top 20 rows\n\n返回字段pid_value是一个稀疏向量类型数据 pyspark.ml.linalg.SparseVector\n\nfrom pyspark.ml.linalg import SparseVector\n# 参数：维度、索引列表、值列表\nprint(SparseVector(4, [1, 3], [3.0, 4.0]))\nprint(SparseVector(4, [1, 3], [3.0, 4.0]).toArray())\nprint(\"*********\")\nprint(new_df.select(\"pid_value\").first())\nprint(new_df.select(\"pid_value\").first().pid_value.toArray())\n\n显示结果:\n(4,[1,3],[3.0,4.0])\n[0. 3. 0. 4.]\n*********\nRow(pid_value=SparseVector(2, {0: 1.0}))\n[1. 0.]\n\n查看最大时间\n\nnew_df.sort(\"timestamp\", ascending=False).show()\n\n+------+----------+---------+-----------+------+---+-----------+-------------+\n|userId| timestamp|adgroupId|        pid|nonclk|clk|pid_feature|    pid_value|\n+------+----------+---------+-----------+------+---+-----------+-------------+\n|177002|1494691186|   593001|430548_1007|     1|  0|        0.0|(2,[0],[1.0])|\n|243671|1494691186|   600195|430548_1007|     1|  0|        0.0|(2,[0],[1.0])|\n|488527|1494691184|   494312|430548_1007|     1|  0|        0.0|(2,[0],[1.0])|\n|488527|1494691184|   431082|430548_1007|     1|  0|        0.0|(2,[0],[1.0])|\n| 17054|1494691184|   742741|430548_1007|     1|  0|        0.0|(2,[0],[1.0])|\n| 17054|1494691184|   756665|430548_1007|     1|  0|        0.0|(2,[0],[1.0])|\n|488527|1494691184|   687854|430548_1007|     1|  0|        0.0|(2,[0],[1.0])|\n|839493|1494691183|   561681|430548_1007|     1|  0|        0.0|(2,[0],[1.0])|\n|704223|1494691183|   624504|430539_1007|     1|  0|        1.0|(2,[1],[1.0])|\n|839493|1494691183|   582235|430548_1007|     1|  0|        0.0|(2,[0],[1.0])|\n|704223|1494691183|   675674|430539_1007|     1|  0|        1.0|(2,[1],[1.0])|\n|628998|1494691180|   618965|430548_1007|     1|  0|        0.0|(2,[0],[1.0])|\n|674444|1494691179|   427579|430548_1007|     1|  0|        0.0|(2,[0],[1.0])|\n|627200|1494691179|   782038|430548_1007|     1|  0|        0.0|(2,[0],[1.0])|\n|627200|1494691179|   420769|430548_1007|     1|  0|        0.0|(2,[0],[1.0])|\n|674444|1494691179|   588664|430548_1007|     1|  0|        0.0|(2,[0],[1.0])|\n|738335|1494691179|   451004|430539_1007|     1|  0|        1.0|(2,[1],[1.0])|\n|627200|1494691179|   817569|430548_1007|     1|  0|        0.0|(2,[0],[1.0])|\n|322244|1494691179|   820018|430548_1007|     1|  0|        0.0|(2,[0],[1.0])|\n|322244|1494691179|   735220|430548_1007|     1|  0|        0.0|(2,[0],[1.0])|\n+------+----------+---------+-----------+------+---+-----------+-------------+\nonly showing top 20 rows\n\n# 本样本数据集共计8天数据\n# 前七天为训练数据、最后一天为测试数据\n\nfrom datetime import datetime\ndatetime.fromtimestamp(1494691186)\nprint(\"该时间之前的数据为训练样本，该时间以后的数据为测试样本：\", datetime.fromtimestamp(1494691186-24*60*60))\n\n显示结果:\n该时间之前的数据为训练样本，该时间以后的数据为测试样本： 2017-05-12 23:59:46\n\n训练样本\n\n# 训练样本：\ntrain_sample = raw_sample_df.filter(raw_sample_df.timestamp(1494691186-24*60*60))\nprint(\"测试样本个数：\")\nprint(test_sample.count())\n\n# 注意：还需要加入广告基本特征和用户基本特征才能做程一份完整的样本数据集\n\n显示结果:\n训练样本个数：\n23249291\n测试样本个数：\n3308670\n3.2 分析并预处理ad_feature数据集\n# 从HDFS中加载广告基本信息数据，返回spark dafaframe对象\ndf = spark.read.csv(\"hdfs://localhost:9000/data/ad_feature.csv\", header=True)\ndf.show()    # 展示数据，默认前20条\n\n显示结果:\n+----------+-------+-----------+--------+------+-----+\n|adgroup_id|cate_id|campaign_id|customer| brand|price|\n+----------+-------+-----------+--------+------+-----+\n|     63133|   6406|      83237|       1| 95471|170.0|\n|    313401|   6406|      83237|       1| 87331|199.0|\n|    248909|    392|      83237|       1| 32233| 38.0|\n|    208458|    392|      83237|       1|174374|139.0|\n|    110847|   7211|     135256|       2|145952|32.99|\n|    607788|   6261|     387991|       6|207800|199.0|\n|    375706|   4520|     387991|       6|  NULL| 99.0|\n|     11115|   7213|     139747|       9|186847| 33.0|\n|     24484|   7207|     139744|       9|186847| 19.0|\n|     28589|   5953|     395195|      13|  NULL|428.0|\n|     23236|   5953|     395195|      13|  NULL|368.0|\n|    300556|   5953|     395195|      13|  NULL|639.0|\n|     92560|   5953|     395195|      13|  NULL|368.0|\n|    590965|   4284|      28145|      14|454237|249.0|\n|    529913|   4284|      70206|      14|  NULL|249.0|\n|    546930|   4284|      28145|      14|  NULL|249.0|\n|    639794|   6261|      70206|      14| 37004| 89.9|\n|    335413|   4284|      28145|      14|  NULL|249.0|\n|    794890|   4284|      70206|      14|454237|249.0|\n|    684020|   6261|      70206|      14| 37004| 99.0|\n+----------+-------+-----------+--------+------+-----+\nonly showing top 20 rows\n\n# 注意：由于本数据集中存在NULL字样的数据，无法直接设置schema，只能先将NULL类型的数据处理掉，然后进行类型转换\n\nfrom pyspark.sql.types import StructType, StructField, IntegerType, FloatType\n\n# 替换掉NULL字符串，替换掉\ndf = df.replace(\"NULL\", \"-1\")\n\n# 打印df结构信息\ndf.printSchema()   \n# 更改df表结构：更改列类型和列名称\nad_feature_df = df.\\\n    withColumn(\"adgroup_id\", df.adgroup_id.cast(IntegerType())).withColumnRenamed(\"adgroup_id\", \"adgroupId\").\\\n    withColumn(\"cate_id\", df.cate_id.cast(IntegerType())).withColumnRenamed(\"cate_id\", \"cateId\").\\\n    withColumn(\"campaign_id\", df.campaign_id.cast(IntegerType())).withColumnRenamed(\"campaign_id\", \"campaignId\").\\\n    withColumn(\"customer\", df.customer.cast(IntegerType())).withColumnRenamed(\"customer\", \"customerId\").\\\n    withColumn(\"brand\", df.brand.cast(IntegerType())).withColumnRenamed(\"brand\", \"brandId\").\\\n    withColumn(\"price\", df.price.cast(FloatType()))\nad_feature_df.printSchema()\nad_feature_df.show()\n\n显示结果:\nroot\n |-- adgroup_id: string (nullable = true)\n |-- cate_id: string (nullable = true)\n |-- campaign_id: string (nullable = true)\n |-- customer: string (nullable = true)\n |-- brand: string (nullable = true)\n |-- price: string (nullable = true)\n\nroot\n |-- adgroupId: integer (nullable = true)\n |-- cateId: integer (nullable = true)\n |-- campaignId: integer (nullable = true)\n |-- customerId: integer (nullable = true)\n |-- brandId: integer (nullable = true)\n |-- price: float (nullable = true)\n\n+---------+------+----------+----------+-------+-----+\n|adgroupId|cateId|campaignId|customerId|brandId|price|\n+---------+------+----------+----------+-------+-----+\n|    63133|  6406|     83237|         1|  95471|170.0|\n|   313401|  6406|     83237|         1|  87331|199.0|\n|   248909|   392|     83237|         1|  32233| 38.0|\n|   208458|   392|     83237|         1| 174374|139.0|\n|   110847|  7211|    135256|         2| 145952|32.99|\n|   607788|  6261|    387991|         6| 207800|199.0|\n|   375706|  4520|    387991|         6|     -1| 99.0|\n|    11115|  7213|    139747|         9| 186847| 33.0|\n|    24484|  7207|    139744|         9| 186847| 19.0|\n|    28589|  5953|    395195|        13|     -1|428.0|\n|    23236|  5953|    395195|        13|     -1|368.0|\n|   300556|  5953|    395195|        13|     -1|639.0|\n|    92560|  5953|    395195|        13|     -1|368.0|\n|   590965|  4284|     28145|        14| 454237|249.0|\n|   529913|  4284|     70206|        14|     -1|249.0|\n|   546930|  4284|     28145|        14|     -1|249.0|\n|   639794|  6261|     70206|        14|  37004| 89.9|\n|   335413|  4284|     28145|        14|     -1|249.0|\n|   794890|  4284|     70206|        14| 454237|249.0|\n|   684020|  6261|     70206|        14|  37004| 99.0|\n+---------+------+----------+----------+-------+-----+\nonly showing top 20 rows\n\n\n查看各项数据的特征\n\nprint(\"总广告条数：\",df.count())   # 数据条数\n_1 = ad_feature_df.groupBy(\"cateId\").count().count()\nprint(\"cateId数值个数：\", _1)\n_2 = ad_feature_df.groupBy(\"campaignId\").count().count()\nprint(\"campaignId数值个数：\", _2)\n_3 = ad_feature_df.groupBy(\"customerId\").count().count()\nprint(\"customerId数值个数：\", _3)\n_4 = ad_feature_df.groupBy(\"brandId\").count().count()\nprint(\"brandId数值个数：\", _4)\nad_feature_df.sort(\"price\").show()\nad_feature_df.sort(\"price\", ascending=False).show()\nprint(\"价格高于1w的条目个数：\", ad_feature_df.select(\"price\").filter(\"price>10000\").count())\nprint(\"价格低于1的条目个数\", ad_feature_df.select(\"price\").filter(\"price\n显示结果:\n总广告条数： 846811\ncateId数值个数： 6769\ncampaignId数值个数： 423436\ncustomerId数值个数： 255875\nbrandId数值个数： 99815\n+---------+------+----------+----------+-------+-----+\n|adgroupId|cateId|campaignId|customerId|brandId|price|\n+---------+------+----------+----------+-------+-----+\n|   485749|  9970|    352666|    140520|     -1| 0.01|\n|    88975|  9996|    198424|    182415|     -1| 0.01|\n|   109704| 10539|     59774|     90351| 202710| 0.01|\n|    49911|  7032|    129079|    172334|     -1| 0.01|\n|   339334|  9994|    310408|    211292| 383023| 0.01|\n|     6636|  6703|    392038|     46239| 406713| 0.01|\n|    92241|  6130|     72781|    149714|     -1| 0.01|\n|    20397| 10539|    410958|     65726|  79971| 0.01|\n|   345870|  9995|    179595|    191036|  79971| 0.01|\n|    77797|  9086|    218276|     31183|     -1| 0.01|\n|    14435|  1136|    135610|     17788|     -1| 0.01|\n|    42055|  9994|     43866|    113068| 123242| 0.01|\n|    41925|  7032|     85373|    114532|     -1| 0.01|\n|    67558|  9995|     90141|     83948|     -1| 0.01|\n|   149570|  7043|    126746|    176076|     -1| 0.01|\n|   518883|  7185|    403318|     58013|     -1| 0.01|\n|     2246|  9996|    413653|     60214| 182966| 0.01|\n|   290675|  4824|    315371|    240984|     -1| 0.01|\n|   552638| 10305|    403318|     58013|     -1| 0.01|\n|    89831| 10539|     90141|     83948| 211816| 0.01|\n+---------+------+----------+----------+-------+-----+\nonly showing top 20 rows\n\n+---------+------+----------+----------+-------+-----------+\n|adgroupId|cateId|campaignId|customerId|brandId|      price|\n+---------+------+----------+----------+-------+-----------+\n|   658722|  1093|    218101|    207754|     -1|      1.0E8|\n|   468220|  1093|    270719|    207754|     -1|      1.0E8|\n|   179746|  1093|    270027|    102509| 405447|      1.0E8|\n|   443295|  1093|     44251|    102509| 300681|      1.0E8|\n|    31899|   685|    218918|     31239| 278301|      1.0E8|\n|   243384|   685|    218918|     31239| 278301|      1.0E8|\n|   554311|  1093|    266086|    207754|     -1|      1.0E8|\n|   513942|   745|      8401|     86243|     -1|8.8888888E7|\n|   201060|   745|      8401|     86243|     -1|5.5555556E7|\n|   289563|   685|     37665|    120847| 278301|      1.5E7|\n|    35156|   527|    417722|     72273| 278301|      1.0E7|\n|    33756|   527|    416333|     70894|     -1|  9900000.0|\n|   335495|   739|    170121|    148946| 326126|  9600000.0|\n|   218306|   206|    162394|      4339| 221720|  8888888.0|\n|   213567|  7213|    239302|    205612| 406125|  5888888.0|\n|   375920|   527|    217512|    148946| 326126|  4760000.0|\n|   262215|   527|    132721|     11947| 417898|  3980000.0|\n|   154623|   739|    170121|    148946| 326126|  3900000.0|\n|   152414|   739|    170121|    148946| 326126|  3900000.0|\n|   448651|   527|    422260|     41289| 209959|  3800000.0|\n+---------+------+----------+----------+-------+-----------+\nonly showing top 20 rows\n\n价格高于1w的条目个数： 6527\n价格低于1的条目个数 5762\n\n特征选择\n\ncateId：脱敏过的商品类目ID；\ncampaignId：脱敏过的广告计划ID；\ncustomerId:脱敏过的广告主ID；\nbrandId：脱敏过的品牌ID；\n\n以上四个特征均属于分类特征，但由于分类值个数均过于庞大，如果去做热独编码处理，会导致数据过于稀疏 且当前我们缺少对这些特征更加具体的信息，（如商品类目具体信息、品牌具体信息等），从而无法对这些特征的数据做聚类、降维处理 因此这里不选取它们作为特征\n而只选取price作为特征数据，因为价格本身是一个统计类型连续数值型数据，且能很好的体现广告的价值属性特征，通常也不需要做其他处理(离散化、归一化、标准化等)，所以这里直接将当做特征数据来使用\n\n\n3.3 分析并预处理user_profile数据集\n# 从HDFS加载用户基本信息数据\ndf = spark.read.csv(\"hdfs://localhost:9000/data/user_profile.csv\", header=True)\n# 发现pvalue_level和new_user_class_level存在空值：（注意此处的null表示空值，而如果是NULL，则往往表示是一个字符串）\n# 因此直接利用schema就可以加载进该数据，无需替换null值\ndf.show()\n\n显示结果:\n+------+---------+------------+-----------------+---------+------------+--------------+----------+---------------------+\n|userid|cms_segid|cms_group_id|final_gender_code|age_level|pvalue_level|shopping_level|occupation|new_user_class_level |\n+------+---------+------------+-----------------+---------+------------+--------------+----------+---------------------+\n|   234|        0|           5|                2|        5|        null|             3|         0|                    3|\n|   523|        5|           2|                2|        2|           1|             3|         1|                    2|\n|   612|        0|           8|                1|        2|           2|             3|         0|                 null|\n|  1670|        0|           4|                2|        4|        null|             1|         0|                 null|\n|  2545|        0|          10|                1|        4|        null|             3|         0|                 null|\n|  3644|       49|           6|                2|        6|           2|             3|         0|                    2|\n|  5777|       44|           5|                2|        5|           2|             3|         0|                    2|\n|  6211|        0|           9|                1|        3|        null|             3|         0|                    2|\n|  6355|        2|           1|                2|        1|           1|             3|         0|                    4|\n|  6823|       43|           5|                2|        5|           2|             3|         0|                    1|\n|  6972|        5|           2|                