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Discrimination between tasks with user activity patterns during information search

Published: 03 July 2014 Publication History

Abstract

Can the activity patterns of page use during information search sessions discriminate between different types of information seeking tasks? We model sequences of interactions with search result and content pages during information search sessions. Two representations are created: the sequences of page use and a cognitive representation of page interactions. The cognitive representation is based on logged eye movement patterns of textual information acquisition via the reading process. Page sequence actions from task sessions (n=109) in a user study are analyzed. The study tasks differed from one another in basic dimensions of complexity, specificity,level, and the type of information product (intellectual or factual). The results show that differences in task types can be measured at both the level of observations of page type sequences and at the level of cognitive activity on the pages. We discuss the implications for personalization of search systems, measurement of task similarity and the development of user-centered information systems that can support the user's current and expected search intentions.

References

[1]
Alonso, O., Gertz, M., and Baeza-Yates, R. A. On the value of temporal information in information retrieval. SIGIR Forum 41, 2 (2007), 35--41.
[2]
Bierig, R., Gwizdka, J., and Cole, M. J. A user-centered experiment and logging framework for interactive information retrieval. In Proceedings of the SIGIR 2009 Workshop on Understanding the User: Logging and interpreting user interactions in information search and retrieval. (July 2009), N. J. Belkin, R. Bierig, G. Buscher, L. van Elst, J. Gwizdka, J. Jose, and J. Teevan, Eds., CEUR, p. 8--11.
[3]
Borlund, P. The IIR evaluation model: A framework for evaluation of interactive information retrieval systems. Information Research 8, 3 (2003).
[4]
Buscher, G., Dengel, A., and van Elst, L. Query expansion using gaze-based feedback on the subdocument level. In Proceedings of SIGIR '08 (Singapore, 2008), ACM, pp. 387--394.
[5]
Cole, M. J., Gwizdka, J., Liu, C., Belkin, N. J., Bierig, R., and Zhang, X. Task and user effects on reading patterns in information search. Interacting with Computers 23, 4 (July 2011), 346--362.
[6]
Cole, M. J., Gwizdka, J., Liu, C., and Belkin, N. J. Dynamic assessment of information acquisition effort during interactive search. In Proceedings of the American Society for Information Science and Technology Conference (2011) (New Orleans, LA, October 2011), vol. 48, ASIST, p. 1--10.
[7]
Cole, M. J., Gwizdka, J., Liu, C., Belkin, N. J.,Bierig, R., and Zhang, X. Inferring user knowledge level from eye movement patterns, Information Processing & Management 49 (November 2012), 1075--1091.
[8]
Findlay, J., and Gilchrist, I. Active Vision: ThePsychology of Looking and Seeing. Oxford University Press, New York, 2003.
[9]
Gao, B., Liu, T.-Y., Liu, Y., Wang, T., Ma, Z.-M., and Li, H. Page importance computation based on Markov processes. Information Retrieval 14, 5 (Oct.2011), 488--514.
[10]
Gwidka, J. Cognitive load and web search tasks. HCIR, 2009. Washington D.C.
[11]
Gwizdka, J. and Spence, I. What can searching behavior tell us about the difficulty of information tasks? A study of Web navigation. Proceedings of the American Society for Information Science and Technology 43, 1 (2006), 1--22.
[12]
Han, S., Yue, Z., and He, D. Automatic detection of search tactic in individual information seeking: A Hidden Markov Model Approach. In iConference 2013 Proceedings (2013), pp. 712--716.
[13]
Hendahewa, C., and Shah, C. Segmental analysis and evaluation of user focused search process. In International Conference on Machine Learning and Applications (ICMLA) (2013), IEEE, pp. 291--294.
[14]
Inhoff, A. W., and Liu, W. The perceptual span and oculomotor activity during the reading of Chinese sentences. Journal of Experimental Psychology. Human Perception and Performance 24, 1 (1998), 20--34.
[15]
Kellar, M., Watters, C., and Shepherd, M. A field study characterizing Web-based information-seeking tasks. JASIST 58, 7 (2007), 999--1018.
[16]
Li, Y. Relationships among work tasks, search tasks,and interactive information searching behavior. PhD thesis, Rutgers University, 2008.
[17]
Li, Y. Exploring the relationships between work task and search task in information search. JASIST, pp. 275--291, 2009.
[18]
Liu, Y., Ma, Z.-M., and Zhou, C. Web Markov skeleton processes and their applications. Tohoku Mathematical Journal 63, 4 (2011), 665--695.
[19]
Marchionini, G. Information-seeking strategies of novices using a full-text electronic encyclopedia. JASIST 40, 1 (1989), 54--66.
[20]
Marchionini, G., and Maurer, H. The roles of digital libraries in teaching and learning.Communications of the ACM 38, 4 (1995), 67--75.
[21]
Morris, R. Lexical and message-level sentence context effects on fixation times in reading. Journal of Experimental Psychology: Learning, Memory, and Cognition 20, 1 (1994), 92.
[22]
Pollatsek, A., Rayner, K., and Balota, D. A. Inferences about eye movement control from the perceptual span in reading. Perception & Psychophysics 40, 2 (1986), 123--130.
[23]
Qiu, L. Markov models of search state patterns in a hypertext information retrieval system. JASIST 44, 7 (1993), 413--427.
[24]
Rayner, K. Eye movements in reading and information processing: 20 years of research. Psychological Bulletin 124 (1998), 372--422.
[25]
Rayner, K., Chace, K. H., Slattery, T. J., and Ashby, J. Eye movements as reflections of comprehension processes in reading. Scientific Studiesof Reading 10, 3 (2006), 241--255.
[26]
Rayner, K., and Pollatsek, A. The Psychology of Reading. Lawrence Erlbaum Associates, Mahwah, New Jersey, 1989.
[27]
Reichle, E. D., Pollatsek, A., and Rayner, K. EZ Reader: A cognitive-control, serial-attention model of eye-movement behavior during reading. Cognitive Systems Research 7, 1 (2006), 4--22.
[28]
Reichle, E. D., Rayner, K., and Pollatsek, A. The EZ Reader model of eye-movement control in reading: Comparisons to other models. Behavioral and Brain Sciences 26, 04 (2004), 445--476.
[29]
Reingold, E., and Rayner, K. Examining the word identification stages hypothesized by the EZ Reader model. Psychological Science 17, 9 (2006), 742--746.
[30]
Sereno, S. C., O'Donnell, P., and Rayner, K. Eye movements and lexical ambiguity resolution: Investigating the subordinate-bias effect. Journal of Experimental Psychology: Human Perception and Performance 32, 2 (2006), 335.
[31]
Triesch, J., Ballard, D. H., Hayhoe, M. M., and Sullivan, B. T. What you see is what you need. Journal of Vision 3 (2003), 86--94.
[32]
Tsai, C., and McConkie, G. The perceptual span in reading Chinese text: A moving window study. In Seventh International Conference on the Cognitive Processing of Chinese and Other Asian Languages, Hong Kong (1995).
[33]
White, R. W., and Kelly, D. A study on the effects of personalization and task information on implicit feedback performance. In CKIM'06 (Arlington, Virginia,USA, November 2006), ACM, pp. 297--306.
[34]
Williams, R., and Morris, R. Eye movements, word familiarity, and vocabulary acquisition. Journal of Cognitive Psychology 16, 1 (2004), 312--339.

