  In this paper, we develop a neural attentive interpretable recommendation
system, named NAIRS. A self-attention network, as a key component of the
system, is designed to assign attention weights to interacted items of a user.
This attention mechanism can distinguish the importance of the various
interacted items in contributing to a user profile. Based on the user profiles
obtained by the self-attention network, NAIRS offers personalized high-quality
recommendation. Moreover, it develops visual cues to interpret recommendations.
This demo application with the implementation of NAIRS enables users to
interact with a recommendation system, and it persistently collects training
data to improve the system. The demonstration and experimental results show the
effectiveness of NAIRS.
