Unbiased Learning to Rank: On Recent Advances and Practical Applications
Shashank Gupta, Philipp Hager, Jin Huang, Ali Vardasbi, and Harrie Oosterhuis Published in Proceedings of the 17th ACM International Conference on Web Search and Data Mining (WSDM ’24), 2024. [pdf, website]
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.
Find the official tutorial website here.
Recommended citation:
Gupta, S., Hager, P., Huang, J., Vardasbi, A., & Oosterhuis, H. (2024, March). Unbiased Learning to Rank: On Recent Advances and Practical Applications. In Proceedings of the 17th ACM International Conference on Web Search and Data Mining (pp. 1118-1121).