Book Recommendation System

Overview

This project is a simple implementation of a Book Recommendation System that applies basic collaborative filtering techniques (user-based, item-based, and model-based using SVD) along with a popularity-based recommendation approach. The system utilizes the Book-Crossing dataset.

Features

• Collaborative Filtering
  • User-based filtering
  • Item-based filtering
  • Model-based filtering using SVD
• Popularity-Based Recommendations
• Book-Crossing Dataset for real-world book rating data
• Considers factors such as the number of ratings, average rating scores, experienced users and the total number of users who have interacted with a book.
• Flask Backend
  • Handles user requests and processes recommendation logic

Dataset

The project uses the Book-Crossing dataset

Technologies Used

• Python
• Flask (backend)
• Pandas & NumPy
• SciPy (for collaborative filtering & SVD)
• Scikit-learn (for model-based filtering)