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.

翻译：自该领域创立以来，无偏学习排序（ULTR）一直保持活跃，并在近年来取得了若干影响深远的进展。本教程既介绍了该领域的核心概念，又概述了其基础理论的最新进展及多种方法的应用。教程分为四部分：首先，概述可用ULTR方法解决的不同形式的偏差；其次，全面讨论ULTR领域最新的估计技术；第三，调研ULTR在实际应用中的已发表成果；第四，探讨ULTR与排序公平性之间的联系。最后，我们简要反思了ULTR研究及其应用的未来方向。本教程旨在惠及对开发新ULTR解决方案或在实际应用中运用这些方案感兴趣的研究人员与行业从业者。