NAIRS: A Neural Attentive Interpretable Recommendation System
This work addresses the need for interpretable and interactive recommendation systems for users, though it appears incremental as it builds on existing attention-based methods.
The paper tackles the problem of providing personalized recommendations by developing NAIRS, a neural attentive interpretable recommendation system that uses self-attention to weight user-interacted items, resulting in effective personalized recommendations as shown in demonstrations and experiments.
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.