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https://github.com/al-chris/movie-recommendation-system
Demonstration on different types of recommender systems
https://github.com/al-chris/movie-recommendation-system
Last synced: 12 days ago
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Demonstration on different types of recommender systems
- Host: GitHub
- URL: https://github.com/al-chris/movie-recommendation-system
- Owner: al-chris
- License: mit
- Created: 2023-12-16T01:04:11.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-12-31T18:19:29.000Z (about 1 month ago)
- Last Synced: 2024-12-31T19:22:52.034Z (about 1 month ago)
- Language: Jupyter Notebook
- Size: 17.5 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Movie Recommendation System
This repository demonstrates various types of recommender systems for movies, including demographic filtering, content-based filtering, and collaborative filtering.
## Project Overview
The project explores different recommendation techniques:
- **Demographic Filtering**: Recommends popular movies based on general trends.
- **Content-Based Filtering**: Suggests movies similar to those a user has liked, based on movie attributes.
- **Collaborative Filtering**: Recommends movies by analyzing user-item interactions to find patterns among users and items.
## Repository Structure
- `Demographic_Filtering_Recommender.ipynb`: Notebook demonstrating demographic filtering techniques.
- `Content_Based_Filtering.ipynb`: Notebook implementing content-based filtering methods.
- `Collaborative_Filtering.ipynb`: Notebook showcasing collaborative filtering approaches.
- `data/`: Directory containing datasets used in the project.
- `LICENSE`: License information.
- `README.md`: Project overview and instructions.
## Getting Started
To explore the recommender systems:
1. **Clone the repository**:
```bash
git clone https://github.com/al-chris/Movie-Recommendation-System.git
cd Movie-Recommendation-System
```
2. **Set up a virtual environment** (optional but recommended):
```bash
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
```
3. **Install the required packages**:
```bash
pip install -r requirements.txt
```
4. **Run the Jupyter Notebooks**:
```bash
jupyter notebook
```
Open the desired notebook to explore the corresponding recommender system.
## Dataset
The project utilizes a movie dataset containing information such as movie titles, genres, and user ratings. The dataset is located in the `data/` directory.
## Contributing
Contributions are welcome. Feel free to open issues or submit pull requests.
## License
This project is licensed under the MIT License. See the `LICENSE` file for details.
## Acknowledgments
- Dataset: [MovieLens](https://grouplens.org/datasets/movielens/)
- Inspiration: Various open-source movie recommendation projects on GitHub.
For any questions or suggestions, please contact [Christopher Aliu](https://github.com/al-chris).