CrowDC: A Divide-and-Conquer Approach for Paired Comparisons in Crowdsourcing
Human-Computer Interaction
2023-02-24 v1 Social and Information Networks
Abstract
Ranking a set of samples based on subjectivity, such as the experience quality of streaming video or the happiness of images, has been a typical crowdsourcing task. Numerous studies have employed paired comparison analysis to solve challenges since it reduces the workload for participants by allowing them to select a single solution. Nonetheless, to thoroughly compare all target combinations, the number of tasks increases quadratically. This paper presents ``CrowDC'', a divide-and-conquer algorithm for paired comparisons. Simulation results show that when ranking more than 100 items, CrowDC can reduce 40-50% in the number of tasks while maintaining 90-95% accuracy compared to the baseline approach.
Cite
@article{arxiv.2302.11722,
title = {CrowDC: A Divide-and-Conquer Approach for Paired Comparisons in Crowdsourcing},
author = {Ming-Hung Wang and Chia-Yuan Zhang and Jia-Ru Song},
journal= {arXiv preprint arXiv:2302.11722},
year = {2023}
}