Project case study
🎵 AI-Powered Music Recommendation System
Recommendation system case study for ranking songs from listening behavior and collaborative signals
Music platforms win when discovery feels personal without becoming repetitive. This case study uses listening behaviour from the Million Song Dataset to build recommendation models that rank songs for each user.
The work compares collaborative filtering, matrix factorization and ranking-oriented baselines so the final recommendations can be judged by retrieval quality, coverage and practical usability.
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