Cross-domain recommendation (CDR) offers an effective strategy for improving recommendation quality in a target domain by leveraging auxiliary signals from source domains. Nonetheless, emerging evidence shows that CDR can inadvertently heighten group-level unfairness. In this work, we conduct a comprehensive theoretical and empirical analysis to uncover why these fairness issues arise. Specifically, we identify two key challenges: (i) Cross-Domain Disparity Transfer, wherein existing group-level disparities in the source domain are systematically propagated to the target domain; and (ii) Unfairness from Cross-Domain Information Gain, where the benefits derived from cross-domain knowledge are unevenly allocated among distinct groups. To address these two challenges, we propose a Cross-Domain Fairness Augmentation (CDFA) framework composed of two key components. Firstly, it mitigates cross-domain disparity transfer by adaptively integrating unlabeled data to equilibrate the informativeness of training signals across groups. Secondly, it redistributes cross-domain information gains via an information-theoretic approach to ensure equitable benefit allocation across groups. Extensive experiments on multiple datasets and baselines demonstrate that our framework significantly reduces unfairness in CDR without sacrificing overall recommendation performance, while even enhancing it.

翻译：跨域推荐（CDR）通过利用源域的辅助信号，为提高目标域推荐质量提供了一种有效策略。然而，新出现的证据表明，CDR可能无意中加剧群体层面的不公平性。在本工作中，我们进行了全面的理论与实证分析，以揭示这些公平性问题产生的原因。具体而言，我们识别出两个关键挑战：（i）跨域差异转移，即源域中现有的群体层面差异被系统地传播到目标域；（ii）跨域信息增益导致的不公平，即从跨域知识中获得的收益在不同群体间分配不均。为应对这两个挑战，我们提出了一个由两个关键组件构成的跨域公平性增强（CDFA）框架。首先，该框架通过自适应地整合未标记数据来平衡跨群体训练信号的信息量，从而缓解跨域差异转移。其次，它通过一种信息论方法重新分配跨域信息增益，以确保收益在不同群体间的公平分配。在多个数据集和基线模型上的大量实验表明，我们的框架在不牺牲整体推荐性能（甚至能提升性能）的前提下，显著降低了CDR中的不公平性。