Python personal Project--personalized recommendation of watercress Books

Project Name: personalized recommendation of watercress Books

Requirements Brief: from a given watercress user name, get the user all Watercress friends list, from the Watercress friends to find out they have read and rated 5 stars of the book, if the same book by different friends rated 5 stars, the higher the rating of the more recommended.

input: watercress user Name

output: the top rated by the Watercress friends, the number of people who scored the most, and I have not read 10 books

Step conception:
1. With the given user name, replace the link below douban_id to see the list of friends that the user is following (access to the path requires login first)
Https://www.douban.com/people/douban_id/contacts

But if you're looking at your friends, you'll jump to:
Https://www.douban.com/contacts/list

The two pages show different forms

2. Get the user information by accessing Https://www.douban.com/people/douban_id/after getting the list of friends, save in user table, fields include: username, place of residence, time to join, signature, read book, book to read, book to read, Last Update time

3. After getting a friend list, visit the following links to see the books that your friends have read, get information about your books, and score information about your friends ' books.
Https://book.douban.com/people/douban_id/collect

Get a list of all the book IDs you've read (you need to page back and turn the pages to 15 books each,https://book.douban.com/people/fenng/collect?start=0&sort=time& Rating=all&filter=all&mode=grid, starting from start=0 first page, start=15 second page, start=30 third page, etc.)
Information on the book: Https://book.douban.com/subject/book_id/including ISBN, title, author, date of publication, pages, pricing, cover image, watercress overall score, number of reviews, content profile, author profile, etc.
The information of the book is static information, deposited in the books table

Friend ratings, friend essays, read time:
Https://book.douban.com/people/Fenng/collect?start=300&sort=time&rating=all&filter=all&mode= Grid

Friends to book evaluation information into rating table, user_id, book_id do foreign key

4. In the rating table to find the friend Evaluation 5 stars of the book, and I have not seen, and then ranked by the number of reviews DESC, take the top 10

Python personal Project--personalized recommendation of watercress Books