2025/03/11 by Guanrong Li, Li, Guanrong, Haolin Yang +7
Computer Science · #Artificial Intelligence (cs.AI) #Explainable Artificial Intelligence (XAI) #FOS: Computer and information sciences #Semantic Web and Ontologies #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2503.08051
openalex publication_date 2025/03/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Explainable recommendation systems leverage transparent reasoning to foster user trust and improve decision-making processes. Current approaches typically decouple recommendation generation from explanation creation, violating causal precedence principles where explanatory factors should logically precede outcomes. This paper introduces a novel framework integrating structural causal models with large language models to establish causal consistency in recommendation pipelines. Our methodology enforces explanation factors as causal antecedents to recommendation predictions through causal graph construction and counterfactual adjustment. We particularly address the confounding effect of item popularity that distorts personalization signals in explanations, developing a debiasing mechanism that disentangles genuine user preferences from conformity bias. Through comprehensive experiments across multiple recommendation scenarios, we demonstrate that CausalX achieves superior performance in recommendation accuracy, explanation plausibility, and bias mitigation compared to baselines.