1,101,5.0
1,102,3.0
1,103,2.52,101,2.0
2,102,2.5
2,103,5.0
2,104,2.03,101,2.5
3,104,4.0
3,105,4.5
3,107,5.04,101,5.0
4,103,3.0
4,104,4.5
4,106,4.05,101,4.0
5,102,3.0
5,103,2.0
5,104,4.0
5,105,3.5
5,106,4.0

package com.xxx;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.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.RecommendedItem;
import org.apache.mahout.cf.taste.recommender.Recommender;
import org.apache.mahout.cf.taste.similarity.UserSimilarity;/*** 简单的使用皮尔逊相关系数进行推荐* @author **/
public class RecommenderIntro {public static void main(String[] args) throws IOException, TasteException {String projectDir = System.getProperty("user.dir");DataModel model = new FileDataModel(new File(projectDir + "/src/main/intro.csv"));UserSimilarity similarity = new PearsonCorrelationSimilarity(model);UserNeighborhood neighborhood = new NearestNUserNeighborhood(2, similarity, model);Recommender recommender = new GenericUserBasedRecommender(model, neighborhood, similarity);List<RecommendedItem> recommendedItems = recommender.recommend(1, 1);for (RecommendedItem recommendedItem : recommendedItems) {System.out.println(recommendedItem);}}
}