In recent years, graph contrastive learning (GCL) has received increasing attention in recommender systems due to its effectiveness in reducing bias caused by data sparsity. However, most existing GCL models rely on heuristic approaches and usually assume entity independence when constructing contrastive views. We argue that these methods struggle to strike a balance between semantic invariance and view hardness across the dynamic training process, both of which are critical factors in graph contrastive learning. To address the above issues, we propose a novel GCL-based recommendation framework RGCL, which effectively maintains the semantic invariance of contrastive pairs and dynamically adapts as the model capability evolves through the training process. Specifically, RGCL first introduces decision boundary-aware adversarial perturbations to constrain the exploration space of contrastive augmented views, avoiding the decrease of task-specific information. Furthermore, to incorporate global user-user and item-item collaboration relationships for guiding on the generation of hard contrastive views, we propose an adversarial-contrastive learning objective to construct a relation-aware view-generator. Besides, considering that unsupervised GCL could potentially narrower margins between data points and the decision boundary, resulting in decreased model robustness, we introduce the adversarial examples based on maximum perturbations to achieve margin maximization. We also provide theoretical analyses on the effectiveness of our designs. Through extensive experiments on five public datasets, we demonstrate the superiority of RGCL compared against twelve baseline models.

翻译：近年来，图对比学习（GCL）因其在缓解数据稀疏性导致的偏差方面的有效性，在推荐系统中受到越来越多的关注。然而，现有的大多数GCL模型依赖于启发式方法，且在构建对比视图时通常假设实体独立性。我们认为，这些方法难以在动态训练过程中平衡语义不变性与视图难度，而这两者均是图对比学习中的关键因素。为解决上述问题，我们提出了一种新颖的基于GCL的推荐框架RGCL，该框架能有效保持对比对的语义不变性，并随着模型能力在训练过程中的演变而动态适应。具体而言，RGCL首先引入决策边界感知的对抗性扰动来约束对比增强视图的探索空间，避免任务特定信息的减少。此外，为融入全局的用户-用户与物品-物品协作关系以指导生成困难对比视图，我们提出了一种对抗-对比学习目标来构建关系感知的视图生成器。另外，考虑到无监督GCL可能缩小数据点与决策边界之间的间隔，导致模型鲁棒性下降，我们引入了基于最大扰动的对抗样本来实现间隔最大化。我们还对所提设计的有效性进行了理论分析。通过在五个公开数据集上的大量实验，我们证明了RGCL相较于十二个基线模型的优越性。