research-article

Supporting Exploratory Search Through Interaction Modeling

Author:

Kumaripaba AthukoralaAuthors Info & Claims

PIKM '14: Proceedings of the 7th Workshop on Ph.D Students

Pages 19 - 24

https://doi.org/10.1145/2663714.2668049

Published: 03 November 2014 Publication History

Abstract

With the explosive growth of information available in the Web, locating needed and relevant information remains a difficult task, whether the information is textual or visual. Although information retrieval techniques have improved a lot in providing relevant information, exploratory information search still remains difficult due to its inherently open-ended and dynamic nature. Modeling the user behavior and predicting dynamically changing information needs in exploratory search is hard. Over the past decade there has been increasing attention on rich user interfaces, retrieval techniques, and studies of exploratory search. However, existing work does not yet support the dynamic aspects of exploratory search. The objective of this research is to understand how user interaction modeling can be applied to provide better support in exploratory search tasks. In this research, we focus on building user models that can predict user intent in search using only implicit interactions with search results. One outcome of this research is a personalized search tool that predicts user intent with implicit interactions and dynamically adjusts search results.

References

[1]

E. Agichtein, E. Brill, S. Dumais, and R. Ragno. Learning user interaction models for predicting web search result preferences. In SIGIR, pages 3--10, 2006.

Digital Library

[2]

O. Alonso, R. Baeza-Yates, and M. Gertz. Exploratory search using timelines. In CHI Workshop on Exploratory Search and HCI, 2007.

[3]

K. Athukorala, E. Hoggan, A. Lehtiö, T. Ruotsalo, and G. Jacucci. Information-seeking behaviors of computer scientists: Challenges for electronic literature search tools. In ASIST, 2013.

Digital Library

[4]

M. J. Bates. The design of browsing and berrypicking techniques for the online search interface. Onl. Inf. Rev., 13(5):407--424, 1989.

[5]

M. J. Bates. What is browsing-really? a model drawing from behavioural science research. Inf. Res., 12(4), 2007.

[6]

D. H. Chau, A. Kittur, J. I. Hong, and C. Faloutsos. Apolo: making sense of large network data by combining rich user interaction and machine learning. In CHI, pages 167--176, 2011.

Digital Library

[7]

M. J. Cole, J. Gwizdka, C. Liu, N. J. Belkin, and X. Zhang. Inferring user knowledge level from eye movement patterns. Inf. Proc. Manag., 2012.

Digital Library

[8]

M. J. Cole, J. Gwizdka, C. Liu, R. Bierig, N. J. Belkin, and X. Zhang. Task and user effects on reading patterns in information search. Int. with Comp., 23(4):346--362, 2011.

[9]

M. J. Cole, X. Zhang, C. Liu, N. J. Belkin, and J. Gwizdka. Knowledge effects on document selection in search results pages. In SIGIR, 2011.

Digital Library

[10]

D. R. Cutting, D. R Karger, J. O. Pedersen, and J. W. Tukey. Scatter/gather: A cluster-based approach to browsing large document collections. In SIGIR, pages 318--329, 1992.

Digital Library

[11]

Paolo Ferragina and Antonio Gulli. A personalized search engine based on web-snippet hierarchical clustering. Software: Practice and Experience, 38(2):189--225, 2008.

Digital Library

[12]

W.-T. Fu and P. Pirolli. Snif-act: A cognitive model of user navigation on the world wide web. Hum.-Comp. Int., 22(4):355--412, 2007.

Digital Library

[13]

D. Gł owacka, T. Ruotsalo, K. Konyushkova, K. Athukorala, S. Kaski, and G. Jacucci. Directing exploratory search: Reinforcement learning from user interactions with keywords. In IUI, 2013.

Digital Library

[14]

D. Gł owacka and J. Shawe-Taylor. Content-based image retrieval with multinomial relevance feedback. In ACML, pages 111--125, 2010.

[15]

M. Hearst. Search user interfaces. Cambridge University Press, 2009.

Digital Library

[16]

C. Hölscher and G. Strube. Web search behavior of internet experts and newbies. Computer Networks, 33(1):337--346, 2000.

Digital Library

[17]

I. Hsieh-Yee. Effects of search experience and subject knowledge on the search tactics of novice and experienced searchers. J. Assoc. Inf. Sci. Tech., 44(3):161--174, 1993.

[18]

I. Hsieh-Yee. Research on web search behavior. Libr. Inf. Sci. Res., 23(2):167--185, 2001.

[19]

M. Kaki. Findex: search result categories help users when document ranking fails. In CHI, pages 131--140, 2005.

Digital Library

[20]

M. Karimzadehgan and C. Zhai. Exploration exploitation tradeoff in interactive relevance feedback. In CIKM, pages 1397--1400, 2010.

Digital Library

[21]

D. Kelly and X. Fu. Elicitation of term relevance feedback: an investigation of term source and context. In SIGIR, pages 453--460, 2006.

