用Hadoop构建电影推荐系统

[align=left]问题导读：[/align]

1.
推荐系统概述；
[align=left]2. 推荐系统指标设计；[/align]
[align=left]3. Hadoop并行算法；[/align]
[align=left]4. 推荐系统架构；[/align]
[align=left]5. MapReduce程序实现。[/align]



[align=left]前言[/align]
[align=left]Netflix电影推荐的百万美金比赛，把“推荐”变成了时下最热门的数据挖掘算法之一。也正是由于Netflix的比赛，让企业界和学科界有了更深层次的技术碰撞。引发了各种网站“推荐”热，个性时代已经到来。[/align]

一、 推荐系统概述
[align=left]电子商务网站是个性化推荐系统重要地应用的领域之一，亚马逊就是个性化推荐系统的积极应用者和推广者，亚马逊的推荐系统深入到网站的各类商品，为亚马逊带来了至少30%的销售额。[/align]
[align=left]不光是电商类，推荐系统无处不在。QQ，人人网的好友推荐；新浪微博的你可能感觉兴趣的人；优酷，土豆的电影推荐；豆瓣的图书推荐；大从点评的餐饮推荐；世纪佳缘的相亲推荐；天际网的职业推荐等。[/align]

[align=left]推荐算法分类：[/align]

[align=left]按数据使用划分：[/align]
[align=left]协同过滤算法：UserCF, ItemCF, ModelCF[/align]
[align=left]基于内容的推荐: 用户内容属性和物品内容属性[/align]
[align=left]社会化过滤：基于用户的社会网络关系[/align]

[align=left]按模型划分：[/align]
[align=left]最近邻模型:基于距离的协同过滤算法[/align]
[align=left]Latent Factor Mode(SVD)：基于矩阵分解的模型[/align]
[align=left]Graph：图模型，社会网络图模型[/align]

[align=left]基于用户的协同过滤算法UserCF[/align]
[align=left]基于用户的协同过滤，通过不同用户对物品的评分来评测用户之间的相似性，基于用户之间的相似性做出推荐。简单来讲就是：给用户推荐和他兴趣相似的其他用户喜欢的物品。[/align]
[align=left]用例说明：[/align]



[align=left]算法实现及使用介绍，请参考文章：Mahout推荐算法API详解[/align]

[align=left]基于物品的协同过滤算法ItemCF[/align]
[align=left]基于item的协同过滤，通过用户对不同item的评分来评测item之间的相似性，基于item之间的相似性做出推荐。简单来讲就是：给用户推荐和他之前喜欢的物品相似的物品。[/align]
[align=left]用例说明：[/align]

[align=left]算法实现及使用介绍，请参考文章：Mahout推荐算法API详解[/align]
[align=left]注：基于物品的协同过滤算法，是目前商用最广泛的推荐算法。[/align]

[align=left]协同过滤算法实现，分为2个步骤[/align]

1. 计算物品之间的相似度
2. 根据物品的相似度和用户的历史行为给用户生成推荐列表

[align=left]有关协同过滤的另一篇文章，请参考：RHadoop实践系列之三 R实现MapReduce的协同过滤算法[/align]

二、 需求分析：推荐系统指标设计

[align=left]下面我们将从一个公司案例出发来全面的解释，如何进行推荐系统指标设计。[/align]

[align=left]案例介绍[/align]
Netflix电影推荐百万奖金比赛，http://www.netflixprize.com/

Netflix官方网站：www.netflix.com

[align=left]Netflix，2006年组织比赛是的时候，是一家以在线电影租赁为生的公司。他们根据网友对电影的打分来判断用户有可能喜欢什么电影，并结合会员看过的电影以及口味偏好设置做出判断，混搭出各种电影风格的需求。[/align]
[align=left]收集会员的一些信息，为他们指定个性化的电影推荐后，有许多冷门电影竟然进入了候租榜单。从公司的电影资源成本方面考量，热门电影的成本一般较高，如果Netflix公司能够在电影租赁中增加冷门电影的比例，自然能够提升自身盈利能力。[/align]
[align=left]Netflix公司曾宣称60%左右的会员根据推荐名单定制租赁顺序，如果推荐系统不能准确地猜测会员喜欢的电影类型，容易造成多次租借冷门电影而并不符合个人口味的会员流失。为了更高效地为会员推荐电影，Netflix一直致力于不断改进和完善个性化推荐服务，在2006年推出百万美元大奖，无论是谁能最好地优化Netflix推荐算法就可获奖励100万美元。到2009年，奖金被一个7人开发小组夺得，Netflix随后又立即推出第二个百万美金悬赏。这充分说明一套好的推荐算法系统是多么重要，同时又是多么困难。[/align]



