On Dynamically Pricing Crowdsourcing Tasks

Abstract

Crowdsourcing techniques have been extensively explored in the past decade, including task allocation, quality assessment, and so on. Most of professional crowdsourcing platforms adopt the fixed pricing scheme to offer a fixed price for crowd tasks. It is neither incentive for crowd workers to produce good performance, nor profitable for the requester to gain high utility with low budget. In this article, we study the problem of pricing crowdsourcing tasks with optional bonuses. We propose a dynamic pricing mechanism, named CrowdPricer for incentively delivering bonuses to the crowd workers of completing tasks, in addition to offering a base payment for completing a task. We leverage a deep time sequence model to learn the effect of bonuses on workers’ quality for crowd tasks. CrowdPricer makes decisions on whether to provide bonuses on workers, so as to maximize the requester’s utility in expectation. We present an efficient bonus delivery algorithm under the help of beam search technique, in order to efficiently solve the decision making problem. Extensive experiments using both a real crowdsourcing platform and simulations demonstrate that CrowdPricer yields the higher utility for the requester. It also obtains more correct crowd answers than the state-of-the-art pricing methods.