Contextual Procurement in Online Crowdsourcing Markets

Designing pricing mechanisms for recruitment of work-ers is a central challenge in online crowdsourcing mar-kets. We consider a novel and realistic setting for such markets, where the private cost of the workers and util-ity 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 contex-tual partial monitoring game, a generic framework for online learning problems that allows to deal with com-plex feedback structure. We present simulation results comparing our approach to the classical contextual ban-dit approach, demonstrating the complexity of the prob-lem and the need for the partial monitoring framework