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

Haiming Liu; Paul Mulholland; Dawei Song; Victoria Uren; Stefan Rüger

doi:10.1145/1840784.1840805

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

Haiming Liu, Paul Mulholland, Dawei Song, Victoria Uren, Stefan Rüger

Research output: Chapter in Book/Published conference output › Conference publication

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.

Original language	English
Title of host publication	Proceeding IIiX 2010 : proceedings of the 2010 Information Interaction in Context Symposium
Place of Publication	New York, NY (US)
Publisher	ACM
Pages	135-144
Number of pages	10
ISBN (Print)	978-1-4503-0247-0
DOIs	https://doi.org/10.1145/1840784.1840805
Publication status	Published - 18 Aug 2010
Event	2010 Information Interaction in Context Symposium - New Brunswick, NJ, United States Duration: 18 Aug 2010 → 21 Aug 2010

Conference

Conference	2010 Information Interaction in Context Symposium
Abbreviated title	IIiX 2010
Country/Territory	United States
City	New Brunswick, NJ
Period	18/08/10 → 21/08/10

Bibliographical note

© 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

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10.1145/1840784.1840805

Applying Information Foraging Theory to Understand User
© 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
Accepted author manuscript, 961 KB

Cite this

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title = "Applying Information Foraging Theory to understand user interaction with content-based image retrieval",

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.",

author = "Haiming Liu and Paul Mulholland and Dawei Song and Victoria Uren and Stefan R{\"u}ger",

note = "{\textcopyright} 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 ; 2010 Information Interaction in Context Symposium, IIiX 2010 ; Conference date: 18-08-2010 Through 21-08-2010",

year = "2010",

month = aug,

day = "18",

doi = "10.1145/1840784.1840805",

language = "English",

isbn = "978-1-4503-0247-0",

pages = "135--144",

booktitle = "Proceeding IIiX 2010 : proceedings of the 2010 Information Interaction in Context Symposium",

publisher = "ACM",

address = "United States",

}

Liu, H, Mulholland, P, Song, D, Uren, V & Rüger, S 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. ACM, New York, NY (US) , pp. 135-144, 2010 Information Interaction in Context Symposium, New Brunswick, NJ, United States, 18/08/10. https://doi.org/10.1145/1840784.1840805

Applying Information Foraging Theory to understand user interaction with content-based image retrieval. / Liu, Haiming; Mulholland, Paul; Song, Dawei et al.
Proceeding IIiX 2010 : proceedings of the 2010 Information Interaction in Context Symposium. New York, NY (US) : ACM, 2010. p. 135-144.

Research output: Chapter in Book/Published conference output › Conference publication

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T1 - Applying Information Foraging Theory to understand user interaction with content-based image retrieval

AU - Liu, Haiming

AU - Mulholland, Paul

AU - Song, Dawei

AU - Uren, Victoria

AU - Rüger, Stefan

N1 - © 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

PY - 2010/8/18

Y1 - 2010/8/18

N2 - 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.

AB - 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.

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BT - Proceeding IIiX 2010 : proceedings of the 2010 Information Interaction in Context Symposium

PB - ACM

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Y2 - 18 August 2010 through 21 August 2010

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

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

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