Contextual Procurement in Online Crowdsourcing Markets

Authors

  • Adish Singla Eidgenössische Technische Hochschule Zürich (ETH Zurich)
  • Ian Lienert Eidgenössische Technische Hochschule Zürich (ETH Zurich)
  • Gábor Bartók Eidgenössische Technische Hochschule Zürich (ETH Zurich)
  • Andreas Krause Eidgenössische Technische Hochschule Zürich (ETH Zurich)

Keywords:

Crowdsourcing, mechanism design, partial monitoring, procurement auctions, contextual features

Abstract

Designing pricing mechanisms for recruitment of workers is a central challenge in online crowdsourcing markets. We consider a novel and realistic setting for such markets, where the private cost of the workers and utility derived from them is unknown to the mechanism; however, a set of the workers' features can be observed before making the price offer. How should the offered price be adapted to maximize the utility from recruited workers, while minimizing the cost of payments and the idling cost of failure to recruit? In this paper, we address these questions by formulating the problem as a {\em contextual partial monitoring game}, a generic framework for online learning problems that allows to deal with complex feedback structure. We present simulation results comparing our approach to the classical contextual bandit approach, demonstrating the complexity of the problem and the need for the partial monitoring framework.

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Published

2014-09-05

How to Cite

Singla, A., Lienert, I., Bartók, G., & Krause, A. (2014). Contextual Procurement in Online Crowdsourcing Markets. Proceedings of the AAAI Conference on Human Computation and Crowdsourcing, 2(1). Retrieved from https://ojs.aaai.org/index.php/HCOMP/article/view/13195