Track Mix Generation on Music Streaming Services using Transformers
This addresses music discovery for users on streaming services, but it is incremental as it applies an existing Transformer method to a new domain-specific task.
The paper tackles the problem of personalized playlist generation for music streaming by introducing Track Mix, a system that uses a Transformer model trained on user playlist sequences to automatically generate 'mix' playlists based on initial tracks, resulting in daily playlist generation for millions of users on Deezer.
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.