Abstract: In recent years, the field of recommendation systems has witnessed significant advancements, with explainable recommendation systems gaining prominence as a crucial area of research. These systems aim to enhance user experience by providing transparent and compelling recommendations, accompanied by explanations. However, a persistent challenge lies in addressing biases that can influence the recommendations and explanations offered by these systems. Such biases often stem from a tendency to favor popular items and generate explanations that highlight their common attributes, thereby deviating from the objective of delivering personalized recommendations and explanations. While existing debiasing methods have been applied in explainable recommendation systems, they often overlook the model-generated explanations in tackling biases. Consequently, biases in model-generated explanations may persist, potentially compromising system performance and user satisfaction.To address biases in both model-generated explanations and recommended items, we discern the impact of model-generated explanations in recommendation through a formulated causal graph. Inspired by this causal perspective, we propose a novel approach termed Causal Explainable Recommendation System (CERS), which incorporates model-generated explanations into the debiasing process and enacts causal interventions based on user feedback on the explanations. By utilizing model-generated explanations as intermediaries between user-item interactions and recommendation results, we adeptly mitigate the biases via targeted causal interventions. Experimental results demonstrate the efficacy of CERS in reducing popularity bias while simultaneously improving recommendation performance, leading to more personalized and tailored recommendations. Human evaluation further affirms that CERS generates explanations tailored to individual users, thereby enhancing the persuasiveness of the system.
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