Embracing Genre Fluidity: Enhancing Music Recommendation through Audio Features and Vector Label Propagation

Concerning the subjectivity of genres and potential users’ preferences in streaming platforms, our study examines the capabilities of audio features integrated with user-attributed tags in improving the quality of a Recommender System. With this paper, we propose a novel approach to music genre prediction that exploits the interconnected nature of sub-genres. While we have implemented various supervised, unsupervised and semi-supervised algorithms in search of an efficient solution, the Agent-based Vector Label Propagation Algorithm (AVPRA) has yielded promising preliminary results, allowing for a more nuanced characterization of song genres and supporting the identification of sub-genres rather than or single-genre assignment.