https://github.com/sudothearkknight/book-recommendation-system
Along simal line to the MRS but i am exploring a different approach to the recommendation system here
https://github.com/sudothearkknight/book-recommendation-system
Last synced: 26 days ago
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Along simal line to the MRS but i am exploring a different approach to the recommendation system here
- Host: GitHub
- URL: https://github.com/sudothearkknight/book-recommendation-system
- Owner: sudoTheArkKnight
- Created: 2023-08-24T15:40:02.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-08-26T06:30:55.000Z (over 1 year ago)
- Last Synced: 2024-08-01T13:10:33.084Z (10 months ago)
- Language: Jupyter Notebook
- Size: 38.7 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Book Recommendation System
Welcome to the Book Recommendation System project, where we've developed a recommendation engine to suggest books based on different principles. This project leverages the power of Python and data analysis techniques to provide tailored book recommendations to users.
## Overview
The Book Recommendation System is designed to help users discover new books that align with their preferences. This system utilizes various principles to generate recommendations, ensuring a personalized experience for each user.
## Principles of Recommendation
### Collaborative Filtering
Collaborative Filtering is a technique that recommends items based on the preferences and behavior of other users. It identifies users with similar tastes and suggests books that those similar users have enjoyed.
### Content-Based Filtering
Content-Based Filtering suggests books based on the characteristics and features of the books themselves. It considers factors such as genre, author, language, and more to match user preferences.
### Hybrid Approach
The Hybrid Approach combines both Collaborative Filtering and Content-Based Filtering to provide well-rounded recommendations. By leveraging the strengths of both methods, this approach offers a more comprehensive and accurate recommendation system.
## How to Use
1. Clone the repository to your local machine.
2. Install the required dependencies using `pip install -r requirements.txt`.
3. Run the recommendation system application using `python app.py`.
4. Enter your preferences or book details to receive personalized recommendations.
## Future Enhancements
- Incorporating Natural Language Processing (NLP) for improved content-based recommendations.
- Implementing Matrix Factorization for advanced collaborative filtering.
- Enhancing user interface and interactivity for a seamless experience.
## Contact
For inquiries or collaboration opportunities, feel free to contact us at [[email protected]](mailto:[email protected]).
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