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0.8
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 list = re.recommend(1, 2);
for(RecommendedItem recommendedItem :list)
{
System.out.println(recommendedItem);
}
}
}