title: Bandit-Based Task Assignment for Heterogeneous Crowdsourcing.
abstract: 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; for example, a worker may be good at recognizing the names of sports teams but not be familiar with cosmetics brands. We refer to this practical setting as heterogeneous crowdsourcing. In this letter, we propose a contextual bandit formulation for task assignment in heterogeneous crowdsourcing that 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.
authors.affiliation: Department of Computer Science, Tokyo Institute of Technology, Meguro-ku, Tokyo 152-8552, Japan zhang.h.ae@m.titech.ac.jp.
authors.forename: Hao
authors.initials: H
authors.lastname: Zhang
authors.affiliation: Department of Computer Science, Tokyo Institute of Technology, Meguro-ku, Tokyo 152-8552, Japan mycw45@gmail.com.
authors.forename: Yao
authors.initials: Y
authors.lastname: Ma
authors.affiliation: Department of Complexity Science and Engineering, University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan sugi@k.u-tokyo.ac.jp.
authors.forename: Masashi
authors.initials: M
authors.lastname: Sugiyama