转】Mahout构建图书推荐系统

　　原博文出自于：　　http://blog.fens.me/hadoop-mahout-recommend-book/　　　　　　感谢！

Mahout构建图书推荐系统

Hadoop家族系列文章，主要介绍Hadoop家族产品，常用的项目包括Hadoop, Hive, Pig, HBase, Sqoop, Mahout, Zookeeper, Avro, Ambari, Chukwa，新增加的项目包括，YARN, Hcatalog, Oozie, Cassandra, Hama, Whirr, Flume, Bigtop, Crunch, Hue等。

从2011年开始，中国进入大数据风起云涌的时代，以Hadoop为代表的家族软件，占据了大数据处理的广阔地盘。开源界及厂商，所有数据软件，无一不向Hadoop靠拢。Hadoop也从小众的高富帅领域，变成了大数据开发的标准。在Hadoop原有技术基础之上，出现了Hadoop家族产品，通过“大数据”概念不断创新，推出科技进步。

作为IT界的开发人员，我们也要跟上节奏，抓住机遇，跟着Hadoop一起雄起！

关于作者：

张丹(Conan), 程序员Java,R,PHP,Javascript
weibo：@Conan_Z
blog: http://blog.fens.me
email: bsspirit@gmail.com

转载请注明出处：
http://blog.fens.me/hadoop-mahout-recommend-book/

前言

本文是Mahout实现推荐系统的又一案例，用Mahout构建图书推荐系统。与之前的两篇文章，思路上面类似，侧重点在于图书的属性如何利用。本文的数据在自于Amazon网站，由爬虫抓取获得。

项目背景
需求分析
数据说明
算法模型
程序开发

1. 项目背景

Amazon是最早的电子商务网站之一，以网上图书起家，最后发展成为音像，电子消费品，游戏，生活用品等的综合性电子商务平台。Amazon的推荐系统，是互联网上最早的商品推荐系统，它为Amazon带来了至少30%的流量，和可观的销售利润。

如今推荐系统已经成为电子商务网站的标配，如果还没有推荐系统都不好意思，说自己是做电商的。

2. 需求分析

推荐系统如此重要，我们应该如果理解？

打开Amazon的Mahout In Action图书页面：
http://www.amazon.com/Mahout-Action-Sean-Owen/dp/1935182684/ref=pd_sim_b_1?ie=UTF8&refRID=0H4H2NSSR8F34R76E2TP

网页上的元素：

广告位：广告商投放广告的位置，网站可以靠网络广告赚钱，一般是网页最好的位置。
平均分：用户对图书的打分
关联规则：通过关联规则，推荐位
协同过滤：通过基于物品的协同过滤算法的，推荐位
图书属性：包括页数，出版社，ISBN，语言等
作者介绍：有关作者的介绍，和作者的其他著作
用户评分：用户评分行为
用户评论：用户评论的内容

在网页上，其他的推荐位：

结合上面2张截图，我们不难发现，推荐对于Amazon的重要性。除了最明显的广告位给了能直接带来利润的广告商，网页中有4处推荐位，分别从不同的维度，用不同的推荐算法，猜用户喜欢的商品。

3. 数据说明

2个数据文件：

rating.csv ：用户评分行为数据
users.csv ：用户属性数据

1). book-ratings.csv

3列数据：用户ID，图书ID, 用户对图书的评分
记录数: 4000次的图书评分
用户数: 200个
图书数: 1000个
评分：1-10

数据示例


1,565,3
1,807,2
1,201,1
1,557,9
1,987,10
1,59,5
1,305,6
1,153,3
1,139,7
1,875,5
1,722,10
2,977,4
2,806,3
2,654,8
2,21,8
2,662,5
2,437,6
2,576,3
2,141,8
2,311,4
2,101,3
2,540,9
2,87,3
2,65,8
2,501,6
2,710,5
2,331,9
2,542,4
2,757,9
2,590,7

2). users.csv

3列数据：用户ID，用户性别，用户年龄
用户数: 200个
用户性别: M为男性，F为女性
用户年龄: 11-80岁之间

数据示例


1,M,40
2,M,27
3,M,41
4,F,43
5,F,16
6,M,36
7,F,36
8,F,46
9,M,50
10,M,21
11,F,11
12,M,42
13,F,40
14,F,28
15,M,25
16,M,68
17,M,53
18,F,69
19,F,48
20,F,56
21,F,36

