Ensuring User-side Fairness in Dynamic Recommender Systems

Hyunsik Yoo, Zhichen Zeng, Jian Kang, Ruizhong Qiu, David Zhou, Zhining Liu, Fei Wang, Charlie Xu, Eunice Chan, Hanghang Tong

Keywords: recommender systems, user-side fairness, dynamic updates

Abstract:

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.

Track: User Modeling and Recommendation

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Submission Number: 1296