Personalized fairness in recommendations has been attracting increasing attention from researchers. The existing works often treat a fairness requirement, represented as a collection of sensitive attributes, as a hyper-parameter, and pursue extreme fairness by completely removing information of sensitive attributes from the learned fair embedding, which suffer from two challenges: huge training cost incurred by the explosion of attribute combinations, and the suboptimal trade-off between fairness and accuracy. In this paper, we propose a novel Adaptive Fair Representation Learning (AFRL) model, which achieves a real personalized fairness due to its advantage of training only one model to adaptively serve different fairness requirements during inference phase. Particularly, AFRL treats fairness requirements as inputs and can learn an attribute-specific embedding for each attribute from the unfair user embedding, which endows AFRL with the adaptability during inference phase to determine the non-sensitive attributes under the guidance of the user's unique fairness requirement. To achieve a better trade-off between fairness and accuracy in recommendations, AFRL conducts a novel Information Alignment to exactly preserve discriminative information of non-sensitive attributes and incorporate a debiased collaborative embedding into the fair embedding to capture attribute-independent collaborative signals, without loss of fairness. Finally, the extensive experiments conducted on real datasets together with the sound theoretical analysis demonstrate the superiority of AFRL.

翻译：推荐系统中的个性化公平性正日益受到研究人员的关注。现有工作通常将表示为敏感属性集合的公平性需求视为超参数，并通过从所学公平嵌入中完全移除敏感属性信息来追求极端公平，这面临两大挑战：属性组合爆炸导致的巨大训练成本，以及公平性与准确性之间的次优权衡。本文提出一种新颖的自适应公平表示学习模型（AFRL），该模型通过仅训练一个模型即可在推理阶段自适应地服务于不同公平性需求，从而实现真正的个性化公平。具体而言，AFRL将公平性需求作为输入，并从非公平用户嵌入中为每个属性学习属性特定嵌入，这使得AFRL在推理阶段具备自适应能力，能够在用户特定公平性需求引导下确定非敏感属性。为在推荐中实现公平性与准确性的更好权衡，AFRL通过创新的信息对齐方法精确保留非敏感属性的判别性信息，并将去偏协同嵌入融入公平嵌入以捕获独立于属性的协同信号，同时不损害公平性。最后，在真实数据集上进行的广泛实验及严谨的理论分析证明了AFRL的优越性。