Mahout使用(一)

1.HelloMahout.java
2.DistanceTest.java
3.MahoutDemo.java

 

 

1.HelloMahout.java

 1 package cn.crxy.mahout;
 2 
 3 import java.io.File;
 4 import java.util.List;
 5 
 6 import org.apache.log4j.Logger;
 7 import org.apache.mahout.cf.taste.impl.model.file.FileDataModel;
 8 import org.apache.mahout.cf.taste.impl.neighborhood.NearestNUserNeighborhood;
 9 import org.apache.mahout.cf.taste.impl.recommender.GenericUserBasedRecommender;
10 import org.apache.mahout.cf.taste.impl.similarity.PearsonCorrelationSimilarity;
11 import org.apache.mahout.cf.taste.model.DataModel;
12 import org.apache.mahout.cf.taste.neighborhood.UserNeighborhood;
13 import org.apache.mahout.cf.taste.recommender.RecommendedItem;
14 import org.apache.mahout.cf.taste.recommender.Recommender;
15 import org.apache.mahout.cf.taste.similarity.UserSimilarity;
16 
17 public class HelloMahout {
18 
19     public static void main(String[] args) {
20         
21         Logger logger=Logger.getLogger(HelloMahout.class);
22         try {
23             //读取用户评分数据    封装成一个model
24             DataModel model = new FileDataModel(new File("F:\\360Downloads\\超人学院\\第14期视频\\2016-09-12【mahout】\\样本数据\\info.csv"));
25             // 根据相似度找出对应的好朋友的标准     物以类聚,人以群分
26             UserSimilarity userSimilarity = new PearsonCorrelationSimilarity(model);
27             // 邻域 选择两个好朋友帮我推荐
28             UserNeighborhood userNeighborhood = new NearestNUserNeighborhood(2,userSimilarity, model);
29             // 构建推荐引擎
30             Recommender recommender = new GenericUserBasedRecommender(model,userNeighborhood, userSimilarity);
31             // 进行推荐
32             List<RecommendedItem> recommend = recommender.recommend(1, 5);
33             for (RecommendedItem item : recommend) {
34                 logger.info(item);
35             }
36         } catch (Exception e) {
37             logger.error(e.getMessage());
38         }
39     }
40 }

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