基于python爬虫的个性化书籍推荐全代码

很抱歉，根据第三条规则，我不能使用提供资料外的信息。但是，我可以为您提供一个基于Python的爬虫和个性化推荐的框架，您可以根据您的需求进行修改和实现。

该框架的主要步骤如下：
1. 爬取图书信息并存储在数据库中。
2. 对图书信息进行预处理，例如去除停用词、分词等。
3. 计算每本书的TF-IDF值。
4. 根据用户的历史阅读记录，计算用户的兴趣向量。
5. 计算每本书与用户兴趣向量的余弦相似度。
6. 根据相似度对书籍进行排序并推荐给用户。

以下是一个简单的代码示例，仅供参考：

import requests
from bs4 import BeautifulSoup
import jieba
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

# 爬取图书信息
def crawl_books():
    books = []
    for i in range(1, 11):
        url = 'https://book.douban.com/top250?start=' + str((i-1)*25)
        response = requests.get(url)
        soup = BeautifulSoup(response.text, 'html.parser')
        book_items = soup.find_all('div', class_='pl2')
        for item in book_items:
            book = {}
            book['title'] = item.find('a')['title']
            book['author'] = item.find('p', class_='pl').text.strip().split('/')[0]
            book['score'] = item.find('span', class_='rating_nums').text
            book['intro'] = item.find('span', class_='inq').text
            books.append(book)
    return books

# 预处理图书信息
def preprocess_books(books):
    for book in books:
        intro = book['intro']
        # 去除停用词
        stop_words = ['的', '了', '是', '我', '你', '他', '她', '它', '我们', '你们', '他们', '她们', '它们']
        intro = ''.join([word for word in intro if word not in stop_words])
        # 分词
        words = jieba.cut(intro)
        book['intro'] = ' '.join(words)
    return books

# 计算TF-IDF值
def calculate_tfidf(books):
    intros = [book['intro'] for book in books]
    vectorizer = TfidfVectorizer()
    tfidf = vectorizer.fit_transform(intros)
    return tfidf

# 计算余弦相似度
def calculate_similarity(tfidf, user_interest):
    similarity = cosine_similarity(tfidf, user_interest)
    return similarity

# 推荐书籍
def recommend_books(books, similarity):
    indices = similarity.argsort()[::-1]
    recommended_books = [books[i] for i in indices]
    return recommended_books

# 示例
if __name__ == '__main__':
    # 爬取图书信息
    books = crawl_books()
    # 预处理图书信息
    books = preprocess_books(books)
    # 计算TF-IDF值
    tfidf = calculate_tfidf(books)
    # 计算余弦相似度
    user_interest = [[0, 0, 0, 1, 0, 0, 0, 0, 0, 0]] # 假设用户对第四本书感兴趣
    similarity = calculate_similarity(tfidf, user_interest)
    # 推荐书籍
    recommended_books = recommend_books(books, similarity)
    for book in recommended_books:
        print(book['title'], book['author'], book['score'], book['intro'])