NAIRS: A Neural Attentive Interpretable Recommendation System
arXiv:1902.07494
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.
This paper was published as a demonstration paper on WSDM'19. In this version, we added a detailed related work section
References in corpus (5)
- BPR: Bayesian Personalized Ranking from Implicit Feedback
- NAIS: Neural Attentive Item Similarity Model for Recommendation
- Session-based Recommendations with Recurrent Neural Networks
- ATRank: An Attention-Based User Behavior Modeling Framework for Recommendation
- MV-RNN: A Multi-View Recurrent Neural Network for Sequential Recommendation