Social Recommendation via Graph-Level Counterfactual Augmentation

Traditional recommendation system focus more on the correlations between users and items (user-item relationships), while research on user-user relationships has received significant attention these years, which is also known as social recommendation. Graph-based models have achieved a great success in this task by utilizing the complex topological information of the social networks. However, these models still face the insufficient expressive and overfitting problems. Counterfactual approaches are proven effective as information augmentation strategies towards above issues in various scenarios, but not fully utilized in social recommendations. To this end, we propose a novel social recommendation method, termed SR-GCA, via a plug-and-play Graph-Level Counterfactual Augmentation mechanism. Specifically, we first generate counterfactual social and item links by constructing a counterfactual matrix for data aug-mentation. Then, we employ a supervised learning strategy to refine data both factual and counterfactual links. Thirdly, we enhance representations learning between users via an alignment and self-supervised optimization techniques. Extensive experiments demonstrate the promising capacity of our model from five aspects: superiority, effectiveness, transferability, complexity, and robustness. In particular, the transferability is well-proven by extending our GCA module to three typical social recommendation models.