User-side group fairness is crucial for modern recommender systems, as it aims to alleviate performance disparity between groups of users defined by sensitive attributes such as gender, race, or age. We find that the disparity tends to persist or even increase over time. This calls for effective ways to address user-side fairness in a dynamic environment, which has been infrequently explored in the literature. However, fairness-constrained re-ranking, a typical method to ensure user-side fairness (i.e., reducing performance disparity), faces two fundamental challenges in the dynamic setting: (1) non-differentiability of the ranking-based fairness constraint, which hinders the end-to-end training paradigm, and (2) time-inefficiency, which impedes quick adaptation to changes in user preferences. In this paper, we propose FAir Dynamic rEcommender (FADE), an end-to-end framework with fine-tuning strategy to dynamically alleviate performance disparity. To tackle the above challenges, FADE uses a novel fairness loss designed to be differentiable and lightweight to fine-tune model parameters to ensure both user-side fairness and high-quality recommendations. Via extensive experiments on the real-world dataset, we empirically demonstrate that FADE effectively and efficiently reduces performance disparity, and furthermore, FADE improves overall recommendation quality over time compared to not using any new data.

翻译：用户侧群体公平性对于现代推荐系统至关重要，其目标在于缓解由敏感属性（如性别、种族或年龄）定义的用户群体之间的性能差异。我们发现这种差异往往随着时间的推移持续存在甚至加剧。这要求在动态环境中有效解决用户侧公平性，而现有文献对此鲜有探讨。然而，在动态场景下，作为确保用户侧公平性（即减少性能差异）典型方法的公平性约束重排序面临两个根本性挑战：（1）基于排序的公平性约束不可微分，阻碍了端到端训练范式；（2）时间效率低下，妨碍了对用户偏好变化的快速适应。本文提出FAir Dynamic rEcommender (FADE)，一种采用微调策略的端到端框架，用于动态缓解性能差异。为应对上述挑战，FADE采用新型公平性损失函数，该函数具有可微分且轻量级的特点，可通过微调模型参数同时确保用户侧公平性与高质量推荐。通过在真实数据集上的大量实验，我们实证证明FADE能有效且高效地降低性能差异，此外，与不使用新数据相比，FADE还能随时间推移提升整体推荐质量。