2|        2|           2|             3|         1|                    2|\n|  9293|        0|           5|                2|        5|        null|             3|         0|                    4|\n|  9510|       55|           8|                1|        2|           2|             2|         0|                    2|\n| 10122|       33|           4|                2|        4|           2|             3|         0|                    2|\n| 10549|        0|           4|                2|        4|           2|             3|         0|                 null|\n| 10812|        0|           4|                2|        4|        null|             2|         0|                 null|\n| 10912|        0|           4|                2|        4|           2|             3|         0|                 null|\n| 10996|        0|           5|                2|        5|        null|             3|         0|                    4|\n| 11256|        8|           2|                2|        2|           1|             3|         0|                    3|\n| 11310|       31|           4|                2|        4|           1|             3|         0|                    4|\n+------+---------+------------+-----------------+---------+------------+--------------+----------+---------------------+\n\n# 注意：这里的null会直接被pyspark识别为None数据，也就是na数据，所以这里可以直接利用schema导入数据\n\nfrom pyspark.sql.types import StructType, StructField, StringType, IntegerType, LongType, FloatType\n\n# 构建表结构schema对象\nschema = StructType([\n    StructField(\"userId\", IntegerType()),\n    StructField(\"cms_segid\", IntegerType()),\n    StructField(\"cms_group_id\", IntegerType()),\n    StructField(\"final_gender_code\", IntegerType()),\n    StructField(\"age_level\", IntegerType()),\n    StructField(\"pvalue_level\", IntegerType()),\n    StructField(\"shopping_level\", IntegerType()),\n    StructField(\"occupation\", IntegerType()),\n    StructField(\"new_user_class_level\", IntegerType())\n])\n# 利用schema从hdfs加载\nuser_profile_df = spark.read.csv(\"hdfs://localhost:9000/data/user_profile.csv\", header=True, schema=schema)\nuser_profile_df.printSchema()\nuser_profile_df.show()\n\n显示结果:\nroot\n |-- userId: integer (nullable = true)\n |-- cms_segid: integer (nullable = true)\n |-- cms_group_id: integer (nullable = true)\n |-- final_gender_code: integer (nullable = true)\n |-- age_level: integer (nullable = true)\n |-- pvalue_level: integer (nullable = true)\n |-- shopping_level: integer (nullable = true)\n |-- occupation: integer (nullable = true)\n |-- new_user_class_level: integer (nullable = true)\n\n+------+---------+------------+-----------------+---------+------------+--------------+----------+--------------------+\n|userId|cms_segid|cms_group_id|final_gender_code|age_level|pvalue_level|shopping_level|occupation|new_user_class_level|\n+------+---------+------------+-----------------+---------+------------+--------------+----------+--------------------+\n|   234|        0|           5|                2|        5|        null|             3|         0|                   3|\n|   523|        5|           2|                2|        2|           1|             3|         1|                   2|\n|   612|        0|           8|                1|        2|           2|             3|         0|                null|\n|  1670|        0|           4|                2|        4|        null|             1|         0|                null|\n|  2545|        0|          10|                1|        4|        null|             3|         0|                null|\n|  3644|       49|           6|                2|        6|           2|             3|         0|                   2|\n|  5777|       44|           5|                2|        5|           2|             3|         0|                   2|\n|  6211|        0|           9|                1|        3|        null|             3|         0|                   2|\n|  6355|        2|           1|                2|        1|           1|             3|         0|                   4|\n|  6823|       43|           5|                2|        5|           2|             3|         0|                   1|\n|  6972|        5|           2|                2|        2|           2|             3|         1|                   2|\n|  9293|        0|           5|                2|        5|        null|             3|         0|                   4|\n|  9510|       55|           8|                1|        2|           2|             2|         0|                   2|\n| 10122|       33|           4|                2|        4|           2|             3|         0|                   2|\n| 10549|        0|           4|                2|        4|           2|             3|         0|                null|\n| 10812|        0|           4|                2|        4|        null|             2|         0|                null|\n| 10912|        0|           4|                2|        4|           2|             3|         0|                null|\n| 10996|        0|           5|                2|        5|        null|             3|         0|                   4|\n| 11256|        8|           2|                2|        2|           1|             3|         0|                   3|\n| 11310|       31|           4|                2|        4|           1|             3|         0|                   4|\n+------+---------+------------+-----------------+---------+------------+--------------+----------+--------------------+\nonly showing top 20 rows\n\n\n显示特征情况\n\nprint(\"分类特征值个数情况: \")\nprint(\"cms_segid: \", user_profile_df.groupBy(\"cms_segid\").count().count())\nprint(\"cms_group_id: \", user_profile_df.groupBy(\"cms_group_id\").count().count())\nprint(\"final_gender_code: \", user_profile_df.groupBy(\"final_gender_code\").count().count())\nprint(\"age_level: \", user_profile_df.groupBy(\"age_level\").count().count())\nprint(\"shopping_level: \", user_profile_df.groupBy(\"shopping_level\").count().count())\nprint(\"occupation: \", user_profile_df.groupBy(\"occupation\").count().count())\n\nprint(\"含缺失值的特征情况: \")\nuser_profile_df.groupBy(\"pvalue_level\").count().show()\nuser_profile_df.groupBy(\"new_user_class_level\").count().show()\n\nt_count = user_profile_df.count()\npl_na_count = t_count - user_profile_df.dropna(subset=[\"pvalue_level\"]).count()\nprint(\"pvalue_level的空值情况：\", pl_na_count, \"空值占比：%0.2f%%\"%(pl_na_count/t_count*100))\nnul_na_count = t_count - user_profile_df.dropna(subset=[\"new_user_class_level\"]).count()\nprint(\"new_user_class_level的空值情况：\", nul_na_count, \"空值占比：%0.2f%%\"%(nul_na_count/t_count*100))\n\n显示内容:\n分类特征值个数情况: \ncms_segid:  97\ncms_group_id:  13\nfinal_gender_code:  2\nage_level:  7\nshopping_level:  3\noccupation:  2\n含缺失值的特征情况: \n+------------+------+\n|pvalue_level| count|\n+------------+------+\n|        null|575917|\n|           1|154436|\n|           3| 37759|\n|           2|293656|\n+------------+------+\n\n+--------------------+------+\n|new_user_class_level| count|\n+--------------------+------+\n|                null|344920|\n|                   1| 80548|\n|                   3|173047|\n|                   4|138833|\n|                   2|324420|\n+--------------------+------+\n\npvalue_level的空值情况： 575917 空值占比：54.24%\nnew_user_class_level的空值情况： 344920 空值占比：32.49%\n\n\n缺失值处理\n\n注意，一般情况下：\n\n缺失率低于10%：可直接进行相应的填充，如默认值、均值、算法拟合等等；\n高于10%：往往会考虑舍弃该特征\n特征处理，如1维转多维\n\n但根据我们的经验，我们的广告推荐其实和用户的消费水平、用户所在城市等级都有比较大的关联，因此在这里pvalue_level、new_user_class_level都是比较重要的特征，我们不考虑舍弃\n\n\n\n缺失值处理方案：\n\n填充方案：结合用户的其他特征值，利用随机森林算法进行预测；但产生了大量人为构建的数据，一定程度上增加了数据的噪音\n把变量映射到高维空间：如pvalue_level的1维数据，转换成是否1、是否2、是否3、是否缺失的4维数据；这样保证了所有原始数据不变，同时能提高精确度，但这样会导致数据变得比较稀疏，如果样本量很小，反而会导致样本效果较差，因此也不能滥用\n\n\n填充方案\n\n利用随机森林对pvalue_level的缺失值进行预测\n\n\n\nfrom pyspark.mllib.regression import LabeledPoint\n\n# 剔除掉缺失值数据，将余下的数据作为训练数据\n# user_profile_df.dropna(subset=[\"pvalue_level\"])： 将pvalue_level中的空值所在行数据剔除后的数据，作为训练样本\ntrain_data = user_profile_df.dropna(subset=[\"pvalue_level\"]).rdd.map(\n    lambda r:LabeledPoint(r.pvalue_level-1, [r.cms_segid, r.cms_group_id, r.final_gender_code, r.age_level, r.shopping_level, r.occupation])\n)\n\n# 注意随机森林输入数据时，由于label的分类数是从0开始的，但pvalue_level的目前只分别是1，2，3，所以需要对应分别-1来作为目标值\n# 自然那么最终得出预测值后，需要对应+1才能还原回来\n\n# 我们使用cms_segid, cms_group_id, final_gender_code, age_level, shopping_level, occupation作为特征值，pvalue_level作为目标值\n\n\nLabeled point\n\nA labeled point is a local vector, either dense or sparse, associated with a label/response. In MLlib, labeled points are used in supervised learning algorithms. We use a double to store a label, so we can use labeled points in both regression and classification. For binary classification, a label should be either 0 (negative) or 1 (positive). For multiclass classification, labels should be class indices starting from zero: 0, 1, 2, …. \n标记点是与标签/响应相关联的密集或稀疏的局部矢量。在MLlib中，标记点用于监督学习算法。我们使用double来存储标签，因此我们可以在回归和分类中使用标记点。对于二分类情况，目标值应为0（负）或1（正）。对于多分类，标签应该是从零开始的类索引：0, 1, 2, …。\nPython \nA labeled point is represented by LabeledPoint. \n标记点表示为 LabeledPoint。 \nRefer to the LabeledPoint Python docs for more details on the API. \n有关API的更多详细信息，请参阅LabeledPointPython文档。\nfrom pyspark.mllib.linalg import SparseVector\nfrom pyspark.mllib.regression import LabeledPoint\n\n# Create a labeled point with a positive label and a dense feature vector.\npos = LabeledPoint(1.0, [1.0, 0.0, 3.0])\n\n# Create a labeled point with a negative label and a sparse feature vector.\nneg = LabeledPoint(0.0, SparseVector(3, [0, 2], [1.0, 3.0]))\n\n\n随机森林：pyspark.mllib.tree.RandomForest\n\nfrom pyspark.mllib.tree import RandomForest\n# 训练分类模型\n# 参数1 训练的数据\n#参数2 目标值的分类个数 0,1,2\n#参数3 特征中是否包含分类的特征 {2:2,3:7} {2:2} 表示 在特征中 第二个特征是分类的: 有两个分类\n#参数4 随机森林中 树的棵数\nmodel = RandomForest.trainClassifier(train_data, 3, {}, 5)\n\n\n随机森林模型：pyspark.mllib.tree.RandomForestModel\n\n# 预测单个数据\n# 注意用法：https://spark.apache.org/docs/latest/api/python/pyspark.mllib.html?highlight=tree%20random#pyspark.mllib.tree.RandomForestModel.predict\nmodel.predict([0.0, 4.0 ,2.0 , 4.0, 1.0, 0.0])\n\n显示结果:\n1.0\n\n\n筛选出缺失值条目\n\npl_na_df = user_profile_df.na.fill(-1).where(\"pvalue_level=-1\")\npl_na_df.show(10)\n\ndef row(r):\n    return r.cms_segid, r.cms_group_id, r.final_gender_code, r.age_level, r.shopping_level, r.occupation\n\n# 转换为普通的rdd类型\nrdd = pl_na_df.rdd.map(row)\n# 预测全部的pvalue_level值:\npredicts = model.predict(rdd)\n# 查看前20条\nprint(predicts.take(20))\nprint(\"预测值总数\", predicts.count())\n\n# 这里注意predict参数，如果是预测多个，那么参数必须是直接有列表构成的rdd参数，而不能是dataframe.rdd类型\n# 因此这里经过map函数处理，将每一行数据转换为普通的列表数据\n\n显示结果:\n+------+---------+------------+-----------------+---------+------------+--------------+----------+--------------------+\n|userId|cms_segid|cms_group_id|final_gender_code|age_level|pvalue_level|shopping_level|occupation|new_user_class_level|\n+------+---------+------------+-----------------+---------+------------+--------------+----------+--------------------+\n|   234|        0|           5|                2|        5|          -1|             3|         0|                   3|\n|  1670|        0|           4|                2|        4|          -1|             1|         0|                  -1|\n|  2545|        0|          10|                1|        4|          -1|             3|         0|                  -1|\n|  6211|        0|           9|                1|        3|          -1|             3|         0|                   2|\n|  9293|        0|           5|                2|        5|          -1|             3|         0|                   4|\n| 10812|        0|           4|                2|        4|          -1|             2|         0|                  -1|\n| 10996|        0|           5|                2|        5|          -1|             3|         0|                   4|\n| 11602|        0|           5|                2|        5|          -1|             3|         0|                   2|\n| 11727|        0|           3|                2|        3|          -1|             3|         0|                   1|\n| 12195|        0|          10|                1|        4|          -1|             3|         0|                   2|\n+------+---------+------------+-----------------+---------+------------+--------------+----------+--------------------+\nonly showing top 10 rows\n\n[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0]\n预测值总数 575917\n\n\n转换为pandas dataframe\n\n# 这里数据量比较小，直接转换为pandas dataframe来处理，因为方便，但注意如果数据量较大不推荐，因为这样会把全部数据加载到内存中\ntemp = predicts.map(lambda x:int(x)).collect()\npdf = pl_na_df.toPandas()\nimport numpy as np\n # 在pandas df的基础上直接替换掉列数据\npdf[\"pvalue_level\"] = np.array(temp) + 1  # 注意+1 还原预测值\npdf\n\n\n与非缺失数据进行拼接，完成pvalue_level的缺失值预测\n\nnew_user_profile_df = user_profile_df.dropna(subset=[\"pvalue_level\"]).unionAll(spark.createDataFrame(pdf, schema=schema))\nnew_user_profile_df.show()\n\n# 注意：unionAll的使用，两个df的表结构必须完全一样\n\n显示结果:\n+------+---------+------------+-----------------+---------+------------+--------------+----------+--------------------+\n|userId|cms_segid|cms_group_id|final_gender_code|age_level|pvalue_level|shopping_level|occupation|new_user_class_level|\n+------+---------+------------+-----------------+---------+------------+--------------+----------+--------------------+\n|   523|        5|           2|                