  User interests are usually dynamic in the real world, which poses both
theoretical and practical challenges for learning accurate preferences from
rich behavior data. Among existing user behavior modeling solutions, attention
networks are widely adopted for its effectiveness and relative simplicity.
Despite being extensively studied, existing attentions still suffer from two
limitations: i) conventional attentions mainly take into account the spatial
correlation between user behaviors, regardless the distance between those
behaviors in the continuous time space; and ii) these attentions mostly provide
a dense and undistinguished distribution over all past behaviors then
attentively encode them into the output latent representations. This is however
not suitable in practical scenarios where a user's future actions are relevant
to a small subset of her/his historical behaviors. In this paper, we propose a
novel attention network, named self-modulating attention, that models the
complex and non-linearly evolving dynamic user preferences. We empirically
demonstrate the effectiveness of our method on top-N sequential recommendation
tasks, and the results on three large-scale real-world datasets show that our
model can achieve state-of-the-art performance.
