Zhen-Heng Fu

Latent Preference Inference and Bilevel Fairness Optimization for Hybrid Expert-Crowd Ranking Systems

A probabilistic inverse framework for latent preference recovery and a bilevel scheme for fairness-aware ranking optimization are presented, which reaches a 99.71 percent elimination-prediction hit rate under steady-state noise and improves fairness by about 40 percent.