基于Spark的大数据电商推荐系统源码+学习说明.zip

package com.smallbell.business.rest; import com.smallbell.business.model.domain.Product; import com.smallbell.business.model.recom.Recommendation; import com.smallbell.business.model.request.*; import com.smallbell.business.service.*; import com.smallbell.business.model.domain.User; import com.smallbell.business.model.request.*; import com.smallbell.business.service.ProductService; import com.smallbell.business.service.RatingService; import com.smallbell.business.service.RecommenderService; import com.smallbell.business.service.UserService; import com.smallbell.business.utils.Constant; import org.springframework.beans.factory.annotation.Autowired; import org.springframework.stereotype.Controller; import org.springframework.ui.Model; import org.springframework.web.bind.annotation.*; import org.apache.log4j.Logger; import java.util.List; @RequestMapping("/rest/product") @Controller public class ProductRestApi { private static Logger logger = Logger.getLogger(ProductRestApi.class.getName()); @Autowired private RecommenderService recommenderService; @Autowired private ProductService productService; @Autowired private UserService userService; @Autowired private RatingService ratingService; /** * 获取热门推荐 * @param model * @return */ @RequestMapping(value = "/hot", produces = "application/json", method = RequestMethod.GET ) @ResponseBody public Model getHotProducts(@RequestParam("num")int num, Model model) { List<Recommendation> recommendations = recommenderService.getHotRecommendations(new HotRecommendationRequest(num)); model.addAttribute("success",true); model.addAttribute("products", productService.getRecommendProducts(recommendations)); return model; } /** * 获取打分最多的商品 * @param model * @return */ @RequestMapping(value = "/rate", produces = "application/json", method = RequestMethod.GET ) @ResponseBody public Model getRateMoreProducts(@RequestParam("num")int num, Model model) { List<Recommendation> recommendations = recommenderService.getRateMoreRecommendations(new RateMoreRecommendationRequest(num)); model.addAttribute("success",true); model.addAttribute("products", productService.getRecommendProducts(recommendations)); return model; } // 基于物品的协同过滤 @RequestMapping(value = "/itemcf/{id}", produces = "application/json", method = RequestMethod.GET) @ResponseBody public Model getItemCFProducts(@PathVariable("id")int id, Model model) { List<Recommendation> recommendations = recommenderService.getItemCFRecommendations(new ItemCFRecommendationRequest(id)); model.addAttribute("success", true); model.addAttribute("products", productService.getRecommendProducts(recommendations)); return model; } // 基于内容的推荐 @RequestMapping(value = "/contentbased/{id}", produces = "application/json", method = RequestMethod.GET) @ResponseBody public Model getContentBasedProducts(@PathVariable("id")int id, Model model) { List<Recommendation> recommendations = recommenderService.getContentBasedRecommendations(new ContentBasedRecommendationRequest(id)); model.addAttribute("success", true); model.addAttribute("products", productService.getRecommendProducts(recommendations)); return model; } /** * 获取单个商品的信息 * @param id * @param model * @return */ @RequestMapping(value = "/info/{id}", produces = "application/json", method = RequestMethod.GET ) @ResponseBody public Model getProductInfo(@PathVariable("id")int id, Model model) { model.addAttribute("success",true); model.addAttribute("product", productService.findByProductId(id)); return model; } /** * 模糊查询商品 * @param query * @param model * @return */ @RequestMapping(value = "/search", produces = "application/json", method = RequestMethod.GET ) @ResponseBody public Model getSearchProducts(@RequestParam("query")String query, Model model) { try { query = new String(query.getBytes("ISO-8859-1"),"UTF-8"); } catch(java.io.UnsupportedEncodingException e) { e.printStackTrace(); } List<Product> products = productService.findByProductName(query); model.addAttribute("success",true); model.addAttribute("products", products); return model; } @RequestMapping(value = "/rate/{id}", produces = "application/json", method = RequestMethod.GET ) @ResponseBody public Model rateToProduct(@PathVariable("id")int id, @RequestParam("score")Double score, @RequestParam("username")String username, Model model) { User user = userService.findByUsername(username); ProductRatingRequest request = new ProductRatingRequest(user.getUserId(), id, score); boolean complete = ratingService.productRating(request); //埋点日志 if(complete) { System.out.print("=========埋点========="); logger.info(Constant.PRODUCT_RATING_PREFIX + ":" + user.getUserId() +"|"+ id +"|"+ request.getScore() +"|"+ System.currentTimeMillis()/1000); } model.addAttribute("success",true); model.addAttribute("message"," 已完成评分！"); return model; } // 离线推荐 @RequestMapping(value = "/offline", produces = "application/json", method = RequestMethod.GET ) @ResponseBody public Model getOfflineProducts(@RequestParam("username")String username,@RequestParam("num")int num, Model model) { User user = userService.findByUsername(username); List<Recommendation> recommendations = recommenderService.getCollaborativeFilteringRecommendations(new UserRecommendationRequest(user.getUserId(), num)); model.addAttribute("success",true); model.addAttribute("products", productService.getRecommendProducts(recommendations)); return model; } // 实时推荐 @RequestMapping(value = "/stream", produces = "application/json", method = RequestMethod.GET ) @ResponseBody public Model getStreamProducts(@RequestParam("username")String username,@RequestParam("num")int num, Model model) { User user = userService.findByUsername(username); List<Recommendation> recommendations = recommenderService.getStreamRecommendations(new UserRecommendationRequest(user.getUserId(), num)); model.addAttribute("success",true); model.addAttribute("products", productService.getRecommendProducts(recommendations)); return model; } }