通过对比反事实推理，让推荐解释更准确可信。
Comparative Explanations via Counterfactual Reasoning in Recommendations
- 基于软交换操作生成反事实数据，实现任意物品对的解释。
- 相比现有方法，显著减少解释中的事实错误。
- 适合需要可信赖推荐解释的场景，如电商、内容平台。
通过反事实推理实现可解释推荐，旨在识别影响推荐结果的关键物品特征，从而生成解释。然而，当前先进方法在最小化物品特征变化的同时，依据聚合决策边界分数反转推荐结果，常导致解释存在事实性错误。为此，本文提出一种新型推荐对比反事实解释方法（CoCountER）。CoCountER基于软交换操作生成反事实数据，能够为任意一对比较性物品提供解释。实验证明该方法有效提升了解释的准确性与可信度。
原文摘要 · Abstract (English)
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.
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