Mahout协同推荐简单实例

    上篇文章介绍了协同过滤的安装与配置，这篇找了几个协同过滤的简单例子，看一下

Mahout给我们提供的强大的协同过滤算法。需要新建一个基于Maven的工程，下面是

pom.xml需要导入的包。

<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"

  xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">

  <modelVersion>4.0.0</modelVersion>

  <groupId>mahouttest</groupId>

  <artifactId>mahouttest</artifactId>

  <version>0.0.1-SNAPSHOT</version>

  <packaging>jar</packaging>

  <name>mahouttest</name>

  <url>http://maven.apache.org</url>

  <properties>

    <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>

  </properties>

  <dependencies>

    <dependency>

      <groupId>junit</groupId>

      <artifactId>junit</artifactId>

      <version>4.8.1</version>

      <scope>test</scope>

    </dependency>

    <dependency>

    	<groupId>org.apache.mahout</groupId>

    	<artifactId>mahout-core</artifactId>

    	<version>0.8-SNAPSHOT</version>

    	<type>jar</type>

    	<scope>compile</scope>

    </dependency>

  </dependencies>

　　这里我们导入的是最新的Mahout包，需要在本地的maven库中安装好。

首先我们需要准备好测试的数据，我们就用《Mahout  in action》中的例子：

1,101,5

1,102,3

1,103,2.5

2,101,2

2,102,2.5

2,103,5

2,104,2

3,101,2.5

3,104,4

3,105,4.5

3,107,5

4,101,5

4,103,3

4,104,4.5

4,106,4

5,101,4

5,102,3

5,103,2

5,104,4

5,105,3.5

5,106,4

具体对应的关系图如下：

下面我们用Mahout中三种不同的推荐代码来执行以下刚才给出的数据，看看Mahout中的推荐接口是

如何使用的。

1. 基于用户的协同推荐的代码：

DataModel  model =new FileDataModel(new File("data/intro.csv"));

UserSimilarity similarity =new PearsonCorrelationSimilarity(model);

UserNeighborhood neighborhood =new NearestNUserNeighborhood(2,similarity,model);

Recommender recommender= new GenericUserBasedRecommender(model,neighborhood,similarity);

List<RecommendedItem> recommendations =recommender.recommend(1, 1);

for(RecommendedItem recommendation :recommendations){

	System.out.println(recommendation);

}

执行后的结果是：RecommendedItem[item:104, value:4.257081]

2. 基于Item的协同过滤的代码：

DataModel  model =new FileDataModel(new File("data/intro.csv"));

ItemSimilarity similarity =new PearsonCorrelationSimilarity(model);

Recommender recommender= new GenericItemBasedRecommender(model,similarity);

List<RecommendedItem> recommendations =recommender.recommend(1, 1);

for(RecommendedItem recommendation :recommendations){

    System.out.println(recommendation);

}

执行后的结果是：RecommendedItem[item:104, value:5.0]

3. SlopeOne推荐算法

DataModel  model =new FileDataModel(new File("data/intro.csv"));

Recommender recommender= new SlopeOneRecommender(model);

List<RecommendedItem> recommendations =recommender.recommend(1, 1);

for(RecommendedItem recommendation :recommendations){

    System.out.println(recommendation);

}

执行结果是：RecommendedItem[item:105, value:5.75]