Denoising Implicit Feedback for Graph Collaborative Filtering via Causal Intervention

The performance of graph collaborative filtering (GCF) models could be affected by noisy user-item interactions. Existing studies on data denoising either ignore the nature of noise in implicit feedback or seldom consider the long-tail distribution of historical interaction data. For the first challenge, we analyze the role of noise from a causal perspective: noise is an unobservable confounder. Therefore, we use the instrumental variable for causal intervention without requiring confounder observation. For the second challenge, we consider degree distribution of nodes in the course of causal intervention. And then we propose a model named causal graph collaborative filtering (CausalGCF) to denoise implicit feedback for GCF. Specifically, we design a degree augmentation strategy as the instrumental variable. First, we divide nodes into head and tail nodes according to their degree. Then, we purify the interactions of the head nodes and enrich those of the tail nodes based on similarity. We perform degree augmentation strategy from the user and item sides to obtain two different graph structures, which are trained together with self-supervised learning. Empirical studies on four real and four synthetic datasets demonstrate the effectiveness of CausalGCF, which is more robust against noisy interactions in implicit feedback than the baselines.