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.

翻译：基于主观性对一组样本进行排序（例如流视频的体验质量或图像带来的愉悦感）已成为典型的众包任务。大量研究采用成对比较分析来解决这一挑战，因为该方法允许参与者选择单一方案，从而减轻了其工作量。然而，为了全面比较所有目标组合，任务数量会呈二次方增长。本文提出了一种名为“CrowDC”的成对比较分治算法。仿真结果表明，在对100个以上项目进行排序时，与基线方法相比，CrowDC可在保持90-95%准确率的同时，将任务数量减少40-50%。