Causal-augmented disentanglement for contrastive recommendation

Graph neural networks (GNNs) combined with contrastive learning have shown promise in mitigating data sparsity and noise in recommender systems. However, current approaches face two key challenges: (1) they often conflate genuine user preferences with group-level influences, introducing confounding bias in representation learning; (2) conventional graph contrastive methods rely on random perturbations that can compromise semantic structure and amplify noise. To overcome these issues, we propose CaDCR — Causal-Augmented Disentanglement for Contrastive Recommendation. CaDCR introduces a dual-channel mechanism to separate individual and collective intent, applying causal interventions to eliminate bias from factors like popularity. Additionally, it employs dual contrastive regularization in both interaction and intent spaces, preserving semantic integrity and enhancing representation quality without manual augmentation. Experiments on Gowalla and other datasets show CaDCR outperforms 14 mainstream methods on metrics like Recall and NDCG, validating its effectiveness and practical relevance.