# AsGCL: Attentive and Simple Graph Contrastive Learning for Recommendation
## 2 Related Work  
Neural Collaborative Filtering (NeuMF) [4] was the first to integrate multi-layer perceptrons (MLPs) with recommendation systems, establishing a dual network that separately models users and items with the aim of capturing their nonlinear relationships. Linear models excel at memorization, while Deep Neural Networks (DNNs) are proficient in generalization. The Deep Matrix Factorization (DeepFM) [5] model combines the advantages of both by integrating a linear regression model with a multi-layer fully connected network, and it replaces the linear regression component with Factorization Machines (FMs) to eliminate the need for manual feature interactions. The Deep Cross-Network (DCN) [7] further addresses the wide component by designing a specific cross-network that autonomously learns feature fusion information. xDeepFM [8] takes this a step further by constructing a Compressed Interaction Network (CIN) layer to learn high-order interaction features.  
Graph neural networks (GNNs) [9,10] have revolutionized the data modeling approaches in recommendation systems, giving rise to models that have excelled across various scenarios, such as those discussed in references [11,12]. The graph convolutional network (GCN) innovatively introduces convolutional methods into the realm of recommendations, enabling multi-layer node aggregation operations that result in rich and interpretable graph embeddings. Such recommendation models continue to receive significant attention, exemplified by models like Neural Graph Collaborative Filtering (NGCF) [6], LightGCN [13], Liner Residual Graph Convolutional Network (LR-GCCF) [14], and Low-pass Collaborative Filtering (LCF) [15]. Notably, LightGCN has emerged as a foundational framework for numerous subsequent studies due to its simplicity and efficiency.  
The graph convolutional network (GCN) faces challenges in assigning different learning weights to neighboring nodes, whereas the Graph Attention Network (GAT) excels in better integrating the correlations among vertex features into the model. The Attention-based Factorization Machine (AFM) [16] is one of the earlier works to introduce attention mechanisms into recommendation systems, positing that different cross-features exert varying influences on the outcomes. The Deep Interest Network (DIN) [17] proposes a mechanism that employs attention to compute user interest distributions for different items, effectively capturing the diversity of a user's interests.  
The self-supervised Hypergraph Convolutional Network (DHCN) [18] proposed a dual-channel hypergraph convolutional network for the social-based recommendation (SBR) task, marking the first time that the concept of self-supervised learning was integrated into the network training for recommendation tasks. The Multi-Channel Hypergraph Convolutional Network (MHCN) [12] combined multi-channel high-order user relationships with self-supervised learning, demonstrating that self-supervised auxiliary tasks can significantly enhance social recommendation performance. Self-supervised Graph Learning (SGL) [19] approached the problem from a graph structure perspective, designing three different types of data augmentation to construct auxiliary contrastive tasks. Neighborhood-enriched Contrastive Learning (NCL) [20] incorporated both graph structure contrastive tasks and semantic neighbor contrastive tasks into the main recommendation task for holistic training. LightGCL [21] posits that heuristic-based augmentation techniques for generating contrastive views do not effectively preserve the intrinsic semantic structure. Graph Contrastive Learning with Cohesive Subgraph Awareness [22] proposes a unified framework called CTAug, which seamlessly integrates cohesive subgraph awareness into existing graph contrastive learning mechanisms.