4. 算法模型

本文主要介绍Mahout的基于物品的协同过滤模型，其他的算法模型将不再这里解释。

针对上面的数据，我将用7种算法组合进行测试：有关Mahout算法组合的详细解释，请参考文章：从源代码剖析Mahout推荐引擎

7种算法组合

userCF1: EuclideanSimilarity+ NearestNUserNeighborhood+ GenericUserBasedRecommender
userCF2: LogLikelihoodSimilarity+ NearestNUserNeighborhood+ GenericUserBasedRecommender
userCF3: EuclideanSimilarity+ NearestNUserNeighborhood+ GenericBooleanPrefUserBasedRecommender
itemCF1: EuclideanSimilarity + GenericItemBasedRecommender
itemCF2: LogLikelihoodSimilarity + GenericItemBasedRecommender
itemCF3: EuclideanSimilarity + GenericBooleanPrefItemBasedRecommender
slopeOne：SlopeOneRecommender

对上面的算法进行算法评估，有关于算法评估的详细解释，请参考文章：Mahout推荐算法API详解

查准率:
召回率(查全率):

5. 程序开发

系统架构：Mahout中推荐过滤算法支持单机算法和分步式算法两种。

单机算法: 在单机内存计算，支持多种算法推荐算法，部署运行简单，修正处理数据量有限
分步式算法: 基于Hadoop集群运行，支持有限的几种推荐算法，部署运行复杂，支持海量数据

开发环境

Win7 64bit
Java 1.6.0_45
Maven3
Eclipse Juno Service Release 2
Mahout-0.8
Hadoop-1.1.2

开发环境mahout版本为0.8。请参考文章：用Maven构建Mahout项目

新建Java类：

BookEvaluator.java, 选出“评估推荐器”验证得分较高的算法
BookResult.java, 对指定数量的结果人工比较
BookFilterGenderResult.java，只保留男性用户的图书列表

1). BookEvaluator.java, 选出“评估推荐器”验证得分较高的算法

源代码


package org.conan.mymahout.recommendation.book;

import java.io.IOException;

import org.apache.mahout.cf.taste.common.TasteException;
import org.apache.mahout.cf.taste.eval.RecommenderBuilder;
import org.apache.mahout.cf.taste.model.DataModel;
import org.apache.mahout.cf.taste.neighborhood.UserNeighborhood;
import org.apache.mahout.cf.taste.similarity.ItemSimilarity;
import org.apache.mahout.cf.taste.similarity.UserSimilarity;

public class BookEvaluator {

    final static int NEIGHBORHOOD_NUM = 2;
    final static int RECOMMENDER_NUM = 3;

    public static void main(String[] args) throws TasteException, IOException {
        String file = "datafile/book/rating.csv";
        DataModel dataModel = RecommendFactory.buildDataModel(file);
        userEuclidean(dataModel);
        userLoglikelihood(dataModel);
        userEuclideanNoPref(dataModel);
        itemEuclidean(dataModel);
        itemLoglikelihood(dataModel);
        itemEuclideanNoPref(dataModel);
        slopeOne(dataModel);
    }

    public static RecommenderBuilder userEuclidean(DataModel dataModel) throws TasteException, IOException {
        System.out.println("userEuclidean");
        UserSimilarity userSimilarity = RecommendFactory.userSimilarity(RecommendFactory.SIMILARITY.EUCLIDEAN, dataModel);
        UserNeighborhood userNeighborhood = RecommendFactory.userNeighborhood(RecommendFactory.NEIGHBORHOOD.NEAREST, userSimilarity, dataModel, NEIGHBORHOOD_NUM);
        RecommenderBuilder recommenderBuilder = RecommendFactory.userRecommender(userSimilarity, userNeighborhood, true);

        RecommendFactory.evaluate(RecommendFactory.EVALUATOR.AVERAGE_ABSOLUTE_DIFFERENCE, recommenderBuilder, null, dataModel, 0.7);
        RecommendFactory.statsEvaluator(recommenderBuilder, null, dataModel, 2);
        return recommenderBuilder;
    }
    
    public static RecommenderBuilder userLoglikelihood(DataModel dataModel) throws TasteException, IOException {
        System.out.println("userLoglikelihood");
        UserSimilarity userSimilarity = RecommendFactory.userSimilarity(RecommendFactory.SIMILARITY.LOGLIKELIHOOD, dataModel);
        UserNeighborhood userNeighborhood = RecommendFactory.userNeighborhood(RecommendFactory.NEIGHBORHOOD.NEAREST, userSimilarity, dataModel, NEIGHBORHOOD_NUM);
        RecommenderBuilder recommenderBuilder = RecommendFactory.userRecommender(userSimilarity, userNeighborhood, true);

