# CrowDC: A Divide-and-Conquer Approach for Paired Comparisons in Crowdsourcing

## TL;DR

CrowDC is a divide-and-conquer algorithm that significantly reduces the number of paired comparison tasks in crowdsourcing while maintaining high ranking accuracy, especially for large item sets.

## Contribution

It introduces a novel divide-and-conquer method for paired comparisons that decreases task workload by 40-50% for over 100 items without sacrificing accuracy.

## Key findings

- Reduces comparison tasks by 40-50% for large datasets
- Maintains 90-95% accuracy compared to baseline methods
- Effective for ranking over 100 items in crowdsourcing

## 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.