2|        2|           1|             3|         1|                   2|\n|   612|        0|           8|                1|        2|           2|             3|         0|                null|\n|  3644|       49|           6|                2|        6|           2|             3|         0|                   2|\n|  5777|       44|           5|                2|        5|           2|             3|         0|                   2|\n|  6355|        2|           1|                2|        1|           1|             3|         0|                   4|\n|  6823|       43|           5|                2|        5|           2|             3|         0|                   1|\n|  6972|        5|           2|                2|        2|           2|             3|         1|                   2|\n|  9510|       55|           8|                1|        2|           2|             2|         0|                   2|\n| 10122|       33|           4|                2|        4|           2|             3|         0|                   2|\n| 10549|        0|           4|                2|        4|           2|             3|         0|                null|\n| 10912|        0|           4|                2|        4|           2|             3|         0|                null|\n| 11256|        8|           2|                2|        2|           1|             3|         0|                   3|\n| 11310|       31|           4|                2|        4|           1|             3|         0|                   4|\n| 11739|       20|           3|                2|        3|           2|             3|         0|                   4|\n| 12549|       33|           4|                2|        4|           2|             3|         0|                   2|\n| 15155|       36|           5|                2|        5|           2|             1|         0|                null|\n| 15347|       20|           3|                2|        3|           2|             3|         0|                   3|\n| 15455|        8|           2|                2|        2|           2|             3|         0|                   3|\n| 15783|        0|           4|                2|        4|           2|             3|         0|                null|\n| 16749|        5|           2|                2|        2|           1|             3|         1|                   4|\n+------+---------+------------+-----------------+---------+------------+--------------+----------+--------------------+\nonly showing top 20 rows\n\n\n利用随机森林对new_user_class_level的缺失值进行预测\n\nfrom pyspark.mllib.regression import LabeledPoint\n\n# 选出new_user_class_level全部的\ntrain_data2 = user_profile_df.dropna(subset=[\"new_user_class_level\"]).rdd.map(\n    lambda r:LabeledPoint(r.new_user_class_level - 1, [r.cms_segid, r.cms_group_id, r.final_gender_code, r.age_level, r.shopping_level, r.occupation])\n)\nfrom pyspark.mllib.tree import RandomForest\nmodel2 = RandomForest.trainClassifier(train_data2, 4, {}, 5)\nmodel2.predict([0.0, 4.0 ,2.0 , 4.0, 1.0, 0.0])\n# 预测值实际应该为2\n\n显示结果:\n1.0\n\nnul_na_df = user_profile_df.na.fill(-1).where(\"new_user_class_level=-1\")\nnul_na_df.show(10)\n\ndef row(r):\n    return r.cms_segid, r.cms_group_id, r.final_gender_code, r.age_level, r.shopping_level, r.occupation\n\nrdd2 = nul_na_df.rdd.map(row)\npredicts2 = model.predict(rdd2)\npredicts2.take(20)\n\n\n显示结果:\n\n+------+---------+------------+-----------------+---------+------------+--------------+----------+--------------------+\n|userId|cms_segid|cms_group_id|final_gender_code|age_level|pvalue_level|shopping_level|occupation|new_user_class_level|\n+------+---------+------------+-----------------+---------+------------+--------------+----------+--------------------+\n|   612|        0|           8|                1|        2|           2|             3|         0|                  -1|\n|  1670|        0|           4|                2|        4|          -1|             1|         0|                  -1|\n|  2545|        0|          10|                1|        4|          -1|             3|         0|                  -1|\n| 10549|        0|           4|                2|        4|           2|             3|         0|                  -1|\n| 10812|        0|           4|                2|        4|          -1|             2|         0|                  -1|\n| 10912|        0|           4|                2|        4|           2|             3|         0|                  -1|\n| 12620|        0|           4|                2|        4|          -1|             2|         0|                  -1|\n| 14437|        0|           5|                2|        5|          -1|             3|         0|                  -1|\n| 14574|        0|           1|                2|        1|          -1|             2|         0|                  -1|\n| 14985|        0|          11|                1|        5|          -1|             2|         0|                  -1|\n+------+---------+------------+-----------------+---------+------------+--------------+----------+--------------------+\nonly showing top 10 rows\n\n[1.0,\n 1.0,\n 1.0,\n 1.0,\n 1.0,\n 1.0,\n 1.0,\n 1.0,\n 0.0,\n 1.0,\n 1.0,\n 1.0,\n 1.0,\n 1.0,\n 1.0,\n 0.0,\n 1.0,\n 0.0,\n 0.0,\n 1.0]\n\n总结：可以发现由于这两个字段的缺失过多，所以预测出来的值已经大大失真，但如果缺失率在10%以下，这种方法是比较有效的一种\n\nuser_profile_df = user_profile_df.na.fill(-1)\nuser_profile_df.show()\n# new_df = new_df.withColumn(\"pvalue_level\", new_df.pvalue_level.cast(StringType()))\\\n#     .withColumn(\"new_user_class_level\", new_df.new_user_class_level.cast(StringType()))\n\n显示结果:\n+------+---------+------------+-----------------+---------+------------+--------------+----------+--------------------+\n|userId|cms_segid|cms_group_id|final_gender_code|age_level|pvalue_level|shopping_level|occupation|new_user_class_level|\n+------+---------+------------+-----------------+---------+------------+--------------+----------+--------------------+\n|   234|        0|           5|                2|        5|          -1|             3|         0|                   3|\n|   523|        5|           2|                2|        2|           1|             3|         1|                   2|\n|   612|        0|           8|                1|        2|           2|             3|         0|                  -1|\n|  1670|        0|           4|                2|        4|          -1|             1|         0|                  -1|\n|  2545|        0|          10|                1|        4|          -1|             3|         0|                  -1|\n|  3644|       49|           6|                2|        6|           2|             3|         0|                   2|\n|  5777|       44|           5|                2|        5|           2|             3|         0|                   2|\n|  6211|        0|           9|                1|        3|          -1|             3|         0|                   2|\n|  6355|        2|           1|                2|        1|           1|             3|         0|                   4|\n|  6823|       43|           5|                2|        5|           2|             3|         0|                   1|\n|  6972|        5|           2|                2|        2|           2|             3|         1|                   2|\n|  9293|        0|           5|                2|        5|          -1|             3|         0|                   4|\n|  9510|       55|           8|                1|        2|           2|             2|         0|                   2|\n| 10122|       33|           4|                2|        4|           2|             3|         0|                   2|\n| 10549|        0|           4|                2|        4|           2|             3|         0|                  -1|\n| 10812|        0|           4|                2|        4|          -1|             2|         0|                  -1|\n| 10912|        0|           4|                2|        4|           2|             3|         0|                  -1|\n| 10996|        0|           5|                2|        5|          -1|             3|         0|                   4|\n| 11256|        8|           2|                2|        2|           1|             3|         0|                   3|\n| 11310|       31|           4|                2|        4|           1|             3|         0|                   4|\n+------+---------+------------+-----------------+---------+------------+--------------+----------+--------------------+\nonly showing top 20 rows\n\n\n低维转高维方式\n我们接下来采用将变量映射到高维空间的方法来处理数据，即将缺失项也当做一个单独的特征来对待，保证数据的原始性\n由于该思想正好和热独编码实现方法一样，因此这里直接使用热独编码方式处理数据\n\n\n\nfrom pyspark.ml.feature import OneHotEncoder\nfrom pyspark.ml.feature import StringIndexer\nfrom pyspark.ml import Pipeline\n\n# 使用热独编码转换pvalue_level的一维数据为多维，其中缺失值单独作为一个特征值\n\n# 需要先将缺失值全部替换为数值，与原有特征一起处理\nfrom pyspark.sql.types import StringType\nuser_profile_df = user_profile_df.na.fill(-1)\nuser_profile_df.show()\n\n# 热独编码时，必须先将待处理字段转为字符串类型才可处理\nuser_profile_df = user_profile_df.withColumn(\"pvalue_level\", user_profile_df.pvalue_level.cast(StringType()))\\\n    .withColumn(\"new_user_class_level\", user_profile_df.new_user_class_level.cast(StringType()))\nuser_profile_df.printSchema()\n\n# 对pvalue_level进行热独编码，求值\nstringindexer = StringIndexer(inputCol='pvalue_level', outputCol='pl_onehot_feature')\nencoder = OneHotEncoder(dropLast=False, inputCol='pl_onehot_feature', outputCol='pl_onehot_value')\npipeline = Pipeline(stages=[stringindexer, encoder])\npipeline_fit = pipeline.fit(user_profile_df)\nuser_profile_df2 = pipeline_fit.transform(user_profile_df)\n# pl_onehot_value列的值为稀疏向量，存储热独编码的结果\nuser_profile_df2.printSchema()\nuser_profile_df2.show()\n\n显示结果:\n+------+---------+------------+-----------------+---------+------------+--------------+----------+--------------------+\n|userId|cms_segid|cms_group_id|final_gender_code|age_level|pvalue_level|shopping_level|occupation|new_user_class_level|\n+------+---------+------------+-----------------+---------+------------+--------------+----------+--------------------+\n|   234|        0|           5|                2|        5|          -1|             3|         0|                   3|\n|   523|        5|           2|                2|        2|           1|             3|         1|                   2|\n|   612|        0|           8|                1|        2|           2|             3|         0|                  -1|\n|  1670|        0|           4|                2|        4|          -1|             1|         0|                  -1|\n|  2545|        0|          10|                1|        4|          -1|             3|         0|                  -1|\n|  3644|       49|           6|                2|        6|           2|             3|         0|                   2|\n|  5777|       44|           5|                2|        5|           2|             3|         0|                   2|\n|  6211|        0|           9|                1|        3|          -1|             3|         0|                   2|\n|  6355|        2|           1|                2|        1|           1|             3|         0|                   4|\n|  6823|       43|           5|                2|        5|           2|             3|         0|                   1|\n|  6972|        5|           2|                2|        2|           2|             3|         1|                   2|\n|  9293|        0|           5|                2|        5|          -1|             3|         0|                   4|\n|  9510|       55|           8|                1|        2|           2|             2|         0|                   2|\n| 10122|       33|           4|                2|        4|           2|             3|         0|                   2|\n| 10549|        0|           4|                2|        4|           2|             3|         0|                  -1|\n| 10812|        0|           4|                2|        4|          -1|             2|         0|                  -1|\n| 10912|        0|           4|                2|        4|           2|             3|         0|                  -1|\n| 10996|        0|           5|                2|        5|          -1|             3|         0|                   4|\n| 11256|        8|           2|                2|        2|           1|             3|         0|                   3|\n| 11310|       31|           4|                2|        4|           1|             3|         0|                   4|\n+------+---------+------------+-----------------+---------+------------+--------------+----------+--------------------+\nonly showing top 20 rows\n\nroot\n |-- userId: integer (nullable = true)\n |-- cms_segid: integer (nullable = true)\n |-- cms_group_id: integer (nullable = true)\n |-- final_gender_code: integer (nullable = true)\n |-- age_level: integer (nullable = true)\n |-- pvalue_level: string (nullable = true)\n |-- shopping_level: integer (nullable = true)\n |-- occupation: integer (nullable = true)\n |-- new_user_class_level: string (nullable = true)\n\nroot\n |-- userId: integer (nullable = true)\n |-- cms_segid: integer (nullable = true)\n |-- cms_group_id: integer (nullable = true)\n |-- final_gender_code: integer (nullable = true)\n |-- age_level: integer (nullable = true)\n |-- pvalue_level: