  The application of machine learning techniques to large-scale personalized
recommendation problems is a challenging task. Such systems must make sense of
enormous amounts of implicit feedback in order to understand user preferences
across numerous product categories. This paper presents a deep learning based
solution to this problem within the collaborative filtering with implicit
feedback framework. Our approach combines neural attention mechanisms, which
allow for context dependent weighting of past behavioral signals, with
representation learning techniques to produce models which obtain extremely
high coverage, can easily incorporate new information as it becomes available,
and are computationally efficient. Offline experiments demonstrate significant
performance improvements when compared to several alternative methods from the
literature. Results from an online setting show that the approach compares
favorably with current production techniques used to produce personalized
product recommendations.
