/* * 这段程序是使用GroupLens定制的DataModel数据模型 * 因为这里的数据是以逗号隔开的。 * 这里我把数据量加大,变成了20M的数据 * 这里使用的数据模型是对于GroupLens定制的GroupLensDataModel * */ package byuser; 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.eval.LoadEvaluator; 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.similarity.precompute.example.GroupLensDataModel; public class GenericUserRecommeder_GroupLens { public static void main(String[] args) { // TODO Auto-generated method stub try { //进行数据的装载 //生成数据模型 DataModel model = new GroupLensDataModel(new File("E:\\mahout项目\\examples\\ratings.dat")); UserSimilarity similarity = new org.apache.mahout.cf.taste.impl.similarity.PearsonCorrelationSimilarity(model); UserNeighborhood neighborhood = new NearestNUserNeighborhood(100, similarity, model); //生成推荐引擎 Recommender recommender = new GenericUserBasedRecommender(model, neighborhood, similarity); LoadEvaluator.runLoad(recommender); //为用户已推荐一件商品recommend( , );其中参数的意思是:第几个人,然后推荐几件商品 List<RecommendedItem> recommendations = recommender.recommend(1, 1); for(RecommendedItem recommendation : recommendations){ System.out.println("GroupLens定制,为您推荐的商品是:" + recommendation); } } catch (IOException e) { // TODO Auto-generated catch block e.printStackTrace(); } catch (TasteException e) { // TODO Auto-generated catch block e.printStackTrace(); } } } //这里的数据结果会出现很慢,因为这里的数据我跑的是1M的数据。
如图:
时间: 2024-10-10 10:29:20