        RecommendFactory.evaluate(RecommendFactory.EVALUATOR.AVERAGE_ABSOLUTE_DIFFERENCE, recommenderBuilder, null, dataModel, 0.7);
        RecommendFactory.statsEvaluator(recommenderBuilder, null, dataModel, 2);
        return recommenderBuilder;
    }
    
    public static RecommenderBuilder userEuclideanNoPref(DataModel dataModel) throws TasteException, IOException {
        System.out.println("userEuclideanNoPref");
        UserSimilarity userSimilarity = RecommendFactory.userSimilarity(RecommendFactory.SIMILARITY.EUCLIDEAN, dataModel);
        UserNeighborhood userNeighborhood = RecommendFactory.userNeighborhood(RecommendFactory.NEIGHBORHOOD.NEAREST, userSimilarity, dataModel, NEIGHBORHOOD_NUM);
        RecommenderBuilder recommenderBuilder = RecommendFactory.userRecommender(userSimilarity, userNeighborhood, false);

        RecommendFactory.evaluate(RecommendFactory.EVALUATOR.AVERAGE_ABSOLUTE_DIFFERENCE, recommenderBuilder, null, dataModel, 0.7);
        RecommendFactory.statsEvaluator(recommenderBuilder, null, dataModel, 2);
        return recommenderBuilder;
    }

    public static RecommenderBuilder itemEuclidean(DataModel dataModel) throws TasteException, IOException {
        System.out.println("itemEuclidean");
        ItemSimilarity itemSimilarity = RecommendFactory.itemSimilarity(RecommendFactory.SIMILARITY.EUCLIDEAN, dataModel);
        RecommenderBuilder recommenderBuilder = RecommendFactory.itemRecommender(itemSimilarity, true);

        RecommendFactory.evaluate(RecommendFactory.EVALUATOR.AVERAGE_ABSOLUTE_DIFFERENCE, recommenderBuilder, null, dataModel, 0.7);
        RecommendFactory.statsEvaluator(recommenderBuilder, null, dataModel, 2);
        return recommenderBuilder;
    }

    public static RecommenderBuilder itemLoglikelihood(DataModel dataModel) throws TasteException, IOException {
        System.out.println("itemLoglikelihood");
        ItemSimilarity itemSimilarity = RecommendFactory.itemSimilarity(RecommendFactory.SIMILARITY.LOGLIKELIHOOD, dataModel);
        RecommenderBuilder recommenderBuilder = RecommendFactory.itemRecommender(itemSimilarity, true);

        RecommendFactory.evaluate(RecommendFactory.EVALUATOR.AVERAGE_ABSOLUTE_DIFFERENCE, recommenderBuilder, null, dataModel, 0.7);
        RecommendFactory.statsEvaluator(recommenderBuilder, null, dataModel, 2);
        return recommenderBuilder;
    }
    
    public static RecommenderBuilder itemEuclideanNoPref(DataModel dataModel) throws TasteException, IOException {
        System.out.println("itemEuclideanNoPref");
        ItemSimilarity itemSimilarity = RecommendFactory.itemSimilarity(RecommendFactory.SIMILARITY.EUCLIDEAN, dataModel);
        RecommenderBuilder recommenderBuilder = RecommendFactory.itemRecommender(itemSimilarity, false);

        RecommendFactory.evaluate(RecommendFactory.EVALUATOR.AVERAGE_ABSOLUTE_DIFFERENCE, recommenderBuilder, null, dataModel, 0.7);
        RecommendFactory.statsEvaluator(recommenderBuilder, null, dataModel, 2);
        return recommenderBuilder;
    }

    public static RecommenderBuilder slopeOne(DataModel dataModel) throws TasteException, IOException {
        System.out.println("slopeOne");
        RecommenderBuilder recommenderBuilder = RecommendFactory.slopeOneRecommender();

