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mahout推荐1
1、准备数据:
intro.csv:
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
2、编程实现:
目的:为用户1推荐一件商品看看:
package 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.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;import org.apache.mahout.cf.taste.impl.similarity.PearsonCorrelationSimilarity;/** * 基于用户的推荐程序 * @author Administrator * */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("data/intro.csv")); //用户相似度,给出两个用户的相似度,有多种度量方式 UserSimilarity similarity = new PearsonCorrelationSimilarity(model); //用户邻居,与给定用户最相似的一组用户 UserNeighborhood neighborhood = new NearestNUserNeighborhood(2, similarity, model); //推荐引擎,合并这些组件,实现推荐 Recommender recommender = new GenericUserBasedRecommender(model, neighborhood, similarity); //为用户1推荐一件物品1,1 List<RecommendedItem> recommendedItems = recommender.recommend(1, 1); //输出 for (RecommendedItem item : recommendedItems) { System.out.println(item); } }}
输出结果:
14/08/04 08:46:31 INFO file.FileDataModel: Creating FileDataModel for file data\intro.csv14/08/04 08:46:31 INFO file.FileDataModel: Reading file info...14/08/04 08:46:31 INFO file.FileDataModel: Read lines: 2114/08/04 08:46:31 INFO model.GenericDataModel: Processed 5 usersRecommendedItem[item:104, value:4.257081]
当然也可以推荐多件商品,那就是将recommender.recommend(1,N)即可。
推荐效果不错。
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