Collaborative Filtering Book Recommender

This is a Book Recommendation System that suggests books to users using two main techniques: Popularity-Based Filtering and Collaborative Filtering. It demonstrates the application of recommender system logic with real-world datasets and includes a simple web interface built with Flask.

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Dataset

• Source: Book Recommendation Dataset – Kaggle
• The dataset includes:
  • Books.csv: Book metadata (title, author, publisher)
  • Users.csv: User demographic information
  • Ratings.csv: User ratings for books
• The dataset was cleaned and filtered for meaningful recommendations (based on minimum number of ratings by users and for books).

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Libraries Used

• pandas, numpy – data manipulation and analysis
• sklearn – building collaborative filtering model (cosine similarity)
• flask – for creating a simple web interface
• pickle – saving and loading precomputed models/data

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Recommendation Techniques

Popularity-Based Recommender

• Filters books that received at least 200 ratings.
• Sorts them by average rating.
• Displays the top 50 most popular books universally.

Collaborative Filtering Recommender

• Filters users who rated 150+ books and books with 50+ ratings.
• Constructs a user-book matrix and computes cosine similarity between users.
• Provides personalized recommendations based on similar user behavior.

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Pickle Files (Model Artifacts)

The following preprocessed models and data are stored as .pkl files and included in the repo via Git LFS:

• top_50_books.pkl – for popularity-based recommendations
• pvt.pkl – user-book pivot table
• similarity.pkl – cosine similarity matrix
• books.pkl – book metadata used in recommendation display

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Web App

A simple Flask-based interface is included:

• The homepage shows the top 50 most popular books.
• The recommendation page allows users to input a book and receive similar book suggestions.

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Notes

• The project uses Git LFS to manage large .pkl files — make sure Git LFS is installed to access them.
• Further enhancements can include:
  • Using matrix factorization (e.g., SVD)
  • Adding login/user profiles
  • Cloud deployment for public access

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Author

• Ali Shayan – GitHub Profile

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Feel free to fork the repo, open issues, or suggest improvements!