A Bayesian Concept Learning Approach to Crowdsourcing

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

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Abstrakt

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.
OriginalsprogEngelsk
TidsskriftCEUR Workshop Proceedings
Vol/bind756
Antal sider8
ISSN1613-0073
StatusUdgivet - 2011
Begivenhed9th Workshop on Intelligent Techniques for Web Personalization and Recommender Systems - Barcelona, Spanien
Varighed: 16 jul. 2011 → …
Konferencens nummer: 9

Workshop

Workshop9th Workshop on Intelligent Techniques for Web Personalization and Recommender Systems
Nummer9
LandSpanien
ByBarcelona
Periode16/07/2011 → …

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