Proactive Guiding Strategy for Item-side Fairness in Interactive Recommendation
New AI framework uses hierarchical reinforcement learning to guide users toward long-tail items without sacrificing satisfaction.
A research team led by Chongjun Xia has introduced HRL4PFG, a novel framework designed to tackle the critical challenge of item-side fairness in interactive recommender systems. Traditional approaches that force long-tail items into recommendations often create a misalignment with user preferences, ultimately harming engagement and recommendation effectiveness. This new proactive strategy aims to actively guide user preferences toward underrepresented items while maintaining satisfaction throughout the interactive process, moving beyond simple exposure-based fairness.
The HRL4PFG framework leverages hierarchical reinforcement learning (HRL) with a two-tiered architecture: a macro-level process generates long-term, fairness-guided targets using multi-step feedback, while a micro-level process fine-tunes recommendations in real-time based on these targets and evolving user preferences. Extensive experiments demonstrate that HRL4PFG outperforms current state-of-the-art methods by achieving a larger margin of improvement in cumulative interaction rewards and maximum user interaction length. This represents a significant step toward building recommendation systems that are both fair to item providers and effective for end-users, potentially influencing platforms from e-commerce to streaming services.
- Proposes HRL4PFG, a hierarchical reinforcement learning framework for proactive fairness guidance in recommendations.
- Uses a two-level process: macro for multi-step target generation and micro for real-time preference tuning.
- Outperforms state-of-the-art methods, improving cumulative rewards and user interaction length in experiments.
Why It Matters
Enables platforms to promote diverse, long-tail content without alienating users, balancing business goals with ethical AI.