一、个人简介
💖💖作者：计算机编程果茶熊 💙💙个人简介：曾长期从事计算机专业培训教学，担任过编程老师，同时本人也热爱上课教学，擅长Java、微信小程序、Python、Golang、安卓Android等多个IT方向。会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法，也喜欢交流技术，大家有技术代码这一块的问题可以问我！ 💛💛想说的话：感谢大家的关注与支持！ 💜💜 网站实战项目 安卓/小程序实战项目 💕💕文末获取源码联系计算机编程果茶熊
二、系统介绍
开发语言：Java+Python 数据库：MySQL 系统架构：B/S 后端框架：SpringBoot(Spring+SpringMVC+Mybatis)+Django 前端：Vue+HTML+CSS+JavaScript+jQuery
本儿童图书推荐系统是一款基于B/S架构的综合性图书推荐平台，采用Java/Python双语言支持。
分别基于SpringBoot(Spring+SpringMVC+Mybatis)和Django框架进行后端开发，前端则采用Vue+ElementUI+HTML技术栈构建友好交互界面，数据持久层使用MySQL数据库。系统核心功能模块包括首页展示、用户管理、图书分类管理、儿童图书管理、热销图书展示、公告信息发布、系统管理、订单管理以及个人信息维护等。其中，最具特色的是基于协同过滤算法的智能推荐功能，该算法通过分析用户的浏览历史、购买记录和评分数据，为不同年龄段的儿童用户提供个性化的图书推荐，有效提升用户体验和图书匹配度。系统整体设计注重用户友好性和操作便捷性，管理员可以轻松进行图书分类管理、库存监控和订单处理，而普通用户则能够便捷地浏览图书信息、查看热销排行、进行图书订购并获取智能推荐。通过这套完整的功能设计，系统实现了儿童图书资源的高效管理和精准推荐，为儿童读者选择适合的图书提供了专业化的技术支持。
三、基于协同过滤算法的儿童图书推荐系统-视频解说
毕设没思路？儿童图书推荐系统帮你解决：Python+Django+协同过滤算法全流程指南
四、基于协同过滤算法的儿童图书推荐系统-功能展示
五、基于协同过滤算法的儿童图书推荐系统-代码展示
// 1. Book Recommendation (Collaborative Filtering Algorithm)
public List<Book> recommendBooks(Long userId) {
List<Book> recommendedBooks = new ArrayList<>();
// Fetch user's historical data (e.g., ratings or purchase history) from MySQL
List<UserRating> userRatings = userRatingRepository.findByUserId(userId);
if (userRatings.isEmpty()) {
// Fallback to hot books if no user history
return bookRepository.findTop10ByOrderBySalesDesc();
}
// Build user-item rating map for collaborative filtering
Map<Long, Double> userRatingMap = userRatings.stream()
.collect(Collectors.toMap(UserRating::getBookId, UserRating::getRating));
// Fetch all users' ratings to compute similarity
List<UserRating> allRatings = userRatingRepository.findAll();
Map<Long, Map<Long, Double>> userItemMatrix = new HashMap<>();
for (UserRating rating : allRatings) {
userItemMatrix.computeIfAbsent(rating.getUserId(), k -> new HashMap<>())
.put(rating.getBookId(), rating.getRating());
}
// Calculate cosine similarity between target user and others
Map<Long, Double> similarities = new HashMap<>();
for (Long otherUserId : userItemMatrix.keySet()) {
if (!otherUserId.equals(userId)) {
double similarity = calculateCosineSimilarity(userRatingMap, userItemMatrix.get(otherUserId));
similarities.put(otherUserId, similarity);
}
}
// Sort users by similarity and select top 5 similar users
List<Long> similarUsers = similarities.entrySet().stream()
.sorted(Map.Entry.<Long, Double>comparingByValue().reversed())
.limit(5)
.map(Map.Entry::getKey)
.collect(Collectors.toList());
// Aggregate book recommendations from similar users
Map<Long, Double> bookScores = new HashMap<>();
for (Long similarUserId : similarUsers) {
Map<Long, Double> otherUserRatings = userItemMatrix.get(similarUserId);
for (Map.Entry<Long, Double> entry : otherUserRatings.entrySet()) {
if (!userRatingMap.containsKey(entry.getKey())) {
bookScores.merge(entry.getKey(), entry.getValue() * similarities.get(similarUserId), Double::sum);
}
}
}
// Sort books by score and fetch top 10
List<Long> recommendedBookIds = bookScores.entrySet().stream()
.sorted(Map.Entry.<Long, Double>comparingByValue().reversed())
.limit(10)
.map(Map.Entry::getKey)
.collect(Collectors.toList());
recommendedBooks = bookRepository.findByIdIn(recommendedBookIds);
return recommendedBooks;
}
// Helper method for cosine similarity calculation
private double calculateCosineSimilarity(Map<Long, Double> user1Ratings, Map<Long, Double> user2Ratings) {
double dotProduct = 0.0, norm1 = 0.0, norm2 = 0.0;
for (Long bookId : user1Ratings.keySet()) {
if (user2Ratings.containsKey(bookId)) {
dotProduct += user1Ratings.get(bookId) * user2Ratings.get(bookId);
}
