基于Java与协同过滤推荐算法实现电影推荐系统

package com.rcd.model.recommender; import java.util.List; import org.apache.mahout.cf.taste.impl.common.FastByIDMap; import org.apache.mahout.cf.taste.impl.model.GenericDataModel; import org.apache.mahout.cf.taste.impl.model.GenericUserPreferenceArray; 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.model.PreferenceArray; 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 RecommenderIntro { public static void main(String[] args) throws Exception { FastByIDMap<PreferenceArray> preferences = new FastByIDMap<PreferenceArray>(); PreferenceArray prefsForUser1 = new GenericUserPreferenceArray(3);// 注意这里的数字 // 这里是用来存储一个用户的元数据，这些元数据通常来自日志文件，比如浏览历史，等等，不同的业务场合，它的业务语义是不一样 prefsForUser1.setUserID(0, 1); prefsForUser1.setItemID(0, 101); prefsForUser1.setValue(0, 5.0f);//<1, 101, 5.0f> < 用户 ID, 物品 ID, 用户偏好 > prefsForUser1.setItemID(1, 102); prefsForUser1.setValue(1, 3.0f);//<1, 102, 3.0f> prefsForUser1.setItemID(2, 103); prefsForUser1.setValue(2, 2.5f);//<1, 103, 2.5f> preferences.put(1l, prefsForUser1);// 在这里添加数据，userID作为key PreferenceArray prefsForUser2 = new GenericUserPreferenceArray(4); prefsForUser2.setUserID(0, 2); prefsForUser2.setItemID(0, 101);//<2, 101, 2.0f> prefsForUser2.setValue(0, 2.0f); prefsForUser2.setItemID(1, 102); prefsForUser2.setValue(1, 2.5f);//<2, 102, 2.5f> prefsForUser2.setItemID(2, 103); prefsForUser2.setValue(2, 5.0f);//<2, 103, 5.0f> prefsForUser2.setItemID(3, 104); prefsForUser2.setValue(3, 2.0f);//<2, 104, 2.0f> preferences.put(2l, prefsForUser2); PreferenceArray prefsForUser3 = new GenericUserPreferenceArray(4); prefsForUser3.setUserID(0, 3); prefsForUser3.setItemID(0, 101); prefsForUser3.setValue(0, 2.5f); prefsForUser3.setItemID(1, 104); prefsForUser3.setValue(1, 4.0f); prefsForUser3.setItemID(2, 105); prefsForUser3.setValue(2, 4.5f); prefsForUser3.setItemID(3, 107); prefsForUser3.setValue(3, 5.0f); preferences.put(3l, prefsForUser3); PreferenceArray prefsForUser4 = new GenericUserPreferenceArray(4); prefsForUser4.setUserID(0, 4); prefsForUser4.setItemID(0, 101); prefsForUser4.setValue(0, 5.0f); prefsForUser4.setItemID(1, 103); prefsForUser4.setValue(1, 3.0f); prefsForUser4.setItemID(2, 104); prefsForUser4.setValue(2, 4.5f); prefsForUser4.setItemID(3, 106); prefsForUser4.setValue(3, 4.0f); preferences.put(4l, prefsForUser4); PreferenceArray prefsForUser5 = new GenericUserPreferenceArray(6); prefsForUser5.setUserID(0, 5); prefsForUser5.setItemID(0, 101); prefsForUser5.setValue(0, 4.0f); prefsForUser5.setItemID(1, 102); prefsForUser5.setValue(1, 3.0f); prefsForUser5.setItemID(2, 103); prefsForUser5.setValue(2, 2.0f); prefsForUser5.setItemID(3, 104); prefsForUser5.setValue(3, 4.0f); prefsForUser5.setItemID(4, 105); prefsForUser5.setValue(4, 3.5f); prefsForUser5.setItemID(5, 106); prefsForUser5.setValue(5, 4.0f); preferences.put(5l, prefsForUser5); DataModel model = new GenericDataModel(preferences);// DataModel的建立 UserSimilarity similarity = new PearsonCorrelationSimilarity(model);//计算相似度 UserNeighborhood neighborhood = new NearestNUserNeighborhood(2,similarity, model);//计算邻居 // 创建推荐引擎 Recommender recommender = new GenericUserBasedRecommender(model,neighborhood, similarity); //为用户1推荐2个 List<RecommendedItem> recommendations = recommender.recommend(1, 2); for (RecommendedItem recommendation : recommendations) { System.out.println(recommendation); } } }