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The study aims to enhance fairness in information retrieval systems while maintaining relevance.

April 1, 2026Open Access

UDInfoLab at the NTCIR-17 FairWeb-1 Task

Key Points

The study aims to enhance fairness in information retrieval systems while maintaining relevance.
Participation in NTCIR-17 FairWeb-1 Task
Submission of five runs based on the DLF fair ranking framework
Construction of experimental setups to measure fairness and relevance
DLF improves fairness in many cases while maintaining relevance
Identified need for more exploration regarding ordinal fairness
Highlighted challenges with longer text documents

Abstract

Providing relevant, diverse, and fair results is crucial for informationretrieval systems. It has attracted more and more attentionbecause of issues caused by traditional relevance-centric retrieval systems.These issues include the problem of echo chambers and theincreasingly polarized online communities. Therefore, we participatedin the NTCIR-17 FairWeb-1 Task to provide group fairness toresearchers, movies, and YouTube content and submitted five runs.The runs are based on a recently proposed fair ranking framework,DLF. The experimental results demonstrate that, in many cases, DLF can improve fairness while maintaining relevance but stillneeds more exploration for ordinal fairness groups and documentswith longer text. This paper reports how the runs were constructedand discusses their performance and future work.

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