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arXiv · 1902.07494

NAIRS: A Neural Attentive Interpretable Recommendation System

Shuai Yu·Yongbo Wang·Min Yang·Baocheng Li·Qiang Qu·Jialie Shen
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Abstract

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.

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Shuai Yu, Yongbo Wang, Min Yang, Baocheng Li, Qiang Qu, Jialie Shen. 2019-02-20. NAIRS: A Neural Attentive Interpretable Recommendation System. https://arxiv.org/abs/1902.07494

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