This GitHub repository provides a simple implementation of an item-to-item recommendation system using the MovieLens dataset. Item-to-item recommendation systems, also known as collaborative filtering, aim to recommend items to users based on the similarity between items. In this case, we leverage the MovieLens dataset to recommend movies to users based on the similarity of movies.
Make sure you have the following dependencies installed:
- Python 3.x
- NumPy
- pandas
- cossine-similarity
Implementation The recommendation system is implemented in the itcf.ipynb file. The system calculates item similarity using the cosine similarity measure and recommends movies based on user preferences.
Contributing If you find any issues or have suggestions for improvement, please open an issue or create a pull request. Your contributions are highly welcome!
License This project is licensed under the MIT License - see the LICENSE file for details.