An Artist Recommendation System built using the Bayesian Personalized Ranking (BPR) algorithm from the Implicit library. This project leverages user-artist interaction data to generate personalized artist recommendations, making it a great example of how collaborative filtering can be applied in the music domain.
The system uses implicit feedback (such as listening history) rather than explicit ratings to learn user preferences. Employing the BPR algorithm effectively ranks artists based on each user's historical interactions, allowing for accurate and scalable recommendations even without explicit ratings.
- Personalized Recommendations: Generates tailored artist suggestions based on user behavior.
- Implicit Feedback Modeling: Utilizes Bayesian Personalized Ranking to work with implicit interaction data.
- Scalable: Designed to handle large datasets efficiently.
- Extensible: Easy-to-modify codebase for further experimentation with alternative algorithms or datasets.