norm1 += Math.pow(user1Ratings.get(bookId), 2);
}
for (Double rating : user2Ratings.values()) {
norm2 += Math.pow(rating, 2);
}
norm1 = Math.sqrt(norm1);
norm2 = Math.sqrt(norm2);
if (norm1 == 0 || norm2 == 0) {
return 0.0;
}
return dotProduct / (norm1 * norm2);
}
// 2. Book Category Management (Add or Update Category)
public Category addOrUpdateCategory(CategoryDTO categoryDTO) {
Category category;
if (categoryDTO.getId() != null) {
// Update existing category
category = categoryRepository.findById(categoryDTO.getId())
.orElseThrow(() -> new RuntimeException("Category not found"));
if (!category.getName().equals(categoryDTO.getName())) {
// Check for duplicate category name
if (categoryRepository.existsByName(categoryDTO.getName())) {
throw new RuntimeException("Category name already exists");
}
category.setName(categoryDTO.getName());
}
} else {
// Add new category
if (categoryRepository.existsByName(categoryDTO.getName())) {
throw new RuntimeException("Category name already exists");
}
category = new Category();
category.setName(categoryDTO.getName());
}
// Update other fields
category.setDescription(categoryDTO.getDescription());
category.setAgeRange(categoryDTO.getAgeRange());
category.setUpdateTime(new Date());
category.setStatus(categoryDTO.getStatus() != null ? categoryDTO.getStatus() : "ACTIVE");
// Validate age range for children (e.g., 0-12 years)
if (!category.getAgeRange().matches("^[0-9]{1,2}-[0-9]{1,2}$")) {
throw new RuntimeException("Invalid age range format");
}
// Save to MySQL database
category = categoryRepository.save(category);
// Update related books' category if necessary
if (categoryDTO.getId() != null) {
List<Book> books = bookRepository.findByCategoryId(categoryDTO.getId());
for (Book book : books) {
book.setCategoryName(category.getName());
bookRepository.save(book);
}
}
return category;
}
// 3. Order Management (Create Order)
public Order createOrder(OrderDTO orderDTO, Long userId) {
// Validate user existence
User user = userRepository.findById(userId)
.orElseThrow(() -> new RuntimeException("User not found"));
Order order = new Order();
order.setUserId(userId);
order.setOrderTime(new Date());
order.setStatus("PENDING");
order.setTotalAmount(0.0);
List<OrderItem> orderItems = new ArrayList<>();
// Process each book in the order
for (OrderItemDTO itemDTO : orderDTO.getItems()) {
Book book = bookRepository.findById(itemDTO.getBookId())
.orElseThrow(() -> new RuntimeException("Book not found"));
// Check stock availability
if (book.getStock() < itemDTO.getQuantity()) {
throw new RuntimeException("Insufficient stock for book: " + book.getTitle());
}
// Calculate item price
double itemTotal = book.getPrice() * itemDTO.getQuantity();
OrderItem orderItem = new OrderItem();
orderItem.setBookId(book.getBookId());
orderItem.setQuantity(itemDTO.getQuantity());
orderItem.setPrice(book.getPrice());
orderItem.setTotal(itemTotal);
orderItems.add(orderItem);
// Update total amount
order.setTotalAmount(order.getTotalAmount() + itemTotal);
// Decrease book stock
book.setStock(book.getStock() - itemDTO.getQuantity());
bookRepository.save(book);
}
// Save order items and order to MySQL
order.setOrderItems(orderItems);
order = orderRepository.save(order);
for (OrderItem item : orderItems) {
item.setOrderId(order.getOrderId());
orderItemRepository.save(item);
}
// Update user's purchase history for recommendation
user.setLastPurchaseTime(new Date());
userRepository.save(user);
// Send order confirmation (simulated)
notificationService.sendOrderConfirmation(user.getEmail(), order.getOrderId());
return order;
}
六、基于协同过滤算法的儿童图书推荐系统-文档展示
七、END
💕💕文末获取源码联系计算机编程果茶熊
