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