  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.
