Graph Neural Networks in Recommender Systems: A Survey

TL;DR

This survey reviews recent advances in applying graph neural networks to recommender systems, highlighting models, challenges, and future directions in leveraging graph-structured data for personalized recommendations.

Contribution

It provides a comprehensive taxonomy and analysis of GNN-based recommendation models, addressing challenges and proposing new perspectives in the field.

Findings

GNNs effectively model complex user-item interactions.

Challenges include data heterogeneity and scalability.

Open-source implementations facilitate further research.

Abstract

With the explosive growth of online information, recommender systems play a key role to alleviate such information overload. Due to the important application value of recommender systems, there have always been emerging works in this field. In recommender systems, the main challenge is to learn the effective user/item representations from their interactions and side information (if any). Recently, graph neural network (GNN) techniques have been widely utilized in recommender systems since most of the information in recommender systems essentially has graph structure and GNN has superiority in graph representation learning. This article aims to provide a comprehensive review of recent research efforts on GNN-based recommender systems. Specifically, we provide a taxonomy of GNN-based recommendation models according to the types of information used and recommendation tasks. Moreover, we systematically analyze the challenges of applying GNN on different types of data and discuss how existing works in this field address these challenges. Furthermore, we state new perspectives pertaining to the development of this field. We collect the representative papers along with their open-source implementations in https://github.com/wusw14/GNN-in-RS.