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Inducing Conceptual User Models

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https://osnadocs.ub.uni-osnabrueck.de/handle/urn:nbn:de:gbv:700-2002042911
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dc.contributor.advisorProf. Dr. C. Rollinger
dc.creatorMüller, Martin Eric
dc.date.accessioned2010-01-30T14:55:00Z
dc.date.available2010-01-30T14:55:00Z
dc.date.issued2002-04-29T14:59:36Z
dc.date.submitted2002-04-29T14:59:36Z
dc.identifier.urihttps://osnadocs.ub.uni-osnabrueck.de/handle/urn:nbn:de:gbv:700-2002042911-
dc.description.abstractUser Modeling and Machine Learning for User Modeling have both become important research topics and key techniques in recent adaptive systems. One of the most intriguing problems in the `information age´ is how to filter relevant information from the huge amount of available data. This problem is tackled by using models of the user´s interest in order to increase precision and discriminate interesting information from un-interesting data. However, any user modeling approach suffers from several major drawbacks: User models built by the system need to be inspectable and understandable by the user himself. Secondly, users in general are not willing to give feedback concerning user satisfaction by the delivered results. Without any evidence for the user´s interest, it is hard to induce a hypothetical user model at all. Finally, most current systems do not draw a line of distinction between domain knowledge and user model which makes the adequacy of a user model hard to determine. This thesis presents the novel approach of conceptual user models. Conceptual user models are easy to inspect and understand and allow for the system to explain its actions to the user. It is shown, that ILP can be applied for the task of inducing user models from feedback, and a method for using mutual feedback for sample enlargement is introduced. Results are evaluated independently of domain knowledge within a clear machine learning problem definition. The whole concept presented is realized in a meta web search engine called OySTER.eng
dc.language.isoeng
dc.subjectMachine Learning for User Modeling
dc.subjectUser Modeling
dc.subjectAdaptive User Interfaces
dc.subjectWeb information retrieval
dc.subjectWeb Search engines
dc.subjectDocument filtering
dc.subject.ddc000 - Informatik, Wissen, Systemeger
dc.titleInducing Conceptual User Modelseng
dc.typeDissertation oder Habilitation [doctoralThesis]-
thesis.locationOsnabrück-
thesis.institutionUniversität-
thesis.typeDissertation [thesis.doctoral]-
thesis.date2001-12-17T12:00:00Z-
elib.elibid188-
elib.marc.edtfangmeier-
elib.dct.accessRightsa-
elib.dct.created2002-03-20T09:26:35Z-
elib.dct.modified2002-04-29T14:59:36Z-
dc.contributor.refereePD Dr. habil H. Gust
dc.contributor.refereeProf. Dr. I. Düntsch
dc.subject.dnb28 - Informatik, Datenverarbeitungger
dc.subject.ccsI.2.6 - Learningeng
dc.subject.ccsH.5.2 - User Interfaceseng
dc.subject.ccsI.3.6 - Methodology and Techniqueseng
dc.subject.ccsH.3.3 - Information Search and Retrievaleng
dc.subject.ccsH.3.5 - Online Information Serviceseng
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