Post-Training Fairness Control: A Single-Train Framework for Dynamic Fairness in Recommendation
It addresses the problem of adapting to diverse and changing fairness demands from stakeholders in real-world recommender systems, offering a more flexible solution than incremental retraining approaches.
The paper tackles the inflexibility of existing fairness-aware recommender systems by proposing Cofair, a single-train framework that enables dynamic fairness control post-training, achieving comparable or better fairness-accuracy curves than state-of-the-art baselines without retraining for each new fairness requirement.
Despite growing efforts to mitigate unfairness in recommender systems, existing fairness-aware methods typically fix the fairness requirement at training time and provide limited post-training flexibility. However, in real-world scenarios, diverse stakeholders may demand differing fairness requirements over time, so retraining for different fairness requirements becomes prohibitive. To address this limitation, we propose Cofair, a single-train framework that enables post-training fairness control in recommendation. Specifically, Cofair introduces a shared representation layer with fairness-conditioned adapter modules to produce user embeddings specialized for varied fairness levels, along with a user-level regularization term that guarantees user-wise monotonic fairness improvements across these levels. We theoretically establish that the adversarial objective of Cofair upper bounds demographic parity and the regularization term enforces progressive fairness at user level. Comprehensive experiments on multiple datasets and backbone models demonstrate that our framework provides dynamic fairness at different levels, delivering comparable or better fairness-accuracy curves than state-of-the-art baselines, without the need to retrain for each new fairness requirement. Our code is publicly available at https://github.com/weixinchen98/Cofair.