<project xmlns="http://maven.apache.org/POM/4.0.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
	<modelVersion>4.0.0</modelVersion>

	<groupId>cn.netkiller</groupId>
	<artifactId>mahout</artifactId>
	<version>0.0.1-SNAPSHOT</version>
	<packaging>jar</packaging>

	<name>mahout</name>
	<url>http://maven.apache.org</url>

	<properties>
		<project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
	</properties>

	<dependencies>
		<dependency>
			<groupId>org.apache.mahout</groupId>
			<artifactId>mahout-core</artifactId>
			<version>0.9</version>
		</dependency>
		<dependency>
			<groupId>org.slf4j</groupId>
			<artifactId>slf4j-nop</artifactId>
			<version>1.7.30</version>
			<scope>test</scope>
		</dependency>
		<dependency>
			<groupId>junit</groupId>
			<artifactId>junit</artifactId>
			<version>3.8.1</version>
			<scope>test</scope>
		</dependency>
	</dependencies>
</project>
			
			
			

			
package cn.netkiller.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 App {

	public App() {
		// TODO Auto-generated constructor stub
	}

	public static void main(String[] args) {
		// TODO Auto-generated method stub
		try {
			// 从文件加载数据
			DataModel model = new FileDataModel(new File("target/classes/test.csv"));
			// 指定用户相似度计算方法，这里采用皮尔森相关度
			UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
			// 指定用户邻居数量，这里为2
			UserNeighborhood neighborhood = new NearestNUserNeighborhood(2, similarity, model);
			// 构建基于用户的推荐系统
			Recommender recommender = new GenericUserBasedRecommender(model, neighborhood, similarity);
			// 得到指定用户的推荐结果，这里是得到用户1的两个推荐
			List<RecommendedItem> recommendations = recommender.recommend(1, 2);
			// 打印推荐结果
			for (RecommendedItem recommendation : recommendations) {
				System.out.println(recommendation);
			}
		} catch (Exception e) {
			System.out.println(e);
		}
	}

}
			
			
			

			
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