基于欧式距离定义相似度推荐算法的评估

/*
 * 这段程序对于基于欧式距离定义相似度的评估
 * */
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() {
			@Override
			public 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);
				
				UserNeighborhood neighborhood = new NearestNUserNeighborhood(100, similarity1, model);
				return new GenericUserBasedRecommender(model, neighborhood, similarity1);
			}
		};
		double score = evaluator.evaluate(recommenderBuilder, null, model, 0.95, 0.05);
		System.out.println("基于欧式距离定义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 stub
		GenericRecByGroupLens_Evalu eva = new GenericRecByGroupLens_Evalu();
	}

}

如图：

这里是基于皮尔逊算法的评估：

这个是基于欧式距离定义相似度的评估：

可以看出，欧式的算法更加的优于皮尔逊的推荐算法