Mahout--最基本的推荐系统的JAVA代码

package mp05.com;
import java.io.File;
import java.io.IOException;
import java.util.List;

import org.apache.mahout.cf.taste.common.TasteException;
import org.apache.mahout.cf.taste.eval.RecommenderBuilder;
import org.apache.mahout.cf.taste.eval.RecommenderEvaluator;
import org.apache.mahout.cf.taste.impl.eval.AverageAbsoluteDifferenceRecommenderEvaluator;
import org.apache.mahout.cf.taste.impl.model.file.FileDataModel;
import org.apache.mahout.cf.taste.impl.neighborhood.NearestNUserNeighborhood;
import org.apache.mahout.cf.taste.impl.neighborhood.ThresholdUserNeighborhood;
import org.apache.mahout.cf.taste.impl.recommender.GenericItemBasedRecommender;
import org.apache.mahout.cf.taste.impl.recommender.GenericUserBasedRecommender;
import org.apache.mahout.cf.taste.impl.similarity.EuclideanDistanceSimilarity;
import org.apache.mahout.cf.taste.impl.similarity.PearsonCorrelationSimilarity;
import org.apache.mahout.cf.taste.impl.similarity.TanimotoCoefficientSimilarity;
import org.apache.mahout.cf.taste.model.DataModel;
import org.apache.mahout.cf.taste.neighborhood.UserNeighborhood;
import org.apache.mahout.cf.taste.recommender.RecommendedItem;
import org.apache.mahout.cf.taste.recommender.Recommender;
import org.apache.mahout.cf.taste.similarity.ItemSimilarity;
import org.apache.mahout.cf.taste.similarity.UserSimilarity;
public class RecommenderIntro {
    //下面是一个基于用户的简单的推荐
    //探究用户与用户之间的相似性，简单的说就是你有一个好基友，他喜欢这首歌，那么你喜欢这首歌的可能性很大。
    public static void main(String[] args) throws TasteException, Exception {
        try {
            DataModel model=new FileDataModel(new File("/home/xuyao/mahout/test_data/intro.csv"));
            //UserSimilarity封装了用户间相似性的概念
            UserSimilarity similarity=new PearsonCorrelationSimilarity(model);
            //UserNeighborhood封装了最相似用户组的概念.  2是用户的邻域，指的是最相似的几个用户
            UserNeighborhood neighborhood=new NearestNUserNeighborhood(2,similarity,model);
            //Recommender推荐引擎
            Recommender recommender=new GenericUserBasedRecommender(model,neighborhood,similarity);
            List<RecommendedItem> recommendations=recommender.recommend(1,1);
            for(RecommendedItem recommendation : recommendations)
                System.out.println(recommendation);
        } catch (IOException e) {
            // TODO Auto-generated catch block
            e.printStackTrace();
        }
        evaluator();

    }
    //配置并评估一个推荐程序，这里也是基于用户的推荐
    public static void evaluator() throws IOException, TasteException{
        DataModel model=new FileDataModel(new File("/home/xuyao/mahout/ua.base"));
        RecommenderEvaluator evaluator=new AverageAbsoluteDifferenceRecommenderEvaluator();
        RecommenderBuilder builder =new RecommenderBuilder() {

            public Recommender buildRecommender(DataModel model) throws TasteException {

                //PearsonCorrelationSimilarity：相似性度量标准--皮尔逊相关系数
                UserSimilarity similarity=new PearsonCorrelationSimilarity(model);

                //EuclideanDistanceSimilarity: 相似性度量标准--欧式距离
                UserSimilarity similarity_2=new EuclideanDistanceSimilarity(model);

                //TanimotoCoefficientSimilarity: 相似性度量标准--谷本系数--完全抛开偏好值
                UserSimilarity similarity_3=new TanimotoCoefficientSimilarity(model);

                //NearestNUserNeighborhood ：固定大小的邻域。。改变这个100可以得到不同的打分，所以这个是可以用来调优的
                UserNeighborhood neighborhood=new NearestNUserNeighborhood(100,similarity,model);

                //下面是另一个表示邻域的，用的是基于阈值的邻域。。其中0.5为可调优。
                UserNeighborhood neighborhood_2=new ThresholdUserNeighborhood(0.5, similarity, model);

                return new GenericUserBasedRecommender(model, neighborhood, similarity);
            }
        };
        //0.9指的是训练90%的数据，测试10%的数据。 而1.0指的是输入的数据的比例。  这里表示数据集全部输入，其中90%用来训练，另外10%用来测试。
        double socre =evaluator.evaluate(builder, null, model, 0.9, 1.0);
        //这个socre表示这个模型的打分，分数越小表示这个模型越好。
        System.out.println(socre);
    }
    //下面是基于物品的推荐，简单的说就是你的电脑有360安全卫士，360杀毒，360浏览器，于是说你比较喜欢360的产品，就给你推荐360WIFI。
    public static void evaluator_2() throws IOException{
        DataModel model=new FileDataModel(new File("/home/xuyao/mahout/ua.base"));
        RecommenderBuilder builder =new RecommenderBuilder() {

            public Recommender buildRecommender(DataModel model) throws TasteException {
                ItemSimilarity similarity =new PearsonCorrelationSimilarity(model);
                return new GenericItemBasedRecommender(model, similarity);
            }
        };
    }
}

到这个地址下面 http://grouplens.org/datasets/movielens/
下载100K的。解压找到ua.base
intro.csv数据如下：

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