Applying Information Foraging Theory to understand user interaction with content-based image retrieval

Liu, Haiming, Mulholland, Paul, Song, Dawei, Uren, Victoria and Rüger, Stefan (2010). Applying Information Foraging Theory to understand user interaction with content-based image retrieval. IN: Proceeding IIiX 2010 : proceedings of the 2010 Information Interaction in Context Symposium. New York, NY (US): ACM.

Abstract

The 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.

Publication DOI: https://doi.org/10.1145/1840784.1840805
Divisions: Aston Business School > Operations & information management
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Aston Business School > Operations & information management research group
Additional Information: © ACM, 2010. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in IIiX '10 Proceedings of the third symposium on Information interaction in context, http://doi.acm.org/10.1145/1840784.1840805
Event Title: 2010 Information Interaction in Context Symposium
Event Type: Other
Event Dates: 2010-08-18 - 2010-08-21
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
Published Date: 2010-08-18
Authors: Liu, Haiming
Mulholland, Paul
Song, Dawei
Uren, Victoria
Rüger, Stefan

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