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 2,151 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.

社交推荐系统 (SocialRS) 同时利用用户到项目的交互以及用户到用户的社交关系来完成向用户生成项目推荐的任务。此外，由于同质性和社会影响力的影响，利用社会关系显然可以有效地了解用户的品味。正因如此，SocialRS日益受到关注。特别是，随着图神经网络（GNN）的进步，最近开发了许多基于GNN的SocialRS方法。因此，我们对基于 GNN 的 SocialRS 的文献进行了全面、系统的回顾。

在本次调查中，我们按照 PRISMA 框架（系统评价和荟萃分析的首选报告项目）对 2,151 篇论文进行注释后，首先确定了 84 篇基于 GNN 的 SocialRS 论文。然后，我们从输入和架构方面全面审查它们，提出一种新的分类法：（1）输入分类法包括 5 组输入类型符号和 7 组输入表示符号； (2) 架构分类包括 8 组 GNN 编码器符号、2 组解码器符号和 12 组损失函数符号。我们根据分类法将基于 GNN 的 SocialRS 方法分为几类并描述它们的细节。此外，我们总结了广泛用于评估基于 GNN 的 SocialRS 方法的基准数据集和指标。最后，我们通过提出一些未来的研究方向来结束本次调查。包含精选论文列表的 GitHub 存储库可在 https://github.com/claws-lab/awesome-GNN-social-recsys 上获取。