Efficiently identifying a well-performing crowd process for a given problem
| Autor: | Abraham Bernstein, Patrick De Boer |
|---|---|
| Přispěvatelé: | University of Zurich |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Optimization problem
business.industry Computer science End user Process (engineering) 10009 Department of Informatics media_common.quotation_subject Bayesian optimization Collective intelligence 02 engineering and technology 000 Computer science knowledge & systems Crowdsourcing computer.software_genre 1712 Software 1709 Human-Computer Interaction 020204 information systems 1705 Computer Networks and Communications 0202 electrical engineering electronic engineering information engineering Search cost 020201 artificial intelligence & image processing Data mining Function (engineering) business computer media_common |
| Zdroj: | University of Zurich CSCW |
| DOI: | 10.5167/uzh-126988 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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