Comparative Explanations via Counterfactual Reasoning in Recommendations
This work addresses the issue of inaccurate explanations in recommendation systems for users, representing an incremental improvement over existing methods.
The paper tackles the problem of factual inaccuracies in explainable recommendation systems using counterfactual reasoning by proposing CoCountER, a method that creates counterfactual data via soft swap operations to enable explanations for arbitrary item pairs, with empirical experiments validating its effectiveness.
Explainable recommendation through counterfactual reasoning seeks to identify the influential aspects of items in recommendations, which can then be used as explanations. However, state-of-the-art approaches, which aim to minimize changes in product aspects while reversing their recommended decisions according to an aggregated decision boundary score, often lead to factual inaccuracies in explanations. To solve this problem, in this work we propose a novel method of Comparative Counterfactual Explanations for Recommendation (CoCountER). CoCountER creates counterfactual data based on soft swap operations, enabling explanations for recommendations of arbitrary pairs of comparative items. Empirical experiments validate the effectiveness of our approach.