[align=left]上图为比赛的各支队伍的排名！[/align]

[align=left]补充说明：[/align]
[align=left]1. Netflix的比赛是基于静态数据的，就是给定“训练级”，匹配“结果集”，“结果集”也是提前就做好的，所以这与我们每天运营的系统，其实是不一样的。[/align]
[align=left]2. Netflix用于比赛的数据集是小量的，整个全集才666MB，而实际的推荐系统都要基于大量历史数据的，动不动就会上GB,TB等[/align]

Netflix数据下载

部分训练集：http://graphlab.org/wp-content/uploads/2013/07/smallnetflix_mm.train_.gz

部分结果集：http://graphlab.org/wp-content/uploads/2013/07/smallnetflix_mm.validate.gz

完整数据集：http://www.lifecrunch.biz/wp-content/uploads/2011/04/nf_prize_dataset.tar.gz
[align=left]所以，我们在真实的环境中设计推荐的时候，要全面考量数据量，算法性能，结果准确度等的指标。[/align]

[align=left]推荐算法选型：基于物品的协同过滤算法ItemCF，并行实现[/align]
[align=left]数据量：基于Hadoop架构，支持GB,TB,PB级数据量[/align]
[align=left]算法检验：可以通过 准确率，召回率，覆盖率，流行度 等指标评判。[/align]
[align=left]结果解读：通过ItemCF的定义，合理给出结果解释[/align]

三、 算法模型：Hadoop并行算法

[align=left]这里我使用”Mahout In Action”书里，第一章第六节介绍的分步式基于物品的协同过滤算法进行实现。Chapter 6: Distributing recommendation computations[/align]
[align=left]测试数据集:small.csv[/align]

1,101,5.0

1,102,3.0

1,103,2.5

2,101,2.0

2,102,2.5

2,103,5.0

2,104,2.0

3,101,2.0

3,104,4.0

3,105,4.5

3,107,5.0

4,101,5.0

4,103,3.0

4,104,4.5

4,106,4.0

5,101,4.0

5,102,3.0

5,103,2.0

5,104,4.0

5,105,3.5

5,106,4.0

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[align=left]每行3个字段，依次是用户ID,电影ID,用户对电影的评分(0-5分，每0.5为一个评分点！)[/align]
[align=left]算法的思想：[/align]
[align=left]1. 建立物品的同现矩阵[/align]
[align=left]2. 建立用户对物品的评分矩阵[/align]
[align=left]3. 矩阵计算推荐结果[/align]

[align=left]1). 建立物品的同现矩阵[/align]
[align=left]按用户分组，找到每个用户所选的物品，单独出现计数及两两一组计数。[/align]

[101] [102] [103] [104] [105] [106] [107]

[101] 5 3 4 4 2 2 1

[102] 3 3 3 2 1 1 0

[103] 4 3 4 3 1 2 0

[104] 4 2 3 4 2 2 1

[105] 2 1 1 2 2 1 1

[106] 2 1 2 2 1 2 0

[107] 1 0 0 1 1 0 1

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[align=left]2). 建立用户对物品的评分矩阵[/align]
[align=left]按用户分组，找到每个用户所选的物品及评分[/align]

U3

[101] 2.0

[102] 0.0

[103] 0.0

[104] 4.0

[105] 4.5

[106] 0.0

[107] 5.0

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[align=left]3). 矩阵计算推荐结果[/align]
[align=left]同现矩阵*评分矩阵=推荐结果[/align]



[align=left]图片摘自”Mahout In Action”[/align]

[align=left]MapReduce任务设计[/align]



[align=left]图片摘自”Mahout In Action”[/align]

[align=left]解读MapRduce任务：[/align]
[align=left]步骤1: 按用户分组，计算所有物品出现的组合列表，得到用户对物品的评分矩阵[/align]
[align=left]步骤2: 对物品组合列表进行计数，建立物品的同现矩阵[/align]
[align=left]步骤3: 合并同现矩阵和评分矩阵[/align]
[align=left]步骤4: 计算推荐结果列表[/align]

四、 架构设计：推荐系统架构









[align=left]上图中，左边是Application业务系统，右边是Hadoop的HDFS, MapReduce。[/align]

