A survey on knowledge graph-based recommender systems

Abstract

Recommender system (RS) targets at providing accurate item recommendations to users with respect to their preferences; it has been widely employed in various online applications for addressing the problem of information explosion and improving user experience. In the past decades, while tremendous efforts have been made in enhancing the performance of RSs, some long-standing challenges, such as data sparsity, cold start, and result diversity, are unaddressed. Along this line, an emerging research trend is to exploit the rich semantic information contained in the knowledge graph (KG); it has been proven to be an effective way to enhance the capability of RSs. To this end, we provide a focused survey on KG-based RS via a holistic perspective of both technologies and applications. Specifically, firstly, we briefly review the core concepts and classical algorithms of the RSs and KGs. Secondly, we comprehensively introduce the representative and state-of-the-art works in this field based on different strategies of exploiting KGs for RSs. Meanwhile, we also summarize some typical application scenarios of KG-based RSs, for facilitating the hands-on practices of corresponding algorithms. Finally, we present our opinions on the prospects of KG-based RS and suggest some future research directions in this area.