用springboot写一个图书推荐算法

首先，需要确定推荐算法的实现方式。常见的推荐算法有基于内容的推荐、协同过滤推荐、混合推荐等。

假设我们采用协同过滤推荐算法，具体实现步骤如下：

1. 数据准备：获取图书信息数据集，包括图书的名称、作者、出版社、标签等信息。

2. 用户数据准备：获取用户对图书的评分数据集，包括用户ID、图书ID、评分等信息。

3. 数据预处理：将用户评分数据集转化为用户-图书评分矩阵。

4. 相似度计算：计算用户之间的相似度，可以采用余弦相似度等算法。

5. 推荐结果生成：根据用户历史评分信息和相似度矩阵，生成推荐结果。

6. 结果展示：将推荐结果展示给用户。

在Spring Boot中，可以使用MyBatis等ORM框架操作数据库获取数据，并使用Jupyter Notebook等数据分析工具进行数据预处理和算法实现。可以将推荐结果以JSON格式返回给前端，供用户查看。

具体代码实现可以参考以下示例：

1. 获取图书信息数据集

@Repository
public interface BookRepository extends JpaRepository<Book, Long> {
    // 查询所有图书
    List<Book> findAll();
}

2. 获取用户对图书的评分数据集

@Repository
public interface RatingRepository extends JpaRepository<Rating, Long> {
    // 根据用户ID查询评分记录
    List<Rating> findByUserId(Long userId);
    // 根据图书ID查询评分记录
    List<Rating> findByBookId(Long bookId);
}

3. 数据预处理

public class DataProcessor {
    public static Map<Long, Map<Long, Double>> process(List<Rating> ratings) {
        Map<Long, Map<Long, Double>> userData = new HashMap<>();
        for (Rating rating : ratings) {
            Long userId = rating.getUserId();
            Long bookId = rating.getBookId();
            Double score = rating.getScore();
            if (!userData.containsKey(userId)) {
                userData.put(userId, new HashMap<>());
            }
            userData.get(userId).put(bookId, score);
        }
        return userData;
    }
}

4. 相似度计算

public class SimilarityCalculator {
    public static double cosineSimilarity(Map<Long, Double> user1, Map<Long, Double> user2) {
        double dotProduct = 0.0;
        double norm1 = 0.0;
        double norm2 = 0.0;
        for (Long bookId : user1.keySet()) {
            if (user2.containsKey(bookId)) {
                dotProduct += user1.get(bookId) * user2.get(bookId);
            }
            norm1 += user1.get(bookId) * user1.get(bookId);
        }
        for (Long bookId : user2.keySet()) {
            norm2 += user2.get(bookId) * user2.get(bookId);
        }
        return dotProduct / (Math.sqrt(norm1) * Math.sqrt(norm2));
    }
}

5. 推荐结果生成

public class RecommendationGenerator {
    public static List<Book> generate(Long userId, Map<Long, Map<Long, Double>> userData, List<Book> bookList) {
        Map<Long, Double> userRatings = userData.get(userId);
        Map<Long, Double> similarities = new HashMap<>();
        for (Long otherUserId : userData.keySet()) {
            if (!otherUserId.equals(userId)) {
                Map<Long, Double> otherUserRatings = userData.get(otherUserId);
                double similarity = SimilarityCalculator.cosineSimilarity(userRatings, otherUserRatings);
                similarities.put(otherUserId, similarity);
            }
        }
        List<Book> recommendedBooks = new ArrayList<>();
        for (Book book : bookList) {
            if (!userRatings.containsKey(book.getId())) {
                double weightedScoreSum = 0.0;
                double similaritySum = 0.0;
                for (Long otherUserId : similarities.keySet()) {
                    Map<Long, Double> otherUserRatings = userData.get(otherUserId);
                    if (otherUserRatings.containsKey(book.getId())) {
                        double similarity = similarities.get(otherUserId);
                        double score = otherUserRatings.get(book.getId());
                        weightedScoreSum += similarity * score;
                        similaritySum += similarity;
                    }
                }
                if (similaritySum > 0.0) {
                    double weightedScore = weightedScoreSum / similaritySum;
                    book.setScore(weightedScore);
                    recommendedBooks.add(book);
                }
            }
        }
        Collections.sort(recommendedBooks, Comparator.comparing(Book::getScore).reversed());
        return recommendedBooks;
    }
}

6. 结果展示

@RestController
@RequestMapping("/recommendation")
public class RecommendationController {
    @Autowired
    private BookRepository bookRepository;
    @Autowired
    private RatingRepository ratingRepository;

    @GetMapping("/{userId}")
    public List<Book> recommend(@PathVariable Long userId) {
        List<Book> bookList = bookRepository.findAll();
        List<Rating> ratings = ratingRepository.findByUserId(userId);
        Map<Long, Map<Long, Double>> userData = DataProcessor.process(ratings);
        List<Book> recommendedBooks = RecommendationGenerator.generate(userId, userData, bookList);
        return recommendedBooks;
    }
}