Article

A data analysis and modelling framework for the evaluation of interactive information retrieval

Authors:

Ralf Bierig,

Michael Cole,

Jacek Gwizdka,

Nicholas J. BelkinAuthors Info & Claims

ECIR'2010: Proceedings of the 32nd European conference on Advances in Information Retrieval

Pages 673 - 674

https://doi.org/10.1007/978-3-642-12275-0_76

Published: 28 March 2010 Publication History

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Abstract

Over the last two decades, Interactive Information Retrieval (IIR) has established a new direction within the long tradition of IR that introduces the user at its center and poses new challenges for system evaluation. IR systems can improve performance by utilizing information about the entire interactive process of search. This approach has so far only been initially explored [1,2] with much potential for the future. This demonstration describes an extensible data analysis and modelling framework that enables researchers to integrate, explore and analyze interactive experiment data obtained from task-based IIR experiments and build and test models of interactive user behavior.

References

[1]

Belkin, N., Keynote: Some(what) Grand Challenges for Information Retrieval. In: 30th European Conference on Information Retrieval Research. Glasgow, Scotland, 2008.

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[2]

Belkin, N., Bierig, R., Buscher, G., Van Elst, L., Gwizdka, J., Jose, J., Teevan, J.,: Workshop on Understanding the User. In: 32nd Annual ACM SIGIR Conference, Boston, USA, 2009.

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Cole MGwizdka JLiu CBelkin NZhang X(2013)Inferring user knowledge level from eye movement patternsInformation Processing and Management: an International Journal10.1016/j.ipm.2012.08.00449:5(1075-1091)Online publication date: 1-Sep-2013
https://dl.acm.org/doi/10.1016/j.ipm.2012.08.004
Cole MZhang XLiu CBelkin NGwizdka JMa WNie JBaeza-Yates RChua TCroft W(2011)Knowledge effects on document selection in search results pagesProceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval10.1145/2009916.2010128(1219-1220)Online publication date: 24-Jul-2011
https://dl.acm.org/doi/10.1145/2009916.2010128
Cole MZhang XLiu JLiu CBelkin NBierig RGwizdka JMarshall CToms EGrove A(2010)Are self-assessments reliable indicators of topic knowledge?Proceedings of the 73rd ASIS&T Annual Meeting on Navigating Streams in an Information Ecosystem - Volume 4710.5555/1920331.1920375(1-10)Online publication date: 22-Oct-2010
https://dl.acm.org/doi/10.5555/1920331.1920375

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Published In

ECIR'2010: Proceedings of the 32nd European conference on Advances in Information Retrieval

March 2010

677 pages

ISBN:3642122744

Editors:
Cathal Gurrin
Adaptive Information Cluster, Dublin City University, Dublin, 9, Ireland
,
Yulan He
Adaptive Information Cluster, The Open University, Walton Hall, Milton Keynes, UK
,
Gabriella Kazai
Adaptive Information Cluster, Microsoft Research Ltd, 7 JJ Thomson Avenue, Cambridge, UK
,
Udo Kruschwitz
Department of Computer Science, University of Essex, Wivenhoe Park, Colchester, UK
,
Suzanne Little
Department of Computer Science, The Open University, Walton Hall, Milton Keynes, UK

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Springer-Verlag

Berlin, Heidelberg

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Published: 28 March 2010

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Cole MGwizdka JLiu CBelkin NZhang X(2013)Inferring user knowledge level from eye movement patternsInformation Processing and Management: an International Journal10.1016/j.ipm.2012.08.00449:5(1075-1091)Online publication date: 1-Sep-2013
https://dl.acm.org/doi/10.1016/j.ipm.2012.08.004
Cole MZhang XLiu CBelkin NGwizdka JMa WNie JBaeza-Yates RChua TCroft W(2011)Knowledge effects on document selection in search results pagesProceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval10.1145/2009916.2010128(1219-1220)Online publication date: 24-Jul-2011
https://dl.acm.org/doi/10.1145/2009916.2010128
Cole MZhang XLiu JLiu CBelkin NBierig RGwizdka JMarshall CToms EGrove A(2010)Are self-assessments reliable indicators of topic knowledge?Proceedings of the 73rd ASIS&T Annual Meeting on Navigating Streams in an Information Ecosystem - Volume 4710.5555/1920331.1920375(1-10)Online publication date: 22-Oct-2010
https://dl.acm.org/doi/10.5555/1920331.1920375

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Statistical information retrieval modelling: from the probability ranking principle to recent advances in diversity, portfolio theory, and beyond

The influence of relevance levels on the effectiveness of interactive information retrieval

A commonsense aboutness theory for information retrieval modeling

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