Recently, learning to rank (LTR) has also seen significant growth from the application side. The focus of the previous tutorials was on the fundamentals of unbiased learning to rank (ULTR) with a limited emphasis on practical applications. To bridge this gap between theory and practice, we plan to discuss recent applications of ULTR.
Given the advancements in the area of ULTR and its related areas, we believe it is the right time to conduct this tutorial. The field has significantly matured with recent fundamental contributions and has seen several advances in applications that are highly relevant to the IR community. While the focus of LTR was traditionally on relevance ranking, it is now commonly acknowledged that optimizing for relevance alone can result in unfairness issues. In this regard, we believe that the objective of a similar area, such as fair LTR, aligns with ULTR’s mission, which is to provide fair and unbiased rankings to the user. To scale up to large-scale applications, fair LTR work relies on unbiased LTR, and we hope that our tutorial will encourage further exploration in this area.
Our tutorial is expected to benefit both academic researchers and industry practitioners who are either interested in developing new ULTR solutions or applying them in real-world applications.
- Shashank Gupta
- Philipp Hager
- Jin Huang
- Ali Vardasbi
- Harrie Oosterhuis
In due course, we will add the following materials: slides, notebooks (for a guided lab session), pre-trained models (to allow attendees to focus on inference and result interpretation during the hands-on session), an annotated bibliography, and code.