Proactive Guiding Strategy for Item-side Fairness in Interactive Recommendation

Researchers propose HRL4PFG, a new interactive recommendation framework using hierarchical reinforcement learning to promote fairness by guiding user preferences toward long-tail items. The approach aims to balance item-side fairness with user satisfaction, showing improved performance in cumulative interaction rewards and user engagement length compared to existing methods.

• →HRL4PFG uses hierarchical reinforcement learning to proactively guide users toward long-tail items rather than forcing exposure through direct recommendations.
• →The framework operates through macro-level fairness target generation and micro-level real-time recommendation fine-tuning.
• →Experiments demonstrate improved cumulative interaction rewards and maximum user interaction length compared to state-of-the-art methods.
• →The approach addresses the misalignment problem between user preferences and recommended long-tail items that reduces recommendation effectiveness.
• →The research focuses on preserving user satisfaction while achieving item-side fairness in interactive recommender systems.