What question did this study set out to answer?

The main aim is to evaluate the effectiveness of search result diversification and ranking fusion techniques for achieving fair search results.

April 1, 2026Open Access

RMITIR at the NTCIR-17 FairWeb-1 Task

Key Points

The main aim is to evaluate the effectiveness of search result diversification and ranking fusion techniques for achieving fair search results.
Participated in the NTCIR-17 FairWeb-1 task with five submitted runs.
Explored explicit search result diversification (SRD) and ranking fusion approaches.
Investigated a linear combination-based technique for relevance in re-ranking.
Compared results against various retrieval baselines across different topic types.
No statistically significant improvement over the retrieval baselines was observed for the SRD-based runs.
Linear combination-based runs also did not show significant enhancements.
Results were compared separately along topic types: Researcher, Movie, YouTube.

Abstract

This report describes the participation of the RMIT IR group at the NTCIR-17 FairWeb-1 task. We submitted five runs with the aim of exploring the role of explicit search result diversification (SRD) and ranking fusion to generate fair rankings considering multiple fairness attributes. We also explored the use of a linear combination-based technique (LC) to take into consideration the relevance while re-ranking. In this report, we compared results from all our submitted runs against each other and the retrieval baselines along each topic type separately (i.e., Researcher, Movie, YouTube). Overall, our results show that neither the SRD-based runs nor the linear combination-based runs show any statistically significant improvement over the retrieval baselines. The source code of the framework for generating group memberships is made available at https://github.com/rmit-ir/fairweb-1.

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