论文标题
达到无偏见的结束：揭示基于点击学习的隐性限制以排名
Reaching the End of Unbiasedness: Uncovering Implicit Limitations of Click-Based Learning to Rank
论文作者
论文摘要
基于点击的学习来排名（LTR）可以解决项目上点击频率之间的不匹配及其实际相关性。先前工作的方法是假设点击行为模型，然后引入一种方法，以无偏估计该假定模型下的偏好。这种方法的成功很明显，因为已经发现了越来越多的行为模型和偏见类型的无偏方法。这项工作旨在揭示反事实LTR领域中高级普遍方法的隐含局限性。因此，与明确假设所遵循的局限性相反，我们的目的是认识到该领域目前不知道该领域的局限性。我们通过反转现有方法来做到这一点：我们首先以通用术语捕获现有方法，然后从这些通用描述中，我们得出了这些方法可以公正的点击行为。我们的倒置方法表明，反事实LTR方法确实存在隐式局限性：我们发现反事实估计只能基于仿射转换产生无偏见的点击行为方法。此外，我们还认识到以前未被发现的点击模型和基于点击LTR的成对方法的限制。我们的发现表明，现有方法不可能为所有合理的点击行为模型提供无偏的保证。
Click-based learning to rank (LTR) tackles the mismatch between click frequencies on items and their actual relevance. The approach of previous work has been to assume a model of click behavior and to subsequently introduce a method for unbiasedly estimating preferences under that assumed model. The success of this approach is evident in that unbiased methods have been found for an increasing number of behavior models and types of bias. This work aims to uncover the implicit limitations of the high-level prevalent approach in the counterfactual LTR field. Thus, in contrast with limitations that follow from explicit assumptions, our aim is to recognize limitations that the field is currently unaware of. We do this by inverting the existing approach: we start by capturing existing methods in generic terms, and subsequently, from these generic descriptions we derive the click behavior for which these methods can be unbiased. Our inverted approach reveals that there are indeed implicit limitations to the counterfactual LTR approach: we find counterfactual estimation can only produce unbiased methods for click behavior based on affine transformations. In addition, we also recognize previously undiscussed limitations of click-modelling and pairwise approaches to click-based LTR. Our findings reveal that it is impossible for existing approaches to provide unbiasedness guarantees for all plausible click behavior models.
