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Fairness Begins with State: Purifying Latent Preferences for Hierarchical Reinforcement Learning in Interactive Recommendation

arXiv:2603.03820v1 Announce Type: new Abstract: Interactive recommender systems (IRS) are increasingly optimized with Reinforcement Learning (RL) to capture the sequential nature of user-system dynamics. However, existing fairness-aware methods often suffer from a fundamental oversight: they assume the observed user state is a faithful representation of true preferences. In reality, implicit feedback is contaminated by popularity-driven noise and exposure bias, creating a distorted state that misleads the RL agent. We argue that the persistent conflict between accuracy and fairness is not merely a reward-shaping issue, but a state estimation failure. In this work, we propose \textbf{DSRM-HRL}, a framework that reformulates fairness-aware recommendation as a latent state purification problem followed by decoupled hierarchical decision-making. We introduce a Denoising State Representation Module (DSRM) based on diffusion models to recover the low-entropy latent preference manifold from

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Yun Lu, Xiaoyu Shi, Hong Xie, Xiangyu Zhao, Mingsheng Shang · · 1 min read · 9 views
#cs.LG #cs.AI

arXiv:2603.03820v1 Announce Type: new Abstract: Interactive recommender systems (IRS) are increasingly optimized with Reinforcement Learning (RL) to capture the sequential nature of user-system dynamics. However, existing fairness-aware methods often suffer from a fundamental oversight: they assume the observed user state is a faithful representation of true preferences. In reality, implicit feedback is contaminated by popularity-driven noise and exposure bias, creating a distorted state that misleads the RL agent. We argue that the persistent conflict between accuracy and fairness is not merely a reward-shaping issue, but a state estimation failure. In this work, we propose \textbf{DSRM-HRL}, a framework that reformulates fairness-aware recommendation as a latent state purification problem followed by decoupled hierarchical decision-making. We introduce a Denoising State Representation Module (DSRM) based on diffusion models to recover the low-entropy latent preference manifold from high-entropy, noisy interaction histories. Built upon this purified state, a Hierarchical Reinforcement Learning (HRL) agent is employed to decouple conflicting objectives: a high-level policy regulates long-term fairness trajectories, while a low-level policy optimizes short-term engagement under these dynamic constraints. Extensive experiments on high-fidelity simulators (KuaiRec, KuaiRand) demonstrate that DSRM-HRL effectively breaks the "rich-get-richer" feedback loop, achieving a superior Pareto frontier between recommendation utility and exposure equity.

Executive Summary

The article proposes a novel framework, DSRM-HRL, to address fairness in interactive recommendation systems. It introduces a Denoising State Representation Module to recover the true preference manifold from noisy interaction histories and then employs Hierarchical Reinforcement Learning to decouple conflicting objectives, achieving a superior balance between recommendation utility and exposure equity.

Key Points

  • ▸ The article identifies the limitation of existing fairness-aware methods in interactive recommendation systems
  • ▸ It proposes a novel framework, DSRM-HRL, to address this limitation
  • ▸ The framework achieves a superior balance between recommendation utility and exposure equity

Merits

Effective Fairness Achievement

The proposed framework effectively breaks the 'rich-get-richer' feedback loop, achieving a superior Pareto frontier between recommendation utility and exposure equity.

Novel Approach

The introduction of a Denoising State Representation Module based on diffusion models is a novel approach to recover the true preference manifold from noisy interaction histories.

Demerits

Complexity

The proposed framework may be complex to implement and require significant computational resources, which could be a limitation for practical applications.

Expert Commentary

The article presents a significant contribution to the field of interactive recommendation systems, addressing a critical limitation of existing fairness-aware methods. The proposed framework, DSRM-HRL, demonstrates a nuanced understanding of the complex interplay between user preferences, popularity-driven noise, and exposure bias. By introducing a Denoising State Representation Module and decoupling conflicting objectives through Hierarchical Reinforcement Learning, the authors provide a compelling solution to the persistent conflict between accuracy and fairness. The results of the extensive experiments on high-fidelity simulators are particularly noteworthy, as they demonstrate the effectiveness of the proposed framework in achieving a superior Pareto frontier between recommendation utility and exposure equity.

Recommendations

  • ✓ Further research is needed to explore the applicability of the proposed framework to other domains and applications
  • ✓ The development of more efficient and scalable implementations of the proposed framework would be beneficial for practical applications

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