  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.

Information Retrieval;Computers and Society;Machine Learning