[2603.03094] Proactive Guiding Strategy for Item-side Fairness in Interactive Recommendation

Computer Science > Information Retrieval arXiv:2603.03094 (cs) [Submitted on 3 Mar 2026] Title:Proactive Guiding Strategy for Item-side Fairness in Interactive Recommendation Authors:Chongjun Xia, Xiaoyu Shi, Hong Xie, Xianzhi Wang, yun lu, Mingsheng Shang View a PDF of the paper titled Proactive Guiding Strategy for Item-side Fairness in Interactive Recommendation, by Chongjun Xia and 5 other authors View PDF HTML (experimental) Abstract: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 ex...