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dc.contributor.authorLiu, Haimingen_GB
dc.contributor.authorMulholland, Paulen_GB
dc.contributor.authorSong, Daweien_GB
dc.contributor.authorUren, Victoriaen_GB
dc.contributor.authorRüger, Stefanen_GB
dc.date.accessioned2013-08-12T08:25:07Z
dc.date.available2013-08-12T08:25:07Z
dc.date.issued2010
dc.identifier.citationLiu, H., Mulholland, P, Song, D., Uren, V. and Rüger, S. (2010) 'Applying information foraging theory to understand user interaction with content-based image retrieval', IIiX '10 Proceedings of the third symposium on Information interaction in context, pp. 135-144en_GB
dc.identifier.isbn9781450302470
dc.identifier.doi10.1145/1840784.1840805
dc.identifier.urihttp://hdl.handle.net/10547/297889
dc.description.abstractThe paper proposes an ISE (Information goal, Search strategy, Evaluation threshold) user classification model based on Information Foraging Theory for understanding user interaction with content-based image retrieval (CBIR). The proposed model is verified by a multiple linear regression analysis based on 50 users' interaction features collected from a task-based user study of interactive CBIR systems. To our best knowledge, this is the first principled user classification model in CBIR verified by a formal and systematic qualitative analysis of extensive user interaction data.
dc.language.isoenen
dc.publisherACMen_GB
dc.relation.urlhttp://portal.acm.org/citation.cfm?doid=1840784.1840805en_GB
dc.titleApplying information foraging theory to understand user interaction with content-based image retrievalen
dc.typeConference papers, meetings and proceedingsen
html.description.abstractThe paper proposes an ISE (Information goal, Search strategy, Evaluation threshold) user classification model based on Information Foraging Theory for understanding user interaction with content-based image retrieval (CBIR). The proposed model is verified by a multiple linear regression analysis based on 50 users' interaction features collected from a task-based user study of interactive CBIR systems. To our best knowledge, this is the first principled user classification model in CBIR verified by a formal and systematic qualitative analysis of extensive user interaction data.


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