string (nullable = true)\n |-- shopping_level: integer (nullable = true)\n |-- occupation: integer (nullable = true)\n |-- new_user_class_level: string (nullable = true)\n |-- pl_onehot_feature: double (nullable = false)\n |-- pl_onehot_value: vector (nullable = true)\n\n+------+---------+------------+-----------------+---------+------------+--------------+----------+--------------------+-----------------+---------------+\n|userId|cms_segid|cms_group_id|final_gender_code|age_level|pvalue_level|shopping_level|occupation|new_user_class_level|pl_onehot_feature|pl_onehot_value|\n+------+---------+------------+-----------------+---------+------------+--------------+----------+--------------------+-----------------+---------------+\n|   234|        0|           5|                2|        5|          -1|             3|         0|                   3|              0.0|  (4,[0],[1.0])|\n|   523|        5|           2|                2|        2|           1|             3|         1|                   2|              2.0|  (4,[2],[1.0])|\n|   612|        0|           8|                1|        2|           2|             3|         0|                  -1|              1.0|  (4,[1],[1.0])|\n|  1670|        0|           4|                2|        4|          -1|             1|         0|                  -1|              0.0|  (4,[0],[1.0])|\n|  2545|        0|          10|                1|        4|          -1|             3|         0|                  -1|              0.0|  (4,[0],[1.0])|\n|  3644|       49|           6|                2|        6|           2|             3|         0|                   2|              1.0|  (4,[1],[1.0])|\n|  5777|       44|           5|                2|        5|           2|             3|         0|                   2|              1.0|  (4,[1],[1.0])|\n|  6211|        0|           9|                1|        3|          -1|             3|         0|                   2|              0.0|  (4,[0],[1.0])|\n|  6355|        2|           1|                2|        1|           1|             3|         0|                   4|              2.0|  (4,[2],[1.0])|\n|  6823|       43|           5|                2|        5|           2|             3|         0|                   1|              1.0|  (4,[1],[1.0])|\n|  6972|        5|           2|                2|        2|           2|             3|         1|                   2|              1.0|  (4,[1],[1.0])|\n|  9293|        0|           5|                2|        5|          -1|             3|         0|                   4|              0.0|  (4,[0],[1.0])|\n|  9510|       55|           8|                1|        2|           2|             2|         0|                   2|              1.0|  (4,[1],[1.0])|\n| 10122|       33|           4|                2|        4|           2|             3|         0|                   2|              1.0|  (4,[1],[1.0])|\n| 10549|        0|           4|                2|        4|           2|             3|         0|                  -1|              1.0|  (4,[1],[1.0])|\n| 10812|        0|           4|                2|        4|          -1|             2|         0|                  -1|              0.0|  (4,[0],[1.0])|\n| 10912|        0|           4|                2|        4|           2|             3|         0|                  -1|              1.0|  (4,[1],[1.0])|\n| 10996|        0|           5|                2|        5|          -1|             3|         0|                   4|              0.0|  (4,[0],[1.0])|\n| 11256|        8|           2|                2|        2|           1|             3|         0|                   3|              2.0|  (4,[2],[1.0])|\n| 11310|       31|           4|                2|        4|           1|             3|         0|                   4|              2.0|  (4,[2],[1.0])|\n+------+---------+------------+-----------------+---------+------------+--------------+----------+--------------------+-----------------+---------------+\nonly showing top 20 rows\n\n\n使用热编码转换new_user_class_level的一维数据为多维\n\nstringindexer = StringIndexer(inputCol='new_user_class_level', outputCol='nucl_onehot_feature')\nencoder = OneHotEncoder(dropLast=False, inputCol='nucl_onehot_feature', outputCol='nucl_onehot_value')\npipeline = Pipeline(stages=[stringindexer, encoder])\npipeline_fit = pipeline.fit(user_profile_df2)\nuser_profile_df3 = pipeline_fit.transform(user_profile_df2)\nuser_profile_df3.show()\n\n显示结果:\n+------+---------+------------+-----------------+---------+------------+--------------+----------+--------------------+-----------------+---------------+-------------------+-----------------+\n|userId|cms_segid|cms_group_id|final_gender_code|age_level|pvalue_level|shopping_level|occupation|new_user_class_level|pl_onehot_feature|pl_onehot_value|nucl_onehot_feature|nucl_onehot_value|\n+------+---------+------------+-----------------+---------+------------+--------------+----------+--------------------+-----------------+---------------+-------------------+-----------------+\n|   234|        0|           5|                2|        5|          -1|             3|         0|                   3|              0.0|  (4,[0],[1.0])|                2.0|    (5,[2],[1.0])|\n|   523|        5|           2|                2|        2|           1|             3|         1|                   2|              2.0|  (4,[2],[1.0])|                1.0|    (5,[1],[1.0])|\n|   612|        0|           8|                1|        2|           2|             3|         0|                  -1|              1.0|  (4,[1],[1.0])|                0.0|    (5,[0],[1.0])|\n|  1670|        0|           4|                2|        4|          -1|             1|         0|                  -1|              0.0|  (4,[0],[1.0])|                0.0|    (5,[0],[1.0])|\n|  2545|        0|          10|                1|        4|          -1|             3|         0|                  -1|              0.0|  (4,[0],[1.0])|                0.0|    (5,[0],[1.0])|\n|  3644|       49|           6|                2|        6|           2|             3|         0|                   2|              1.0|  (4,[1],[1.0])|                1.0|    (5,[1],[1.0])|\n|  5777|       44|           5|                2|        5|           2|             3|         0|                   2|              1.0|  (4,[1],[1.0])|                1.0|    (5,[1],[1.0])|\n|  6211|        0|           9|                1|        3|          -1|             3|         0|                   2|              0.0|  (4,[0],[1.0])|                1.0|    (5,[1],[1.0])|\n|  6355|        2|           1|                2|        1|           1|             3|         0|                   4|              2.0|  (4,[2],[1.0])|                3.0|    (5,[3],[1.0])|\n|  6823|       43|           5|                2|        5|           2|             3|         0|                   1|              1.0|  (4,[1],[1.0])|                4.0|    (5,[4],[1.0])|\n|  6972|        5|           2|                2|        2|           2|             3|         1|                   2|              1.0|  (4,[1],[1.0])|                1.0|    (5,[1],[1.0])|\n|  9293|        0|           5|                2|        5|          -1|             3|         0|                   4|              0.0|  (4,[0],[1.0])|                3.0|    (5,[3],[1.0])|\n|  9510|       55|           8|                1|        2|           2|             2|         0|                   2|              1.0|  (4,[1],[1.0])|                1.0|    (5,[1],[1.0])|\n| 10122|       33|           4|                2|        4|           2|             3|         0|                   2|              1.0|  (4,[1],[1.0])|                1.0|    (5,[1],[1.0])|\n| 10549|        0|           4|                2|        4|           2|             3|         0|                  -1|              1.0|  (4,[1],[1.0])|                0.0|    (5,[0],[1.0])|\n| 10812|        0|           4|                2|        4|          -1|             2|         0|                  -1|              0.0|  (4,[0],[1.0])|                0.0|    (5,[0],[1.0])|\n| 10912|        0|           4|                2|        4|           2|             3|         0|                  -1|              1.0|  (4,[1],[1.0])|                0.0|    (5,[0],[1.0])|\n| 10996|        0|           5|                2|        5|          -1|             3|         0|                   4|              0.0|  (4,[0],[1.0])|                3.0|    (5,[3],[1.0])|\n| 11256|        8|           2|                2|        2|           1|             3|         0|                   3|              2.0|  (4,[2],[1.0])|                2.0|    (5,[2],[1.0])|\n| 11310|       31|           4|                2|        4|           1|             3|         0|                   4|              2.0|  (4,[2],[1.0])|                3.0|    (5,[3],[1.0])|\n+------+---------+------------+-----------------+---------+------------+--------------+----------+--------------------+-----------------+---------------+-------------------+-----------------+\nonly showing top 20 rows\n\n\n用户特征合并\n\nfrom pyspark.ml.feature import VectorAssembler\nfeature_df = VectorAssembler().setInputCols([\"age_level\", \"pl_onehot_value\", \"nucl_onehot_value\"]).setOutputCol(\"features\").transform(user_profile_df3)\nfeature_df.show()\n\n显示结果:\n+------+---------+------------+-----------------+---------+------------+--------------+----------+--------------------+-----------------+---------------+-------------------+-----------------+--------------------+\n|userId|cms_segid|cms_group_id|final_gender_code|age_level|pvalue_level|shopping_level|occupation|new_user_class_level|pl_onehot_feature|pl_onehot_value|nucl_onehot_feature|nucl_onehot_value|            features|\n+------+---------+------------+-----------------+---------+------------+--------------+----------+--------------------+-----------------+---------------+-------------------+-----------------+--------------------+\n|   234|        0|           5|                2|        5|          -1|             3|         0|                   3|              0.0|  (4,[0],[1.0])|                2.0|    (5,[2],[1.0])|(10,[0,1,7],[5.0,...|\n|   523|        5|           2|                2|        2|           1|             3|         1|                   2|              2.0|  (4,[2],[1.0])|                1.0|    (5,[1],[1.0])|(10,[0,3,6],[2.0,...|\n|   612|        0|           8|                1|        2|           2|             3|         0|                  -1|              1.0|  (4,[1],[1.0])|                0.0|    (5,[0],[1.0])|(10,[0,2,5],[2.0,...|\n|  1670|        0|           4|                2|        4|          -1|             1|         0|                  -1|              0.0|  (4,[0],[1.0])|                0.0|    (5,[0],[1.0])|(10,[0,1,5],[4.0,...|\n|  2545|        0|          10|                1|        4|          -1|             3|         0|                  -1|              0.0|  (4,[0],[1.0])|                0.0|    (5,[0],[1.0])|(10,[0,1,5],[4.0,...|\n|  3644|       49|           6|                2|        6|           2|             3|         0|                   2|              1.0|  (4,[1],[1.0])|                1.0|    (5,[1],[1.0])|(10,[0,2,6],[6.0,...|\n|  5777|       44|           5|                2|        5|           2|             3|         0|                   2|              1.0|  (4,[1],[1.0])|                1.0|    (5,[1],[1.0])|(10,[0,2,6],[5.0,...|\n|  6211|        0|           9|                1|        3|          -1|             3|         0|                   2|              0.0|  (4,[0],[1.0])|                1.0|    (5,[1],[1.0])|(10,[0,1,6],[3.0,...|\n|  6355|        2|           1|                2|        1|           1|             3|         0|                   4|              2.0|  (4,[2],[1.0])|                3.0|    (5,[3],[1.0])|(10,[0,3,8],[1.0,...|\n|  6823|       43|           5|                2|        5|           2|             3|         0|                   1|              1.0|  (4,[1],[1.0])|                4.0|    (5,[4],[1.0])|(10,[0,2,9],[5.0,...|\n|  6972|        5|           2|                2|        2|           2|             3|         1|                   2|              1.0|  (4,[1],[1.0])|                1.0|    (5,[1],[1.0])|(10,[0,2,6],[2.0,...|\n|  9293|        0|           5|                2|        5|          -1|             3|         0|                   4|              0.0|  (4,[0],[1.0])|                3.0|    (5,[3],[1.0])|(10,[0,1,8],[5.0,...|\n|  9510|       55|           8|                1|        2|           2|             2|         0|                   2|              1.0|  (4,[1],[1.0])|                1.0|    (5,[1],[1.0])|(10,[0,2,6],[2.0,...|\n| 10122|       33|           4|                2|        4|           2|             3|         0|                   2|              1.0|  (4,[1],[1.0])|                1.0|    (5,[1],[1.0])|(10,[0,2,6],[4.0,...|\n| 10549|        0|           4|                2|        4|           2|             3|         0|                  -1|              1.0|  (4,[1],[1.0])|                0.0|    (5,[0],[1.0])|(10,[0,2,5],[4.0,...