Recommender systems traditionally assume that user profiles and movie attributes
are static. Temporal dynamics are purely reactive, that is, they are inferred after
they are observed, e.g. after a user's taste has changed or based on
hand-engineered temporal bias corrections for movies. We propose Recurrent
Recommender Networks (RRN) that are able to predict future behavioral trajectories.
This is achieved by endowing both users and movies with a Long Short-Term Memory
(LSTM) autoregressive model that captures dynamics, in addition to a more
traditional low-rank factorization. On multiple real-world datasets, our model
offers excellent prediction accuracy and it is very compact, since we need not
learn latent state but rather just the state transition function.