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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 | |
| Authors |
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| Presentation Type | paper |
| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| 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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