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In this paper, we propose a novel sequence-aware recommendation model. Our model utilizes self-attention mechanism to infer the item-item relationship from user’s historical interactions. With self-attention, it is able to estimate the relative weights of each item in user interaction trajectories to learn better representations for user’s transient interests. The model is finally trained in a metric learning framework, taking both local and global user intentions into consideration. Experiments on a wide range of datasets on different domains demonstrate that our approach outperforms the state-of-theart by a wide margin.