Bandit-Based Task Assignment for Heterogeneous Crowdsourcing
This addresses the challenge of efficiently assigning tasks to workers with diverse expertise in crowdsourcing platforms, though it appears incremental as it applies an existing bandit framework to this specific problem.
The paper tackles the problem of task assignment in heterogeneous crowdsourcing, where workers have varying reliability across different tasks, by proposing a contextual bandit formulation to maximize reliable labels within a budget, and demonstrates its practical usefulness experimentally.
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 formulation 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.