The Recommender system is a vital information service on today's Internet. Recently, graph neural networks have emerged as the leading approach for recommender systems. We try to review recent literature on graph neural network-based recommender systems, covering the background and development of both recommender systems and graph neural networks. Then categorizing recommender systems by their settings and graph neural networks by spectral and spatial models, we explore the motivation behind incorporating graph neural networks into recommender systems. We also analyze challenges and open problems in graph construction, embedding propagation and aggregation, and computation efficiency. This guides us to better explore the future directions and developments in this domain.

翻译：推荐系统是当今互联网上至关重要信息服务。近年来，图神经网络已成为推荐系统的主导方法。本文尝试回顾基于图神经网络的推荐系统的最新文献，涵盖推荐系统与图神经网络的背景与发展历程。随后，按推荐系统的设定类型及图神经网络的谱域与空域模型进行分类，探讨将图神经网络融入推荐系统的动机。同时，我们分析了图构建、嵌入传播与聚合以及计算效率方面的挑战与开放性问题，这有助于更好地探索该领域的未来发展方向与进展。