mahout基于用户推荐的简单例子（1）

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.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;

public class UserRecommer {
public static void main(String[] args) throws Exception {
DataModel model = new FileDataModel(new File("xxx/intro.csv"));
// 皮尔逊相似度算法。其他的还有好多相似度算法
UserSimilarity similarity = new PearsonCorrelationSimilarity(model);

UserNeighborhood neighborhood = new NearestNUserNeighborhood(2, similarity, model);
// 生成推荐系统
Recommender recommender = new GenericUserBasedRecommender(model, neighborhood, similarity);
// 为用户1推荐物品1
List<RecommendedItem> recommendations = recommender.recommend(1, 1);

for (RecommendedItem recommendation : recommendations)

{
System.out.println(recommendation);
}
}
}

intro.csv文件内容：
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

mahout in action（mahout实战）代码已上传到github，自行下载。

https://github.com/liuhuanone/mahout-example/tree/master/mahout-examples/tdunning-MiA-5b8956f