        RecommendFactory.evaluate(RecommendFactory.EVALUATOR.AVERAGE_ABSOLUTE_DIFFERENCE, recommenderBuilder, null, dataModel, 0.7);
        RecommendFactory.statsEvaluator(recommenderBuilder, null, dataModel, 2);
        return recommenderBuilder;
    }
}

控制台输出：


userEuclidean
AVERAGE_ABSOLUTE_DIFFERENCE Evaluater Score:0.33333325386047363
Recommender IR Evaluator: [Precision:0.3010752688172043,Recall:0.08542713567839195]
userLoglikelihood
AVERAGE_ABSOLUTE_DIFFERENCE Evaluater Score:2.5245869159698486
Recommender IR Evaluator: [Precision:0.11764705882352945,Recall:0.017587939698492466]
userEuclideanNoPref
AVERAGE_ABSOLUTE_DIFFERENCE Evaluater Score:4.288461538461536
Recommender IR Evaluator: [Precision:0.09045226130653267,Recall:0.09296482412060306]
itemEuclidean
AVERAGE_ABSOLUTE_DIFFERENCE Evaluater Score:1.408880928305655
Recommender IR Evaluator: [Precision:0.0,Recall:0.0]
itemLoglikelihood
AVERAGE_ABSOLUTE_DIFFERENCE Evaluater Score:2.448554412835434
Recommender IR Evaluator: [Precision:0.0,Recall:0.0]
itemEuclideanNoPref
AVERAGE_ABSOLUTE_DIFFERENCE Evaluater Score:2.5665197873957957
Recommender IR Evaluator: [Precision:0.6005025125628134,Recall:0.6055276381909548]
slopeOne
AVERAGE_ABSOLUTE_DIFFERENCE Evaluater Score:2.6893078179405814
Recommender IR Evaluator: [Precision:0.0,Recall:0.0]

可视化“评估推荐器”输出：

推荐的结果的平均距离

推荐器的评分

只有itemEuclideanNoPref算法评估的结果是非常好的，其他算法的结果都不太好。

2). BookResult.java, 对指定数量的结果人工比较

为得到差异化结果，我们分别取4个算法：userEuclidean,itemEuclidean，userEuclideanNoPref，itemEuclideanNoPref，对推荐结果人工比较。

源代码


package org.conan.mymahout.recommendation.book;

import java.io.IOException;
import java.util.List;

import org.apache.mahout.cf.taste.common.TasteException;
import org.apache.mahout.cf.taste.eval.RecommenderBuilder;
import org.apache.mahout.cf.taste.impl.common.LongPrimitiveIterator;
import org.apache.mahout.cf.taste.model.DataModel;
import org.apache.mahout.cf.taste.recommender.RecommendedItem;

public class BookResult {

    final static int NEIGHBORHOOD_NUM = 2;
    final static int RECOMMENDER_NUM = 3;

    public static void main(String[] args) throws TasteException, IOException {
        String file = "datafile/book/rating.csv";
        DataModel dataModel = RecommendFactory.buildDataModel(file);
        RecommenderBuilder rb1 = BookEvaluator.userEuclidean(dataModel);
        RecommenderBuilder rb2 = BookEvaluator.itemEuclidean(dataModel);
        RecommenderBuilder rb3 = BookEvaluator.userEuclideanNoPref(dataModel);
        RecommenderBuilder rb4 = BookEvaluator.itemEuclideanNoPref(dataModel);
        
        LongPrimitiveIterator iter = dataModel.getUserIDs();
        while (iter.hasNext()) {
            long uid = iter.nextLong();
            System.out.print("userEuclidean       =>");
            result(uid, rb1, dataModel);
            System.out.print("itemEuclidean       =>");
            result(uid, rb2, dataModel);
            System.out.print("userEuclideanNoPref =>");
            result(uid, rb3, dataModel);
            System.out.print("itemEuclideanNoPref =>");
            result(uid, rb4, dataModel);
        }
    }

    public static void result(long uid, RecommenderBuilder recommenderBuilder, DataModel dataModel) throws TasteException {
        List list = recommenderBuilder.buildRecommender(dataModel).recommend(uid, RECOMMENDER_NUM);
        RecommendFactory.showItems(uid, list, false);
    }
}

