Mahout基于余弦相似度的评估

来源：互联网发布：诺维斯基10年数据编辑：程序博客网时间：2024/05/21 17:40
/* * 这段程序对于基于余弦相似度的评估 * */package byuser;import java.io.File;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.neighborhood.NearestNUserNeighborhood;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.model.DataModel;import org.apache.mahout.cf.taste.neighborhood.UserNeighborhood;import org.apache.mahout.cf.taste.recommender.Recommender;import org.apache.mahout.cf.taste.similarity.UserSimilarity;import org.apache.mahout.cf.taste.similarity.precompute.example.GroupLensDataModel;public class GenericRecByGroupLens_Evalu {public GenericRecByGroupLens_Evalu() throws Exception{DataModel model = new GroupLensDataModel(new File("E:\\mahout项目\\examples\\ratings.dat"));RecommenderEvaluator evaluator = new AverageAbsoluteDifferenceRecommenderEvaluator();RecommenderBuilder recommenderBuilder = new RecommenderBuilder() {@Overridepublic Recommender buildRecommender(DataModel model) throws TasteException {//PearsonCoreCOnrrelationSimilarity是皮尔逊相关系数的算法使用UserSimilarity similarity = new PearsonCorrelationSimilarity(model);//这里使用的是基于欧式距离定义相似度的算法UserSimilarity similarity1 =  new EuclideanDistanceSimilarity(model);//这里使用余弦相似性度量,以前的名字是：CosineMeasureSimilarity现在改成了如下的名字：UserSimilarity similarity2 =  new PearsonCorrelationSimilarity(model);UserNeighborhood neighborhood = new NearestNUserNeighborhood(100, similarity2, model);return new GenericUserBasedRecommender(model, neighborhood, similarity2);}};double score = evaluator.evaluate(recommenderBuilder, null, model, 0.95, 0.05);System.out.println("基于余弦相似度的推荐引擎的评测得分是： " + score);}public static void main(String[] args) throws Exception {// TODO Auto-generated method stubGenericRecByGroupLens_Evalu eva = new GenericRecByGroupLens_Evalu();}}
如图：
这里和上一篇博客的图片进行对比：
优于皮尔逊，略逊与欧氏距离。
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