CGCLER: Causality-Enhanced Graph Contrastive Learning for Explainable Recommendation

Abstract

Explainable recommendation has been conceptualized as a joint ranking task encompassing both items and explanations within contemporary recommender system research. The modeling of user–item–explanation triplets can be effectively facilitated by Graph Neural Networks (GNNs) due to their powerful representation learning capabilities. However, various observable and unobservable confounding factors, such as the user’s mood at the time of purchase and product popularity, may cause users to make decisions that diverge from their genuine preferences. These confounding variables significantly affect GNN models, resulting in inconsistent or inaccurate representations of the relationships among users, items, and explanations. To address these challenges, we propose Causality-enhanced Graph Contrastive Learning for Explainable Recommendation (CGCLER). This method enables item–explanation joint ranking by distinguishing causal and confounding features at the graph-node representation level. Guided by the backdoor adjustment principle, CGCLER further introduces a backdoor-inspired graph contrastive learning objective that constructs representation-level perturbation views by combining causal features with randomly sampled confounding features, thereby encouraging representations that are less affected by varying confounding contexts. The effectiveness of CGCLER is evaluated through experiments on three publicly available datasets.