  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.
