TDR-CL：针对无偏推荐的定向双鲁棒协作学习
TDR-CL: Targeted Doubly Robust Collaborative Learning for Debiased Recommendations
摘要：
偏见是推荐系统中普遍存在的一个问题，它与用户的偏好纠缠在一起，对无偏学习提出了很大的挑战。对于去偏置任务，双鲁棒（DR）方法及其变体表现出优越的性能，由于双鲁棒性，即DR是无偏的，当估算误差或学习倾向是准确的。然而，我们的理论分析表明，DR通常有很大的方差。同时，由于不准确的估算误差和学习倾向，DR会出现意想不到的大偏差和泛化能力差，这在实践中是经常发生的。在本文中，我们提出了一个原则性的方法，可以有效地减少偏见和方差同时为现有的DR方法时，错误的插补模型是错误的。此外，我们还提出了一种新的半参数协作学习方法，将估算误差分解为参数和非参数部分，并协同更新它们，从而获得更准确的预测。理论分析和实验结果都表明了该方法相对于现有去偏方法的优越性。
英文摘要：
Bias is a common problem inherent in recommender systems, which is entangled with users' preferences and poses a great challenge to unbiased learning. For debiasing tasks, the doubly robust (DR) method and its variants show superior performance due to the double robustness property, that is, DR is unbiased when either imputed errors or learned propensities are accurate. However, our theoretical analysis reveals that DR usually has a large variance. Meanwhile, DR would suffer unexpectedly large bias and poor generalization caused by inaccurate imputed errors and learned propensities, which usually occur in practice. In this paper, we propose a principled approach that can effectively reduce bias and variance simultaneously for existing DR approaches when the error imputation model is misspecified. In addition, we further propose a novel semi-parametric collaborative learning approach that decomposes imputed errors into parametric and nonparametric parts and updates them collaboratively, resulting in more accurate predictions. Both theoretical analysis and experiments demonstrate the superiority of the proposed methods compared with existing debiasing 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。
