Track Mix Generation on Music Streaming Services using Transformers
Walid Bendada Théo Bontempelli Mathieu Morlon Benjamin Chapus Thibault Cador Thomas Bouabça Guillaume Salha-Galvan
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.