package com.huat.huangjiahao.business.rest;
import com.huat.huangjiahao.business.model.domain.Product;
import com.huat.huangjiahao.business.model.recom.Recommendation;
import com.huat.huangjiahao.business.model.request.*;
import com.huat.huangjiahao.business.service.*;
import com.huat.huangjiahao.business.model.domain.User;
import com.huat.huangjiahao.business.utils.Constant;
import com.huat.huangjiahao.business.model.domain.Product;
import com.huat.huangjiahao.business.model.domain.User;
import com.huat.huangjiahao.business.model.recom.Recommendation;
import com.huat.huangjiahao.business.model.request.*;
import com.huat.huangjiahao.business.service.ProductService;
import com.huat.huangjiahao.business.service.RatingService;
import com.huat.huangjiahao.business.service.RecommenderService;
import com.huat.huangjiahao.business.service.UserService;
import com.huat.huangjiahao.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;
}
/**
* 埋点日志采集
*
* @param id
* @param score
* @param username
* @param model
* @return
*/
@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)
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));
if (recommendations.size() == 0) model = null;
return model;
}
}
基于Spark机器学习的电商推荐系统设计与实现.zip
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2023-08-03
17:47:55
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基于Spark机器学习的电商推荐系统设计与实现.zip （302个子文件） 
OnlineRecommender$.class 17KB OnlineRecommender$.class 17KB OfflineRecommender$.class 15KB OfflineRecommender$.class 10KB ALSTrainer$.class 9KB ALSTrainer$.class 9KB DataLoader$.class 8KB DataLoader$.class 8KB StatisticsRecommender$.class 7KB StatisticsRecommender$.class 7KB Product.class 6KB Product.class 6KB ProductRating.class 6KB ProductRating.class 6KB Rating.class 6KB Rating.class 6KB Rating.class 6KB Rating.class 6KB ProductRecs.class 5KB UserRecs.class 5KB ProductRecs.class 5KB ProductRecs.class 5KB UserRecs.class 5KB UserRecs.class 5KB ProductRecs.class 5KB UserRecs.class 5KB MongoConfig.class 5KB MongoConfig.class 5KB Recommendation.class 5KB MongoConfig.class 5KB MongoConfig.class 5KB MongoConfig.class 5KB Recommendation.class 5KB MongoConfig.class 5KB Recommendation.class 5KB MongoConfig.class 5KB Recommendation.class 5KB MongoConfig.class 5KB OnlineRecommender.class 4KB OnlineRecommender.class 3KB OnlineRecommender$$anonfun$computeProductScore$3.class 3KB OnlineRecommender$$anonfun$computeProductScore$3.class 3KB OnlineRecommender$$anonfun$main$1$$anonfun$apply$2.class 3KB OnlineRecommender$$anonfun$main$1$$anonfun$apply$2.class 3KB OnlineRecommender$$anonfun$computeProductScore$1$$anonfun$apply$mcVI$sp$1.class 3KB OnlineRecommender$$anonfun$computeProductScore$1$$anonfun$apply$mcVI$sp$1.class 3KB Application.class 3KB Application.class 3KB Product$.class 3KB OfflineRecommender$$anonfun$7.class 3KB OfflineRecommender$$anonfun$7.class 2KB Product$.class 2KB ProductRating$.class 2KB OnlineRecommender$$anonfun$1.class 2KB OfflineRecommender$$anonfun$12.class 2KB ProductRating$.class 2KB OfflineRecommender$$anonfun$12.class 2KB Rating$.class 2KB Rating$.class 2KB ProductRecs$.class 2KB OnlineRecommender$$anonfun$1.class 2KB ALSTrainer$$anonfun$2.class 2KB UserRecs$.class 2KB ALSTrainer$$anonfun$2$$anonfun$apply$1.class 2KB Rating$.class 2KB Rating$.class 2KB OnlineRecommender$$anonfun$2.class 2KB ALSTrainer$$anonfun$2.class 2KB OnlineRecommender$$anonfun$computeProductScore$1.class 2KB ProductRecs$.class 2KB OnlineRecommender$$anonfun$2.class 2KB ALSTrainer$$anonfun$2$$anonfun$apply$1.class 2KB ProductRecs$.class 2KB UserRecs$.class 2KB OnlineRecommender$$anonfun$computeProductScore$1.class 2KB UserRecs$.class 2KB ProductRecs$.class 2KB OnlineRecommender$$anonfun$saveDataToMongoDB$1.class 2KB UserRecs$.class 2KB OfflineRecommender$$anonfun$10.class 2KB OfflineRecommender$$anonfun$10.class 2KB OnlineRecommender$$anonfun$saveDataToMongoDB$1.class 2KB StatisticsRecommender.class 2KB MongoConfig$.class 2KB Recommendation$.class 2KB MongoConfig$.class 2KB DataLoader.class 2KB MongoConfig$.class 2KB ConnHelper$.class 2KB Recommendation$.class 2KB StatisticsRecommender.class 2KB ConnHelper$.class 2KB DataLoader.class 2KB MongoConfig$.class 2KB Recommendation$.class 2KB MongoConfig$.class 2KB OnlineRecommender$$anonfun$main$1.class 2KB MongoConfig$.class 2KB LogProcessor.class 2KB MongoConfig$.class 2KB共 302 条
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