  We show that collaborative filtering can be viewed as a sequence prediction
problem, and that given this interpretation, recurrent neural networks offer
very competitive approach. In particular we study how the long short-term
memory (LSTM) can be applied to collaborative filtering, and how it compares to
standard nearest neighbors and matrix factorization methods on movie
recommendation. We show that the LSTM is competitive in all aspects, and
largely outperforms other methods in terms of item coverage and short term
predictions.
