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]