mahout推荐1

1、准备数据：

intro.csv:

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

 

2、编程实现：

　　目的：为用户1推荐一件商品看看：

package mahout;

import java.io.File;
import java.util.List;

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.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;
import org.apache.mahout.cf.taste.impl.similarity.PearsonCorrelationSimilarity;
/**
 * 基于用户的推荐程序
 * @author Administrator
 *
 */
public class RecommenderIntro {

	public static void main(String[] args) throws Exception {
		//装载数据文件，实现存储，并为计算提供所需的所有偏好，用户和物品数据
		DataModel model = new FileDataModel(new File("data/intro.csv"));
		//用户相似度，给出两个用户的相似度，有多种度量方式
		UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
		//用户邻居，与给定用户最相似的一组用户
		UserNeighborhood neighborhood = new NearestNUserNeighborhood(2,
				similarity, model);
		//推荐引擎，合并这些组件，实现推荐
		Recommender recommender = new GenericUserBasedRecommender(model,
				neighborhood, similarity);
		//为用户1推荐一件物品1,1
		List<RecommendedItem> recommendedItems = recommender.recommend(1, 1);
		//输出
		for (RecommendedItem item : recommendedItems) {
			System.out.println(item);
		}
	}
}

 输出结果：

14/08/04 08:46:31 INFO file.FileDataModel: Creating FileDataModel for file data\intro.csv
14/08/04 08:46:31 INFO file.FileDataModel: Reading file info...
14/08/04 08:46:31 INFO file.FileDataModel: Read lines: 21
14/08/04 08:46:31 INFO model.GenericDataModel: Processed 5 users
RecommendedItem[item:104, value:4.257081]

 当然也可以推荐多件商品，那就是将recommender.recommend(1,N)即可。

推荐效果不错。