Unbiased learning to rank has been proposed to alleviate the biases in the search ranking, making it possible to train ranking models with user interaction data. In real applications, search engines are designed to display only the most relevant k documents from the retrieved candidate set. The rest candidates are discarded. As a consequence, position bias and sample selection bias usually occur simultaneously. Existing unbiased learning to rank approaches either focus on one type of bias (e.g., position bias) or mitigate the position bias and sample selection bias with separate components, overlooking their associations. In this study, we first analyze the mechanisms and associations of position bias and sample selection bias from the viewpoint of a causal graph. Based on the analysis, we propose Causal Likelihood Decomposition (CLD), a unified approach to simultaneously mitigating these two biases in top-k learning to rank. By decomposing the log-likelihood of the biased data as an unbiased term that only related to relevance, plus other terms related to biases, CLD successfully detaches the relevance from position bias and sample selection bias. An unbiased ranking model can be obtained from the unbiased term, via maximizing the whole likelihood. An extension to the pairwise neural ranking is also developed. Advantages of CLD include theoretical soundness and a unified framework for pointwise and pairwise unbiased top-k learning to rank. Extensive experimental results verified that CLD, including its pairwise neural extension, outperformed the baselines by mitigating both the position bias and the sample selection bias. Empirical studies also showed that CLD is robust to the variation of bias severity and the click noise.

翻译：无偏排序学习被提出以减轻搜索排序中的偏差，使得利用用户交互数据训练排序模型成为可能。在实际应用中，搜索引擎通常仅从检索到的候选集中展示最相关的k个文档，其余候选文档则被舍弃。因此，位置偏差与样本选择偏差往往同时发生。现有的无偏排序学习方法要么仅关注单一偏差类型（如位置偏差），要么通过独立组件分别处理位置偏差与样本选择偏差，忽略了二者之间的关联。本研究首先从因果图视角分析了位置偏差与样本选择偏差的作用机制及其关联性。基于此分析，我们提出了因果似然分解（CLD）——一种在top-k排序学习中同时缓解这两种偏差的统一方法。通过将有偏数据的对数似然分解为仅与相关性相关的无偏项，加上其他与偏差相关的项，CLD成功地将相关性与位置偏差及样本选择偏差分离。通过最大化整体似然，可从无偏项中获得无偏的排序模型。本文还进一步开发了面向神经网络的成对排序扩展版本。CLD的优势包括理论严谨性，以及为逐点与成对无偏top-k排序学习提供了统一框架。大量实验结果表明，CLD及其神经网络成对扩展版本通过同时缓解位置偏差与样本选择偏差，性能优于基线方法。实证研究还表明，CLD对偏差严重程度的变化及点击噪声具有良好鲁棒性。