Social recommender systems (SocialRS) simultaneously leverage the user-to-item interactions as well as the user-to-user social relations for the task of generating item recommendations to users. Additionally exploiting social relations is clearly effective in understanding users' tastes due to the effects of homophily and social influence. For this reason, SocialRS has increasingly attracted attention. In particular, with the advance of graph neural networks (GNN), many GNN-based SocialRS methods have been developed recently. Therefore, we conduct a comprehensive and systematic review of the literature on GNN-based SocialRS. In this survey, we first identify 84 papers on GNN-based SocialRS after annotating 2151 papers by following the PRISMA framework (preferred reporting items for systematic reviews and meta-analyses). Then, we comprehensively review them in terms of their inputs and architectures to propose a novel taxonomy: (1) input taxonomy includes 5 groups of input type notations and 7 groups of input representation notations; (2) architecture taxonomy includes 8 groups of GNN encoder notations, 2 groups of decoder notations, and 12 groups of loss function notations. We classify the GNN-based SocialRS methods into several categories as per the taxonomy and describe their details. Furthermore, we summarize benchmark datasets and metrics widely used to evaluate the GNN-based SocialRS methods. Finally, we conclude this survey by presenting some future research directions. GitHub repository with the curated list of papers are available at https://github.com/claws-lab/awesome-GNN-social-recsys.

翻译：社交推荐系统（SocialRS）同时利用用户-项目交互以及用户-用户社交关系来为用户生成项目推荐。由于同质性和社会影响的作用，额外利用社交关系在理解用户偏好方面显然十分有效。因此，社交推荐系统日益受到关注。特别是随着图神经网络（GNN）的发展，近年来涌现出许多基于GNN的社交推荐方法。为此，我们对基于GNN的社交推荐系统文献进行了全面且系统的综述。本综述中，我们首先按照PRISMA框架（系统评价和元分析的首选报告项目）标注了2151篇论文，从中识别出84篇关于基于GNN的社交推荐系统的论文。随后，我们从输入和架构两方面对它们进行全面评述，提出了一种新颖的分类体系：（1）输入分类体系包括5组输入类型标注和7组输入表示标注；（2）架构分类体系包括8组GNN编码器标注、2组解码器标注和12组损失函数标注。我们依据该分类体系将基于GNN的社交推荐方法分为若干类别，并详细描述了它们的细节。此外，我们总结了广泛用于评估基于GNN的社交推荐方法的基准数据集和指标。最后，我们提出了未来的一些研究方向，以此作为本综述的结论。精选论文列表的GitHub仓库链接为：https://github.com/claws-lab/awesome-GNN-social-recsys。