  Social recommender systems (SocialRS) simultaneously leverage user-to-item
interactions as well as 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 80 papers
on GNN-based SocialRS after annotating 2151 papers by following the PRISMA
framework (Preferred Reporting Items for Systematic Reviews and Meta-Analysis).
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, 2 groups of decoder, 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 the benchmark datasets and metrics widely used to evaluate the
GNN-based SocialRS methods. Finally, we conclude this survey by presenting some
future research directions.

Social and Information Networks;Information Retrieval;Machine Learning