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We consider a task assignment problem in crowdsourcing, which is aimed atcollecting as many reliable labels as possible within a limited budget. Achallenge in this scenario is how to cope with the diversity of tasks and thetask-dependent reliability of workers, e.g., a worker may be good atrecognizing the name of sports teams, but not be familiar with cosmeticsbrands. We refer to this practical setting as heterogeneous crowdsourcing. Inthis paper, we propose a contextual bandit formulation for task assignment inheterogeneous crowdsourcing, which is able to deal with theexploration-exploitation trade-off in worker selection. We also theoreticallyinvestigate the regret bounds for the proposed method, and demonstrate itspractical usefulness experimentally.

Bandit-Based Task Assignment for Heterogeneous Crowdsourcing