  User-side group fairness is crucial for modern recommender systems, aiming to
alleviate performance disparities among user groups defined by sensitive
attributes like gender, race, or age. In the ever-evolving landscape of
user-item interactions, continual adaptation to newly collected data is crucial
for recommender systems to stay aligned with the latest user preferences.
However, we observe that such continual adaptation often exacerbates
performance disparities. This necessitates a thorough investigation into
user-side fairness in dynamic recommender systems, an area that has been
unexplored in the literature. This problem is challenging due to distribution
shifts, frequent model updates, and non-differentiability of ranking metrics.
To our knowledge, this paper presents the first principled study on ensuring
user-side fairness in dynamic recommender systems. We start with theoretical
analyses on fine-tuning v.s. retraining, showing that the best practice is
incremental fine-tuning with restart. Guided by our theoretical analyses, we
propose FAir Dynamic rEcommender (FADE), an end-to-end fine-tuning framework to
dynamically ensure user-side fairness over time. To overcome the
non-differentiability of recommendation metrics in the fairness loss, we
further introduce Differentiable Hit (DH) as an improvement over the recent
NeuralNDCG method, not only alleviating its gradient vanishing issue but also
achieving higher efficiency. Besides that, we also address the instability
issue of the fairness loss by leveraging the competing nature between the
recommendation loss and the fairness loss. Through extensive experiments on
real-world datasets, we demonstrate that FADE effectively and efficiently
reduces performance disparities with little sacrifice in the overall
recommendation performance.
