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