业务系统记录了用户的行为和对物品的打分
设置系统定时器CRON，每xx小时，增量向HDFS导入数据(userid,itemid,value,time)。
完成导入后，设置系统定时器，启动MapReduce程序，运行推荐算法。
完成计算后，设置系统定时器，从HDFS导出推荐结果数据到数据库，方便以后的及时查询。

五、 程序开发：MapReduce程序实现

[align=left]win7的开发环境 和 Hadoop的运行环境 ，请参考文章：用Maven构建Hadoop项目[/align]
[align=left]新建Java类：[/align]
[align=left]Recommend.java，主任务启动程序[/align]
[align=left]Step1.java，按用户分组，计算所有物品出现的组合列表，得到用户对物品的评分矩阵[/align]
[align=left]Step2.java，对物品组合列表进行计数，建立物品的同现矩阵[/align]
[align=left]Step3.java，合并同现矩阵和评分矩阵[/align]
[align=left]Step4.java，计算推荐结果列表[/align]
[align=left]HdfsDAO.java，HDFS操作工具类[/align]

[align=left]1). Recommend.java，主任务启动程序[/align]
[align=left]源代码：[/align]

package org.conan.myhadoop.recommend;

import java.util.HashMap;

import java.util.Map;

import java.util.regex.Pattern;

import org.apache.hadoop.mapred.JobConf;

public class Recommend {

public static final String HDFS = "hdfs://192.168.1.210:9000";

public static final Pattern DELIMITER = Pattern.compile("[\t,]");

public static void main(String[] args) throws Exception {

Map<String, String> path = new HashMap<String, String>();

path.put("data", "logfile/small.csv");

path.put("Step1Input", HDFS + "/user/hdfs/recommend");

path.put("Step1Output", path.get("Step1Input") + "/step1");

path.put("Step2Input", path.get("Step1Output"));

path.put("Step2Output", path.get("Step1Input") + "/step2");

path.put("Step3Input1", path.get("Step1Output"));

path.put("Step3Output1", path.get("Step1Input") + "/step3_1");

path.put("Step3Input2", path.get("Step2Output"));

path.put("Step3Output2", path.get("Step1Input") + "/step3_2");

path.put("Step4Input1", path.get("Step3Output1"));

path.put("Step4Input2", path.get("Step3Output2"));

path.put("Step4Output", path.get("Step1Input") + "/step4");

Step1.run(path);

Step2.run(path);

Step3.run1(path);

Step3.run2(path);

Step4.run(path);

System.exit(0);

}

public static JobConf config() {

JobConf conf = new JobConf(Recommend.class);

conf.setJobName("Recommend");

conf.addResource("classpath:/hadoop/core-site.xml");

conf.addResource("classpath:/hadoop/hdfs-site.xml");

conf.addResource("classpath:/hadoop/mapred-site.xml");

return conf;

}

}

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</blockquote></div></font></div><div align="left" style="font-size: 13px;"><font color="#4d4d4f"><b>2). Step1.java，按用户分组，计算所有物品出现的组合列表，得到用户对物品的评分矩阵</b></font></div>

<div align="left" style="font-size: 13px;"><font color="#4d4d4f">源代码：</font></div><div align="left" style="font-size: 13px;"><span style="color: rgb(77, 77, 79); line-height: 1.5;"><div class="blockcode"><blockquote>package org.conan.myhadoop.recommend;

import java.io.IOException;

import java.util.Iterator;

import java.util.Map;

import org.apache.hadoop.fs.Path;

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapred.FileInputFormat;

import org.apache.hadoop.mapred.FileOutputFormat;

import org.apache.hadoop.mapred.JobClient;

import org.apache.hadoop.mapred.JobConf;

import org.apache.hadoop.mapred.MapReduceBase;

import org.apache.hadoop.mapred.Mapper;

import org.apache.hadoop.mapred.OutputCollector;

import org.apache.hadoop.mapred.Reducer;

import org.apache.hadoop.mapred.Reporter;

import org.apache.hadoop.mapred.RunningJob;

import org.apache.hadoop.mapred.TextInputFormat;

import org.apache.hadoop.mapred.TextOutputFormat;

import org.conan.myhadoop.hdfs.HdfsDAO;

public class Step1 {

public static class Step1_ToItemPreMapper extends MapReduceBase implements Mapper<Object, Text, IntWritable, Text> {

private final static IntWritable k = new IntWritable();

private final static Text v = new Text();

@Override

public void map(Object key, Text value, OutputCollector<IntWritable, Text> output, Reporter reporter) throws IOException {