控制台输出：只截取部分结果


...
userEuclidean       =>uid:63,
itemEuclidean       =>uid:63,(984,9.000000)(690,9.000000)(943,8.875000)
userEuclideanNoPref =>uid:63,(4,1.000000)(723,1.000000)(300,1.000000)
itemEuclideanNoPref =>uid:63,(867,3.791667)(947,3.083333)(28,2.750000)
userEuclidean       =>uid:64,
itemEuclidean       =>uid:64,(368,8.615385)(714,8.200000)(290,8.142858)
userEuclideanNoPref =>uid:64,(860,1.000000)(490,1.000000)(64,1.000000)
itemEuclideanNoPref =>uid:64,(409,3.950000)(715,3.830627)(901,3.444048)
userEuclidean       =>uid:65,(939,7.000000)
itemEuclidean       =>uid:65,(550,9.000000)(334,9.000000)(469,9.000000)
userEuclideanNoPref =>uid:65,(939,2.000000)(185,1.000000)(736,1.000000)
itemEuclideanNoPref =>uid:65,(666,4.166667)(96,3.093931)(345,2.958333)
userEuclidean       =>uid:66,
itemEuclidean       =>uid:66,(971,9.900000)(656,9.600000)(918,9.577709)
userEuclideanNoPref =>uid:66,(6,1.000000)(492,1.000000)(676,1.000000)
itemEuclideanNoPref =>uid:66,(185,3.650000)(533,3.617307)(172,3.500000)
userEuclidean       =>uid:67,
itemEuclidean       =>uid:67,(663,9.700000)(987,9.625000)(486,9.600000)
userEuclideanNoPref =>uid:67,(732,1.000000)(828,1.000000)(113,1.000000)
itemEuclideanNoPref =>uid:67,(724,3.000000)(279,2.950000)(890,2.750000)
...

我们查看uid=65的用户推荐信息：

查看user.csv数据集


> user[65,]
userid gender age
65     65      M  14

用户65，男性，14岁。

以itemEuclideanNoPref的算法的推荐结果，查看bookid=666的图书评分情况


> rating[which(rating$bookid==666),]
userid bookid pref
646      44    666   10
1327     89    666    7
2470    165    666    3
2697    179    666    7

发现有4个用户对666的图书评分，查看这4个用户的属性数据


> user[c(44,89,165,179),]
userid gender age
44      44      F  76
89      89      M  40
165    165      F  59
179    179      F  68

这4个用户，3女1男。

我们假设男性和男性有相同的图书兴趣，女性和女性有相同的图书偏好。因为用户65是男性，所以我们接下来排除女性的评分者，只保留男性评分者的评分记录。

3). BookFilterGenderResult.java，只保留男性用户的图书列表

源代码


package org.conan.mymahout.recommendation.book;

import java.io.BufferedReader;
import java.io.File;
import java.io.FileReader;
import java.io.IOException;
import java.util.HashSet;
import java.util.List;
import java.util.Set;

import org.apache.mahout.cf.taste.common.TasteException;
import org.apache.mahout.cf.taste.eval.RecommenderBuilder;
import org.apache.mahout.cf.taste.impl.common.LongPrimitiveIterator;
import org.apache.mahout.cf.taste.model.DataModel;
import org.apache.mahout.cf.taste.recommender.IDRescorer;
import org.apache.mahout.cf.taste.recommender.RecommendedItem;

public class BookFilterGenderResult {

    final static int NEIGHBORHOOD_NUM = 2;
    final static int RECOMMENDER_NUM = 3;

    public static void main(String[] args) throws TasteException, IOException {
        String file = "datafile/book/rating.csv";
        DataModel dataModel = RecommendFactory.buildDataModel(file);
        RecommenderBuilder rb1 = BookEvaluator.userEuclidean(dataModel);
        RecommenderBuilder rb2 = BookEvaluator.itemEuclidean(dataModel);
        RecommenderBuilder rb3 = BookEvaluator.userEuclideanNoPref(dataModel);
        RecommenderBuilder rb4 = BookEvaluator.itemEuclideanNoPref(dataModel);
        
        long uid = 65;
        System.out.print("userEuclidean       =>");
        filterGender(uid, rb1, dataModel);
        System.out.print("itemEuclidean       =>");
        filterGender(uid, rb2, dataModel);
        System.out.print("userEuclideanNoPref =>");
        filterGender(uid, rb3, dataModel);
        System.out.print("itemEuclideanNoPref =>");
        filterGender(uid, rb4, dataModel);
    }

