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：

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]