|\n| 10812|        0|           4|                2|        4|          -1|             2|         0|                  -1|              0.0|  (4,[0],[1.0])|                0.0|    (5,[0],[1.0])|(10,[0,1,5],[4.0,...|\n| 10912|        0|           4|                2|        4|           2|             3|         0|                  -1|              1.0|  (4,[1],[1.0])|                0.0|    (5,[0],[1.0])|(10,[0,2,5],[4.0,...|\n| 10996|        0|           5|                2|        5|          -1|             3|         0|                   4|              0.0|  (4,[0],[1.0])|                3.0|    (5,[3],[1.0])|(10,[0,1,8],[5.0,...|\n| 11256|        8|           2|                2|        2|           1|             3|         0|                   3|              2.0|  (4,[2],[1.0])|                2.0|    (5,[2],[1.0])|(10,[0,3,7],[2.0,...|\n| 11310|       31|           4|                2|        4|           1|             3|         0|                   4|              2.0|  (4,[2],[1.0])|                3.0|    (5,[3],[1.0])|(10,[0,3,8],[4.0,...|\n+------+---------+------------+-----------------+---------+------------+--------------+----------+--------------------+-----------------+---------------+-------------------+-----------------+--------------------+\nonly showing top 20 rows\n\nfeature_df.select(\"features\").show()\n\n显示结果:\n+--------------------+\n|            features|\n+--------------------+\n|(10,[0,1,7],[5.0,...|\n|(10,[0,3,6],[2.0,...|\n|(10,[0,2,5],[2.0,...|\n|(10,[0,1,5],[4.0,...|\n|(10,[0,1,5],[4.0,...|\n|(10,[0,2,6],[6.0,...|\n|(10,[0,2,6],[5.0,...|\n|(10,[0,1,6],[3.0,...|\n|(10,[0,3,8],[1.0,...|\n|(10,[0,2,9],[5.0,...|\n|(10,[0,2,6],[2.0,...|\n|(10,[0,1,8],[5.0,...|\n|(10,[0,2,6],[2.0,...|\n|(10,[0,2,6],[4.0,...|\n|(10,[0,2,5],[4.0,...|\n|(10,[0,1,5],[4.0,...|\n|(10,[0,2,5],[4.0,...|\n|(10,[0,1,8],[5.0,...|\n|(10,[0,3,7],[2.0,...|\n|(10,[0,3,8],[4.0,...|\n+--------------------+\nonly showing top 20 rows\n\n\n特征选取\n\n除了前面处理的pvalue_level和new_user_class_level需要作为特征以外，(能体现出用户的购买力特征)，还有：\n前面分析的以下几个分类特征值个数情况:\n- cms_segid:  97\n- cms_group_id:  13\n- final_gender_code:  2\n- age_level:  7\n- shopping_level:  3\n- occupation:  2\n-pvalue_level\n-new_user_class_level\n-price\n根据经验，以上几个分类特征都一定程度能体现用户在购物方面的特征，且类别都较少，都可以用来作为用户特征\n"},"day07_推荐系统案例/04_逻辑回归实现CTR预估.html":{"url":"day07_推荐系统案例/04_逻辑回归实现CTR预估.html","title":"04_逻辑回归(LR)实现CTR预估","keywords":"","body":"四 LR实现CTR预估\n4.1 Spark逻辑回归(LR)训练点击率预测模型\n\n本小节主要根据广告点击样本数据集(raw_sample)、广告基本特征数据集(ad_feature)、用户基本信息数据集(user_profile)构建出了一个完整的样本数据集，并按日期划分为了训练集(前七天)和测试集(最后一天)，利用逻辑回归进行训练。\n训练模型时，通过对类别特征数据进行处理，一定程度达到提高了模型的效果\n\n\n'''从HDFS中加载样本数据信息'''\n_raw_sample_df1 = spark.read.csv(\"hdfs://localhost:9000/data/raw_sample.csv\", header=True)\n# _raw_sample_df1.show()    # 展示数据，默认前20条\n# 更改表结构，转换为对应的数据类型\nfrom pyspark.sql.types import StructType, StructField, IntegerType, FloatType, LongType, StringType\n\n# 更改df表结构：更改列类型和列名称\n_raw_sample_df2 = _raw_sample_df1.\\\n    withColumn(\"user\", _raw_sample_df1.user.cast(IntegerType())).withColumnRenamed(\"user\", \"userId\").\\\n    withColumn(\"time_stamp\", _raw_sample_df1.time_stamp.cast(LongType())).withColumnRenamed(\"time_stamp\", \"timestamp\").\\\n    withColumn(\"adgroup_id\", _raw_sample_df1.adgroup_id.cast(IntegerType())).withColumnRenamed(\"adgroup_id\", \"adgroupId\").\\\n    withColumn(\"pid\", _raw_sample_df1.pid.cast(StringType())).\\\n    withColumn(\"nonclk\", _raw_sample_df1.nonclk.cast(IntegerType())).\\\n    withColumn(\"clk\", _raw_sample_df1.clk.cast(IntegerType()))\n_raw_sample_df2.printSchema()\n_raw_sample_df2.show()\n\n# 样本数据pid特征处理\nfrom pyspark.ml.feature import OneHotEncoder\nfrom pyspark.ml.feature import StringIndexer\nfrom pyspark.ml import Pipeline\n\nstringindexer = StringIndexer(inputCol='pid', outputCol='pid_feature')\nencoder = OneHotEncoder(dropLast=False, inputCol='pid_feature', outputCol='pid_value')\npipeline = Pipeline(stages=[stringindexer, encoder])\npipeline_fit = pipeline.fit(_raw_sample_df2)\nraw_sample_df = pipeline_fit.transform(_raw_sample_df2)\nraw_sample_df.show()\n\n'''pid和特征的对应关系\n430548_1007：0\n430549_1007：1\n'''\n\n显示结果:\nroot\n |-- userId: integer (nullable = true)\n |-- timestamp: long (nullable = true)\n |-- adgroupId: integer (nullable = true)\n |-- pid: string (nullable = true)\n |-- nonclk: integer (nullable = true)\n |-- clk: integer (nullable = true)\n\n+------+----------+---------+-----------+------+---+\n|userId| timestamp|adgroupId|        pid|nonclk|clk|\n+------+----------+---------+-----------+------+---+\n|581738|1494137644|        1|430548_1007|     1|  0|\n|449818|1494638778|        3|430548_1007|     1|  0|\n|914836|1494650879|        4|430548_1007|     1|  0|\n|914836|1494651029|        5|430548_1007|     1|  0|\n|399907|1494302958|        8|430548_1007|     1|  0|\n|628137|1494524935|        9|430548_1007|     1|  0|\n|298139|1494462593|        9|430539_1007|     1|  0|\n|775475|1494561036|        9|430548_1007|     1|  0|\n|555266|1494307136|       11|430539_1007|     1|  0|\n|117840|1494036743|       11|430548_1007|     1|  0|\n|739815|1494115387|       11|430539_1007|     1|  0|\n|623911|1494625301|       11|430548_1007|     1|  0|\n|623911|1494451608|       11|430548_1007|     1|  0|\n|421590|1494034144|       11|430548_1007|     1|  0|\n|976358|1494156949|       13|430548_1007|     1|  0|\n|286630|1494218579|       13|430539_1007|     1|  0|\n|286630|1494289247|       13|430539_1007|     1|  0|\n|771431|1494153867|       13|430548_1007|     1|  0|\n|707120|1494220810|       13|430548_1007|     1|  0|\n|530454|1494293746|       13|430548_1007|     1|  0|\n+------+----------+---------+-----------+------+---+\nonly showing top 20 rows\n\n+------+----------+---------+-----------+------+---+-----------+-------------+\n|userId| timestamp|adgroupId|        pid|nonclk|clk|pid_feature|    pid_value|\n+------+----------+---------+-----------+------+---+-----------+-------------+\n|581738|1494137644|        1|430548_1007|     1|  0|        0.0|(2,[0],[1.0])|\n|449818|1494638778|        3|430548_1007|     1|  0|        0.0|(2,[0],[1.0])|\n|914836|1494650879|        4|430548_1007|     1|  0|        0.0|(2,[0],[1.0])|\n|914836|1494651029|        5|430548_1007|     1|  0|        0.0|(2,[0],[1.0])|\n|399907|1494302958|        8|430548_1007|     1|  0|        0.0|(2,[0],[1.0])|\n|628137|1494524935|        9|430548_1007|     1|  0|        0.0|(2,[0],[1.0])|\n|298139|1494462593|        9|430539_1007|     1|  0|        1.0|(2,[1],[1.0])|\n|775475|1494561036|        9|430548_1007|     1|  0|        0.0|(2,[0],[1.0])|\n|555266|1494307136|       11|430539_1007|     1|  0|        1.0|(2,[1],[1.0])|\n|117840|1494036743|       11|430548_1007|     1|  0|        0.0|(2,[0],[1.0])|\n|739815|1494115387|       11|430539_1007|     1|  0|        1.0|(2,[1],[1.0])|\n|623911|1494625301|       11|430548_1007|     1|  0|        0.0|(2,[0],[1.0])|\n|623911|1494451608|       11|430548_1007|     1|  0|        0.0|(2,[0],[1.0])|\n|421590|1494034144|       11|430548_1007|     1|  0|        0.0|(2,[0],[1.0])|\n|976358|1494156949|       13|430548_1007|     1|  0|        0.0|(2,[0],[1.0])|\n|286630|1494218579|       13|430539_1007|     1|  0|        1.0|(2,[1],[1.0])|\n|286630|1494289247|       13|430539_1007|     1|  0|        1.0|(2,[1],[1.0])|\n|771431|1494153867|       13|430548_1007|     1|  0|        0.0|(2,[0],[1.0])|\n|707120|1494220810|       13|430548_1007|     1|  0|        0.0|(2,[0],[1.0])|\n|530454|1494293746|       13|430548_1007|     1|  0|        0.0|(2,[0],[1.0])|\n+------+----------+---------+-----------+------+---+-----------+-------------+\nonly showing top 20 rows\n\n'pid和特征的对应关系\\n430548_1007：0\\n430549_1007：1\\n'\n\n\n从HDFS中加载广告基本信息数据\n\n_ad_feature_df = spark.read.csv(\"hdfs://localhost:9000/datasets/ad_feature.csv\", header=True)\n\n# 更改表结构，转换为对应的数据类型\nfrom pyspark.sql.types import StructType, StructField, IntegerType, FloatType\n\n# 替换掉NULL字符串\n_ad_feature_df = _ad_feature_df.replace(\"NULL\", \"-1\")\n\n# 更改df表结构：更改列类型和列名称\nad_feature_df = _ad_feature_df.\\\n    withColumn(\"adgroup_id\", _ad_feature_df.adgroup_id.cast(IntegerType())).withColumnRenamed(\"adgroup_id\", \"adgroupId\").\\\n    withColumn(\"cate_id\", _ad_feature_df.cate_id.cast(IntegerType())).withColumnRenamed(\"cate_id\", \"cateId\").\\\n    withColumn(\"campaign_id\", _ad_feature_df.campaign_id.cast(IntegerType())).withColumnRenamed(\"campaign_id\", \"campaignId\").\\\n    withColumn(\"customer\", _ad_feature_df.customer.cast(IntegerType())).withColumnRenamed(\"customer\", \"customerId\").\\\n    withColumn(\"brand\", _ad_feature_df.brand.cast(IntegerType())).withColumnRenamed(\"brand\", \"brandId\").\\\n    withColumn(\"price\", _ad_feature_df.price.cast(FloatType()))\nad_feature_df.printSchema()\nad_feature_df.show()\n\n显示结果:\nroot\n |-- adgroupId: integer (nullable = true)\n |-- cateId: integer (nullable = true)\n |-- campaignId: integer (nullable = true)\n |-- customerId: integer (nullable = true)\n |-- brandId: integer (nullable = true)\n |-- price: float (nullable = true)\n\n+---------+------+----------+----------+-------+-----+\n|adgroupId|cateId|campaignId|customerId|brandId|price|\n+---------+------+----------+----------+-------+-----+\n|    63133|  6406|     83237|         1|  95471|170.0|\n|   313401|  6406|     83237|         1|  87331|199.0|\n|   248909|   392|     83237|         1|  32233| 38.0|\n|   208458|   392|     83237|         1| 174374|139.0|\n|   110847|  7211|    135256|         2| 145952|32.99|\n|   607788|  6261|    387991|         6| 207800|199.0|\n|   375706|  4520|    387991|         6|     -1| 99.0|\n|    11115|  7213|    139747|         9| 186847| 33.0|\n|    24484|  7207|    139744|         9| 186847| 19.0|\n|    28589|  5953|    395195|        13|     -1|428.0|\n|    23236|  5953|    395195|        13|     -1|368.0|\n|   300556|  5953|    395195|        13|     -1|639.0|\n|    92560|  5953|    395195|        13|     -1|368.0|\n|   590965|  4284|     28145|        14| 454237|249.0|\n|   529913|  4284|     70206|        14|     -1|249.0|\n|   546930|  4284|     28145|        14|     -1|249.0|\n|   639794|  6261|     70206|        14|  37004| 89.9|\n|   335413|  4284|     28145|        14|     -1|249.0|\n|   794890|  4284|     70206|        14| 454237|249.0|\n|   684020|  6261|     70206|        14|  37004| 99.0|\n+---------+------+----------+----------+-------+-----+\nonly showing top 20 rows\n\n\n从HDFS加载用户基本信息数据\n\nfrom pyspark.sql.types import StructType, StructField, StringType, IntegerType, LongType, FloatType\n\n# 构建表结构schema对象\nschema = StructType([\n    StructField(\"userId\", IntegerType()),\n    StructField(\"cms_segid\", IntegerType()),\n    StructField(\"cms_group_id\", IntegerType()),\n    StructField(\"final_gender_code\", IntegerType()),\n    StructField(\"age_level\", IntegerType()),\n    StructField(\"pvalue_level\", IntegerType()),\n    StructField(\"shopping_level\", IntegerType()),\n    StructField(\"occupation\", IntegerType()),\n    StructField(\"new_user_class_level\", IntegerType())\n])\n# 利用schema从hdfs加载\n_user_profile_df1 = spark.read.csv(\"hdfs://localhost:9000/datasets/user_profile.csv\", header=True, schema=schema)\n# user_profile_df.printSchema()\n# user_profile_df.show()\n\n'''对缺失数据进行特征热编码'''\nfrom pyspark.ml.feature import OneHotEncoder\nfrom pyspark.ml.feature import StringIndexer\nfrom pyspark.ml import Pipeline\n\n# 使用热编码转换pvalue_level的一维数据为多维，增加n-1个虚拟变量，n为pvalue_level的取值范围\n\n# 需要先将缺失值全部替换为数值，便于处理，否则会抛出异常\nfrom pyspark.sql.types import StringType\n_user_profile_df2 = _user_profile_df1.na.fill(-1)\n# _user_profile_df2.show()\n\n# 热编码时，必须先将待处理字段转为字符串类型才可处理\n_user_profile_df3 = _user_profile_df2.withColumn(\"pvalue_level\", _user_profile_df2.pvalue_level.cast(StringType()))\\\n    .withColumn(\"new_user_class_level\", _user_profile_df2.new_user_class_level.cast(StringType()))\n# _user_profile_df3.printSchema()\n\n# 对pvalue_level进行热编码，求值\n# 运行过程是先将pvalue_level转换为一列新的特征数据，然后对该特征数据求出的热编码值，存在了新的一列数据中，类型为一个稀疏矩阵\nstringindexer = StringIndexer(inputCol='pvalue_level', outputCol='pl_onehot_feature')\nencoder = OneHotEncoder(dropLast=False, inputCol='pl_onehot_feature', outputCol='pl_onehot_value')\npipeline = Pipeline(stages=[stringindexer, encoder])\npipeline_fit = pipeline.fit(_user_profile_df3)\n_user_profile_df4 = pipeline_fit.transform(_user_profile_df3)\n# pl_onehot_value列的值为稀疏矩阵，存储热编码的结果\n# _user_profile_df4.printSchema()\n# _user_profile_df4.show()\n\n# 使用热编码转换new_user_class_level的一维数据为多维\nstringindexer = StringIndexer(inputCol='new_user_class_level', outputCol='nucl_onehot_feature')\nencoder = OneHotEncoder(dropLast=False, inputCol='nucl_onehot_feature', outputCol='nucl_onehot_value')\npipeline = Pipeline(stages=[stringindexer, encoder])\npipeline_fit = pipeline.fit(_user_profile_df4)\nuser_profile_df = pipeline_fit.transform(_user_profile_df4)\nuser_profile_df.show()\n\n显示结果:\n+------+---------+------------+-----------------+---------+------------+--------------+----------+--------------------+-----------------+---------------+-------------------+-----------------+\n|userId|cms_segid|cms_group_id|final_gender_code|age_level|pvalue_level|shopping_level|occupation|new_user_class_level|pl_onehot_feature|pl_onehot_value|nucl_onehot_feature|nucl_onehot_value|\n+------+---------+------------+-----------------+---------+------------+--------------+----------+--------------------+-----------------+---------------+-------------------+-----------------+\n|   