https://github.com/integeralex/netflix-recommendation-system
This Netflix Recommendation System is a web application developed using Node.js and Express. It utilizes a recommendation engine written in Python
https://github.com/integeralex/netflix-recommendation-system
ai collaborate docker express netflix nodejs pandas recommendation-system scikit-learn
Last synced: 2 months ago
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This Netflix Recommendation System is a web application developed using Node.js and Express. It utilizes a recommendation engine written in Python
- Host: GitHub
- URL: https://github.com/integeralex/netflix-recommendation-system
- Owner: IntegerAlex
- License: gpl-3.0
- Created: 2024-03-20T12:57:17.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-24T19:57:19.000Z (4 months ago)
- Last Synced: 2025-04-16T04:14:08.912Z (3 months ago)
- Topics: ai, collaborate, docker, express, netflix, nodejs, pandas, recommendation-system, scikit-learn
- Language: TypeScript
- Homepage: https://NO MONEY
- Size: 15.4 MB
- Stars: 8
- Watchers: 1
- Forks: 5
- Open Issues: 5
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Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
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README
# Netflix Recommendation System
[](https://app.deepsource.com/gh/IntegerAlex/netflix-recommendation-system/)
[](https://codescene.io/projects/51785)
This Netflix Recommendation System is a web application developed using Node.js and Express. It utilizes a recommendation engine written in Python to provide personalized Netflix recommendations based on user input. The system integrates with the OMDB API to fetch movie details and leverages a large dataset for accurate recommendations. [ALGORITHM](/ALORITHM.md)
## Features
- Personalized Recommendations: Users can input their preferences, and the system provides tailored movie recommendations.
- Advanced Search Functionality: The system offers a comprehensive search feature to help users find specific movies.
- Integration with OMDB API: Utilizes the OMDB API to retrieve detailed information about movies, including posters, release years, genres, and ratings.## Technologies Used
- Node.js: The backend of the application is developed using Node.js, providing a fast and scalable environment.
- Express: Express.js is used to handle routing and middleware functions, facilitating the development of the RESTful API.
- Python: The recommendation engine is implemented in Python, allowing for efficient and accurate movie recommendations.
- EJS: EJS is used as the templating engine to dynamically generate HTML pages.
- OMDB API: Integration with the OMDB API enables access to a vast database of movie information.### Screenshots
Here are some screenshots showcasing our application:
- 
Landing page- 
After Input wait for Loading- 
Recommedations generated after user input## Demo
Here is a demo using the application
[](http://www.youtube.com/watch?v=013HnsjD75w "DEMO")
## Contributing
Contributions are welcome! If you have any ideas, suggestions, or improvements, please feel free to open an issue or submit a pull request.
Please note that this project is released with a Contributor Code of Conduct. By participating in this project, you agree to abide by its terms.
## License
This project is licensed under the [GPL V3](LICENSE).