# Using Graph Neural Networks for Social Recommendations
## 2 Related Work  
With the widespread adoption of online social platforms, social RSs have emerged as a highly promising approach that leverages social networks among users to enhance recommendation performance [14; 15; 16]. Grounded in sociological concepts, such as homophily and social influence [17], this field of study operates under the premise that users' preferences are more profoundly shaped by those of their interconnected peers than by those of unfamiliar users [18]. [PERSON] et al. [15] give a narrow definition of social recommendation as \"any recommendation with online social relations as an additional input, i.e., augmenting an existing recommendation engine with additional social signals\". Researchers have long recognized the influence of social connections on individuals, highlighting the phenomenon of similar preferences among social neighbors as information diffuses within social networks [1; 2; 11; 19; 20; 21; 22; 23]. Social regularization [1; 2] has been shown to be effective in social recommendation scenarios, operating under the assumption that users with similar preferences exhibit shared latent preferences within popular latent factor-based models [24]. In the realm of social recommendations, GNNs have emerged as a powerful tool for capturing the intricate relationships between users, items, and other contextual features, such as time and location [11; 12; 13; 22]. The incorporation of GNNs into social recommendation models allows for a comprehensive understanding of the complex dynamics present in social networks, resulting in more accurate and contextually relevant recommendations. Recent studies investigate various aspects, such as modeling user-item interactions, capturing social influence, incorporating contextual information, and addressing scalability challenges. Through a deeper understanding of these developments, researchers and practitioners can gain insights into the potential benefits and challenges associated with integrating GNNs into social recommendation frameworks, thereby fostering further innovation and advancements in this exciting research area. [PERSON] et al. [22] introduced PinSage, a novel framework based on GNNs, designed specifically for personalized feed recommendations. The motivation behind PinSage stems from the scalability limitations observed in traditional CF methods. PinSage revolutionizes the landscape of personalized feeds (news) recommendations by constructing a graph representation that encompasses both items and users. Leveraging the expressive power of GNNs, PinSage efficiently learns personalized feed representations for each user. This graph-based approach enables the model to capture the complex relationships between items and users, thereby facilitating accurate and relevant pin recommendations. PinSage innovatively combines content-based and collaborative filtering approaches. In their paper, [PERSON] et al. [11] presented GraphRec, an innovative recommendation algorithm that leverages the power of graphs. They highlight the limitations of traditional recommendation techniques, particularly in dealing with the cold-start problem and effectively capturing intricate user-item connections. GraphRec aims to overcome these challenges by using graphs to model user-item interactions in the form of a diverse graph, rating scores, and differentiating the ties strengths by considering the heterogeneous strengths of social relations. [PERSON] et al. [12] proposed Diffnet, a neural influence diffusion model for social recommendations. Please note that this term refers to information diffusion in a social graph in this context (not to be confused with Generative AI diffusion). Diffnet utilizes a user's social network data to provide personalized recommendations. Its neural architecture comprises four main components: the embedding layer, the fusion layer, the layer-wise influence diffusion layers, and the prediction layer. Once the influence diffusion process reaches stability, the output layer predicts the final preference for a user-item pair.