论文标题
TDR-CL：有针对性的鲁棒性协作学习，用于辩护建议
TDR-CL: Targeted Doubly Robust Collaborative Learning for Debiased Recommendations
论文作者
论文摘要
偏见是推荐系统固有的常见问题，它与用户的偏好纠缠在一起，并对无偏见的学习构成了巨大的挑战。对于辩护任务，双重鲁棒（DR）方法及其变体由于双重鲁棒性属性而显示出卓越的性能，也就是说，当估算错误或学习的倾向是否准确时，DR是公正的。但是，我们的理论分析表明，DR通常具有很大的差异。同时，DR会出乎意料地遭受大量偏见和由于估算错误和学识渊博的倾向而导致的概括，这通常是在实践中发生的。在本文中，我们提出了一种有原则的方法，该方法可以有效地减少误解模型的现有DR方法同时降低偏差和差异。此外，我们进一步提出了一种新型的半参数协作学习方法，该方法将估算错误分解为参数和非参数零件，并进行协作，从而进行更准确的预测。理论分析和实验都证明了与现有的辩护方法相比，所提出的方法的优越性。
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.
