TROP: Task Ranking Optimization Problem on Crowdsourcing Service Platform

Jiale Zhang, Haozhen Lu, Xiaofeng Gao, Ailun Song, Guihai Chen

Abstract: Crowdsourcing has the potential to solve complex problems, especially tasks that are easy for humans but difficult for computers. Service providers of emerging crowdsourcing platforms hope that crowdsourcing tasks on their service platforms can be executed as much as possible in available time. We consider from the perspective of the crowdsourcing service platform and study how to rank tasks to minimize the maximum timeout of tasks. We first formalize the Task Ranking Optimization Problem (TROP) and study its offline version. In the case of online scenario, we propose an Iterative Hungarian Algorithm for Task Ranking Optimization Problem, considering task deadline and click transfer rate with ranking amplification. Experiments on a real crowdsourcing service platform and the simulations based on real datasets demonstrate the superiority of proposed algorithm.

External IDs:dblp:conf/dasfaa/ZhangLGSC22