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Efficiently identifying a well-performing crowd process for a given problem


De Boer, Patrick; Bernstein, Abraham (2017). Efficiently identifying a well-performing crowd process for a given problem. In: 20th ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2017), Portland, OR, 25 February 2017 - 1 March 2017.

Abstract

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.

Abstract

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.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Language:English
Event End Date:1 March 2017
Deposited On:02 Nov 2016 16:15
Last Modified:08 Dec 2017 20:40
Publisher:s.n.
Other Identification Number:merlin-id:13964

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