IUG-CF: Neural collaborative filtering with ideal user group labels

Abstract

Demographics are crucial information for recommender systems (RSs). Most existing demographic-based RSs focus on similarity between user profiles. However, they rarely incorporate demographic data to describe an item and establish the connection between items and users. In this paper, we propose the concept of the ideal user group (IUG) as a dynamic label for items. This label indicates the users who are most suitable for an item, based on the demographics of its historical customers. Unlike a general label (such as genre or language), the IUG is dynamically changing with the distribution of historical user demographics and is built based on demographic information that undergoes a split-combine process. To validate our method’s effectiveness, we propose an IUG-based neural collaborative filtering (IUG-CF) model. Experimental results on three real-world datasets show that the IUG is an effective approach for improving recommendation performance.