StableDR：面向非随机缺失数据推荐的稳定双重鲁棒学习
StableDR: Stabilized Doubly Robust Learning for Recommendation on Data Missing Not at Random
AI总结：
针对推荐系统中非随机缺失数据下双重鲁棒方法不稳定、依赖外推致性能次优的问题，提出稳定双重鲁棒学习方法StableDR及循环更新策略，理论上保证有界偏差方差与泛化界，实验性能显著优于现有方法。
摘要：
在推荐系统中，用户总是选择喜爱的物品进行评分，这导致数据非随机缺失，给预测模型的无偏评估与学习带来巨大挑战。目前，双重鲁棒（DR）方法已得到广泛研究并展现出优异性能。但本文指出，DR方法不稳定，在倾向度极小时会存在无界的偏差、方差与泛化界；此外，DR更依赖外推的特性会导致性能次优。为在保留双重鲁棒性的同时解决上述局限，我们提出一种对外推依赖更弱的稳定双重鲁棒（StableDR）学习方法。理论分析表明，在插补误差不准确且倾向度任意小的情况下，StableDR同时具有有界的偏差、方差与泛化误差界。此外，我们为StableDR提出一种新型学习方法，循环更新插补、倾向度与预测模型，实现更稳定准确的预测。大量实验表明，我们的方法显著优于现有方法。
英文摘要：
In recommender systems, users always choose the favorite items to rate, which leads to data missing not at random and poses a great challenge for unbiased evaluation and learning of prediction models. Currently, the doubly robust (DR) methods have been widely studied and demonstrate superior performance. However, in this paper, we show that DR methods are unstable and have unbounded bias, variance, and generalization bounds to extremely small propensities. Moreover, the fact that DR relies more on extrapolation will lead to suboptimal performance. To address the above limitations while retaining double robustness, we propose a stabilized doubly robust (StableDR) learning approach with a weaker reliance on extrapolation. Theoretical analysis shows that StableDR has bounded bias, variance, and generalization error bound simultaneously under inaccurate imputed errors and arbitrarily small propensities. In addition, we propose a novel learning approach for StableDR that updates the imputation, propensity, and prediction models cyclically, achieving more stable and accurate predictions. Extensive experiments show that our approaches significantly outperform the existing methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
