A Bayesian Concept Learning Approach to Crowdsourcing

Paolo Renato Viappiani, Sandra Zilles, Howard J. Hamilton, Craiug Boutilier

Research output: Contribution to journalConference article in JournalResearchpeer-review

Abstract

We develop a Bayesian approach to concept learning for crowdsourcing applications. A probabilistic belief over possible concept definitions is maintained and updated according to (noisy) observations from experts, whose behaviors are modeled using discrete types. We propose recommendation techniques, inference methods, and query selection
strategies to assist a user charged with choosing a configuration that satisfies some (partially known) concept. Our model is able to simultaneously learn
the concept definition and the types of the experts. We evaluate our model with simulations, showing that our Bayesian strategies are effective even in large concept spaces with many uninformative experts.
Original languageEnglish
JournalCEUR Workshop Proceedings
Volume756
Number of pages8
ISSN1613-0073
Publication statusPublished - 2011
EventINTELLIGENT TECHNIQUES FOR WEB PERSONALIZATION & RECOMMENDER SYSTEMS (ITWP'11) - Barcelona, Spain
Duration: 16 Jul 2011 → …
Conference number: 9

Workshop

WorkshopINTELLIGENT TECHNIQUES FOR WEB PERSONALIZATION & RECOMMENDER SYSTEMS (ITWP'11)
Number9
CountrySpain
CityBarcelona
Period16/07/2011 → …

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