NAIRS:一个神经关注可解释推荐系统

Shuai Yu, Yongbo Wang, Min Yang, Baocheng Li, Qiang Qu, Jialie Shen
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引用次数: 17

摘要

在本文中,我们开发了一个神经关注可解释推荐系统,命名为NAIRS。自关注网络作为系统的关键组成部分,用于为用户的交互项分配关注权重。这种注意机制可以区分各种交互项目对用户配置文件的重要性。%,它还提供了可解释的建议。NAIRS基于自关注网络获取的用户资料,提供个性化的高质量推荐。此外,它还开发了视觉线索来解释建议。这个演示应用程序实现了NAIRS,使用户能够与推荐系统进行交互,并且它持续收集训练数据以改进系统。演示和实验结果表明了NAIRS的有效性。
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NAIRS: A Neural Attentive Interpretable Recommendation System
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. %, and it also provides interpretable recommendations. 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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