A Survey on Recommender Systems using Graph Neural Network

Abstract

The expansion of the Internet has resulted in a change in the flow of information. With the vast amount of digital information generated online, it is easy for users to feel overwhelmed. Finding the specific information can be a challenge, and it can be difficult to distinguish credible sources from unreliable ones. This has made recommender system (RS) an integral part of the information services framework. These systems alleviate users from information overload by analyzing users’ past preferences and directing only desirable information toward users. Traditional RSs use approaches like collaborative and content-based filtering to generate recommendations. Recently, these systems have evolved to a whole new level, intuitively optimizing recommendations using deep network models. Graph Neural Networks (GNNs) have become one of the most widely used approaches in RSs, capturing complex relationships between users and items using graphs. In this survey, we provide a literature review of the latest research efforts done on GNN-based RSs. We present an overview of RS, discuss its generalized pipeline and evolution with changing learning approaches. Furthermore, we explore basic GNN architecture and its variants used in RSs, their applications, and some critical challenges for future research.