# A Comprehensive Survey of Recommender Systems Based on Deep Learning
## 3 Overview of the Recommender Systems
### Social Recommendation
#### 3.4.3 Social Recommendation Based on GNN  
Over time, neural networks' applications in the recommendation field have significantly broadened. Graph neural networks (GNNs) represent innovative extensions of neural networks, drawing inspiration from convolutional neural networks (CNNs) and the concept of graph embeddings. They are adept at extracting and representing features in graph-based data. Characterized by their efficiency and scalability, GNNs have demonstrated profound capabilities when handling graph data. Users and items are considered as nodes in the graph, and interactions are represented as edges. This process leads to the transformation of user-item interactions into embedded representations within the graph. By integrating these social relationships into recommender systems and subsequently feeding them into neural networks, more comprehensive and personalized recommendations can be provided. The goal of a GNN is to acquire a state embedding that captures the neighborhood information of each node. The embedding is a z-dimensional real-valued vector associated with node u that can be used to generate a node label output, representing the vector output of node u at a given network layer. Local transfer functions are shared across all nodes and update node state based on input neighborhood. Finally, the output is produced. Then, based on the characteristics of nodes, the characteristics of adjacent edges, and the state embeddings and features of adjacent fixed points, the social relationship model is further improved, for example, by considering the impact of various social relationships on user behaviors. Attention mechanisms, among other techniques, have been introduced [138]. Broadly speaking, the architecture of a social recommendation model comprises three key components: an encoder, a decoder, and a loss function. The encoder [139] utilizes different graph neural network encoders to represent users and items as low-dimensional vectors (i.e., embeddings). The decoder then predicts each user's preference for each item by performing different operations on the obtained user and item embeddings. Optimization is finally achieved using various loss functions [140]. The performance of social recommendation methods based on GNN largely depends on their encoders. Beyond user behaviors, social recommender systems also take various types of information into account, including social interactions and shared interests. They operate on the premise that users are likely to share similar interests with friends or contacts within their social networks, utilizing these social relationships to enhance the accuracy and relevance of recommendations. Additionally, the strength and depth levels of social ties are considered to enhance the produced recommendation outcomes. For new users, the cold-start problem can be mitigated via their social networks. Furthermore, recommendations stemming from friends are more likely to be trusted and accepted by users. However, social recommendations exhibit some limitations. Utilizing users' social network data may raise privacy and security concerns [141]. Moreover, not all social interactions correlate with genuine preferences, necessitating the filtering of irrelevant or misleading information. Graph neural networks offer robust mechanisms for recommender systems to capture the intricate interactions and relationships between users and items [142], with several models demonstrating significant influences and innovations. Initially, GraphSAGE [28], a pioneering spatial GNN model, marked an essential milestone. It moved away from relying on the entirety of graph information and instead strategically sampled the neighbors of target nodes and combined their embeddings. This approach achieved efficient target embedding updates and offered a viable tool for providing social recommendations in large-scale networks.