# AsGCL: Attentive and Simple Graph Contrastive Learning for Recommendation
## 1 Introduction  
Since its inception, recommendation systems have been widely implemented across various social platforms, alleviating users from much of the decision-making burden associated with selecting products or obtaining information. With the support of deep learning, recommendation models are capable of learning more valuable item content from users' historical evaluations and behaviors. This capability represents one of the most practical and valuable directions in the current landscape of model development. Collaborative filtering methods [1, 2] are among the most classic algorithms in recommendation systems. They construct a co-occurrence matrix with users represented on the horizontal axis and items on the vertical axis, calculating the similarity between pairs of columns and ranking them based on similarity scores. While collaborative filtering is an intuitive and highly interpretable algorithm, it suffers from a lack of generalization capability and is unable to effectively address the pervasive issue of sparse matrices. Consequently, matrix factorization techniques [3] have emerged, generating corresponding latent vectors for each user and item, thereby reducing the dimensionality of the co-occurrence matrix and uncovering hidden interest information. This approach aligns closely with the embedding concepts in deep learning, allowing matrix factorization to extend beyond interaction information and to be combined with other features, thereby opening the door to deep learning applications. Whether it is collaborative filtering, matrix factorization, or deep recommendation models [4, 5], the encoding of target nodes typically remains in the first order, focusing only on the immediate neighbors of the target node. This approach often lacks a more precise encoding of the collaborative signals between users and items, which is crucial for revealing the similarities between embedding vectors. When the embedding representations fail to capture deeper interaction knowledge, we need to rely on effective interaction functions to compensate for the shortcomings of suboptimal embeddings. As a result, graph convolutional network (GCN) [6] recommendation methods have begun to gain traction. Taking social networks as an example, users are represented as nodes, and the relationships between users are represented as edges, forming a graph structure. These methods recursively propagate embedding signals across the interaction graph and model the information of multi-hop neighbor nodes through interaction functions. Specifically, high-order connectivity refers to any path with a length greater than 1 that reaches the target node, encapsulating rich semantic and collaborative signals. Models based on graph neural networks (GNNs) offer enhanced embedding expressiveness and interpretability, making them a powerful tool for recommendation tasks. We observe that models based on graph neural networks do not differentiate between the input nodes; however, the initial input nodes inherently possess varying degrees of importance. Specifically, nodes with higher degrees of correlation should be assigned superior values to facilitate differentiation. On the other hand, to mitigate the issue of popularity bias, contrastive learning typically aims to achieve this goal by learning more uniform and homogeneous embedding representations. Therefore, our proposed asGCL model addresses this problem from two perspectives: first, by calculating the weights of the nodes, and second, by incorporating a contrastive learning task. In summary, our contributions are as follows:  
* In this article, we provide a detailed analysis of the connections and distinctions between matrix factorization techniques and the input layer of graph convolutional networks, and we also adapt the attention mechanism from the field of computer vision to recommendation systems.