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    cover image ACM Conferences
    SIGIR '14: Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval
    July 2014
    1330 pages
    ISBN:9781450322577
    DOI:10.1145/2600428
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 03 July 2014

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    Author Tags

    1. cognitive effort
    2. information search behavior
    3. personalization
    4. task
    5. user study

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    SIGIR '14 Paper Acceptance Rate 82 of 387 submissions, 21%;
    Overall Acceptance Rate 792 of 3,983 submissions, 20%

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    • (2023)Quantifying and Measuring Confirmation Bias in Information Retrieval Using SensorsAdjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing10.1145/3594739.3610765(236-240)Online publication date: 8-Oct-2023
    • (2023)Taking Search to TaskProceedings of the 2023 Conference on Human Information Interaction and Retrieval10.1145/3576840.3578288(1-13)Online publication date: 19-Mar-2023
    • (2023)Examining the Impact of Uncontrolled Variables on Physiological Signals in User Studies for Information Processing ActivitiesProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591981(1971-1975)Online publication date: 19-Jul-2023
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    • (2019)Interactive IR User Study Design, Evaluation, and ReportingSynthesis Lectures on Information Concepts, Retrieval, and Services10.2200/S00923ED1V01Y201905ICR06711:2(i-75)Online publication date: 3-Jun-2019
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    • (2018)Predicting readers’ domain knowledge based on eye-tracking measuresThe Electronic Library10.1108/EL-05-2017-010836:6(1027-1042)Online publication date: 10-Dec-2018
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