  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 short-term and
long-term intentions into consideration. Experiments on a wide range of
datasets on different domains demonstrate that our approach outperforms the
state-of-the-art by a wide margin.
