人工智能-项目实践-检索系统-餐馆推荐系统 全文检索 基于张量分解的数据推荐

package cn.com.chenyixiao.rrs.controller; import java.io.BufferedWriter; import java.io.File; import java.io.FileWriter; import java.io.IOException; import java.util.ArrayList; import java.util.List; import org.apache.mahout.cf.taste.common.TasteException; 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.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.recommender.svd.ALSWRFactorizer; import org.apache.mahout.cf.taste.impl.recommender.svd.SVDRecommender; import org.apache.mahout.cf.taste.impl.similarity.AveragingPreferenceInferrer; import org.apache.mahout.cf.taste.impl.similarity.EuclideanDistanceSimilarity; import org.apache.mahout.cf.taste.impl.similarity.LogLikelihoodSimilarity; import org.apache.mahout.cf.taste.impl.similarity.PearsonCorrelationSimilarity; import org.apache.mahout.cf.taste.impl.similarity.TanimotoCoefficientSimilarity; 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; import org.springframework.beans.factory.annotation.Autowired; import org.springframework.stereotype.Controller; import org.springframework.web.bind.annotation.PathVariable; import org.springframework.web.bind.annotation.RequestMapping; import org.springframework.web.bind.annotation.RequestMethod; import org.springframework.web.bind.annotation.ResponseBody; import com.google.gson.Gson; import cn.com.chenyixiao.rrs.entity.Food; import cn.com.chenyixiao.rrs.entity.Preference2d; import cn.com.chenyixiao.rrs.entity.RestaurantFood; import cn.com.chenyixiao.rrs.entity.User; import cn.com.chenyixiao.rrs.service.Preference2dService; import cn.com.chenyixiao.rrs.service.RestaurantFoodService; import cn.com.chenyixiao.rrs.service.UserService; import cn.com.chenyixiao.rrs.vo.RecommendResult; import cn.com.chenyixiao.rrs.vo.RecommendResultVO; @Controller @RequestMapping(produces="application/json;charset=UTF-8") public class RecommendController { private static final String filePath = "./rrs/data/dianping_user-restaurant.csv"; @Autowired private Preference2dService preference2dService; @Autowired private UserService userService; @Autowired private RestaurantFoodService restaurantFoodService; @ResponseBody @RequestMapping(value = "/recommend/user/{userId}", method = RequestMethod.GET) public String recommend(@PathVariable Long userId ) throws TasteException, IOException { DataModel dataModel = getFileDataModel(); UserSimilarity userSimilarity = // new PearsonCorrelationSimilarity(dataModel); //皮尔逊相关系数 // new EuclideanDistanceSimilarity(dataModel); //欧氏距离 // new TanimotoCoefficientSimilarity(dataModel); //谷本系数 new LogLikelihoodSimilarity(dataModel); //对数似然比 // userSimilarity.setPreferenceInferrer(new AveragingPreferenceInferrer(dataModel)); UserNeighborhood userNeighborhood = new NearestNUserNeighborhood(10, userSimilarity, dataModel); Recommender recommender = //new GenericUserBasedRecommender(dataModel, userNeighborhood, userSimilarity); new SVDRecommender(dataModel, new ALSWRFactorizer(dataModel, 10, 0.05, 10)); List<RecommendedItem> recommendedItems = recommender.recommend(userId, 10); Gson gson = new Gson(); List<RecommendResultVO> recommendResultVOs = new ArrayList<RecommendResultVO>(recommendedItems.size()); for (int i = 0; i < recommendedItems.size(); i++) { RecommendResultVO recommendResultVO = new RecommendResultVO(); recommendResultVO.setRestaurantId(recommendedItems.get(i).getItemID()); recommendResultVO.setScore(recommendedItems.get(i).getValue()); recommendResultVOs.add(i, recommendResultVO); } return gson.toJson(recommendResultVOs); } @ResponseBody @RequestMapping(value = "/recommend/user/{userId}/food/{foodId}", method = RequestMethod.GET) public String recommendByFood(@PathVariable Long userId, @PathVariable Long foodId ) throws TasteException, IOException { DataModel dataModel = getFileDataModel(); UserSimilarity userSimilarity = new LogLikelihoodSimilarity(dataModel); //对数似然比 UserNeighborhood userNeighborhood = new NearestNUserNeighborhood(10, userSimilarity, dataModel); Recommender recommender = new SVDRecommender(dataModel, new ALSWRFactorizer(dataModel, 10, 0.05, 10)); List<RecommendedItem> recommendedItems = recommender.recommend(userId, 1000); Gson gson = new Gson(); List<RecommendResultVO> recommendResultVOs = new ArrayList<RecommendResultVO>(); for (int i = 0; i < recommendedItems.size(); i++) { List<RestaurantFood> rfList = restaurantFoodService. getRestaurantFoodsByRestaurantId(recommendedItems.get(i).getItemID()); for (RestaurantFood restaurantFood : rfList) { if (restaurantFood.getFoodId() == foodId) { RecommendResultVO recommendResultVO = new RecommendResultVO(); recommendResultVO.setRestaurantId(recommendedItems.get(i).getItemID()); recommendResultVO.setScore(recommendedItems.get(i).getValue()); recommendResultVOs.add(recommendResultVO); } } } return gson.toJson(recommendResultVOs); } @ResponseBody @RequestMapping(value = "/recommend", method = RequestMethod.GET) public String recommendAll() throws TasteException, IOException { DataModel dataModel = getFileDataModel(); UserSimilarity userSimilarity = new PearsonCorrelationSimilarity(dataModel); UserNeighborhood userNeighborhood = new NearestNUserNeighborhood(2, userSimilarity, dataModel); Recommender recommender = // new GenericUserBasedRecommender(dataModel, userNeighborhood, userSimilarity); new SVDRecommender(dataModel, new ALSWRFactorizer(dataModel, 10, 0.05, 10)); List<User> users = userService.getAllUsers(); List<RecommendResult> recommendResults = new ArrayList<RecommendResult>(); for (int i = 0; i < users.size(); i++) { List<RecommendedItem> recommendedItems = recommender.recommend(users.get(i).getId(), 10); if (!recommendedItems.isEmpty()) { RecommendResult recommendResult = new RecommendResult(); recommendResult.setUserId(users.get(i).getId()); List<RecommendResultVO> recommendResultVOs = new ArrayList<RecommendResultVO>(); for (int j = 0; j < recommendedItems.size(); j++) { RecommendResultVO recommendResultVO = new RecommendResultVO(); recommendResultVO.setRestaurantId(recommendedItems.get(j).getItemID()); recommendResultVO.setScore(recommendedItems.get(j).getValue()); recommendResultVOs.add(j, recommendResultVO); } recommendResult.setRecommendResultVOs(recommendResultVOs); recommendResults.add(recommendResult); } } Gson gson = new Gson(); return gson.toJson(recommendResults); } private DataModel getGenericDataModel() { FastByIDMap<PreferenceArray> preferences = new FastByIDMap<PreferenceArray>(); List<User> users = userService.getAllUsers(); for (int j = 0; j < users.size(); j++) { List<Preference2d> preference2ds = preference2dService.getPreferencesByUserId(users.get(j).getId()); PreferenceArray prefsForUser = new GenericUserPreferenceArray(preference2ds.size()); prefsForUser.setUserID(j, users.get(j).getId()); for (int i = 0; i < preference2ds.size(); i++) { prefsForUser.setItemID(i, preference2ds.get(i).getRestaurant