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Contribution Details

Type Conference or Workshop Paper
Scope Discipline-based scholarship
Published in Proceedings Yes
Title Efficiently identifying a well-performing crowd process for a given problem
Organization Unit
  • Patrick De Boer
  • Abraham Bernstein
Presentation Type paper
Item Subtype Original Work
Refereed Yes
Status Published in final form
  • English
Event Title 20th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2017)
Event Type conference
Event Location Portland, OR
Event Start Date February 25 - 2017
Event End Date March 1 - 2017
Place of Publication Portland, OR
Publisher s.n.
Abstract Text With the increasing popularity of crowdsourcing and crowd computing, the question of how to select a well-performing crowd process for a problem at hand is growing ever more important. Prior work casted crowd process selection to an optimization problem, whose solution is the crowd process performing best for a user’s problem. However, existing approaches require users to probabilistically model aspects of the problem, which may entail a substantial investment of time and may be error-prone. We propose to use black- box optimization instead, a family of techniques that do not require probabilistic modelling by the end user. Specifically, we adopt Bayesian Optimization to approximate the maximum of a utility function quantifying the user’s (business-) objectives while minimizing search cost. Our approach is validated in a simulation and three real-world experiments. The black-box nature of our approach may enable us to reduce the entry barrier for efficiently building crowdsourcing solutions.
Digital Object Identifier 10.1145/2998181.2998263
Other Identification Number merlin-id:13964
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