The application of Unbiased Learning to Rank (ULTR) is widespread in modern
systems for training unbiased ranking models from biased click logs. The key is
to explicitly model a generation process for user behavior and fit click data
based on examination hypothesis. Previous research found empirically that the
true latent relevance can be recovered in most cases as long as the clicks are
perfectly fitted. However, we demonstrate that this is not always achievable,
resulting in a significant reduction in ranking performance. In this work, we
aim to answer if or when the true relevance can be recovered from click data,
which is a foundation issue for ULTR field. We first define a ranking model as
identifiable if it can recover the true relevance up to a scaling
transformation, which is enough for pairwise ranking objective. Then we explore
an equivalent condition for identifiability that can be novely expressed as a
graph connectivity test problem: if and only if a graph (namely identifiability
graph, or IG) constructed on the underlying structure of the dataset is
connected, we can guarantee that the relevance can be correctly recovered. When
the IG is not connected, there may be bad cases leading to poor ranking
performance. To address this issue, we propose two methods, namely node
intervention and node merging, to modify the dataset and restore connectivity
of the IG. Empirical results obtained on a simulation dataset and two LTR
benchmark datasets confirm the validity of our proposed theorems and show the
effectiveness of our methods in mitigating data bias when the relevance model
is unidentifiable.

应用无偏学习排序 (Unbiased Learning to Rank, ULTR) 于训练基于偏差点击日志的无偏排序模型，在明确用户行为生成过程并根据检验假设拟合点击数据的基础上，研究发现点击数据完美拟合时可以恢复真实潜在相关性，但实际上并非总能实现，从而导致排序性能明显降低。本研究旨在解决当数据集的图连通性被破坏时，从点击数据恢复真实相关性的问题，并提出了节点干预和节点合并两种方法来修正数据集并恢复图连通性，通过仿真数据集和两个 LTR 基准数据集的实证结果验证了我们提出的定理的有效性，同时证明了在相关性模型不可辨识时我们的方法在减轻数据偏差方面的有效性。

识别性问题：揭示偏向的学习排序中隐藏的可恢复条件

Identifiability Matters: Revealing the Hidden Recoverable Condition in  Unbiased Learning to Rank

Since its inception, the field of unbiased learning to rank (ULTR) has
remained very active and has seen several impactful advancements in recent
years. This tutorial provides both an introduction to the core concepts of the
field and an overview of recent advancements in its foundations along with
several applications of its methods. The tutorial is divided into four parts:
Firstly, we give an overview of the different forms of bias that can be
addressed with ULTR methods. Secondly, we present a comprehensive discussion of
the latest estimation techniques in the ULTR field. Thirdly, we survey
published results of ULTR in real-world applications. Fourthly, we discuss the
connection between ULTR and fairness in ranking. We end by briefly reflecting
on the future of ULTR research and its applications. This tutorial is intended
to benefit both researchers and industry practitioners who are interested in
developing new ULTR solutions or utilizing them in real-world applications.

本教程介绍了无偏学习排序（ULTR）领域的核心概念和最新发展，包括其方法的若干应用以及在排序公平性方面的相关连接，旨在为对开发新的 ULTR 解决方案或在实际应用中利用 ULTR 方法感兴趣的研究人员和产业从业者提供帮助。