Book recommendations learned from the Book-Crossing ratings: 1,149,780 ratings by 278,858 users on 271,360 books (Kaggle, Book Recommendation Dataset).

The repository holds two versions of the project. v1 is kept exactly as I built it by hand. v2 is being built beside it with Claude Code, on the same data, and will be measured against v1 on the same test split.

• v1/notebooks/: exploratory data analysis, collaborative filtering (user- and item-based) and content-based filtering.
• v1/src/: data loading, cleaning and the collaborative filtering models.
• v1/app/: a FastAPI service that finds books similar to a given title, with covers and descriptions from Open Library. It runs on Fly.io at https://what-else-should-i-read.fly.dev; the machine sleeps when idle, so the first request can take up to 20 seconds.
• The report: Book Recommendation System: Collaborative & Content-Based Filtering Approaches (30 pages, LaTeX).

v1's files are as they were; they only moved into v1/. The last three v1 commits (October 2026) were made with Claude Code while tidying the repository: committing local work, limiting CORS and updating paths. The notebooks read the data from an absolute path on the machine they were written on; to run them, point dataset_folder at datasets/.

Built in stages, each measured on the same test split as v1. v2/README.md says how to run it and what each stage found.

1. Evaluation, done (report): v1 reproduces exactly from its own code, and v1 is scored again next to simple baselines. v1's user-based model, as shipped, recommends best so far: 27.7% of users find one of their held-out books in its top 10, against 14.9% for the most popular books.
2. Data quality: clean titles, merge editions of the same book, fetch book details once.
3. Models: use the implicit interactions (62.3% of the ratings are 0, meaning a book was seen but not rated), add content embeddings for books with few ratings, and combine both.
4. A demo on saricmilos.com that needs no server.

datasets/ holds Books.csv, Ratings.csv and Users.csv. Both versions read them and neither changes them.

MIT