This paper introduces Track Mix, a personalized playlist generation system released in 2022 on the music streaming service Deezer.
Track Mix automatically generates “mix” playlists inspired by initial music tracks, allowing users to discover music similar to their favorite content. To generate these mixes, we consider a Transformer model trained on millions of track sequences from user playlists. In light of the growing popularity of Transformers in recent years, we analyze the advantages, drawbacks, and technical challenges of using such a model for mix generation on the service, compared to a more traditional collaborative filtering approach.
Since its release, Track Mix has been generating playlists for millions of users daily, enhancing their music discovery experience on Deezer.
This paper has been accepted for publication in the proceedings of the 17th ACM Conference on Recommender Systems (RecSys 2023), as an industry paper.