  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.
