FLOR: A Federated Learning-based Music Recommendation Engine

Music recommendations are normally based on a users prior artist and genre preference, or based on a similar users preference. This may result in recommendations for users being limited to subsections of artists and sub-genres, rather than offering an exploration of different genres that are similar to the user's vocal preferences. In this work, we propose a federated learning-based approach that scalably tunes music clusters to accurately describe users' preferences in a particular genre. It uses deep clustering on frequency sets and mel-spectrograms of songs. It can improve song recommendations based on the user's musical tastes.