NAIRS: A Neural Attentive Interpretable Recommendation System

TL;DR

NAIRS is a neural recommendation system that uses self-attention to interpret user preferences and provide personalized, high-quality recommendations with visual explanations, demonstrated through an interactive application.

Contribution

It introduces a neural attentive model with interpretability features for recommendation systems, enhancing personalization and explanation capabilities.

Findings

Effective in distinguishing important user interactions

Provides high-quality personalized recommendations

Enables interactive user engagement and data collection

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.