Unbiased Learning to Rank: On Recent Advances and Practical Applications

Shashank Gupta, Philipp Hager, Jin Huang, Harrie Oosterhuis, Maarten de Rijke

Abstract:

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 interested in developing new ULTR solutions or
utilizing them in real-world applications.