CIEGCL: Counterfactual Intervention Enhancing Graph Contrastive Learning in Implicit Feedback

Graph neural network (GNN) is a powerful approach for graph-based recommender systems. Recently, GNNs integrated with contrastive learning have achieved superior performance in recommendations due to their data augmentation techniques for sparse data. These methods are susceptible to noisy perturbations and are affected by data bias stemming from missing-not-at-random (MNAR) issues, which are common in implicit feedback scenarios. In this paper, we propose a Counterfactual Intervention Enhancing Graph Contrastive Learning (CIEGCL) paradigm. Our model applies counterfactual intervention in the positive sampling process for contrastive augmentation and generates enhanced facts (e.g., random perturbations) for contrast penalty, improving model generalization. Additionally, our dual contrastive channels separately handle click prediction and recommendation tasks to reduce the impact of data bias on the final recommendations. Empirical studies on five real-world datasets demonstrate the effectiveness of our model over the state-of-the-arts. Further theoretical and experimental analysis confirms the rationale behind CIEGCL. We release our code at https://github.com/huang-zi-jian/CIEGCL.