基于物品的协同过滤mysql_使用Mahout基于用户和物品相似度进行协同过滤推荐内容...

基于用户User协同过滤

package mahout;

import java.io.File;

import java.io.IOException;

import java.util.List;

import org.apache.mahout.cf.taste.common.TasteException;

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.CachingRecommender;

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.model.JDBCDataModel;

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;

/**

* 基于用户相似度进行推荐

* mahout基于协同过滤(CF)的推荐

* 用户对物品进行评分，对于无评分只有喜欢、不喜欢的，可以将喜欢置为5分，不喜欢置为1分

* @author itindex

*

*/

public class Mahout {

public static void main(String[] args) throws TasteException, IOException {

//(1)----连接数据库部分

//        MysqlDataSource dataSource = new MysqlDataSource();

//        dataSource.setServerName("localhost");

//        dataSource.setUser("admin");

//        dataSource.setPassword("admin");

//        dataSource.setDatabaseName("mahoutDB");

//      //(2)----使用MySQLJDBCDataModel数据源读取MySQL里的数据

//        JDBCDataModel dataModel = new MySQLJDBCDataModel(dataSource, "table1", "userId", "itemId", "preference", "date");

//(3)----数据模型部分

//把MySQLJDBCDataModel对象赋值给DataModel

//        DataModel model = dataModel;

DataModel model = new FileDataModel(new File("D:/intro.csv"));

//用户相似度UserSimilarity:包含相似性度量和邻居参数

UserSimilarity similarity = new PearsonCorrelationSimilarity(model);

//相邻用户UserNeighborhood

UserNeighborhood neighborhood = new NearestNUserNeighborhood(2, similarity, model);

//一旦确定相邻用户,一个普通的user-based推荐器被构建,构建一个GenericUserBasedRecommender推荐器需要数据源DataModel,用户相似性UserSimilarity,相邻用户相似度UserNeighborhood

Recommender recommender = new CachingRecommender(new GenericUserBasedRecommender(model, neighborhood, similarity));

//向用户1推荐2个商品

List recommendations = recommender.recommend(1, 2);

for(RecommendedItem recommendation : recommendations){

//输出推荐结果

System.out.println(recommendation);

}

}

}

基于物品Item进行协同过滤：

package mahout;

import java.io.File;

import java.util.Collection;

import java.util.List;

import org.apache.mahout.cf.taste.impl.model.file.FileDataModel;

import org.apache.mahout.cf.taste.impl.recommender.CachingRecommender;

import org.apache.mahout.cf.taste.impl.recommender.GenericItemBasedRecommender;

import org.apache.mahout.cf.taste.impl.similarity.EuclideanDistanceSimilarity;

import org.apache.mahout.cf.taste.impl.similarity.GenericItemSimilarity;

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;

/**

* 基于Item 物品相似度进行推荐

* 用户对物品进行评分，对于无评分只有喜欢、不喜欢的，可以将喜欢置为5分，不喜欢置为1分

* @author itindex

*

*/

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("D:/intro.csv"));

//基于物品的推荐

Recommender recommender1 ;

ItemSimilarity otherSimilarity = new EuclideanDistanceSimilarity(model);

GenericItemSimilarity similarity2 = new GenericItemSimilarity(otherSimilarity, model);

recommender1 = new CachingRecommender(new GenericItemBasedRecommender(model, similarity2));

//为用户1推荐一件物品1,1

List recommendedItems = recommender1.recommend(1, 2);

//输出

for (RecommendedItem item : recommendedItems) {

System.out.println(item);

System.out.println(item.getItemID());

System.out.println(item.getValue());

}

}

}

intro.csv文件格式：

用户id,物品id,评分

1,10,5

1,12,5

1,14,1

2,12,5

3,10,5

3,12,5

3,13,5

3,14,1

4,10,5

IT瘾于2015年6月2日 下午11时31分00秒发布

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