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.

本文介绍了一种名为 NAIRS 的神经注意力可解释推荐系统，它采用自注意网络作为主要组成部分，使用注意力机制对用户交互物品进行加权，能够个性化高质量地进行推荐并提供视觉提示来解释推荐，演示应用程序能够与推荐系统进行交互并不断收集训练数据来改进系统。实验结果表明，NAIRS 是有效的。