Bandit-Based Task Assignment for Heterogeneous Crowdsourcing

Hao Zhang, Yao Ma, Masashi Sugiyama
2015 Neural Computation  
We consider a task assignment problem in crowdsourcing, which is aimed at collecting as many reliable labels as possible within a limited budget. A challenge in this scenario is how to cope with the diversity of tasks and the task-dependent reliability of workers, e.g., a worker may be good at recognizing the name of sports teams, but not be familiar with cosmetics brands. We refer to this practical setting as heterogeneous crowdsourcing. In this paper, we propose a contextual bandit
more » ... for task assignment in heterogeneous crowdsourcing, which is able to deal with the exploration-exploitation trade-off in worker selection. We also theoretically investigate the regret bounds for the proposed method, and demonstrate its practical usefulness experimentally.
doi:10.1162/neco_a_00782 pmid:26378878 fatcat:tmriuekiqzbqbgmt6lfuuxgtom