Adaptive Fair Representation Learning for Personalized Fairness in Recommendations via Information Alignment

The core message of this paper is to propose a novel Adaptive Fair Representation Learning (AFRL) model that achieves personalized fairness in recommendations by treating fairness requirements as inputs and learning attribute-specific embeddings and a debiased collaborative embedding, without compromising recommendation accuracy.