Implicit feedback data, such as user clicks, is commonly used in learning-to-rank (LTR) systems because it is easy to collect and it often reflects user preferences. However, this data is prone to various biases, and training an LTR algorithm directly on biased data can result in suboptimal ranking performance. One of the most prominent and well-studied biases in implicit feedback data is position bias, which occurs because users are more likely to interact with higher-ranked items regardless of their true relevance. In this paper, we propose a novel control function-based method that accounts for position bias in a two-stage process. The first stage uses exogenous variation from the residuals of the ranking process to correct for position bias in the second stage click equation. Unlike previous position bias correction methods, our method does not require knowledge of the click or propensity model and allows for nonlinearity in the underlying ranking model. Moreover, our method is general and allows for debiasing any state-of-the-art ranking algorithm by plugging it into the second stage. We also introduce a new technique to debias validation clicks for hyperparameter tuning to select the optimal model in the absence of unbiased validation data. Experimental results show that our method outperforms state-of-the-art approaches in correcting for position bias.

翻译：隐式反馈数据（如用户点击）因其易于收集且常能反映用户偏好，被广泛应用于排序学习（LTR）系统。然而，此类数据易受多种偏差影响，直接在含偏数据上训练LTR算法可能导致排序性能欠佳。位置偏差是隐式反馈数据中最突出且被深入研究的偏差之一，其产生原因是用户更倾向于与排名较高的项目交互，而无论其真实相关性如何。本文提出一种基于控制函数的新方法，通过两阶段流程处理位置偏差：第一阶段利用排序过程残差的外生变异来校正第二阶段点击方程中的位置偏差。与现有位置偏差校正方法不同，本方法无需已知点击模型或倾向得分模型，且允许底层排序模型存在非线性特性。此外，该方法具有通用性，可将任何先进排序算法嵌入第二阶段实现去偏。我们还提出一种新技术，用于对超参数调优中的验证点击进行去偏处理，从而在无偏验证数据缺失时选择最优模型。实验结果表明，本方法在位置偏差校正方面优于现有先进方法。