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.

翻译：项目侧公平性对于确保交互式推荐系统中长尾项目的公平曝光至关重要。现有方法通过直接将长尾项目纳入推荐结果来促进其曝光。这导致用户偏好与推荐的长尾项目之间出现错配，从而阻碍长期用户参与度并降低推荐效果。我们旨在提出一种主动的公平性引导策略，该策略在交互推荐过程中积极引导用户偏好转向长尾项目，同时保持用户满意度。为此，我们提出了HRL4PFG——一个利用分层强化学习逐步引导用户偏好转向长尾项目的交互式推荐框架。HRL4PFG通过宏观层面和微观层面两个过程运作：宏观过程基于多步反馈生成公平性引导目标，微观过程则根据这些目标及动态演化的用户偏好实时微调推荐。大量实验表明，在交互式推荐环境中，与最先进方法相比，HRL4PFG在累积交互奖励和最大用户交互长度方面取得了更显著的提升。