    /**
     * 对用户性别进行过滤
     */
    public static void filterGender(long uid, RecommenderBuilder recommenderBuilder, DataModel dataModel) throws TasteException, IOException {
        Set userids = getMale("datafile/book/user.csv");

        //计算男性用户打分过的图书
        Set bookids = new HashSet();
        for (long uids : userids) {
            LongPrimitiveIterator iter = dataModel.getItemIDsFromUser(uids).iterator();
            while (iter.hasNext()) {
                long bookid = iter.next();
                bookids.add(bookid);
            }
        }

        IDRescorer rescorer = new FilterRescorer(bookids);
        List list = recommenderBuilder.buildRecommender(dataModel).recommend(uid, RECOMMENDER_NUM, rescorer);
        RecommendFactory.showItems(uid, list, false);
    }

    /**
     * 获得男性用户ID
     */
    public static Set getMale(String file) throws IOException {
        BufferedReader br = new BufferedReader(new FileReader(new File(file)));
        Set userids = new HashSet();
        String s = null;
        while ((s = br.readLine()) != null) {
            String[] cols = s.split(",");
            if (cols[1].equals("M")) {// 判断男性用户
                userids.add(Long.parseLong(cols[0]));
            }
        }
        br.close();
        return userids;
    }

}

/**
 * 对结果重计算
 */
class FilterRescorer implements IDRescorer {
    final private Set userids;

    public FilterRescorer(Set userids) {
        this.userids = userids;
    }

    @Override
    public double rescore(long id, double originalScore) {
        return isFiltered(id) ? Double.NaN : originalScore;
    }

    @Override
    public boolean isFiltered(long id) {
        return userids.contains(id);
    }
}

控制台输出:


userEuclidean       =>uid:65,
itemEuclidean       =>uid:65,(784,8.090909)(276,8.000000)(476,7.666667)
userEuclideanNoPref =>uid:65,
itemEuclideanNoPref =>uid:65,(887,2.250000)(356,2.166667)(430,1.866667)

我们发现，由于只保留男性的评分记录，数据量就变得比较少了，基于用户的协同过滤算法，已经没有输出的结果了。基于物品的协同过滤算法，结果集也有所变化。

对于itemEuclideanNoPref算法，输出排名第一条为ID为887的图书。

我再进一步向下追踪：查询哪些用户对图书887进行了打分。


> rating[which(rating$bookid==887),]
userid bookid pref
1280     85    887    2
1743    119    887    8
2757    184    887    4
2791    186    887    5

有4个用户对图书887评分，再分别查看这个用户的属性


> user[c(85,119,184,186),]
userid gender age
85      85      F  31
119    119      F  49
184    184      M  27
186    186      M  35

其中2男，2女。由于我们的算法，已经排除了女性的评分，我们可以推断图书887的推荐应该来自于2个男性的评分者的推荐。

分别计算用户65，与用户184和用户186的评分的图书交集。


rat65<-rating[which(rating$userid==65),]
rat184<-rating[which(rating$userid==184),]
rat186<-rating[which(rating$userid==186),]

> intersect(rat65$bookid ,rat184$bookid)
integer(0)
> intersect(rat65$bookid ,rat186$bookid)
[1]  65 375

最后发现，用户65与用户186都给图书65和图书375打过分。我们再打分出用户186的评分记录。


> rat186
userid bookid pref
2790    186     65    7
2791    186    887    5
2792    186    529    3
2793    186    375    6
2794    186    566    7
2795    186    169    4
2796    186    907    1
2797    186    821    2
2798    186    720    5
2799    186    642    5
2800    186    137    3
2801    186    744    1
2802    186    896    2
2803    186    156    6
2804    186    392    3
2805    186    386    3
2806    186    901    7
2807    186     69    6
2808    186    845    6
2809    186    998    3

用户186，还给图书887打过分，所以对于给65用户推荐图书887，是合理的。

我们通过一个实际的图书推荐的案例，更进一步地了解了如何用Mahout构建推荐系统。

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看文字不过瘾，作者视频讲解，请访问网站：http://onbook.me/video
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转载请注明出处：
http://blog.fens.me/hadoop-mahout-recommend-book/

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原文地址：https://www.cnblogs.com/zlslch/p/6039715.html