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。

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<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：

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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]