Mahout实战---运行第一个推荐引擎

创建输入

创建intro.csv文件，内容如下

1,101,5.0
1,102,3.0
1,103,2.5

2,101,2.0
2,102,2.5
2,103,5.0
2,104,2.0

3,101,2.5
3,104,4.0
3,105,4.5
3,107,5.0

4,101,5.0
4,103,3.0
4,104,4.5
4,106,4.0

5,101,4.0
5,102,3.0
5,103,2.0
5,104,4.0
5,105,3.5
5,106,4.0

创建推荐程序

由于项目在eclipse下，所以先获取项目额根目录String projectDir = System.getProperty("user.dir");

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);
        }
    }
}

推荐程序的步骤是：1，输入user-item矩阵数据 2，选择合适的相似度计算方法（程序中使用的是皮尔逊相关系数）3，构造N最近邻  4，根据邻居产生推荐结果

对应到mahout程序就是上述代码中写的。这个很简单，没毛病，下面是运行结果