234|        0|           5|                2|        5|          -1|             3|         0|                   3|              0.0|  (4,[0],[1.0])|                2.0|    (5,[2],[1.0])|\n|   523|        5|           2|                2|        2|           1|             3|         1|                   2|              2.0|  (4,[2],[1.0])|                1.0|    (5,[1],[1.0])|\n|   612|        0|           8|                1|        2|           2|             3|         0|                  -1|              1.0|  (4,[1],[1.0])|                0.0|    (5,[0],[1.0])|\n|  1670|        0|           4|                2|        4|          -1|             1|         0|                  -1|              0.0|  (4,[0],[1.0])|                0.0|    (5,[0],[1.0])|\n|  2545|        0|          10|                1|        4|          -1|             3|         0|                  -1|              0.0|  (4,[0],[1.0])|                0.0|    (5,[0],[1.0])|\n|  3644|       49|           6|                2|        6|           2|             3|         0|                   2|              1.0|  (4,[1],[1.0])|                1.0|    (5,[1],[1.0])|\n|  5777|       44|           5|                2|        5|           2|             3|         0|                   2|              1.0|  (4,[1],[1.0])|                1.0|    (5,[1],[1.0])|\n|  6211|        0|           9|                1|        3|          -1|             3|         0|                   2|              0.0|  (4,[0],[1.0])|                1.0|    (5,[1],[1.0])|\n|  6355|        2|           1|                2|        1|           1|             3|         0|                   4|              2.0|  (4,[2],[1.0])|                3.0|    (5,[3],[1.0])|\n|  6823|       43|           5|                2|        5|           2|             3|         0|                   1|              1.0|  (4,[1],[1.0])|                4.0|    (5,[4],[1.0])|\n|  6972|        5|           2|                2|        2|           2|             3|         1|                   2|              1.0|  (4,[1],[1.0])|                1.0|    (5,[1],[1.0])|\n|  9293|        0|           5|                2|        5|          -1|             3|         0|                   4|              0.0|  (4,[0],[1.0])|                3.0|    (5,[3],[1.0])|\n|  9510|       55|           8|                1|        2|           2|             2|         0|                   2|              1.0|  (4,[1],[1.0])|                1.0|    (5,[1],[1.0])|\n| 10122|       33|           4|                2|        4|           2|             3|         0|                   2|              1.0|  (4,[1],[1.0])|                1.0|    (5,[1],[1.0])|\n| 10549|        0|           4|                2|        4|           2|             3|         0|                  -1|              1.0|  (4,[1],[1.0])|                0.0|    (5,[0],[1.0])|\n| 10812|        0|           4|                2|        4|          -1|             2|         0|                  -1|              0.0|  (4,[0],[1.0])|                0.0|    (5,[0],[1.0])|\n| 10912|        0|           4|                2|        4|           2|             3|         0|                  -1|              1.0|  (4,[1],[1.0])|                0.0|    (5,[0],[1.0])|\n| 10996|        0|           5|                2|        5|          -1|             3|         0|                   4|              0.0|  (4,[0],[1.0])|                3.0|    (5,[3],[1.0])|\n| 11256|        8|           2|                2|        2|           1|             3|         0|                   3|              2.0|  (4,[2],[1.0])|                2.0|    (5,[2],[1.0])|\n| 11310|       31|           4|                2|        4|           1|             3|         0|                   4|              2.0|  (4,[2],[1.0])|                3.0|    (5,[3],[1.0])|\n+------+---------+------------+-----------------+---------+------------+--------------+----------+--------------------+-----------------+---------------+-------------------+-----------------+\nonly showing top 20 rows\n\n热编码中：\"pvalue_level\"特征对应关系:\n\n+------------+----------------------+\n|pvalue_level|pl_onehot_feature     |\n+------------+----------------------+\n|          -1|                   0.0|\n|           3|                   3.0|\n|           1|                   2.0|\n|           2|                   1.0|\n+------------+----------------------+\n\n\n“new_user_class_level”的特征对应关系\n\n+--------------------+------------------------+\n|new_user_class_level|nucl_onehot_feature     |\n+--------------------+------------------------+\n|                  -1|                     0.0|\n|                   3|                     2.0|\n|                   1|                     4.0|\n|                   4|                     3.0|\n|                   2|                     1.0|\n+--------------------+------------------------+\n\nuser_profile_df.groupBy(\"pvalue_level\").min(\"pl_onehot_feature\").show()\nuser_profile_df.groupBy(\"new_user_class_level\").min(\"nucl_onehot_feature\").show()\n\n显示结果:\n+------------+----------------------+\n|pvalue_level|min(pl_onehot_feature)|\n+------------+----------------------+\n|          -1|                   0.0|\n|           3|                   3.0|\n|           1|                   2.0|\n|           2|                   1.0|\n+------------+----------------------+\n\n+--------------------+------------------------+\n|new_user_class_level|min(nucl_onehot_feature)|\n+--------------------+------------------------+\n|                  -1|                     0.0|\n|                   3|                     2.0|\n|                   1|                     4.0|\n|                   4|                     3.0|\n|                   2|                     1.0|\n+--------------------+------------------------+\n\nDataframe数据合并：pyspark.sql.DataFrame.join\n\n# raw_sample_df和ad_feature_df合并条件\ncondition = [raw_sample_df.adgroupId==ad_feature_df.adgroupId]\n_ = raw_sample_df.join(ad_feature_df, condition, 'outer')\n\n# _和user_profile_df合并条件\ncondition2 = [_.userId==user_profile_df.userId]\ndatasets = _.join(user_profile_df, condition2, \"outer\")\n# 查看datasets的结构\ndatasets.printSchema()\n# 查看datasets条目数\nprint(datasets.count())\n\n显示结果:\nroot\n |-- userId: integer (nullable = true)\n |-- timestamp: long (nullable = true)\n |-- adgroupId: integer (nullable = true)\n |-- pid: string (nullable = true)\n |-- nonclk: integer (nullable = true)\n |-- clk: integer (nullable = true)\n |-- pid_feature: double (nullable = true)\n |-- pid_value: vector (nullable = true)\n |-- adgroupId: integer (nullable = true)\n |-- cateId: integer (nullable = true)\n |-- campaignId: integer (nullable = true)\n |-- customerId: integer (nullable = true)\n |-- brandId: integer (nullable = true)\n |-- price: float (nullable = true)\n |-- userId: integer (nullable = true)\n |-- cms_segid: integer (nullable = true)\n |-- cms_group_id: integer (nullable = true)\n |-- final_gender_code: integer (nullable = true)\n |-- age_level: integer (nullable = true)\n |-- pvalue_level: string (nullable = true)\n |-- shopping_level: integer (nullable = true)\n |-- occupation: integer (nullable = true)\n |-- new_user_class_level: string (nullable = true)\n |-- pl_onehot_feature: double (nullable = true)\n |-- pl_onehot_value: vector (nullable = true)\n |-- nucl_onehot_feature: double (nullable = true)\n |-- nucl_onehot_value: vector (nullable = true)\n\n26557961\n\n\n训练CTRModel_Normal：直接将对应的特征的特征值组合成对应的特征向量进行训练\n\n# 剔除冗余、不需要的字段\nuseful_cols = [\n    # \n    # 时间字段，划分训练集和测试集\n    \"timestamp\",\n    # label目标值字段\n    \"clk\",  \n    # 特征值字段\n    \"pid_value\",       # 资源位的特征向量\n    \"price\",    # 广告价格\n    \"cms_segid\",    # 用户微群ID\n    \"cms_group_id\",    # 用户组ID\n    \"final_gender_code\",    # 用户性别特征，[1,2]\n    \"age_level\",    # 年龄等级，1-\n    \"shopping_level\",\n    \"occupation\",\n    \"pl_onehot_value\",\n    \"nucl_onehot_value\"\n]\n# 筛选指定字段数据，构建新的数据集\ndatasets_1 = datasets.select(*useful_cols)\n# 由于前面使用的是outer方式合并的数据，产生了部分空值数据，这里必须先剔除掉\ndatasets_1 = datasets_1.dropna()\nprint(\"剔除空值数据后，还剩：\", datasets_1.count())\n\n显示结果:\n剔除空值数据后，还剩： 25029435\n\n根据特征字段计算出特征向量，并划分出训练数据集和测试数据集\n\nfrom pyspark.ml.feature import VectorAssembler\n# 根据特征字段计算特征向量\ndatasets_1 = VectorAssembler().setInputCols(useful_cols[2:]).setOutputCol(\"features\").transform(datasets_1)\n# 训练数据集: 约7天的数据\ntrain_datasets_1 = datasets_1.filter(datasets_1.timestamp(1494691186-24*60*60))\n# 所有的特征的特征向量已经汇总到在features字段中\ntrain_datasets_1.show(5)\ntest_datasets_1.show(5)\n\n显示结果:\n+----------+---+-------------+------+---------+------------+-----------------+---------+--------------+----------+---------------+-----------------+--------------------+\n| timestamp|clk|    pid_value| price|cms_segid|cms_group_id|final_gender_code|age_level|shopping_level|occupation|pl_onehot_value|nucl_onehot_value|            features|\n+----------+---+-------------+------+---------+------------+-----------------+---------+--------------+----------+---------------+-----------------+--------------------+\n|1494261938|  0|(2,[1],[1.0])| 108.0|        0|          11|                1|        5|             3|         0|  (4,[0],[1.0])|    (5,[1],[1.0])|(18,[1,2,4,5,6,7,...|\n|1494261938|  0|(2,[1],[1.0])|1880.0|        0|          11|                1|        5|             3|         0|  (4,[0],[1.0])|    (5,[1],[1.0])|(18,[1,2,4,5,6,7,...|\n|1494553913|  0|(2,[1],[1.0])|2360.0|       19|           3|                2|        3|             3|         0|  (4,[1],[1.0])|    (5,[1],[1.0])|(18,[1,2,3,4,5,6,...|\n|1494553913|  0|(2,[1],[1.0])|2200.0|       19|           3|                2|        3|             3|         0|  (4,[1],[1.0])|    (5,[1],[1.0])|(18,[1,2,3,4,5,6,...|\n|1494436784|  0|(2,[1],[1.0])|5649.0|       19|           3|                2|        3|             3|         0|  (4,[1],[1.0])|    (5,[1],[1.0])|(18,[1,2,3,4,5,6,...|\n+----------+---+-------------+------+---------+------------+-----------------+---------+--------------+----------+---------------+-----------------+--------------------+\nonly showing top 5 rows\n\n+----------+---+-------------+-----+---------+------------+-----------------+---------+--------------+----------+---------------+-----------------+--------------------+\n| timestamp|clk|    pid_value|price|cms_segid|cms_group_id|final_gender_code|age_level|shopping_level|occupation|pl_onehot_value|nucl_onehot_value|            features|\n+----------+---+-------------+-----+---------+------------+-----------------+---------+--------------+----------+---------------+-----------------+--------------------+\n|1494677292|  0|(2,[1],[1.0])|176.0|       19|           3|                2|        3|             3|         0|  (4,[1],[1.0])|    (5,[1],[1.0])|(18,[1,2,3,4,5,6,...|\n|1494677292|  0|(2,[1],[1.0])|698.0|       19|           3|                2|        3|             3|         0|  (4,[1],[1.0])|    (5,[1],[1.0])|(18,[1,2,3,4,5,6,...|\n|1494677292|  0|(2,[1],[1.0])|697.0|       19|           3|                2|        3|             3|         0|  (4,[1],[1.0])|    (5,[1],[1.0])|(18,[1,2,3,4,5,6,...|\n|1494684007|  0|(2,[1],[1.0])|247.0|       18|           3|                2|        3|             3|         0|  (4,[1],[1.0])|    (5,[4],[1.0])|(18,[1,2,3,4,5,6,...|\n|1494684007|  0|(2,[1],[1.0])|109.0|       18|           3|                2|        3|             3|         0|  (4,[1],[1.0])|    (5,[4],[1.0])|(18,[1,2,3,4,5,6,...|\n+----------+---+-------------+-----+---------+------------+-----------------+---------+--------------+----------+---------------+-----------------+--------------------+\nonly showing top 5 rows\n\n创建逻辑回归训练器，并训练模型：LogisticRegression、 LogisticRegressionModel\n\nfrom pyspark.ml.classification import LogisticRegression\nlr = LogisticRegression()\n# 设置目标字段、特征值字段并训练\nmodel = lr.setLabelCol(\"clk\").setFeaturesCol(\"features\").fit(train_datasets_1)\n# 对模型进行存储\nmodel.save(\"hdfs://localhost:9000/models/CTRModel_Normal.obj\")\n# 载入训练好的模型\nfrom pyspark.ml.classification import LogisticRegressionModel\nmodel = LogisticRegressionModel.load(\"hdfs://localhost:9000/models/CTRModel_Normal.obj\")\n# 根据测试数据进行预测\nresult_1 = model.transform(test_datasets_1)\n# 按probability升序排列数据，probability表示预测结果的概率\n# 如果预测值是0，其概率是0.9248，那么反之可推出1的可能性就是1-0.9248=0.0752，即点击概率约为7.52%\n# 因为前面提到广告的点击率一般都比较低，所以预测值通常都是0，因此通常需要反减得出点击的概率\nresult_1.select(\"clk\", \"price\", \"probability\", \"prediction\").sort(\"probability\").show(100)\n\n显示结果:\n+---+-----------+--------------------+----------+\n|clk|      price|         probability|prediction|\n+---+-----------+--------------------+----------+\n|  0|      1.0E8|[0.86822033939259...|       0.0|\n|  0|      1.0E8|[0.88410457194969...|       0.0|\n|  0|      1.0E8|[0.89175497837562...|       0.0|\n|  1|5.5555556E7|[0.92481456486873...|       0.0|\n|  0|      1.5E7|[0.93741450446939...|       0.0|\n|  0|      1.5E7|[0.93757135079959...|       0.0|\n|  0|      1.5E7|[0.93834723093801...|       0.0|\n|  0|     1099.0|[0.93972095713786...|       0.0|\n|  0|      338.0|[0.93972134993018...