Fairness-aware recommendation eliminates discrimination issues to build trustworthy recommendation systems.Explaining the causes of unfair recommendations is critical, as it promotes fairness diagnostics, and thus secures users' trust in recommendation models. Existing fairness explanation methods suffer high computation burdens due to the large-scale search space and the greedy nature of the explanation search process. Besides, they perform score-based optimizations with continuous values, which are not applicable to discrete attributes such as gender and race. In this work, we adopt the novel paradigm of counterfactual explanation from causal inference to explore how minimal alterations in explanations change model fairness, to abandon the greedy search for explanations. We use real-world attributes from Heterogeneous Information Networks (HINs) to empower counterfactual reasoning on discrete attributes. We propose a novel Counterfactual Explanation for Fairness (CFairER) that generates attribute-level counterfactual explanations from HINs for recommendation fairness. Our CFairER conducts off-policy reinforcement learning to seek high-quality counterfactual explanations, with an attentive action pruning reducing the search space of candidate counterfactuals. The counterfactual explanations help to provide rational and proximate explanations for model fairness, while the attentive action pruning narrows the search space of attributes. Extensive experiments demonstrate our proposed model can generate faithful explanations while maintaining favorable recommendation performance.

翻译：公平感知推荐通过消除歧视问题来构建可信赖的推荐系统。解释不公平推荐的原因至关重要，因为它能促进公平性诊断，从而增强用户对推荐模型的信任。现有公平性解释方法因搜索空间过大且解释搜索过程具有贪婪性，导致计算负担沉重。此外，这些方法采用连续值的评分优化，不适用于性别、种族等离散属性。本研究采用因果推断中的反事实解释新范式，探索解释的最小化改变如何影响模型公平性，从而避免对解释的贪婪搜索。我们利用异质信息网络中的真实世界属性，增强对离散属性的反事实推理能力。提出了一种面向公平性的新型反事实解释方法，该方法从异质信息网络中生成针对推荐公平性的属性级反事实解释。我们的方法通过离策略强化学习寻求高质量的反事实解释，并采用注意力动作剪枝策略缩小候选反事实的搜索空间。反事实解释有助于为模型公平性提供合理且近似的解释，而注意力动作剪枝则缩小了属性的搜索范围。大量实验证明，该模型能够在生成可靠解释的同时保持优异的推荐性能。