技术栈后端框架Spring Boot简化配置、内嵌Tomcat、Spring MVC请求处理、Spring Security权限控制。数据库MySQL关系型数据存储、Redis缓存热门推荐数据。前端技术Thymeleaf模板引擎、HTML/CSS/JavaScript、Vue.js可选用于动态交互。辅助工具Lombok简化代码、MyBatis/JPA数据库操作、SwaggerAPI文档。背景与意义解决痛点传统美食平台推荐精准度低、用户交互单一。通过个性化算法如协同过滤提升推荐效果。价值用户端根据历史行为浏览、收藏推荐匹配菜品增强体验。商家端精准曝光目标客户提升转化率。技术实践整合Spring Boot全栈技术适合二次开发扩展。核心代码示例推荐算法逻辑协同过滤// 基于用户的协同过滤 public ListDish recommendByUser(Long userId) { // 1. 获取目标用户的历史行为数据 ListUserBehavior targetUserBehaviors behaviorMapper.selectByUser(userId); // 2. 计算与其他用户的相似度余弦相似度 MapLong, Double userSimilarities new HashMap(); ListUser allUsers userMapper.selectAll(); for (User otherUser : allUsers) { if (otherUser.getId().equals(userId)) continue; double similarity calculateCosineSimilarity(targetUserBehaviors, behaviorMapper.selectByUser(otherUser.getId())); userSimilarities.put(otherUser.getId(), similarity); } // 3. 根据相似用户推荐高评分菜品 return findTopRatedDishes(userSimilarities); }接口示例Spring MVCRestController RequestMapping(/api/recommend) public class RecommendController { Autowired private RecommendService recommendService; GetMapping(/user/{userId}) public ResponseEntityListDish getUserRecommendations(PathVariable Long userId) { return ResponseEntity.ok(recommendService.recommendByUser(userId)); } }数据库设计主要表结构用户表userCREATE TABLE user ( id BIGINT PRIMARY KEY AUTO_INCREMENT, username VARCHAR(50) NOT NULL UNIQUE, password VARCHAR(100) NOT NULL, gender VARCHAR(10), create_time DATETIME DEFAULT CURRENT_TIMESTAMP );菜品表dishCREATE TABLE dish ( id BIGINT PRIMARY KEY AUTO_INCREMENT, name VARCHAR(100) NOT NULL, category VARCHAR(50) COMMENT 菜系分类, price DECIMAL(10,2), image_url VARCHAR(255), average_rating FLOAT DEFAULT 0 );用户行为表user_behaviorCREATE TABLE user_behavior ( id BIGINT PRIMARY KEY AUTO_INCREMENT, user_id BIGINT NOT NULL, dish_id BIGINT NOT NULL, behavior_type ENUM(VIEW, COLLECT, RATE) COMMENT 浏览/收藏/评分, rating INT COMMENT 仅当behavior_typeRATE时有效, create_time DATETIME DEFAULT CURRENT_TIMESTAMP, FOREIGN KEY (user_id) REFERENCES user(id), FOREIGN KEY (dish_id) REFERENCES dish(id) );索引优化在user_behavior(user_id, dish_id)上建立联合索引加速查询用户历史行为。对dish(category)建立索引支持按菜系快速筛选。扩展方向实时推荐集成Kafka处理用户实时行为流。多维度推荐结合标签如辣度、口味和时空数据用户位置。可视化通过Echarts展示推荐结果的热力分布。