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0.8

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 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 “厚积薄发，磨刀不误砍柴工” 博客，请务必保留此出处http://tianxingzhe.blog.51cto.com/3390077/1713455

mahout0.11 taste框架推荐引擎api

原文：http://tianxingzhe.blog.51cto.com/3390077/1713455