RecommenderEvaluator实现对推荐程序的评分测试程序

<span style="font-size:18px;">/*
 * 代码mahout实现训练数据和评分
 * 这里输出测试这个推荐程序的评分
 * 评分越低意味着估计值与实际实际偏好值得差别越小
 * 0.0为完美的估计
 * */
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.model.file.FileDataModel;
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.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.common.RandomUtils;

public class RecommenderEvaluatorStudy {
	
	//无参构造
	public RecommenderEvaluatorStudy(){
	}
	
	public static void main(String[] args) {
		try{
			//每次生成的随机数都相同
			//因此随机生成可以重复的结果
			//这里是为了测试，实际代码中请勿使用
			RandomUtils.useTestSeed();
			
			//构建推荐的数据模型
			DataModel model = new FileDataModel(new File("E:\\mahout项目\\examples\\intro.csv"));
			
			RecommenderEvaluator evaluator = new AverageAbsoluteDifferenceRecommenderEvaluator();
			RecommenderBuilder builder = new RecommenderBuilder(){
			
			@Override
			public Recommender buildRecommender(DataModel model) throws TasteException{
				UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
				UserNeighborhood neighborhood = new NearestNUserNeighborhood(2, similarity, model);
				return new GenericUserBasedRecommender(model, neighborhood, similarity);
				}
			};
			
			//这里的数据意思是训练70%，测试30%的数据
			//这里的数据如果显示出现了NAN，就表示计算数据出现了问题NAN: not a number
			//你只需要修改一下参数，我这里改成了0.9
			double score = evaluator.evaluate(builder, null, model, 0.9, 1.0);
			System.out.println("分值为：" + score);
		}catch(Exception e){
			e.printStackTrace();
		}
	}	
}
</span>

原图：

这里为NAN： Not A Number的意思

修改参数后的图片：

RecommenderEvaluator实现对推荐程序的评分测试程序

原文：http://blog.csdn.net/u012965373/article/details/45481443