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This paper introduces Track Mix, a personalized playlist generation systemreleased in 2022 on the music streaming service Deezer. Track Mix automaticallygenerates "mix" playlists inspired by initial music tracks, allowing users todiscover music similar to their favorite content. To generate these mixes, weconsider a Transformer model trained on millions of track sequences from userplaylists. In light of the growing popularity of Transformers in recent years,we analyze the advantages, drawbacks, and technical challenges of using such amodel for mix generation on the service, compared to a more traditionalcollaborative filtering approach. Since its release, Track Mix has beengenerating playlists for millions of users daily, enhancing their musicdiscovery experience on Deezer.

Track Mix Generation on Music Streaming Services using Transformers