论文标题
Stastabled：稳定的双重鲁棒学习，以推荐有关丢失的数据，而不是随机的
StableDR: Stabilized Doubly Robust Learning for Recommendation on Data Missing Not at Random
论文作者
论文摘要
在推荐系统中，用户始终选择要评估的喜欢的项目，这导致数据并非随机丢失，并且对预测模型的无偏评估和学习构成了巨大的挑战。当前，双重鲁棒（DR）方法已被广泛研究并表现出卓越的性能。但是，在本文中，我们表明DR方法是不稳定的，并且对极小的倾向具有无限的偏见，差异和泛化界限。此外，DR更依赖于外推的事实将导致次优性能。为了解决上述局限性，同时保持双重鲁棒性，我们提出了一种稳定的双重鲁棒（Stabledr）学习方法，并且对外推的依赖较弱。理论分析表明，在不准确的估算错误和任意小的倾向下，StabledR具有同时限制的偏差，方差和概括误差。此外，我们为StabledR提出了一种新颖的学习方法，该方法周期性地更新了插补，倾向和预测模型，从而实现了更稳定和准确的预测。广泛的实验表明，我们的方法显着优于现有方法。
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.
