Out-of-distribution (OOD) generalization has emerged as a significant challenge in graph recommender systems. Traditional graph neural network algorithms often fail because they learn spurious environmental correlations instead of stable causal relationships, leading to substantial performance degradation under distribution shifts. While recent advancements in Large Language Models (LLMs) offer a promising avenue due to their vast world knowledge and reasoning capabilities, effectively integrating this knowledge with the fine-grained topology of specific graphs to solve the OOD problem remains a significant challenge. To address these issues, we propose {$\textbf{Inv}$ariant $\textbf{G}$raph $\textbf{C}$ontrastive Learning with $\textbf{LLM}$s for Out-of-Distribution Recommendation (InvGCLLM)}, an innovative causal learning framework that synergistically integrates the strengths of data-driven models and knowledge-driven LLMs. Our framework first employs a data-driven invariant learning model to generate causal confidence scores for each user-item interaction. These scores then guide an LLM to perform targeted graph refinement, leveraging its world knowledge to prune spurious connections and augment missing causal links. Finally, the structurally purified graphs provide robust supervision for a causality-guided contrastive learning objective, enabling the model to learn representations that are resilient to spurious correlations. Experiments conducted on four public datasets demonstrate that InvGCLLM achieves significant improvements in out-of-distribution recommendation, consistently outperforming state-of-the-art baselines.

翻译：分布外（OOD）泛化已成为图推荐系统中的一项重要挑战。传统的图神经网络算法常因学习到虚假的环境相关性而非稳定的因果关系而失效，导致在分布偏移下性能显著下降。尽管大语言模型（LLMs）凭借其广泛的世界知识和推理能力为这一挑战提供了有前景的解决途径，但如何有效整合此类知识与特定图的细粒度拓扑结构以解决OOD问题仍面临重大困难。为应对这些问题，我们提出了{$\textbf{Inv}$ariant $\textbf{G}$raph $\textbf{C}$ontrastive Learning with $\textbf{LLM}$s for Out-of-Distribution Recommendation (InvGCLLM)}，这是一种创新的因果学习框架，协同整合了数据驱动模型与知识驱动LLMs的优势。该框架首先采用数据驱动的不变学习模型为每个用户-物品交互生成因果置信度分数。这些分数随后引导LLM进行有针对性的图优化，利用其世界知识剪除虚假连接并增强缺失的因果链接。最终，结构纯化后的图为因果引导的对比学习目标提供鲁棒监督，使模型能够学习对虚假相关性具有鲁棒性的表征。在四个公开数据集上的实验表明，InvGCLLM在分布外推荐任务中取得了显著提升，持续优于现有最先进的基线方法。