|       0.0|\n|  0|      311.0|[0.93972136386626...|       0.0|\n|  0|      300.0|[0.93972136954393...|       0.0|\n|  0|      278.0|[0.93972138089925...|       0.0|\n|  0|      188.0|[0.93972142735283...|       0.0|\n|  0|      176.0|[0.93972143354663...|       0.0|\n|  0|      168.0|[0.93972143767584...|       0.0|\n|  0|      158.0|[0.93972144283734...|       0.0|\n|  1|      138.0|[0.93972145316035...|       0.0|\n|  0|      125.0|[0.93972145987031...|       0.0|\n|  0|      119.0|[0.93972146296721...|       0.0|\n|  0|       78.0|[0.93972148412937...|       0.0|\n|  0|      59.98|[0.93972149343040...|       0.0|\n|  0|       58.0|[0.93972149445238...|       0.0|\n|  0|       56.0|[0.93972149548468...|       0.0|\n|  0|       38.0|[0.93972150477538...|       0.0|\n|  1|       35.0|[0.93972150632383...|       0.0|\n|  0|       33.0|[0.93972150735613...|       0.0|\n|  0|       30.0|[0.93972150890458...|       0.0|\n|  0|       27.6|[0.93972151014334...|       0.0|\n|  0|       18.0|[0.93972151509838...|       0.0|\n|  0|       30.0|[0.93980311191464...|       0.0|\n|  0|       28.0|[0.93980311294563...|       0.0|\n|  0|       25.0|[0.93980311449212...|       0.0|\n|  0|      688.0|[0.93999362023323...|       0.0|\n|  0|      339.0|[0.93999379960808...|       0.0|\n|  0|      335.0|[0.93999380166395...|       0.0|\n|  0|      220.0|[0.93999386077017...|       0.0|\n|  0|      176.0|[0.93999388338470...|       0.0|\n|  0|      158.0|[0.93999389263610...|       0.0|\n|  0|      158.0|[0.93999389263610...|       0.0|\n|  1|      149.0|[0.93999389726180...|       0.0|\n|  0|      122.5|[0.93999391088191...|       0.0|\n|  0|       99.0|[0.93999392296012...|       0.0|\n|  0|       88.0|[0.93999392861375...|       0.0|\n|  0|       79.0|[0.93999393323945...|       0.0|\n|  0|       75.0|[0.93999393529532...|       0.0|\n|  0|       68.0|[0.93999393889308...|       0.0|\n|  0|       68.0|[0.93999393889308...|       0.0|\n|  0|       59.9|[0.93999394305620...|       0.0|\n|  0|      44.98|[0.93999395072458...|       0.0|\n|  0|       35.5|[0.93999395559698...|       0.0|\n|  0|       33.0|[0.93999395688189...|       0.0|\n|  0|       32.8|[0.93999395698469...|       0.0|\n|  0|       30.0|[0.93999395842379...|       0.0|\n|  0|       28.0|[0.93999395945172...|       0.0|\n|  0|       19.9|[0.93999396361485...|       0.0|\n|  0|       19.8|[0.93999396366625...|       0.0|\n|  0|       19.8|[0.93999396366625...|       0.0|\n|  0|       12.0|[0.93999396767518...|       0.0|\n|  0|        6.7|[0.93999397039920...|       0.0|\n|  0|      568.0|[0.94000369247841...|       0.0|\n|  0|      398.0|[0.94000377983931...|       0.0|\n|  0|      158.0|[0.94000390317214...|       0.0|\n|  0|     5718.0|[0.94001886593718...|       0.0|\n|  0|     5718.0|[0.94001886593718...|       0.0|\n|  1|     5608.0|[0.94001892245145...|       0.0|\n|  0|     4120.0|[0.94001968693052...|       0.0|\n|  0|     1027.5|[0.94002127571285...|       0.0|\n|  0|     1027.5|[0.94002127571285...|       0.0|\n|  0|      989.0|[0.94002129549211...|       0.0|\n|  0|      672.0|[0.94002145834965...|       0.0|\n|  0|      660.0|[0.94002146451460...|       0.0|\n|  0|      598.0|[0.94002149636681...|       0.0|\n|  0|      598.0|[0.94002149636681...|       0.0|\n|  0|      563.0|[0.94002151434789...|       0.0|\n|  0|      509.0|[0.94002154209012...|       0.0|\n|  0|      509.0|[0.94002154209012...|       0.0|\n|  0|      500.0|[0.94002154671382...|       0.0|\n|  0|      498.0|[0.94002154774131...|       0.0|\n|  0|      440.0|[0.94002157753851...|       0.0|\n|  0|      430.0|[0.94002158267595...|       0.0|\n|  0|      388.0|[0.94002160425322...|       0.0|\n|  0|      369.0|[0.94002161401436...|       0.0|\n|  0|      368.0|[0.94002161452811...|       0.0|\n|  0|      368.0|[0.94002161452811...|       0.0|\n|  0|      368.0|[0.94002161452811...|       0.0|\n|  0|      368.0|[0.94002161452811...|       0.0|\n|  0|      366.0|[0.94002161555560...|       0.0|\n|  0|      366.0|[0.94002161555560...|       0.0|\n|  0|      348.0|[0.94002162480299...|       0.0|\n|  0|      299.0|[0.94002164997645...|       0.0|\n|  0|      299.0|[0.94002164997645...|       0.0|\n|  0|      299.0|[0.94002164997645...|       0.0|\n|  0|      298.0|[0.94002165049020...|       0.0|\n|  0|      297.0|[0.94002165100394...|       0.0|\n|  0|      278.0|[0.94002166076508...|       0.0|\n|  1|      275.0|[0.94002166230631...|       0.0|\n|  0|      275.0|[0.94002166230631...|       0.0|\n|  0|      273.0|[0.94002166333380...|       0.0|\n|  0|      258.0|[0.94002167103995...|       0.0|\n|  0|      256.0|[0.94002167206744...|       0.0|\n+---+-----------+--------------------+----------+\nonly showing top 100 rows\n\n\n查看样本中点击的被实际点击的条目的预测情况\n\nresult_1.filter(result_1.clk==1).select(\"clk\", \"price\", \"probability\", \"prediction\").sort(\"probability\").show(100)\n\n显示结果:\n+---+-----------+--------------------+----------+\n|clk|      price|         probability|prediction|\n+---+-----------+--------------------+----------+\n|  1|5.5555556E7|[0.92481456486873...|       0.0|\n|  1|      138.0|[0.93972145316035...|       0.0|\n|  1|       35.0|[0.93972150632383...|       0.0|\n|  1|      149.0|[0.93999389726180...|       0.0|\n|  1|     5608.0|[0.94001892245145...|       0.0|\n|  1|      275.0|[0.94002166230631...|       0.0|\n|  1|       35.0|[0.94002178560473...|       0.0|\n|  1|       49.0|[0.94004219516957...|       0.0|\n|  1|      915.0|[0.94021082858784...|       0.0|\n|  1|      598.0|[0.94021099096349...|       0.0|\n|  1|      568.0|[0.94021100633025...|       0.0|\n|  1|      398.0|[0.94021109340848...|       0.0|\n|  1|      368.0|[0.94021110877521...|       0.0|\n|  1|      299.0|[0.94021114411869...|       0.0|\n|  1|      278.0|[0.94021115487539...|       0.0|\n|  1|      259.0|[0.94021116460765...|       0.0|\n|  1|      258.0|[0.94021116511987...|       0.0|\n|  1|      258.0|[0.94021116511987...|       0.0|\n|  1|      258.0|[0.94021116511987...|       0.0|\n|  1|      195.0|[0.94021119738998...|       0.0|\n|  1|      188.0|[0.94021120097554...|       0.0|\n|  1|      178.0|[0.94021120609778...|       0.0|\n|  1|      159.0|[0.94021121583003...|       0.0|\n|  1|      149.0|[0.94021122095226...|       0.0|\n|  1|      138.0|[0.94021122658672...|       0.0|\n|  1|       58.0|[0.94021126756458...|       0.0|\n|  1|       49.0|[0.94021127217459...|       0.0|\n|  1|       35.0|[0.94021127934572...|       0.0|\n|  1|       25.0|[0.94021128446795...|       0.0|\n|  1|     2890.0|[0.94028789742257...|       0.0|\n|  1|      220.0|[0.94028926340218...|       0.0|\n|  1|      188.0|[0.94031410659516...|       0.0|\n|  1|       68.0|[0.94031416796289...|       0.0|\n|  1|       58.0|[0.94031417307687...|       0.0|\n|  1|      198.0|[0.94035413548387...|       0.0|\n|  1|      208.0|[0.94039204931181...|       0.0|\n|  1|     8888.0|[0.94045237642030...|       0.0|\n|  1|      519.0|[0.94045664687995...|       0.0|\n|  1|      478.0|[0.94045666780037...|       0.0|\n|  1|      349.0|[0.94045673362308...|       0.0|\n|  1|      348.0|[0.94045673413334...|       0.0|\n|  1|      316.0|[0.94045675046144...|       0.0|\n|  1|      298.0|[0.94045675964600...|       0.0|\n|  1|      298.0|[0.94045675964600...|       0.0|\n|  1|      199.0|[0.94045681016104...|       0.0|\n|  1|      199.0|[0.94045681016104...|       0.0|\n|  1|      198.0|[0.94045681067129...|       0.0|\n|  1|      187.1|[0.94045681623305...|       0.0|\n|  1|      176.0|[0.94045682189685...|       0.0|\n|  1|      168.0|[0.94045682597887...|       0.0|\n|  1|      160.0|[0.94045683006090...|       0.0|\n|  1|      158.0|[0.94045683108140...|       0.0|\n|  1|      158.0|[0.94045683108140...|       0.0|\n|  1|      135.0|[0.94045684281721...|       0.0|\n|  1|      129.0|[0.94045684587872...|       0.0|\n|  1|      127.0|[0.94045684689923...|       0.0|\n|  1|      125.0|[0.94045684791973...|       0.0|\n|  1|      124.0|[0.94045684842999...|       0.0|\n|  1|      118.0|[0.94045685149150...|       0.0|\n|  1|      109.0|[0.94045685608377...|       0.0|\n|  1|      108.0|[0.94045685659402...|       0.0|\n|  1|       99.0|[0.94045686118630...|       0.0|\n|  1|       98.0|[0.94045686169655...|       0.0|\n|  1|       79.8|[0.94045687098314...|       0.0|\n|  1|       79.0|[0.94045687139134...|       0.0|\n|  1|       77.0|[0.94045687241185...|       0.0|\n|  1|       72.5|[0.94045687470798...|       0.0|\n|  1|       69.0|[0.94045687649386...|       0.0|\n|  1|       68.0|[0.94045687700412...|       0.0|\n|  1|       60.0|[0.94045688108613...|       0.0|\n|  1|      43.98|[0.94045688926037...|       0.0|\n|  1|       40.0|[0.94045689129118...|       0.0|\n|  1|       39.9|[0.94045689134220...|       0.0|\n|  1|       39.6|[0.94045689149528...|       0.0|\n|  1|       32.0|[0.94045689537319...|       0.0|\n|  1|       31.0|[0.94045689588345...|       0.0|\n|  1|      25.98|[0.94045689844491...|       0.0|\n|  1|       23.0|[0.94045689996546...|       0.0|\n|  1|       19.0|[0.94045690200647...|       0.0|\n|  1|       16.9|[0.94045690307800...|       0.0|\n|  1|       10.0|[0.94045690659874...|       0.0|\n|  1|        3.5|[0.94045690991538...|       0.0|\n|  1|        3.5|[0.94045690991538...|       0.0|\n|  1|        0.4|[0.94045691149716...|       0.0|\n|  1|     3960.0|[0.94055740378069...|       0.0|\n|  1|     3088.0|[0.94055784801535...|       0.0|\n|  1|     1689.0|[0.94055856072019...|       0.0|\n|  1|      998.0|[0.94055891273943...|       0.0|\n|  1|      888.0|[0.94055896877705...|       0.0|\n|  1|      788.0|[0.94055901972029...|       0.0|\n|  1|      737.0|[0.94055904570133...|       0.0|\n|  1|      629.0|[0.94055910071996...|       0.0|\n|  1|      599.0|[0.94055911600291...|       0.0|\n|  1|      599.0|[0.94055911600291...|       0.0|\n|  1|      599.0|[0.94055911600291...|       0.0|\n|  1|      499.0|[0.94055916694603...|       0.0|\n|  1|      468.0|[0.94055918273839...|       0.0|\n|  1|      459.0|[0.94055918732327...|       0.0|\n|  1|      399.0|[0.94055921788912...|       0.0|\n|  1|      399.0|[0.94055921788912...|       0.0|\n+---+-----------+--------------------+----------+\nonly showing top 100 rows\n"},"day07_推荐系统案例/05_离线推荐处理.html":{"url":"day07_推荐系统案例/05_离线推荐处理.html","title":"05_离线推荐处理","keywords":"","body":"五 离线推荐数据缓存\n5.1离线数据缓存之离线召回集\n\n这里主要是利用我们前面训练的ALS模型进行协同过滤召回，但是注意，我们ALS模型召回的是用户最感兴趣的类别，而我们需要的是用户可能感兴趣的广告的集合，因此我们还需要根据召回的类别匹配出对应的广告。\n所以这里我们除了需要我们训练的ALS模型以外，还需要有一个广告和类别的对应关系。\n\n\n# 从HDFS中加载广告基本信息数据，返回spark dafaframe对象\ndf = spark.read.csv(\"hdfs://localhost:9000/data/ad_feature.csv\", header=True)\n\n# 注意：由于本数据集中存在NULL字样的数据，无法直接设置schema，只能先将NULL类型的数据处理掉，然后进行类型转换\n\nfrom pyspark.sql.types import StructType, StructField, IntegerType, FloatType\n\n# 替换掉NULL字符串，替换掉\ndf = df.replace(\"NULL\", \"-1\")\n\n# 更改df表结构：更改列类型和列名称\nad_feature_df = df.\\\n    withColumn(\"adgroup_id\", df.adgroup_id.cast(IntegerType())).withColumnRenamed(\"adgroup_id\", \"adgroupId\").\\\n    withColumn(\"cate_id\", df.cate_id.cast(IntegerType())).withColumnRenamed(\"cate_id\", \"cateId\").\\\n    withColumn(\"campaign_id\", df.campaign_id.cast(IntegerType())).withColumnRenamed(\"campaign_id\", \"campaignId\").\\\n    withColumn(\"customer\", df.customer.cast(IntegerType())).withColumnRenamed(\"customer\", \"customerId\").\\\n    withColumn(\"brand\", df.brand.cast(IntegerType())).withColumnRenamed(\"brand\", \"brandId\").\\\n    withColumn(\"price\", df.price.cast(FloatType()))\n\n# 这里我们只需要adgroupId、和cateId\n_ = ad_feature_df.select(\"adgroupId\", \"cateId\")\n# 由于这里数据集其实很少，所以我们再直接转成Pandas dataframe来处理，把数据载入内存\npdf = _.toPandas()\n\n\n# 手动释放一些内存\ndel df\ndel ad_feature_df\ndel _\nimport gc\ngc.collect()\n\n\n根据指定的类别找到对应的广告\n\nimport numpy as np\npdf.where(pdf.cateId==11156).dropna().adgroupId\n\nnp.random.choice(pdf.where(pdf.cateId==11156).dropna().adgroupId.astype(np.int64), 200)\n\n显示结果:\n313       138953.0\n314       467512.0\n1661      140008.0\n1666      238772.0\n1669      237471.0\n1670      238761.0\n            ...   \n843456    352273.0\n846728    818681.0\n846729    838953.0\n846810    845337.0\nName: adgroupId, Length: 731, dtype: float64\n\n利用ALS模型进行类别的召回\n\n# 加载als模型，注意必须先有spark上下文管理器，即sparkContext，但这里sparkSession创建后，自动创建了sparkContext\n\nfrom pyspark.ml.recommendation import ALSModel\n# 从hdfs加载之前存储的模型\nals_model = ALSModel.load(\"hdfs://localhost:9000/models/userCateRatingALSModel.obj\")\n# 返回模型中关于用户的所有属性   df:   id   features\nals_model.userFactors\n\n显示结果:\nDataFrame[id: int, features: array]\nimport pandas as pd\ncateId_df = pd.DataFrame(pdf.cateId.unique(),columns=[\"cateId\"])\ncateId_df\n\n显示结果:\n    cateId\n0    1\n1    2\n2    3\n3    4\n4    5\n5    6\n6    7\n...    ...\n6766    12948\n6767    12955\n6768    12960\n6769 rows × 1 columns\ncateId_df.insert(0, \"userId\", np.array([8 for i in range(6769)]))\ncateId_df\n\n显示结果:\n userId cateId\n0    8    1\n1    8    2\n2    8    3\n3    8    4\n4    8    5\n...    ...    ...