Station and Track Attribute-Aware Music Personalization

We present a transformer for music personalization that recommends tracks given a station seed (artist) and improves the accuracy vs. a baseline matrix factorization method by 10%. Adding additional embeddings to capture track and station attributes further improves the accuracy of our recommendations by an additional 1% while also improving recommendation diversity, i.e. mitigating popularity bias. We analyze the learned embeddings and find they learn both explicit attributes provided at training and implicit attributes that may inform listener preferences. We also find that incorporating the station context of user feedback helps the model identify and transfer relevant listener preferences across different genres and artists. This particularly helps with music discovery on new stations.