Multi-Armed Bandit Approach to Qualification Task Assignment across Multi Crowdsourcing Platforms
- Resource Type
- Conference
- Authors
- Xiao, Yunyi; Yamashita, Yu; Ito, Hiroyoshi; Matsubara, Masaki; Morishima, Atsuyuki
- Source
- 2022 IEEE International Conference on Big Data (Big Data) Big Data (Big Data), 2022 IEEE International Conference on. :4040-4048 Dec, 2022
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Engineering Profession
Geoscience
Robotics and Control Systems
Signal Processing and Analysis
Crowdsourcing
Big Data
Probability
Planning
Behavioral sciences
Task analysis
Optimization
Multi-Platform Crowdsourcing
Multi-Armed Bandit
Task Assignment Optimization
Qualification Task
- Language
Many existing optimization approaches deal with task assignments on one single crowdsourcing platform. This paper addresses the difficulties of the optimal platform selection for qualification tasks on one single platform. We proposed a novel approach about assigning qualification tasks to workers iteratively on multiple platforms to maximize the total number of collected qualified workers on a limited budget. We applied Multi-Armed Bandit (MAB) algorithms to create strategies for the platform selections to achieve this goal. The conducted experiments revealed that (1) the optimal platform is not always trivial, and (2) the strategies created by MAB algorithms can achieve high-quality assignments under different settings which also satisfied different requesters’ needs.