Revisiting Fairness-aware Interactive Recommendation: Item Lifecycle as a Control Knob

• URL: http://arxiv.org/abs/2511.16248v1
• Date: Thu, 20 Nov 2025 11:27:04 GMT
• Title: Revisiting Fairness-aware Interactive Recommendation: Item Lifecycle as a Control Knob
• Abstract summary: This paper revisits fairness-aware interactive recommendation by introducing a novel control knob, i.e., the lifecycle of items.<n>We introduce LHRL, a lifecycle-aware hierarchical reinforcement learning framework.<n>Experiments on multiple real-world interactive recommendation demonstrate that LHRL significantly improves both fairness and user engagement.
• Score: 11.023574014254663
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper revisits fairness-aware interactive recommendation (e.g., TikTok, KuaiShou) by introducing a novel control knob, i.e., the lifecycle of items. We make threefold contributions. First, we conduct a comprehensive empirical analysis and uncover that item lifecycles in short-video platforms follow a compressed three-phase pattern, i.e., rapid growth, transient stability, and sharp decay, which significantly deviates from the classical four-stage model (introduction, growth, maturity, decline). Second, we introduce LHRL, a lifecycle-aware hierarchical reinforcement learning framework that dynamically harmonizes fairness and accuracy by leveraging phase-specific exposure dynamics. LHRL consists of two key components: (1) PhaseFormer, a lightweight encoder combining STL decomposition and attention mechanisms for robust phase detection; (2) a two-level HRL agent, where the high-level policy imposes phase-aware fairness constraints, and the low-level policy optimizes immediate user engagement. This decoupled optimization allows for effective reconciliation between long-term equity and short-term utility. Third, experiments on multiple real-world interactive recommendation datasets demonstrate that LHRL significantly improves both fairness and user engagement. Furthermore, the integration of lifecycle-aware rewards into existing RL-based models consistently yields performance gains, highlighting the generalizability and practical value of our approach.

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