Language Models for Next-Track Music Recommendation

Next-track music recommendation is the task of automatically determining the next song to play in a music listening session. Almost all music streaming platforms on the web provide their users with such a feature today. In this work, we propose the use of language modeling techniques for this task and investigate how well these techniques perform in the context of popular and also more diverse music. For this, we implement two basic language models, one based on n-grams and the other based on a recurrent neural network. We evaluate these models on two datasets, one limited to popular music and one consisting of more diverse tracks. Further, we also compare them with a nearest-neighbor model. Our results suggest that language models perform well in the context of popular music and can be used both as a basis for more sophisticated models and as a strong comparative baseline.