用Hadoop构建电影推荐系统

jopen 4年前

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-mapreduce-recommend/

hadoop-recommand

前言

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。

目录

  1. 推荐系统概述
  2. 需求分析:推荐系统指标设计
  3. 算法模型:Hadoop并行算法
  4. 架构设计:推荐系统架构
  5. 程序开发:MapReduce程序实现

1. 推荐系统概述

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。

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。

推荐算法分类:

按数据使用划分:

  • 协同过滤算法:UserCF, ItemCF, ModelCF
  • 基于内容的推荐: 用户内容属性和物品内容属性
  • 社会化过滤:基于用户的社会网络关系

按模型划分:

  • 最近邻模型:基于距离的协同过滤算法
  • Latent Factor Mode(SVD):基于矩阵分解的模型
  • Graph:图模型,社会网络图模型

基于用户的协同过滤算法UserCF

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。

用例说明:

image015

算法实现及使用介绍,请参考文章:Mahout推荐算法API详解

基于物品的协同过滤算法ItemCF

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。

用例说明:

image017

算法实现及使用介绍,请参考文章:Mahout推荐算法API详解

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。

协同过滤算法实现,分为2个步骤

  • 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

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

2. 需求分析:推荐系统指标设计

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。

案例介绍

Netflix电影推荐百万奖金比赛,http://www.netflixprize.com/
Netflix官方网站:www.netflix.com

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。

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。

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。

netflix_prize

上图为比赛的各支队伍的排名!

补充说明:

  • 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。
  • 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

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

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。

  • 推荐算法选型:基于物品的协同过滤算法ItemCF,并行实现
  • 数据量:基于Hadoop架构,支持GB,TB,PB级数据量
  • 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。
  • 结果解读:通过ItemCF的定义,合理给出结果解释

3. 算法模型:Hadoop并行算法

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

测试数据集:small.csv

 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

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!)

算法的思想:

  • 1. 建立物品的同现矩阵
  • 2. 建立用户对物品的评分矩阵
  • 3. 矩阵计算推荐结果

1). 建立物品的同现矩阵
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。

       [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

2). 建立用户对物品的评分矩阵
按用户分组,找到每个用户所选的物品及评分

        U3  [101] 2.0  [102] 0.0  [103] 0.0  [104] 4.0  [105] 4.5  [106] 0.0  [107] 5.0

3). 矩阵计算推荐结果
同现矩阵*评分矩阵=推荐结果

alogrithm_1

图片摘自”Mahout In Action”

MapReduce任务设计

aglorithm_2

图片摘自”Mahout In Action”

解读MapRduce任务:

  • 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
  • 步骤2: 对物品组合列表进行计数,建立物品的同现矩阵
  • 步骤3: 合并同现矩阵和评分矩阵
  • 步骤4: 计算推荐结果列表

4. 架构设计:推荐系统架构

hadoop-recommand-architect

上图中,左边是Application业务系统,右边是Hadoop的HDFS, MapReduce。

  1. 业务系统记录了用户的行为和对物品的打分
  2. 设置系统定时器CRON,每xx小时,增量向HDFS导入数据(userid,itemid,value,time)。
  3. 完成导入后,设置系统定时器,启动MapReduce程序,运行推荐算法。
  4. 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。

5. 程序开发:MapReduce程序实现

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

新建Java类:

  • Recommend.java,主任务启动程序
  • 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
  • Step2.java,对物品组合列表进行计数,建立物品的同现矩阵
  • Step3.java,合并同现矩阵和评分矩阵
  • Step4.java,计算推荐结果列表
  • HdfsDAO.java,HDFS操作工具类

1). Recommend.java,主任务启动程序
源代码:

 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;      }    }

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

源代码:

 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();          }      }    }

计算结果:

 ~ 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

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

 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();          }      }  }

计算结果:

 ~ 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

4). Step3.java,合并同现矩阵和评分矩阵
源代码:

 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();          }      }    }

计算结果:

 ~ 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

5). Step4.java,计算推荐结果列表
源代码:

 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;      }    }

计算结果:

 ~ 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

6). HdfsDAO.java,HDFS操作工具类
详细解释,请参考文章:Hadoop编程调用HDFS

源代码:

 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();            }      }  }

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。

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

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

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

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