\n6766    8    12948\n6767    8    12955\n6768    8    12960\n6769 rows × 2 columns\n\n传入 userid、cataId的df，对应预测值进行排序\n\nals_model.transform(spark.createDataFrame(cateId_df)).sort(\"prediction\", ascending=False).na.drop().show()\n\n显示结果:\n+------+------+----------+\n|userId|cateId|prediction|\n+------+------+----------+\n|     8|  7214|  9.917084|\n|     8|   877|  7.479664|\n|     8|  7266| 7.4762917|\n|     8| 10856| 7.3395424|\n|     8|  4766|  7.149538|\n|     8|  7282| 6.6835284|\n|     8|  7270| 6.2145095|\n|     8|   201| 6.0623236|\n|     8|  4267| 5.9155636|\n|     8|  7267|  5.838009|\n|     8|  5392| 5.6882005|\n|     8|  6261| 5.6804466|\n|     8|  6306| 5.2992325|\n|     8| 11050|  5.245261|\n|     8|  8655| 5.1701374|\n|     8|  4610|  5.139578|\n|     8|   932|   5.12694|\n|     8| 12276| 5.0776596|\n|     8|  8071|  4.979195|\n|     8|  6580| 4.8523283|\n+------+------+----------+\nonly showing top 20 rows\nimport numpy as np\nimport pandas as pd\n\nimport redis\n\n# 存储用户召回，使用redis第9号数据库，类型：sets类型\nclient = redis.StrictRedis(host=\"192.168.19.137\", port=6379, db=9)\n# 遍历als_model 中 所有用户的id\nfor r in als_model.userFactors.select(\"id\").collect():\n\n    userId = r.id\n\n    #准备 当前用户 和 所有类别 一一对应的dataframe\n    cateId_df = pd.DataFrame(pdf.cateId.unique(),columns=[\"cateId\"])\n    cateId_df.insert(0, \"userId\", np.array([userId for i in range(6769)]))\n    ret = set()\n\n    # 利用模型，传入datasets(userId, cateId)，这里控制了userId一样，所以相当于是在求某用户对所有分类的兴趣程度\n    cateId_list = als_model.transform(spark.createDataFrame(cateId_df)).sort(\"prediction\", ascending=False).na.drop()\n    # 找到前 20个 最感兴趣的类别 从前20个分类中选出500个进行召回\n    for i in cateId_list.head(20):\n        need = 500 - len(ret)    # 如果不足500个，那么随机选出need个广告\n        ret = ret.union(np.random.choice(pdf.where(pdf.cateId==i.cateId).adgroupId.dropna().astype(np.int64), need))\n        if len(ret) >= 500:    # 如果达到500个则退出\n            break\n    client.sadd(userId, *ret)\n\n# 如果redis所在机器，内存不足，会抛出异常\n\n5.2 离线数据缓存之离线特征\n# \"pid\", 广告资源位，属于场景特征，也就是说，每一种广告通常是可以防止在多种资源外下的\n# 因此这里对于pid，应该是由广告系统发起推荐请求时，向推荐系统明确要推荐的用户是谁，以及对应的资源位，或者说有哪些\n# 这样如果有多个资源位，那么每个资源位都会对应相应的一个推荐列表\n\n# 需要进行缓存的特征值\n\nfeature_cols_from_ad = [\n    \"price\"    # 来自广告基本信息中\n]\n\n# 用户特征\nfeature_cols_from_user = [\n    \"cms_group_id\",\n    \"final_gender_code\",\n    \"age_level\",\n    \"shopping_level\",\n    \"occupation\",\n    \"pvalue_level\",\n    \"new_user_class_level\"\n]\n\n\n从HDFS中加载广告基本信息数据\n\n_ad_feature_df = spark.read.csv(\"hdfs://localhost:9000/data/ad_feature.csv\", header=True)\n\n# 更改表结构，转换为对应的数据类型\nfrom pyspark.sql.types import StructType, StructField, IntegerType, FloatType\n\n# 替换掉NULL字符串\n_ad_feature_df = _ad_feature_df.replace(\"NULL\", \"-1\")\n\n# 更改df表结构：更改列类型和列名称\nad_feature_df = _ad_feature_df.\\\n    withColumn(\"adgroup_id\", _ad_feature_df.adgroup_id.cast(IntegerType())).withColumnRenamed(\"adgroup_id\", \"adgroupId\").\\\n    withColumn(\"cate_id\", _ad_feature_df.cate_id.cast(IntegerType())).withColumnRenamed(\"cate_id\", \"cateId\").\\\n    withColumn(\"campaign_id\", _ad_feature_df.campaign_id.cast(IntegerType())).withColumnRenamed(\"campaign_id\", \"campaignId\").\\\n    withColumn(\"customer\", _ad_feature_df.customer.cast(IntegerType())).withColumnRenamed(\"customer\", \"customerId\").\\\n    withColumn(\"brand\", _ad_feature_df.brand.cast(IntegerType())).withColumnRenamed(\"brand\", \"brandId\").\\\n    withColumn(\"price\", _ad_feature_df.price.cast(FloatType()))\n\ndef foreachPartition(partition):\n\n    import redis\n    import json\n    client = redis.StrictRedis(host=\"192.168.19.137\", port=6379, db=10)\n\n    for r in partition:\n        data = {\n            \"price\": r.price\n        }\n        # 转成json字符串再保存，能保证数据再次倒出来时，能有效的转换成python类型\n        client.hset(\"ad_features\", r.adgroupId, json.dumps(data))\n\nad_feature_df.foreachPartition(foreachPartition)\n\n\n从HDFS加载用户基本信息数据\n\nfrom pyspark.sql.types import StructType, StructField, StringType, IntegerType, LongType, FloatType\n\n# 构建表结构schema对象\nschema = StructType([\n    StructField(\"userId\", IntegerType()),\n    StructField(\"cms_segid\", IntegerType()),\n    StructField(\"cms_group_id\", IntegerType()),\n    StructField(\"final_gender_code\", IntegerType()),\n    StructField(\"age_level\", IntegerType()),\n    StructField(\"pvalue_level\", IntegerType()),\n    StructField(\"shopping_level\", IntegerType()),\n    StructField(\"occupation\", IntegerType()),\n    StructField(\"new_user_class_level\", IntegerType())\n])\n# 利用schema从hdfs加载\nuser_profile_df = spark.read.csv(\"hdfs://localhost:8020/csv/user_profile.csv\", header=True, schema=schema)\nuser_profile_df\n\n显示结果:\nDataFrame[userId: int, cms_segid: int, cms_group_id: int, final_gender_code: int, age_level: int, pvalue_level: int, shopping_level: int, occupation: int, new_user_class_level: int]\ndef foreachPartition2(partition):\n\n    import redis\n    import json\n    client = redis.StrictRedis(host=\"192.168.199.188\", port=6379, db=10)\n\n    for r in partition:\n        data = {\n            \"cms_group_id\": r.cms_group_id,\n            \"final_gender_code\": r.final_gender_code,\n            \"age_level\": r.age_level,\n            \"shopping_level\": r.shopping_level,\n            \"occupation\": r.occupation,\n            \"pvalue_level\": r.pvalue_level,\n            \"new_user_class_level\": r.new_user_class_level\n        }\n        # 转成json字符串再保存，能保证数据再次倒出来时，能有效的转换成python类型\n        client.hset(\"user_features1\", r.userId, json.dumps(data))\n\nuser_profile_df.foreachPartition(foreachPartition2)\n\n"},"day07_推荐系统案例/06_实时推荐.html":{"url":"day07_推荐系统案例/06_实时推荐.html","title":"06_实时推荐","keywords":"","body":"六 实时产生推荐结果\n6.1 推荐任务处理\n\nCTR预测模型 + 特征 ==> 预测结果 ==> TOP-N列表\n\n\n特征获取\n\nimport redis\nimport json\nimport pandas as pd\nfrom pyspark.ml.linalg import DenseVector\n\n\ndef create_datasets(userId, pid):\n    client_of_recall = redis.StrictRedis(host=\"192.168.19.137\", port=6379, db=9)\n    client_of_features = redis.StrictRedis(host=\"192.168.19.137\", port=6379, db=10)\n    # 获取用户特征\n    user_feature = json.loads(client_of_features.hget(\"user_features\", userId))\n\n    # 获取用户召回集\n    recall_sets = client_of_recall.smembers(userId)\n\n    result = []\n\n    # 遍历召回集\n    for adgroupId in recall_sets:\n        adgroupId = int(adgroupId)\n        # 获取该广告的特征值\n        ad_feature = json.loads(client_of_features.hget(\"ad_features\", adgroupId))\n\n        features = {}\n        features.update(user_feature)\n        features.update(ad_feature)\n\n        for k,v in features.items():\n            if v is None:\n                features[k] = -1\n\n        features_col = [\n            # 特征值\n            \"price\",\n            \"cms_group_id\",\n            \"final_gender_code\",\n            \"age_level\",\n            \"shopping_level\",\n            \"occupation\",\n            \"pid\", \n            \"pvalue_level\",\n            \"new_user_class_level\"\n        ]\n        '''\n        \"cms_group_id\", 类别型特征，约13个分类 ==> 13维\n        \"final_gender_code\", 类别型特征，2个分类 ==> 2维\n        \"age_level\", 类别型特征，7个分类 ==>7维\n        \"shopping_level\", 类别型特征，3个分类 ==> 3维\n        \"occupation\", 类别型特征，2个分类 ==> 2维\n        '''\n\n        price = float(features[\"price\"])\n\n        pid_value = [0 for i in range(2)]#[0,0]\n        cms_group_id_value = [0 for i in range(13)]\n        final_gender_code_value = [0 for i in range(2)]\n        age_level_value = [0 for i in range(7)]\n        shopping_level_value = [0 for i in range(3)]\n        occupation_value = [0 for i in range(2)]\n        pvalue_level_value = [0 for i in range(4)]\n        new_user_class_level_value = [0 for i in range(5)]\n\n        pid_value[pid_rela[pid]] = 1\n        cms_group_id_value[cms_group_id_rela[int(features[\"cms_group_id\"])]] = 1\n        final_gender_code_value[final_gender_code_rela[int(features[\"final_gender_code\"])]] = 1\n        age_level_value[age_level_rela[int(features[\"age_level\"])]] = 1\n        shopping_level_value[shopping_level_rela[int(features[\"shopping_level\"])]] = 1\n        occupation_value[occupation_rela[int(features[\"occupation\"])]] = 1\n        pvalue_level_value[pvalue_level_rela[int(features[\"pvalue_level\"])]] = 1\n        new_user_class_level_value[new_user_class_level_rela[int(features[\"new_user_class_level\"])]] = 1\n #         print(pid_value)\n#         print(cms_group_id_value)\n#         print(final_gender_code_value)\n#         print(age_level_value)\n#         print(shopping_level_value)\n#         print(occupation_value)\n#         print(pvalue_level_value)\n#         print(new_user_class_level_value)\n\n        vector = DenseVector([price] + pid_value + cms_group_id_value + final_gender_code_value\\\n        + age_level_value + shopping_level_value + occupation_value + pvalue_level_value + new_user_class_level_value)\n\n        result.append((userId, adgroupId, vector))\n\n    return result\n\n# create_datasets(88, \"430548_1007\")\n\n\n载入训练好的模型\n\nfrom pyspark.ml.classification import LogisticRegressionModel\nCTR_model = LogisticRegressionModel.load(\"hdfs://localhost:9000/models/CTRModel_AllOneHot.obj\")\npdf = pd.DataFrame(create_datasets(8, \"430548_1007\"), columns=[\"userId\", \"adgroupId\", \"features\"])\ndatasets = spark.createDataFrame(pdf)\ndatasets.show()\n\n显示结果:\n+------+---------+--------------------+\n|userId|adgroupId|            features|\n+------+---------+--------------------+\n|     8|   445914|[9.89999961853027...|\n|     8|   258252|[7.59999990463256...|\n|     8|   129682|[8.5,1.0,0.0,1.0,...|\n|     8|   763027|[68.0,1.0,0.0,1.0...|\n|     8|   292027|[16.0,1.0,0.0,1.0...|\n|     8|   430023|[34.2000007629394...|\n|     8|   133457|[169.0,1.0,0.0,1....|\n|     8|   816999|[5.0,1.0,0.0,1.0,...|\n|     8|   221714|[4.80000019073486...|\n|     8|   186334|[106.0,1.0,0.0,1....|\n|     8|   169717|[2.20000004768371...|\n|     8|    31314|[15.8000001907348...|\n|     8|   815312|[2.29999995231628...|\n|     8|   199445|[5.0,1.0,0.0,1.0,...|\n|     8|   746178|[16.7999992370605...|\n|     8|   290950|[6.5,1.0,0.0,1.0,...|\n|     8|   221585|[18.5,1.0,0.0,1.0...|\n|     8|   692672|[47.0,1.0,0.0,1.0...|\n|     8|   797982|[33.0,1.0,0.0,1.0...|\n|     8|   815219|[2.40000009536743...|\n+------+---------+--------------------+\nonly showing top 20 rows\n\nprediction = CTR_model.transform(datasets).sort(\"probability\")\nprediction.show()\n+------+---------+--------------------+--------------------+--------------------+----------+\n|userId|adgroupId|            features|       rawPrediction|         probability|prediction|\n+------+---------+--------------------+--------------------+--------------------+----------+\n|     8|   631204|[19888.0,1.0,0.0,...|[2.69001234046578...|[0.93643471623189...|       0.0|\n|     8|   583215|[3750.0,1.0,0.0,1...|[2.69016170680037...|[0.93644360664433...|       0.0|\n|     8|   275819|[3280.0,1.0,0.0,1...|[2.69016605691669...|[0.93644386554961...|       0.0|\n|     8|   401433|[1200.0,1.0,0.0,1...|[2.69018530849532...|[0.93644501133142...|       0.0|\n|     8|    29466|[640.0,1.0,0.0,1....|[2.69019049161265...|[0.93644531980785...|       0.0|\n|     8|   173327|[356.0,1.0,0.0,1....|[2.69019312019358...|[0.93644547624893...|       0.0|\n|     8|   241402|[269.0,1.0,0.0,1....|[2.69019392542787...|[0.93644552417271...|       0.0|\n|     8|   351366|[246.0,1.0,0.0,1....|[2.69019413830591...|[0.93644553684221...|       0.0|\n|     8|   229827|[238.0,1.0,0.0,1....|[2.69019421235044...|[0.93644554124900...|       0.0|\n|     8|   164807|[228.0,1.0,0.0,1....|[2.69019430490611...|[0.93644554675747...|       0.0|\n|     8|   227731|[199.0,1.0,0.0,1....|[2.69019457331754...|[0.93644556273205...|       0.0|\n|     8|   265403|[198.0,1.0,0.0,1....|[2.69019458257311...|[0.93644556328290...|       0.0|\n|     8|   569939|[188.0,1.0,0.0,1....|[2.69019467512877...|[0.93644556879138...|       0.0|\n|     8|   277335|[181.5,1.0,0.0,1....|[2.69019473528996...|[0.93644557237189...|       0.0|\n|     8|   575633|[180.0,1.0,0.0,1....|[2.69019474917331...|[0.93644557319816...|       0.0|\n|     8|   201867|[179.0,1.0,0.0,1....|[2.69019475842887...|[0.93644557374900...|       0.0|\n|     8|    25542|[176.0,1.0,0.0,1....|[2.69019478619557...|[0.93644557540155...|       0.0|\n|     8|   133457|[169.0,1.0,0.0,1....|[2.69019485098454...|[0.93644557925748...|       0.0|\n|     8|   494224|[169.0,1.0,0.0,1....|[2.69019485098454...|[0.93644557925748...|       0.0|\n|     8|   339382|[163.0,1.0,0.0,1....|[2.69019490651794...|[0.93644558256256...|       0.0|\n+------+---------+--------------------+--------------------+--------------------+----------+\nonly showing top 20 rows\n\n\nTOP-20\n\n# TOP-20\nprediction.select(\"adgroupId\").head(20)\n\n显示结果:\n[Row(adgroupId=631204),\n Row(adgroupId=583215),\n Row(adgroupId=275819),\n Row(adgroupId=401433),\n Row(adgroupId=29466),\n Row(adgroupId=173327),\n Row(adgroupId=241402),\n Row(adgroupId=351366),\n Row(adgroupId=229827),\n Row(adgroupId=164807),\n Row(adgroupId=227731),\n Row(adgroupId=265403),\n Row(adgroupId=569939),\n Row(adgroupId=277335),\n Row(adgroupId=575633),\n Row(adgroupId=201867),\n Row(adgroupId=25542),\n Row(adgroupId=133457),\n Row(adgroupId=494224),\n Row(adgroupId=339382)]\n\n[i.adgroupId for i in prediction.select(\"adgroupId\").head(20)]\n显示结果:\n[631204,\n 583215,\n 275819,\n 401433,\n 29466,\n 173327,\n 241402,\n 351366,\n 229827,\n 164807,\n 227731,\n 265403,\n 569939,\n 277335,\n 575633,\n 201867,\n 25542,\n 133457,\n 494224,\n 339382]\n\n"}}}