  In this paper, we study the problem of modeling users' diverse interests.
Previous methods usually learn a fixed user representation, which has a limited
ability to represent distinct interests of a user. In order to model users'
various interests, we propose a Memory Attention-aware Recommender System
(MARS). MARS utilizes a memory component and a novel attentional mechanism to
learn deep \textit{adaptive user representations}. Trained in an end-to-end
fashion, MARS adaptively summarizes users' interests. In the experiments, MARS
outperforms seven state-of-the-art methods on three real-world datasets in
terms of recall and mean average precision. We also demonstrate that MARS has a
great interpretability to explain its recommendation results, which is
important in many recommendation scenarios.