Digital Library

[22]

C. C. Kuhlthau. Seeking meaning: A process approach to library and information services. 2004.

[23]

B. Kules, M. Wilson, M. C. Schraefel, and B. Shneiderman. From keyword search to exploration: How result visualization aids discovery on the web. Technical Report HCIL-2008-06, U. Maryland, 2008.

[24]

S. Lelis and A. Howes. Informing decisions: how people use online rating information to make choices. In CHI, pages 2285--2294, 2011.

Digital Library

[25]

G. Marchionini. Exploratory search: from finding to understanding. Comm. ACM, 49(4):41--46, 2006.

Digital Library

[26]

J. Matejka, T. Grossman, and G. Fitzmaurice. Citeology: visualizing paper genealogy. In CHI Extended Abstracts, pages 181--190, 2012.

Digital Library

[27]

S. J. Payne and A. Howes. Adaptive interaction: A utility maximization approach to understanding human interaction with technology. In Synt. Lec. Hum.-Cent. Inf., pages 1--111, 2013.

Digital Library

[28]

P. Pirolli and S. Card. Information foraging in information access environments. In CHI, 1995.

Digital Library

[29]

P. Pirolli and S. Card. Information foraging. Psych. rev., 106(4):643, 1999.

[30]

F. Radlinski, R. Kleinberg, and T. Joachims. Learning diverse rankings with multi-armed bandits. In ICML, pages 784--791, 2008.

Digital Library

[31]

J-F. Rouet. The skills of document use: From text comprehension to Web-based learning. Routledge, 2013.

[32]

T. Ruotsalo, K. Athukorala, D. Glowacka, K. Konyushkova, G. Jacucci, and S. Kaski. Supporting exploratory search tasks with interactive user modeling. In In: AGM of Assoc. Inf. Sci. Tech., 2013.

Digital Library

[33]

H. Saito and K. Miwa. A cognitive study of information seeking processes in the www: the effects of searcher's knowledge and experience. In WISE, pages 321--327, 2001.

Digital Library

[34]

A. G. Sutcliffe, M. Ennis, and S. J. Watkinson. Empirical studies of end-user information searching. J. Assoc. Inf. Sci. Tech., 51(13):1211--1231, 2000.

Digital Library

[35]

J. Teevan, C. Alvarado, M. S. Ackerman, and D. R. Karger. The perfect search engine is not enough: a study of orienteering behavior in directed search. In CHI, pages 415--422, 2004.

Digital Library

[36]

P. Vakkari, M. Pennanen, and S. Serola. Changes of search terms and tactics while writing a research proposal: A longitudinal case study. Inf. Proc. Manag., 39(3):445--463, 2003.

Digital Library

[37]

R. W. White, P. N. Bennett, and S. T. Dumais. Predicting short-term interests using activity-based search context. In CIKM, 2010.

Digital Library

[38]

R. W. White, B. Kules, and S. M. Drucker. Supporting exploratory search. Comm. ACM, 49(4):36--39, 2006.

Digital Library

[39]

R. W. White and R. A. Roth. Exploratory search: Beyond the query-response paradigm. Synth. Lec. on Inf. Conc., Retr., and Serv., 1(1):1--98, 2009.

Digital Library

[40]

B. M. Wildemuth. The effects of domain knowledge on search tactic formulation. J. Assoc. Inf. Sci. Tech., 55(3):246--258, 2004.

Digital Library

[41]

K.-P. Yee, K. Swearingen, K. Li, and M. Hearst. Faceted metadata for image search and browsing. In CHI, 2003.

Digital Library

Cited By

de Melo GKacimi MVarde ALi JWang XGarofalakis MSoboroff ISuel TWang M(2014)PIKM 2014Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management10.1145/2661829.2663543(2098-2099)Online publication date: 3-Nov-2014
https://dl.acm.org/doi/10.1145/2661829.2663543

Index Terms

Supporting Exploratory Search Through Interaction Modeling
1. Human-centered computing
  1. Human computer interaction (HCI)

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cover image ACM Conferences

PIKM '14: Proceedings of the 7th Workshop on Ph.D Students

November 2014

70 pages

ISBN:9781450314817

DOI:10.1145/2663714

Program Chairs:
Gerard de Melo
Tsinghua University, China
,
Mouna Kacimi
Free University of Bozen-Bolzano, Italy
,
Aparna Varde
Montclair State University, USA

Copyright © 2014 ACM.

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Publication History

Published: 03 November 2014

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CIKM '14: 2014 ACM Conference on Information and Knowledge Management

November 3, 2014

Shanghai, China

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PIKM '14 Paper Acceptance Rate 4 of 10 submissions, 40%;

Overall Acceptance Rate 25 of 62 submissions, 40%

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Cited By

de Melo GKacimi MVarde ALi JWang XGarofalakis MSoboroff ISuel TWang M(2014)PIKM 2014Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management10.1145/2661829.2663543(2098-2099)Online publication date: 3-Nov-2014
https://dl.acm.org/doi/10.1145/2661829.2663543

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