Large-scale user modeling with recurrent neural networks for music discovery on multiple time scales

@article{Boom2017LargescaleUM,
  title={Large-scale user modeling with recurrent neural networks for music discovery on multiple time scales},
  author={Cedric De Boom and Rohan Agrawal and Samantha Hansen and Esh Kumar and Romain Yon and Ching-Wei Chen and Thomas Demeester and Bart Dhoedt},
  journal={Multimedia Tools and Applications},
  year={2017},
  volume={77},
  pages={15385-15407}
}
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… CONTINUE READING
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