String[] tokens = Recommend.DELIMITER.split(value.toString());

int userID = Integer.parseInt(tokens[0]);

String itemID = tokens[1];

String pref = tokens[2];

k.set(userID);

v.set(itemID + ":" + pref);

output.collect(k, v);

}

}

public static class Step1_ToUserVectorReducer extends MapReduceBase implements Reducer<IntWritable, Text, IntWritable, Text> {

private final static Text v = new Text();

@Override

public void reduce(IntWritable key, Iterator values, OutputCollector<IntWritable, Text> output, Reporter reporter) throws IOException {

StringBuilder sb = new StringBuilder();

while (values.hasNext()) {

sb.append("," + values.next());

}

v.set(sb.toString().replaceFirst(",", ""));

output.collect(key, v);

}

}

public static void run(Map<String, String> path) throws IOException {

JobConf conf = Recommend.config();

String input = path.get("Step1Input");

String output = path.get("Step1Output");

HdfsDAO hdfs = new HdfsDAO(Recommend.HDFS, conf);

hdfs.rmr(input);

hdfs.mkdirs(input);

hdfs.copyFile(path.get("data"), input);

conf.setMapOutputKeyClass(IntWritable.class);

conf.setMapOutputValueClass(Text.class);

conf.setOutputKeyClass(IntWritable.class);

conf.setOutputValueClass(Text.class);

conf.setMapperClass(Step1_ToItemPreMapper.class);

conf.setCombinerClass(Step1_ToUserVectorReducer.class);

conf.setReducerClass(Step1_ToUserVectorReducer.class);

conf.setInputFormat(TextInputFormat.class);

conf.setOutputFormat(TextOutputFormat.class);

FileInputFormat.setInputPaths(conf, new Path(input));

FileOutputFormat.setOutputPath(conf, new Path(output));

RunningJob job = JobClient.runJob(conf);

while (!job.isComplete()) {

job.waitForCompletion();

}

}

}

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[align=left]计算结果：[/align]

~ hadoop fs -cat /user/hdfs/recommend/step1/part-00000

1 102:3.0,103:2.5,101:5.0

2 101:2.0,102:2.5,103:5.0,104:2.0

3 107:5.0,101:2.0,104:4.0,105:4.5

4 101:5.0,103:3.0,104:4.5,106:4.0

5 101:4.0,102:3.0,103:2.0,104:4.0,105:3.5,106:4.0

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[align=left]3). Step2.java，对物品组合列表进行计数，建立物品的同现矩阵[/align]
[align=left]源代码：[/align]

package org.conan.myhadoop.recommend;

import java.io.IOException;

import java.util.Iterator;

import java.util.Map;

import org.apache.hadoop.fs.Path;

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.io.LongWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapred.FileInputFormat;

import org.apache.hadoop.mapred.FileOutputFormat;

import org.apache.hadoop.mapred.JobClient;

import org.apache.hadoop.mapred.JobConf;

import org.apache.hadoop.mapred.MapReduceBase;

import org.apache.hadoop.mapred.Mapper;

import org.apache.hadoop.mapred.OutputCollector;

import org.apache.hadoop.mapred.Reducer;

import org.apache.hadoop.mapred.Reporter;

import org.apache.hadoop.mapred.RunningJob;

import org.apache.hadoop.mapred.TextInputFormat;

import org.apache.hadoop.mapred.TextOutputFormat;

import org.conan.myhadoop.hdfs.HdfsDAO;

public class Step2 {

public static class Step2_UserVectorToCooccurrenceMapper extends MapReduceBase implements Mapper<LongWritable, Text, Text, IntWritable> {

private final static Text k = new Text();

private final static IntWritable v = new IntWritable(1);

@Override

public void map(LongWritable key, Text values, OutputCollector<Text, IntWritable> output, Reporter reporter) throws IOException {

String[] tokens = Recommend.DELIMITER.split(values.toString());

for (int i = 1; i < tokens.length; i++) {

String itemID = tokens[i].split(":")[0];

for (int j = 1; j < tokens.length; j++) {

String itemID2 = tokens[j].split(":")[0];

k.set(itemID + ":" + itemID2);

output.collect(k, v);

}

}

}

}

public static class Step2_UserVectorToConoccurrenceReducer extends MapReduceBase implements Reducer<Text, IntWritable, Text, IntWritable> {

private IntWritable result = new IntWritable();

