FLOR: A Federated Learning-based Music Recommendation Engine
- Resource Type
- Conference
- Authors
- Sha, Jasper; Basara, Nathaniel; Freedman, Joseph; Xu, Hailu
- Source
- 2022 International Conference on Computer Communications and Networks (ICCCN) Computer Communications and Networks (ICCCN), 2022 International Conference on. :1-2 Jul, 2022
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Music
Servers
Recommender systems
Engines
Network systems
Federated Learning
Deep Clustering
Music Recommendation
- Language
- ISSN
- 2637-9430
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.