Proactive Guiding Strategy for Item-side Fairness in Interactive Recommendation
This work addresses the problem of fair exposure of long-tail items for users of interactive recommender systems, which is an incremental improvement over existing methods.
The authors tackled the problem of item-side fairness in interactive recommender systems, achieving improved cumulative interaction rewards and maximum user interaction length compared to state-of-the-art methods. Their approach, HRL4PFG, resulted in a larger margin of improvement in interactive recommendation environments.
Item-side fairness is crucial for ensuring the fair exposure of long-tail items in interactive recommender systems. Existing approaches promote the exposure of long-tail items by directly incorporating them into recommended results. This causes misalignment between user preferences and the recommended long-tail items, which hinders long-term user engagement and reduces the effectiveness of recommendations. We aim for a proactive fairness-guiding strategy, which actively guides user preferences toward long-tail items while preserving user satisfaction during the interactive recommendation process. To this end, we propose HRL4PFG, an interactive recommendation framework that leverages hierarchical reinforcement learning to guide user preferences toward long-tail items progressively. HRL4PFG operates through a macro-level process that generates fairness-guided targets based on multi-step feedback, and a micro-level process that fine-tunes recommendations in real time according to both these targets and evolving user preferences. Extensive experiments show that HRL4PFG improves cumulative interaction rewards and maximum user interaction length by a larger margin when compared with state-of-the-art methods in interactive recommendation environments.