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。

        
       
        4.0.0
         
       
        mahouttest
         
       
        mahouttest
         
       
        0.0.1-SNAPSHOT
         
       
        jar
         
       
        mahouttest
         
       
        http://maven.apache.org
         
           
        
         UTF-8
          
         
           
              
         
          junit
               
         
          junit
               
         
          4.8.1
               
         
          test
             
            
            	
         
          org.apache.mahout
             	
         
          mahout-core
             	
         
          0.8-SNAPSHOT
             	
         
          jar
             	
         
          compile
             
          
       
      

　　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：

1,101,51,102,31,103,2.52,101,22,102,2.52,103,52,104,23,101,2.53,104,43,105,4.53,107,54,101,54,103,34,104,4.54,106,45,101,45,102,35,103,25,104,45,105,3.55,106,4

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
     
       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
     
       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
     
       recommendations =recommender.recommend(1, 1);for(RecommendedItem recommendation :recommendations){    System.out.println(recommendation);}
     

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