A Survey of Graph Neural Networks for Social Recommender Systems

Exploiting social relations in recommendation works well because of the effects of social homophily [61] and social influence [60]: (1) social homophily indicates that a user tends to connect herself to other users with similar attributes and preferences, and (2) social influence indicates that users with direct or indirect relations tend to influence each other to make themselves become more similar. Accordingly, SocialRS can effectively mitigate the data sparsity problem by exploiting social neighbors to capture the preferences of a sparsely interacting user. Literature has shown that SocialRS can be applied successfully in various recommendation domains (e.g., product [101, 103], music [116-118], location [39, 72, 100], and image [86, 99, 102]), thereby improving user satisfaction. Furthermore, techniques and insights explored from SocialRS can also be exploited in real-world applications other than recommendations. For instance, García-Sánchez et al. [20] leveraged SocialRS to design a decision-making system for marketing (e.g., advertisement), while Gasparetti et al. [21] analyzed SocialRS in terms of community detection. Motivated by such wide applicability, there has been an increasing interest in research on developing accurate 40 SocialRS models. In the early days, research focused on matrix factorization (MF) techniques [28, 54-20 57, 84, 112].