NOTICE: Due to a lapse in annual appropriations, most of this website is not being updated. Learn more.
Form submissions will still be accepted but will not receive responses at this time. Sections of this site for programs using non-appropriated funds (such as NVLAP) or those that are excepted from the shutdown (such as CHIPS and NVD) will continue to be updated.
An official website of the United States government
Here’s how you know
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
Secure .gov websites use HTTPS
A lock (
) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.
Sijie Tao, Nuo Chen, Tetsuya Sakai, Zhumin Chu, Hiromi Arai, Ian Soboroff, Nicola Ferro, Maria Maistro
Abstract
This paper provides an overview of the NTCIR-17 FairWeb-1 Task. FairWeb-1 is an English web search task which seeks more than an ad-hoc web search task. Our task considers not only document relevance but also group fairness. We designed three types of search topics for this task: researchers (R), movies (M), and Youtube con- tents (Y). For each topic type, attribute sets are defined for consider- ing group fairness. We utilise a deduped version of the Chuweb21 corpus as the target corpus. We received 28 runs from six teams, in- cluding six runs from the organisers team. In this paper, we describe the task, the test collection construction and the official evalution results of the submitted runs.
Proceedings Title
Proceedings of the 17th NTCIR (NII Testbeds and Community for Informtion Access Research) Conference
Tao, S.
, Chen, N.
, Sakai, T.
, Chu, Z.
, Arai, H.
, Soboroff, I.
, Ferro, N.
and Maistro, M.
(2024),
Overview of the NTCIR-17 FairWeb-1 Task, Proceedings of the 17th NTCIR (NII Testbeds and Community for Informtion Access Research) Conference, Tokyo, JP, [online], https://doi.org/10.20736/0002001318, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=956867
(Accessed October 13, 2025)