AsGCL: Attentive and Simple Graph Contrastive Learning for Recommendation

In contemporary society, individuals are inundated with a vast amount of redundant information, and recommendation systems have undoubtedly opened up new avenues for managing irrelevant data. Graph convolutional networks (GCNs) have demonstrated remarkable performance in the field of recommendation systems by iteratively performing node convolutions to capture information from neighboring nodes, thereby enhancing recommendation efficacy. However, most existing models fail to distinguish the importance of different nodes, which limits their performance. To address this issue, we propose the asGCL model. To mitigate the prevalent issue of popularity bias and to learn more uniform embedding representations, we have integrated a lightweight contrastive learning module into our model. Finally, extensive experiments conducted on four real-world datasets demonstrate the effectiveness of our model. Notably, on the Amazon-Books dataset, our asGCL model achieved improvements of 4.21% and 8.74% in recall@20 and NDCG@20, respectively.