通过渐进式对抗训练生成多样推荐视图，提升模型鲁棒性。
Dual Adversarial Perturbators Generate rich Views for Recommendation
- 用双重对抗机制逐步增强图结构与嵌入的扰动难度
- 在三个真实数据集上显著超越当前最佳方法
- 适合追求高精度推荐系统且关注对抗学习的开发者
图对比学习（GCL）在推荐系统中被广泛研究并作为有效工具。现有基于GCL的推荐方法通常通过修改图结构或引入嵌入扰动来生成对比视图。尽管这些方法能有效缓解稀疏数据问题，但当对比视图差异过大时，可能导致性能下降甚至训练崩溃。为缓解此问题，我们采用课程学习策略，逐步增加对比视图间的差异，使模型逐步适应更复杂场景。本文提出一种双对抗图学习方法AvoGCL，通过渐进式对抗训练模拟课程学习，在图结构上减少冗余，并在嵌入空间生成对抗扰动以构建对比视图，从而逐步提升对比难度并获得更优结果。在三个真实世界数据集上的大量实验表明，AvoGCL显著优于现有最先进方法。
原文摘要 · Abstract (English)
Graph contrastive learning (GCL) has been extensively studied and leveraged as a potent tool in recommender systems. Most existing GCL-based recommenders generate contrastive views by altering the graph structure or introducing perturbations to embedding. While these methods effectively enhance learning from sparse data, they risk performance degradation or even training collapse when the differences between contrastive views become too pronounced. To mitigate this issue, we employ curriculum learning to incrementally increase the disparity between contrastive views, enabling the model to gain from more challenging scenarios. In this paper, we propose a dual-adversarial graph learning approach, AvoGCL, which emulates curriculum learning by progressively applying adversarial training to graph structures and embedding perturbations. Specifically, AvoGCL construct contrastive views by reducing graph redundancy and generating adversarial perturbations in the embedding space, and achieve better results by gradually increasing the difficulty of contrastive views. Extensive experiments on three real-world datasets demonstrate that AvoGCL significantly outperforms the state-of-the-art competitors.
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