2023/02/23 by Ming-Hung Wang, Wang, Ming-Hung, Chia-Yuan Zhang +3
Business, Management and Accounting · Computer Science · #Consumer Market Behavior and Pricing #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Mobile Crowdsensing and Crowdsourcing #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.2302.11722
openalex publication_date 2023/02/23 · openalex created_date 2023/02/25 · openalex updated_date 2026/07/28
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.