Causal-Invariant Cross-Domain Out-of-Distribution Recommendation

Cross-Domain Recommendation (CDR) aims to leverage knowledge from a relatively data-richer source domain to address the data sparsity problem in a relatively data-sparser target domain. While CDR methods need to address the distribution shifts between different domains, i.e., Cross-Domain Distribution Shifts (CDDS) , they typically assume that the training and testing data within the target domain are Independently and Identically Distributed (IID) . However, this IID assumption rarely holds in real-world scenarios due to Single-Domain Distribution Shift (SDDS) . The above two co-existing distribution shifts lead to Out-of-Distribution (OOD) environments that hinder effective knowledge transfer and generalization, ultimately degrading recommendation performance in CDR. To address these co-existing distribution shifts, we propose a novel Causal-Invariant Cross-Domain OOD Recommendation (CICDOR) framework. In CICDOR, we first learn dual-level causal structures to infer domain-specific and domain-shared causal-invariant user preferences for tackling both CDDS and SDDS under OOD environments in CDR. Then, we propose an LLM-guided confounder discovery module that seamlessly integrates LLMs with a conventional causal discovery method to extract observed confounders for effective deconfounding, thereby enabling accurate causal-invariant preference inference. Extensive experiments on two real-world datasets demonstrate the superior recommendation accuracy of CICDOR over state-of-the-art methods across various OOD scenarios.