@Override

public void reduce(Text key, Iterator values, OutputCollector<Text, IntWritable> output, Reporter reporter) throws IOException {

int sum = 0;

while (values.hasNext()) {

sum += values.next().get();

}

result.set(sum);

output.collect(key, result);

}

}

public static void run(Map<String, String> path) throws IOException {

JobConf conf = Recommend.config();

String input = path.get("Step2Input");

String output = path.get("Step2Output");

HdfsDAO hdfs = new HdfsDAO(Recommend.HDFS, conf);

hdfs.rmr(output);

conf.setOutputKeyClass(Text.class);

conf.setOutputValueClass(IntWritable.class);

conf.setMapperClass(Step2_UserVectorToCooccurrenceMapper.class);

conf.setCombinerClass(Step2_UserVectorToConoccurrenceReducer.class);

conf.setReducerClass(Step2_UserVectorToConoccurrenceReducer.class);

conf.setInputFormat(TextInputFormat.class);

conf.setOutputFormat(TextOutputFormat.class);

FileInputFormat.setInputPaths(conf, new Path(input));

FileOutputFormat.setOutputPath(conf, new Path(output));

RunningJob job = JobClient.runJob(conf);

while (!job.isComplete()) {

job.waitForCompletion();

}

}

}

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[align=left]计算结果：[/align]

~ hadoop fs -cat /user/hdfs/recommend/step2/part-00000

101:101 5

101:102 3

101:103 4

101:104 4

101:105 2

101:106 2

101:107 1

102:101 3

102:102 3

102:103 3

102:104 2

102:105 1

102:106 1

103:101 4

103:102 3

103:103 4

103:104 3

103:105 1

103:106 2

104:101 4

104:102 2

104:103 3

104:104 4

104:105 2

104:106 2

104:107 1

105:101 2

105:102 1

105:103 1

105:104 2

105:105 2

105:106 1

105:107 1

106:101 2

106:102 1

106:103 2

106:104 2

106:105 1

106:106 2

107:101 1

107:104 1

107:105 1

107:107 1

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[align=left]4). Step3.java，合并同现矩阵和评分矩阵[/align]

[align=left]源代码：[/align]

package org.conan.myhadoop.recommend;

import java.io.IOException;

import java.util.Map;

import org.apache.hadoop.fs.Path;

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.io.LongWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapred.FileInputFormat;

import org.apache.hadoop.mapred.FileOutputFormat;

import org.apache.hadoop.mapred.JobClient;

import org.apache.hadoop.mapred.JobConf;

import org.apache.hadoop.mapred.MapReduceBase;

import org.apache.hadoop.mapred.Mapper;

import org.apache.hadoop.mapred.OutputCollector;

import org.apache.hadoop.mapred.Reporter;

import org.apache.hadoop.mapred.RunningJob;

import org.apache.hadoop.mapred.TextInputFormat;

import org.apache.hadoop.mapred.TextOutputFormat;

import org.conan.myhadoop.hdfs.HdfsDAO;

public class Step3 {

public static class Step31_UserVectorSplitterMapper extends MapReduceBase implements Mapper<LongWritable, Text, IntWritable, Text> {

private final static IntWritable k = new IntWritable();

private final static Text v = new Text();

@Override

public void map(LongWritable key, Text values, OutputCollector<IntWritable, Text> output, Reporter reporter) throws IOException {

String[] tokens = Recommend.DELIMITER.split(values.toString());

for (int i = 1; i < tokens.length; i++) {

String[] vector = tokens[i].split(":");

int itemID = Integer.parseInt(vector[0]);

String pref = vector[1];

k.set(itemID);

v.set(tokens[0] + ":" + pref);

output.collect(k, v);

}

}

}

public static void run1(Map<String, String> path) throws IOException {

JobConf conf = Recommend.config();

String input = path.get("Step3Input1");

String output = path.get("Step3Output1");

HdfsDAO hdfs = new HdfsDAO(Recommend.HDFS, conf);

hdfs.rmr(output);

conf.setOutputKeyClass(IntWritable.class);

conf.setOutputValueClass(Text.class);

conf.setMapperClass(Step31_UserVectorSplitterMapper.class);

conf.setInputFormat(TextInputFormat.class);

conf.setOutputFormat(TextOutputFormat.class);

FileInputFormat.setInputPaths(conf, new Path(input));

FileOutputFormat.setOutputPath(conf, new Path(output));

RunningJob job = JobClient.runJob(conf);

while (!job.isComplete()) {

job.waitForCompletion();

