Mahout对于定制的GroupLens推荐进行评估

/*
 * 这段程序写的是测试定制的GroupLens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.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 {
				UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
				UserNeighborhood neighborhood = new NearestNUserNeighborhood(100, similarity, model);
				return new GenericUserBasedRecommender(model, neighborhood, similarity);
			}
		};
		double score = evaluator.evaluate(recommenderBuilder, null, model, 0.95, 0.05);
		System.out.println("GroupLens定制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();
	}

}