针对隐性混淆导致的推荐偏差，提出个性化敏感度边界方法提升推荐鲁棒性。
Robust Personalized Recommendation under Hidden Confounding in MNAR
- 基于用户-物品级敏感度边界，放宽全局同质性假设
- 在三个真实数据集上显著优于传统全局方法
- 无需实验数据，适合真实场景中存在隐藏混淆的推荐
推荐系统依赖观察到的用户-物品交互数据，但用户选择性互动易引入选择偏差。逆倾向权重和双重稳健估计器在可观测混淆因子下有效缓解偏差，但在隐藏混淆存在时不可靠。现有依赖随机对照试验（RCT）或全局敏感度边界的方案受限：RCT需昂贵实验数据，而全局敏感度边界假设未测量混淆因子对倾向性的效应均匀有界，忽略了用户-物品交互间的异质性。为此，我们提出新框架PUID，估计用户-物品层级的敏感度边界，显著放松全局敏感度边界的同质性假设。为兼顾鲁棒性与预测精度，进一步设计对抗优化策略，并提出融合预训练模型作为稳定参考的基准引导变体BPUID。在三个真实世界数据集上的大量实验表明，该方法在存在隐藏混淆时显著优于全局方法，且无需RCT数据。
原文摘要 · Abstract (English)
Recommender systems often rely on observational user--item interaction data, which is prone to selection bias due to users' selective interactions with items. Inverse propensity weighting and doubly robust estimators effectively mitigate selection bias under observed confounding, but are unreliable in the presence of hidden confounders. Existing approaches relying on randomized controlled trials (RCTs) or global sensitivity bounds are constrained in practice: RCTs demand costly experimental data, while global sensitivity bounds presume a uniformly bounded effect of unmeasured confounders on propensities through sensitivity analysis, thereby neglecting heterogeneity across user--item interactions. To overcome this limitation, we propose a novel framework, which estimates user--item level sensitivity bounds, thereby substantially relaxing the homogeneity assumption inherent in global sensitivity bounds named Personalized Unobserved-Confounding-aware Interaction Deconfounder (PUID). To ensure both robustness and predictive accuracy, we further develop an adversarial optimization strategy and propose a benchmark-guided variant (BPUID) that incorporates pre-trained models as stabilizing references. Extensive experiments on three real-world datasets demonstrate that our approach significantly outperforms global methods under hidden confounding, without requiring RCT data.
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