Fairness Begins with State: Purifying Latent Preferences for Hierarchical Reinforcement Learning in Interactive Recommendation

Researchers propose DSRM-HRL, a new framework that uses diffusion models to purify user preference data and hierarchical reinforcement learning to balance recommendation accuracy with fairness. The system addresses bias in interactive recommendation systems by separating state estimation from decision-making, achieving better outcomes on both utility and exposure equity.