}

}

public static class Step32_CooccurrenceColumnWrapperMapper extends MapReduceBase implements Mapper<LongWritable, Text, Text, IntWritable> {

private final static Text k = new Text();

private final static IntWritable v = new IntWritable();

@Override

public void map(LongWritable key, Text values, OutputCollector<Text, IntWritable> output, Reporter reporter) throws IOException {

String[] tokens = Recommend.DELIMITER.split(values.toString());

k.set(tokens[0]);

v.set(Integer.parseInt(tokens[1]));

output.collect(k, v);

}

}

public static void run2(Map<String, String> path) throws IOException {

JobConf conf = Recommend.config();

String input = path.get("Step3Input2");

String output = path.get("Step3Output2");

HdfsDAO hdfs = new HdfsDAO(Recommend.HDFS, conf);

hdfs.rmr(output);

conf.setOutputKeyClass(Text.class);

conf.setOutputValueClass(IntWritable.class);

conf.setMapperClass(Step32_CooccurrenceColumnWrapperMapper.class);

conf.setInputFormat(TextInputFormat.class);

conf.setOutputFormat(TextOutputFormat.class);

FileInputFormat.setInputPaths(conf, new Path(input));

FileOutputFormat.setOutputPath(conf, new Path(output));

RunningJob job = JobClient.runJob(conf);

while (!job.isComplete()) {

job.waitForCompletion();

}

}

}

复制代码

[align=left]计算结果：[/align]

~ hadoop fs -cat /user/hdfs/recommend/step3_1/part-00000

101 5:4.0

101 1:5.0

101 2:2.0

101 3:2.0

101 4:5.0

102 1:3.0

102 5:3.0

102 2:2.5

103 2:5.0

103 5:2.0

103 1:2.5

103 4:3.0

104 2:2.0

104 5:4.0

104 3:4.0

104 4:4.5

105 3:4.5

105 5:3.5

106 5:4.0

106 4:4.0

107 3:5.0

~ hadoop fs -cat /user/hdfs/recommend/step3_2/part-00000

101:101 5

101:102 3

101:103 4

101:104 4

101:105 2

101:106 2

101:107 1

102:101 3

102:102 3

102:103 3

102:104 2

102:105 1

102:106 1

103:101 4

103:102 3

103:103 4

103:104 3

103:105 1

103:106 2

104:101 4

104:102 2

104:103 3

104:104 4

104:105 2

104:106 2

104:107 1

105:101 2

105:102 1

105:103 1

105:104 2

105:105 2

105:106 1

105:107 1

106:101 2

106:102 1

106:103 2

106:104 2

106:105 1

106:106 2

107:101 1

107:104 1

107:105 1

107:107 1

复制代码

[align=left]5). Step4.java，计算推荐结果列表[/align]
[align=left]源代码：[/align]

package org.conan.myhadoop.recommend;

import java.io.IOException;

import java.util.ArrayList;

import java.util.HashMap;

import java.util.Iterator;

import java.util.List;

import java.util.Map;

import org.apache.hadoop.fs.Path;

import org.apache.hadoop.io.IntWritable;

import org.apache.hadoop.io.LongWritable;

import org.apache.hadoop.io.Text;

import org.apache.hadoop.mapred.FileInputFormat;

import org.apache.hadoop.mapred.FileOutputFormat;

import org.apache.hadoop.mapred.JobClient;

import org.apache.hadoop.mapred.JobConf;

import org.apache.hadoop.mapred.MapReduceBase;

import org.apache.hadoop.mapred.Mapper;

import org.apache.hadoop.mapred.OutputCollector;

import org.apache.hadoop.mapred.Reducer;

import org.apache.hadoop.mapred.Reporter;

import org.apache.hadoop.mapred.RunningJob;

import org.apache.hadoop.mapred.TextInputFormat;

import org.apache.hadoop.mapred.TextOutputFormat;

import org.conan.myhadoop.hdfs.HdfsDAO;

public class Step4 {

public static class Step4_PartialMultiplyMapper extends MapReduceBase implements Mapper<LongWritable, Text, IntWritable, Text> {

private final static IntWritable k = new IntWritable();

private final static Text v = new Text();

private final static Map<Integer, List> cooccurrenceMatrix = new HashMap<Integer, List>();

@Override

public void map(LongWritable key, Text values, OutputCollector<IntWritable, Text> output, Reporter reporter) throws IOException {

String[] tokens = Recommend.DELIMITER.split(values.toString());

String[] v1 = tokens[0].split(":");

