Mahout协同推荐简单实例

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：

Mahout协同推荐简单实例

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：

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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：

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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

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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]