- Created TrackRecommendationService with ML-based algorithms
- Collaborative filtering (40%) using similar users' preferences
- Content-based filtering (30%) using track metadata (genre, artist, year)
- Popularity scoring (20%) based on play_count and like_count
- Recency scoring (10%) for recently uploaded tracks
- Support for seed tracks, genre filtering, and track exclusion
- Added unit tests for scoring algorithms
- Combines multiple algorithms for personalized recommendations
