# A Comprehensive Survey of Recommender Systems Based on Deep Learning
## 3 Overview of the Recommender Systems
### Social Recommendation  
Users' decision-making processes are influenced by their social circles, and numerous researchers have integrated social relations into recommender systems. The main goal is to integrate users' social information as auxiliary data into conventional recommendation frameworks, thereby improving the accuracy of the resulting recommendations. Distinct from other recommendation strategies, social recommendation focuses on leveraging users' social network information to generate more personalized and accurate recommendations. The framework for social recommendation is illustrated in Figure 7.  
The primary concept involves aggregating features from both a node itself and its neighboring nodes to generate a consolidated representation. The inputs of this approach are users, items, and the social relationship network among the users. After performing embedding, neural networks are generally employed to handle the relationships between users. The final output consists of item recommendations.  
Numerous studies have integrated social relational information into collaborative filtering-based recommendations with the goal of modelling the interrelationships between users. The evolution of social recommendation has spanned three phases. Initially, researchers began to recognize the potential value of trust relationships within a user's social network for recommendation purposes. Leveraging random walks, a trust-based recommendation method was proposed in [129]. The trust relationships among users were employed as weights to adjust the recommendation outcomes. Subsequently, with the application of matrix factorization techniques, models integrating matrix factorization with social networks gained popularity. Building upon trust relationships, these models endeavored to incorporate both strong and weak social ties into a unified framework, allowing recommendations to benefit from both types of relations [130]. With the help of deep learning, the field has progressed into the current era of graph-based social recommendations. These models usually take data from a user's social network as input and subsequently utilize neural networks to forecast users' interests [131].  
A graph-based social recommendation has emerged as a focal area of academic interest. The advantages of GNNs in terms of mining graph-structured data have facilitated research on social network recommender systems. GNNs possess the capability to efficiently learn and extract features from graph structures, demonstrating commendable results in user-item representation learning tasks. Consequently, numerous scholars have integrated social networks into recommender system research and employed GNNs to construct recommender system models grounded in graph representation learning. For instance, the GC-MC model [132] and the NGCF model [27] graph convolutions to identify user-item interactions in the original space, yielding enhanced recommendation outcomes in practical applications. Moreover, some researchers have found that adding nonlinear activation functions to GNNs does not significantly improve the accuracy of the resulting recommendations. This observation led to the proposal of a streamlined GNN recommendation model, LightGCN [42], which outperforms NGCF in terms of recommendation performance. LR-GCCF [43] eliminates nonlinear transformations from graph neural networks and substitutes them with linear embedding propagation, thereby reducing unnecessary  
Figure 7: Social recommendation process.  
operations, simplifying the complexity of the model, and still ensuring that satisfactory performance metrics are produced.  
Social recommendation algorithms can be broadly classified into three main categories: traditional collaborative filtering methods, deep social recommendation methods relying on graph embeddings, and social recommendation methods based on graph neural networks.