# Graph-Augmentation-Free Self-Supervised Learning for Social Recommendation
## 3 Noise in social relationships  
Existing recommendation models are generally based on the assumption that users in social networks have similar item preferences. However, the formation of social relationships is a complicated process that can be based on interests or pure social relationships. Users may have completely different preferences for certain items, but this is not always the case. The model becomes noisier as a result of this assumption, which makes it difficult to effectively incorporate user characteristics and recommendation targets in social networks. **Present work. ** In view of the above limitations and challenges, this paper proposes an improved social recommendation model--graph-augmentation-free self-supervised learning for social recommendation (GAFSRC). Here, we applied self-supervised contrastive learning to recommendation with the goal of increasing the mutual information for the same user/item view, thereby reducing the reliance on labels and resolving the first and second issues mentioned above. Most contrastive learning techniques currently in use (such as random edge/node loss) improve the consistency of nodes across views through structural perturbations [4; 7; 8; 9]. However, the most recent research demonstrates [10] that data augmentation can still be accomplished without structural perturbation by adding the proper amount of random uniform noise to the original image. As a result, we employed the method of adding sufficiently small and uniform noise to the graph convolution layer of the recommendation task to achieve cross-layer comparative learning. This technique can operate in the embedding space, making it more effective and simpler to use, and it can subtly attenuate the long-tail phenomenon. The third problem was solved by using both explicit and implicit social relationships, employing hypergraph convolutional networks to mine users' high-order social relationships, and adding the role of key opinion leaders to prevent extra social noise from affecting user preferences. To improve efficiency, we employed a multi-task training technique with recommendation as the primary task and self-supervised contrastive learning as an auxiliary task. We first created four views using a social network graph and a user-item interaction graph: user-item interaction graph, explicit friend social graph, implicit friend social graph, and user-item sharing graph with explicit friends. Graph encoders (a graph convolutional network and hypergraph convolutional network) were then built for each view to learn the users' high-order relational representation. To avoid the difficulties caused by data sparsity in modeling, we incorporated cross-layer contrastive learning without graph augmentation into GAFSRC, amplified the variance via a graph convolutional neural network, and regularized the representation of recommendations with contrast-augmented views. Finally, we combined the recommendation task and the self-supervised task within the framework of master-assisted learning. The performance of the recommendation task was significantly improved after jointly optimizing these two tasks and utilizing the interactions between all components. The main contributions of this paper can be summarized as follows:  
1. We designed a high-order heterogeneous graph based on motifs, integrated social relations and item ratings, comprehensively modeled relational information in the network, and undertook modeling through graph convolution to capture high-order relations between users;
2. We incorporated the cross-layer self-supervised contrastive learning task without graph augmentation into network training, enabling it to run more efficiently while ensuring the reliability of recommendations;
3. We conducted extensive experiments with multiple real datasets, and the comparative results showed that the proposed model was superior and that the model was effective in ablation experiments. The rest of this paper is organized as follows. Section 2 presents related work.