Graph Collaborative Filtering (GCF), one of the most widely adopted recommendation system methods, effectively captures intricate relationships between user and item interactions. Graph Contrastive Learning (GCL) based GCF has gained significant attention as it leverages self-supervised techniques to extract valuable signals from real-world scenarios. However, many methods usually learn the instances of discrimination tasks that involve the construction of contrastive pairs through random sampling. GCL approaches suffer from sampling bias issues, where the negatives might have a semantic structure similar to that of the positives, thus leading to a loss of effective feature representation. To address these problems, we present the \underline{Proto}typical contrastive learning through \underline{A}lignment and \underline{U}niformity for recommendation, which is called \textbf{ProtoAU}. Specifically, we first propose prototypes (cluster centroids) as a latent space to ensure consistency across different augmentations from the origin graph, aiming to eliminate the need for random sampling of contrastive pairs. Furthermore, the absence of explicit negatives means that directly optimizing the consistency loss between instance and prototype could easily result in dimensional collapse issues. Therefore, we propose aligning and maintaining uniformity in the prototypes of users and items as optimization objectives to prevent falling into trivial solutions. Finally, we conduct extensive experiments on four datasets and evaluate their performance on the task of link prediction. Experimental results demonstrate that the proposed ProtoAU outperforms other representative methods. The source codes of our proposed ProtoAU are available at \url{https://github.com/oceanlvr/ProtoAU}.

翻译：图协同过滤（GCF）作为最广泛采用的推荐系统方法之一，能有效捕捉用户与物品交互之间的复杂关系。基于图对比学习（GCL）的GCF方法因利用自监督技术从现实场景中提取有价值信号而受到广泛关注。然而，许多方法通常通过随机采样构建对比对来学习实例判别任务。GCL方法存在采样偏差问题，即负样本可能具有与正样本相似的语义结构，从而导致有效特征表征的损失。为解决这些问题，我们提出了基于对齐与均匀性的原型对比学习方法用于推荐系统，简称为**ProtoAU**。具体而言，我们首先引入原型（聚类中心）作为潜在空间，确保原始图谱中不同数据增强之间的一致性，旨在消除随机采样对比对的需求。此外，由于缺乏显式负样本，直接优化实例与原型之间的一致性损失容易导致维度坍缩问题。因此，我们提出将用户和物品原型的对齐与均匀性保持作为优化目标，以防止陷入平凡解。最后，我们在四个数据集上开展大量实验，并在链接预测任务上评估其性能。实验结果表明，所提出的ProtoAU方法优于其他代表性方法。ProtoAU的源代码发布于 \url{https://github.com/oceanlvr/ProtoAU}。