https://github.com/2003harsh/book_recommender_system

This repository contains a collaborative filtering-based book recommender system built with Python Flask. 📚✨ The system utilizes cosine similarity to suggest books based on user preferences and historical data. 📊🔍
https://github.com/2003harsh/book_recommender_system

cosine-similarity flask machine-learning recommendation-system

Last synced: about 2 months ago
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This repository contains a collaborative filtering-based book recommender system built with Python Flask. 📚✨ The system utilizes cosine similarity to suggest books based on user preferences and historical data. 📊🔍

• Host: GitHub
• URL: https://github.com/2003harsh/book_recommender_system
• Owner: 2003HARSH
• License: mit
• Created: 2023-07-23T06:42:12.000Z (over 1 year ago)
• Default Branch: master
• Last Pushed: 2024-07-06T06:08:25.000Z (6 months ago)
• Last Synced: 2024-07-06T07:25:31.052Z (6 months ago)
• Topics: cosine-similarity, flask, machine-learning, recommendation-system
• Language: HTML
• Homepage:
• Size: 15.6 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Book Recommender System using Collaborative Filtering

This repository contains a collaborative filtering-based book recommender system built with Python Flask. The recommender system utilizes cosine similarity to suggest books based on user preferences and historical data.

## Features

- Collaborative filtering algorithm for personalized recommendations.

- Utilizes cosine similarity for efficient book similarity calculations.

- User-friendly interface built with Flask for easy interaction.

## Requirements

- Python 3.x

- Flask

- Pandas

- NumPy

- scikit-learn

## Installation

1. Clone the repository: `git clone https://github.com/your_username/book-recommender.git`

2. Navigate to the project directory: `cd book-recommender`

3. Install dependencies: `pip install -r requirements.txt`

## Usage

1. Run the Flask app: `python app.py`

2. Access the book recommender system in your web browser at `http://localhost:5000`

## How it Works

The recommender system analyzes user book ratings and similarities between books using cosine similarity. Based on this analysis, it generates personalized recommendations for users.

## Future Improvements

- Integration with additional data sources for broader book recommendations.

- Enhanced user interface with improved design and usability.

- Implementation of more advanced recommendation algorithms.

## Contributions

Contributions are welcome! Feel free to submit issues, feature requests, or pull requests to help improve the project.