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Bandit-Based Task Assignment for Heterogeneous Crowdsourcing

机译:基于强盗的异构众包任务分配

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摘要

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.
机译:我们考虑了众包中的任务分配问题,该问题旨在在有限的预算内收集尽可能多的可靠标签。这种情况下的挑战是如何应对任务的多样性和工人依赖任务的可靠性。例如,工人可能擅长识别运动队的名称,但不熟悉化妆品品牌。我们将这种实际环境称为异构众包。在这封信中,我们提出了一种在不同类型的众包中用于任务分配的上下文强盗公式,该公式能够处理工人选择中的勘探与开发权衡。我们还从理论上研究了该方法的遗憾界限,并通过实验证明了其实用性。

著录项

  • 来源
    《Neural computation》 |2015年第11期|2447-2475|共29页
  • 作者单位

    Department of Computer Science, Tokyo Institute of Technology, Meguro-ku, Tokyo 152-8552, Japan zhang.h.ae@m.titech.ac.jp;

  • 收录信息 美国《科学引文索引》(SCI);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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