EXPLORE: EXPLainable item-tag CO-REcommendation

Abstract Tag-based recommendation has become increasingly important in recent years owing to the popularization of social tagging systems. Related studies on this subject can be categorized into item recommendation and tag recommendation based on the objective of the recommendation. In both categories, most existing recommendation approaches focus on improving prediction accuracy. But they ignore that the explanations of recommendations also greatly affect the decision-making of users. In social tagging systems, tags not only behave as auxiliary information of items but also show the implicit preferences of users. Therefore, they can be used to improve prediction accuracy and provide explanations to recommended items. Items and tags have interrelation and mutual effects. By focusing only on either item recommendation or tag recommendation, users may miss some information and can only achieve marginal gains. Fusing the two types of recommendations can improve the performance of both approaches. On the basis of the above ideas, in this study, we propose an EXPLainable item-tag CO-REcommendation (EXPLORE) framework that jointly recommends items and the corresponding tags. Different from conventional recommendations that utilize a single source of content, EXPLORE takes advantage of users’ interests, item contents, and item tags. The experiments conducted on three real-world datasets demonstrate that EXPLORE outperforms the state-of-the-art methods. Importantly, the recommended tags can provide explanations to the recommended items, thus making the recommendation results explainable.