<ul><li>Graph Neural Networks (GNNs) struggle with out-of-distribution (OOD) recommendation due to unstable correlations.</li><li>Researchers propose a novel approach, graph representation learning via causal diffusion (CausalDiffRec) for OOD recommendation.</li><li>CausalDiffRec eliminates environmental confounders and learns invariant graph representations.</li><li>Experimental results show up to 22.41% improvement in generalization on popular recommendation datasets.</li></ul>

Graph Representation Learning via Causal Diffusion for Out-of-Distribution Recommendation

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