Large-Scale User Modeling with Recurrent Neural Networks for Music Discovery on Multiple Time Scales
This addresses the challenge of improving music discovery for users on streaming platforms by overcoming limitations of existing methods like collaborative filtering and item co-occurrence algorithms.
The paper tackles the problem of modeling users for music recommendation by using recurrent neural networks to process sequential consumption data, resulting in semantically rich user representations that capture musical taste over time and can predict future songs on multiple time scales.
The amount of content on online music streaming platforms is immense, and most users only access a tiny fraction of this content. Recommender systems are the application of choice to open up the collection to these users. Collaborative filtering has the disadvantage that it relies on explicit ratings, which are often unavailable, and generally disregards the temporal nature of music consumption. On the other hand, item co-occurrence algorithms, such as the recently introduced word2vec-based recommenders, are typically left without an effective user representation. In this paper, we present a new approach to model users through recurrent neural networks by sequentially processing consumed items, represented by any type of embeddings and other context features. This way we obtain semantically rich user representations, which capture a user's musical taste over time. Our experimental analysis on large-scale user data shows that our model can be used to predict future songs a user will likely listen to, both in the short and long term.