This repository demonstrates various types of recommender systems for movies, including demographic filtering, content-based filtering, and collaborative filtering.
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.
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.
To explore the recommender systems:
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Clone the repository:
git clone https://github.com/al-chris/Movie-Recommendation-System.git cd Movie-Recommendation-System -
Set up a virtual environment (optional but recommended):
python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate
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Install the required packages:
pip install -r requirements.txt
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Run the Jupyter Notebooks:
jupyter notebook
Open the desired notebook to explore the corresponding recommender system.
The project utilizes a movie dataset containing information such as movie titles, genres, and user ratings. The dataset is located in the data/ directory.
Contributions are welcome. Feel free to open issues or submit pull requests.
This project is licensed under the MIT License. See the LICENSE file for details.
- Dataset: MovieLens
- Inspiration: Various open-source movie recommendation projects on GitHub.
For any questions or suggestions, please contact Christopher Aliu.