Graph Neural Networks (GNNs)-based recommendation algorithms typically assume that training and testing data are drawn from independent and identically distributed (IID) spaces. However, this assumption often fails in the presence of out-of-distribution (OOD) data, resulting in significant performance degradation. In this study, we construct a Structural Causal Model (SCM) to analyze interaction data, revealing that environmental confounders (e.g., the COVID-19 pandemic) lead to unstable correlations in GNN-based models, thus impairing their generalization to OOD data. To address this issue, we propose a novel approach, graph representation learning via causal diffusion (CausalDiffRec) for OOD recommendation. This method enhances the model's generalization on OOD data by eliminating environmental confounding factors and learning invariant graph representations. Specifically, we use backdoor adjustment and variational inference to infer the real environmental distribution, thereby eliminating the impact of environmental confounders. This inferred distribution is then used as prior knowledge to guide the representation learning in the reverse phase of the diffusion process to learn the invariant representation. In addition, we provide a theoretical derivation that proves optimizing the objective function of CausalDiffRec can encourage the model to learn environment-invariant graph representations, thereby achieving excellent generalization performance in recommendations under distribution shifts. Our extensive experiments validate the effectiveness of CausalDiffRec in improving the generalization of OOD data, and the average improvement is up to 10.69% on Food, 18.83% on KuaiRec, 22.41% on Yelp2018, and 11.65% on Douban datasets.

翻译：基于图神经网络（GNN）的推荐算法通常假设训练数据和测试数据来自独立同分布（IID）的空间。然而，当存在分布外（OOD）数据时，这一假设往往不成立，导致性能显著下降。在本研究中，我们构建了一个结构因果模型（SCM）来分析交互数据，揭示了环境混杂因子（例如COVID-19疫情）会导致基于GNN的模型中存在不稳定的相关性，从而损害其向OOD数据的泛化能力。为解决这一问题，我们提出了一种新颖的方法——基于因果扩散的图表示学习（CausalDiffRec）用于OOD推荐。该方法通过消除环境混杂因子并学习不变的图表示，来增强模型在OOD数据上的泛化能力。具体而言，我们使用后门调整和变分推理来推断真实的环境分布，从而消除环境混杂因子的影响。随后，将推断出的分布作为先验知识，在扩散过程的逆向阶段指导表示学习，以学习不变的表示。此外，我们提供了理论推导，证明优化CausalDiffRec的目标函数可以促使模型学习环境不变的图表示，从而在分布偏移下的推荐任务中实现优异的泛化性能。我们的大量实验验证了CausalDiffRec在提升OOD数据泛化能力方面的有效性，在Food、KuaiRec、Yelp2018和Douban数据集上的平均提升分别高达10.69%、18.83%、22.41%和11.65%。