String[] v2 = tokens[1].split(":");

if (v1.length > 1) {// cooccurrence

int itemID1 = Integer.parseInt(v1[0]);

int itemID2 = Integer.parseInt(v1[1]);

int num = Integer.parseInt(tokens[1]);

List list = null;

if (!cooccurrenceMatrix.containsKey(itemID1)) {

list = new ArrayList();

} else {

list = cooccurrenceMatrix.get(itemID1);

}

list.add(new Cooccurrence(itemID1, itemID2, num));

cooccurrenceMatrix.put(itemID1, list);

}

if (v2.length > 1) {// userVector

int itemID = Integer.parseInt(tokens[0]);

int userID = Integer.parseInt(v2[0]);

double pref = Double.parseDouble(v2[1]);

k.set(userID);

for (Cooccurrence co : cooccurrenceMatrix.get(itemID)) {

v.set(co.getItemID2() + "," + pref * co.getNum());

output.collect(k, v);

}

}

}

}

public static class Step4_AggregateAndRecommendReducer extends MapReduceBase implements Reducer<IntWritable, Text, IntWritable, Text> {

private final static Text v = new Text();

@Override

public void reduce(IntWritable key, Iterator values, OutputCollector<IntWritable, Text> output, Reporter reporter) throws IOException {

Map<String, Double> result = new HashMap<String, Double>();

while (values.hasNext()) {

String[] str = values.next().toString().split(",");

if (result.containsKey(str[0])) {

result.put(str[0], result.get(str[0]) + Double.parseDouble(str[1]));

} else {

result.put(str[0], Double.parseDouble(str[1]));

}

}

Iterator iter = result.keySet().iterator();

while (iter.hasNext()) {

String itemID = iter.next();

double score = result.get(itemID);

v.set(itemID + "," + score);

output.collect(key, v);

}

}

}

public static void run(Map<String, String> path) throws IOException {

JobConf conf = Recommend.config();

String input1 = path.get("Step4Input1");

String input2 = path.get("Step4Input2");

String output = path.get("Step4Output");

HdfsDAO hdfs = new HdfsDAO(Recommend.HDFS, conf);

hdfs.rmr(output);

conf.setOutputKeyClass(IntWritable.class);

conf.setOutputValueClass(Text.class);

conf.setMapperClass(Step4_PartialMultiplyMapper.class);

conf.setCombinerClass(Step4_AggregateAndRecommendReducer.class);

conf.setReducerClass(Step4_AggregateAndRecommendReducer.class);

conf.setInputFormat(TextInputFormat.class);

conf.setOutputFormat(TextOutputFormat.class);

FileInputFormat.setInputPaths(conf, new Path(input1), new Path(input2));

FileOutputFormat.setOutputPath(conf, new Path(output));

RunningJob job = JobClient.runJob(conf);

while (!job.isComplete()) {

job.waitForCompletion();

}

}

}

class Cooccurrence {

private int itemID1;

private int itemID2;

private int num;

public Cooccurrence(int itemID1, int itemID2, int num) {

super();

this.itemID1 = itemID1;

this.itemID2 = itemID2;

this.num = num;

}

public int getItemID1() {

return itemID1;

}

public void setItemID1(int itemID1) {

this.itemID1 = itemID1;

}

public int getItemID2() {

return itemID2;

}

public void setItemID2(int itemID2) {

this.itemID2 = itemID2;

}

public int getNum() {

return num;

}

public void setNum(int num) {

this.num = num;

}

}

复制代码

[align=left]计算结果：[/align]

~ hadoop fs -cat /user/hdfs/recommend/step4/part-00000

1 107,5.0

1 106,18.0

1 105,15.5

1 104,33.5

1 103,39.0

1 102,31.5

1 101,44.0

2 107,4.0

2 106,20.5

2 105,15.5

2 104,36.0

2 103,41.5

2 102,32.5

2 101,45.5

3 107,15.5

3 106,16.5

3 105,26.0

3 104,38.0

3 103,24.5

3 102,18.5

3 101,40.0

4 107,9.5

4 106,33.0

4 105,26.0

4 104,55.0

4 103,53.5

4 102,37.0

4 101,63.0

5 107,11.5

5 106,34.5

5 105,32.0

5 104,59.0

5 103,56.5

5 102,42.5

5 101,68.0

复制代码

[align=left]对Step4过程优化，请参考本文最后的补充内容。[/align]

