Causal Denoising Framework for Generalizable Recommendation System using Graph Neural Network

Yibowen Zhao, Yonghui Xu, Ning Liu, Yixin Zhang, Wei Guo, Xudong Lu, Lizhen Cui

Abstract: Graph Neural Networks (GNNs) have significantly advanced recommendation systems by capturing complex interplays between user-item relationships and dependencies. However, inherent noise in user behaviors, manifesting as random clicks and diverse browsing patterns, disrupts the structural integrity of graphs, thereby degrading the accuracy and reliability of GNN-based recommendation systems. Traditional graph pruning methods, which remove or reweight connections, often fail to adequately address this noise because they neglect the deeper causal factors influencing user choices, resulting in biased outcomes. To confront these challenges, this paper presents the GNN-based Causal Denoising Framework (GCDF). GCDF employs causal relationships to filter out noisy connections, thus enhancing the performance of GNNs. By utilizing a denoised graph that more accurately reflects the causal interactions among items, GCDF significantly improves the accuracy and reliability of recommendations, as evidenced by comprehensive empirical evaluations.