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The amount of content on online music streaming platforms is immense, andmost users only access a tiny fraction of this content. Recommender systems arethe application of choice to open up the collection to these users.Collaborative filtering has the disadvantage that it relies on explicitratings, which are often unavailable, and generally disregards the temporalnature of music consumption. On the other hand, item co-occurrence algorithms,such as the recently introduced word2vec-based recommenders, are typically leftwithout an effective user representation. In this paper, we present a newapproach to model users through recurrent neural networks by sequentiallyprocessing consumed items, represented by any type of embeddings and othercontext features. This way we obtain semantically rich user representations,which capture a user's musical taste over time. Our experimental analysis onlarge-scale user data shows that our model can be used to predict future songsa user will likely listen to, both in the short and long term.

Large-Scale User Modeling with Recurrent Neural Networks for Music Discovery on Multiple Time Scales