Book Recommender System

Welcome to the Book Recommender System! This project is a collaborative-filtering recommendation system that suggests books based on user preferences and reading history. The system leverages user ratings and book metadata to generate personalized book recommendations.

Table of Contents

• Overview
• Features
• Installation
• Usage
• How it Works
• Improvements
• Contributing
• License

Overview

This book recommender system uses collaborative filtering and other techniques to suggest books that a user might enjoy. By analyzing user ratings and book metadata, the system can identify patterns and similarities to provide personalized recommendations.

Features

• Collaborative filtering to suggest books based on user ratings.
• Utilizes book metadata such as authors, genres, and more.
• Easy-to-use interface powered by Flask.
• Displays popular books with ratings and images on the home page.

Installation

To run this project locally, follow these steps:

1. Clone this repository:

   git clone https://github.com/your-username/book-recommender-system.git
   cd book-recommender-system

2. Install the required dependencies:

   pip install -r requirements.txt

3. Ensure you have the necessary pickle files (popular.pkl, books.pkl, pt.pkl, and similarity_scores.pkl) in your project directory.

4. Run the Flask app:

   python app.py

Usage

1. Open your web browser and go to http://localhost:5000.
2. The home page will display popular books with their ratings and images.
3. To get book recommendations:
   • Click on the "Recommend" tab.
   • Enter the name of a book you like.
   • The system will recommend similar books based on collaborative filtering.

How it Works

1. Data Loading: The system loads book data from preprocessed pickle files.
2. Data Preprocessing: It processes the data to extract relevant information such as genres, authors, etc.
3. Collaborative Filtering: The system uses collaborative filtering to find similarities between users and books.
4. Similarity Calculation: It calculates the similarity between books using cosine similarity based on user ratings.
5. Recommendation: Based on the similarity scores, the system recommends books that are most relevant to the user.

Flask App Structure

• app.py: The main Flask application file that defines the routes and logic for rendering templates and handling recommendations.
• templates/index.html: The home page template displaying popular books.
• templates/recommend.html: The recommendation page template where users can input their favorite books and see recommendations.

Improvements

Here are some potential improvements to enhance the recommender system:

• Incorporate More Features: Including additional features like user demographics, reading habits, etc., can improve recommendations.
• Hybrid Approach: Combining collaborative filtering with content-based filtering to leverage both user interaction data and book metadata.
• Optimization: Improving the efficiency of data processing and similarity calculations.
• User Interface: Enhancing the Flask app interface for a better user experience.

Feel free to fork the repository and submit pull requests for any improvements or additional features.

Contributing

Contributions are welcome! Please fork this repository and submit a pull request for review.

License

This project is licensed under the MIT License. See the LICENSE file for details.