  Recommender systems objectives can be broadly characterized as modeling user
preferences over short-or long-term time horizon. A large body of previous
research studied long-term recommendation through dimensionality reduction
techniques applied to the historical user-item interactions. A recently
introduced session-based recommendation setting highlighted the importance of
modeling short-term user preferences. In this task, Recurrent Neural Networks
(RNN) have shown to be successful at capturing the nuances of user's
interactions within a short time window. In this paper, we evaluate RNN-based
models on both short-term and long-term recommendation tasks. Our experimental
results suggest that RNNs are capable of predicting immediate as well as
distant user interactions. We also find the best performing configuration to be
a stacked RNN with layer normalization and tied item embeddings.