[align=left]6). HdfsDAO.java，HDFS操作工具类[/align]
[align=left]详细解释，请参考文章：Hadoop编程调用HDFS[/align]
[align=left]源代码：[/align]

package org.conan.myhadoop.hdfs;

import java.io.IOException;

import java.net.URI;

import org.apache.hadoop.conf.Configuration;

import org.apache.hadoop.fs.FSDataInputStream;

import org.apache.hadoop.fs.FSDataOutputStream;

import org.apache.hadoop.fs.FileStatus;

import org.apache.hadoop.fs.FileSystem;

import org.apache.hadoop.fs.Path;

import org.apache.hadoop.io.IOUtils;

import org.apache.hadoop.mapred.JobConf;

public class HdfsDAO {

private static final String HDFS = "hdfs://192.168.1.210:9000/";

public HdfsDAO(Configuration conf) {

this(HDFS, conf);

}

public HdfsDAO(String hdfs, Configuration conf) {

this.hdfsPath = hdfs;

this.conf = conf;

}

private String hdfsPath;

private Configuration conf;

public static void main(String[] args) throws IOException {

JobConf conf = config();

HdfsDAO hdfs = new HdfsDAO(conf);

hdfs.copyFile("datafile/item.csv", "/tmp/new");

hdfs.ls("/tmp/new");

}

public static JobConf config(){

JobConf conf = new JobConf(HdfsDAO.class);

conf.setJobName("HdfsDAO");

conf.addResource("classpath:/hadoop/core-site.xml");

conf.addResource("classpath:/hadoop/hdfs-site.xml");

conf.addResource("classpath:/hadoop/mapred-site.xml");

return conf;

}

public void mkdirs(String folder) throws IOException {

Path path = new Path(folder);

FileSystem fs = FileSystem.get(URI.create(hdfsPath), conf);

if (!fs.exists(path)) {

fs.mkdirs(path);

System.out.println("Create: " + folder);

}

fs.close();

}

public void rmr(String folder) throws IOException {

Path path = new Path(folder);

FileSystem fs = FileSystem.get(URI.create(hdfsPath), conf);

fs.deleteOnExit(path);

System.out.println("Delete: " + folder);

fs.close();

}

public void ls(String folder) throws IOException {

Path path = new Path(folder);

FileSystem fs = FileSystem.get(URI.create(hdfsPath), conf);

FileStatus[] list = fs.listStatus(path);

System.out.println("ls: " + folder);

System.out.println("==========================================================");

for (FileStatus f : list) {

System.out.printf("name: %s, folder: %s, size: %d\n", f.getPath(), f.isDir(), f.getLen());

}

System.out.println("==========================================================");

fs.close();

}

public void createFile(String file, String content) throws IOException {

FileSystem fs = FileSystem.get(URI.create(hdfsPath), conf);

byte[] buff = content.getBytes();

FSDataOutputStream os = null;

try {

os = fs.create(new Path(file));

os.write(buff, 0, buff.length);

System.out.println("Create: " + file);

} finally {

if (os != null)

os.close();

}

fs.close();

}

public void copyFile(String local, String remote) throws IOException {

FileSystem fs = FileSystem.get(URI.create(hdfsPath), conf);

fs.copyFromLocalFile(new Path(local), new Path(remote));

System.out.println("copy from: " + local + " to " + remote);

fs.close();

}

public void download(String remote, String local) throws IOException {

Path path = new Path(remote);

FileSystem fs = FileSystem.get(URI.create(hdfsPath), conf);

fs.copyToLocalFile(path, new Path(local));

System.out.println("download: from" + remote + " to " + local);

fs.close();

}

public void cat(String remoteFile) throws IOException {

Path path = new Path(remoteFile);

FileSystem fs = FileSystem.get(URI.create(hdfsPath), conf);

FSDataInputStream fsdis = null;

System.out.println("cat: " + remoteFile);

try {

fsdis =fs.open(path);

IOUtils.copyBytes(fsdis, System.out, 4096, false);

} finally {

IOUtils.closeStream(fsdis);

fs.close();

}

}

}

复制代码

[align=left]这样我们就自己编程实现了MapReduce化基于物品的协同过滤算法。[/align]

[align=left]RHadoop的实现方案，请参考文章：RHadoop实践系列之三 R实现MapReduce的协同过滤算法[/align]
[align=left]Mahout的实现方案，请参考文章：Mahout分步式程序开发 基于物品的协同过滤ItemCF[/align]
我已经把整个MapReduce的实现都放到了github上面：

https://github.com/bsspirit/maven_hadoop_template/releases/tag/recommend