论文标题
成对无偏学习的一般框架排名
A General Framework for Pairwise Unbiased Learning to Rank
论文作者
论文摘要
成对辩论是减少学习对秩（LTR）模型中位置偏见的最有效策略之一。但是，限制此策略的范围是许多成对辩论方法所需的基本假设。在本文中，我们基于简约的假设集开发了一种方法，该假设可以应用于更广泛的用户浏览模式和任意演示布局。我们将方法作为无偏的Lambdamart的简化版本实现，并证明它保留了比原始算法更广泛的设置中的潜在无偏。最后，使用带有“黄金”相关性标签的仿真，我们将证明，当对排名列表中对不同位置的检查并非独立时，简化的版本与原始无偏见的Lambdamart进行了比较。
Pairwise debiasing is one of the most effective strategies in reducing position bias in learning-to-rank (LTR) models. However, limiting the scope of this strategy, are the underlying assumptions required by many pairwise debiasing approaches. In this paper, we develop an approach based on a minimalistic set of assumptions that can be applied to a much broader range of user browsing patterns and arbitrary presentation layouts. We implement the approach as a simplified version of the Unbiased LambdaMART and demonstrate that it retains the underlying unbiasedness property in a wider variety of settings than the original algorithm. Finally, using simulations with "golden" relevance labels, we will show that the simplified version compares favourably with the original Unbiased LambdaMART when the examination of different positions in a ranked list is not assumed to be independent.
