mahout java api_mahout0.11 taste框架推荐引擎api

mahout0.11 taste框架推荐引擎api

发布时间：2020-08-08 15:47:04

来源：51CTO

阅读：857

作者：jethai

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 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 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 list = re.recommend(1, 2);

for(RecommendedItem recommendedItem :list)

{

System.out.println(recommendedItem);

}

}

}