mahout0.11 taste框架推荐引擎api

所需jar包





数据格式以逗号分隔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
3,101,2.0
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
6,102,4.0
6,103,2.0
6,105,3.5
6,107,4.0

基于用户推荐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 UserItemRecommend {
public static void main(String[] args) throws Exception{
    //创建数据模型
    DataModel dm = new FileDataModel(new File("C:/test.txt"));
    //使用user来推荐，计算相似度
    UserSimilarity us=new PearsonCorrelationSimilarity(dm);
    //查找K(3)近邻
    UserNeighborhood unb=new NearestNUserNeighborhood(3, us, dm);
 //构造推荐引擎
    Recommender re =new GenericUserBasedRecommender(dm, unb, us);
//显示推荐结果,为1号用户推荐两个商品
    List<RecommendedItem> list = re.recommend(1, 2);
    for(RecommendedItem recommendedItem :list)
    {
        System.out.println(recommendedItem);
    }
}
    
}推荐结果RecommendedItem[item:104, value:4.257081]
RecommendedItem[item:106, value:4.0]

基于商品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.recommender.GenericItemBasedRecommender;
import org.apache.mahout.cf.taste.impl.similarity.PearsonCorrelationSimilarity;
import org.apache.mahout.cf.taste.model.DataModel;
import org.apache.mahout.cf.taste.recommender.RecommendedItem;
import org.apache.mahout.cf.taste.recommender.Recommender;
import org.apache.mahout.cf.taste.similarity.ItemSimilarity;

public class ItemUserRecommend {
public static void main(String[] args) throws Exception{
    //创建数据模型
    DataModel dm = new FileDataModel(new File("C:/test.txt"));

    ItemSimilarity is=new PearsonCorrelationSimilarity(dm);
    
 //构造推荐引擎
    Recommender re =new GenericItemBasedRecommender(dm,is);
//显示推荐结果,为1号用户推荐两个商品
    List<RecommendedItem> list = re.recommend(1, 2);
    for(RecommendedItem recommendedItem :list)
    {
        System.out.println(recommendedItem);
    }
}
    
}

slopeone算法，0.9版本已移除，要使用只能用0.8import java.io.File;
import java.util.List;

import org.apache.mahout.cf.taste.impl.model.file.FileDataModel;
import org.apache.mahout.cf.taste.impl.recommender.GenericItemBasedRecommender;
import org.apache.mahout.cf.taste.impl.similarity.PearsonCorrelationSimilarity;
import org.apache.mahout.cf.taste.model.DataModel;
import org.apache.mahout.cf.taste.recommender.RecommendedItem;
import org.apache.mahout.cf.taste.recommender.Recommender;
import org.apache.mahout.cf.taste.similarity.ItemSimilarity;

public class SlopeOneRecommend {
public static void main(String[] args) throws Exception{
    //创建数据模型
    DataModel dm = new FileDataModel(new File("C:/test.txt"));

    
    
 //构造推荐引擎
    Recommender re =new SlopeOneRecommender(dm);;
//显示推荐结果,为1号用户推荐两个商品
    List<RecommendedItem> list = re.recommend(1, 2);
    for(RecommendedItem recommendedItem :list)
    {
        System.out.println(recommendedItem);
    }
}
    
}

本文出自 “点滴积累” 博客，请务必保留此出处http://tianxingzhe.blog.51cto.com/3390077/1713455