Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1145/2600428.2609631acmconferencesArticle/Chapter ViewAbstractPublication PagesirConference Proceedingsconference-collections
research-article
Open access

Towards better measurement of attention and satisfaction in mobile search

Published: 03 July 2014 Publication History

Abstract

Web Search has seen two big changes recently: rapid growth in mobile search traffic, and an increasing trend towards providing answer-like results for relatively simple information needs (e.g., [weather today]). Such results display the answer or relevant information on the search page itself without requiring a user to click. While clicks on organic search results have been used extensively to infer result relevance and search satisfaction, clicks on answer-like results are often rare (or meaningless), making it challenging to evaluate answer quality. Together, these call for better measurement and understanding of search satisfaction on mobile devices. In this paper, we studied whether tracking the browser viewport (visible portion of a web page) on mobile phones could enable accurate measurement of user attention at scale, and provide good measurement of search satisfaction in the absence of clicks. Focusing on answer-like results in web search, we designed a lab study to systematically vary answer presence and relevance (to the user's information need), obtained satisfaction ratings from users, and simultaneously recorded eye gaze and viewport data as users performed search tasks. Using this ground truth, we identified increased scrolling past answer and increased time below answer as clear, measurable signals of user dissatisfaction with answers. While the viewport may contain three to four results at any given time, we found strong correlations between gaze duration and viewport duration on a per result basis, and that the average user attention is focused on the top half of the phone screen, suggesting that we may be able to scalably and reliably identify which specific result the user is looking at, from viewport data alone.

References

[1]
A. Aula, P. Majaranta, and K.-J. Räihä. Eye-tracking reveals the personal styles for search result evaluation. In Proc. of Human-Computer Interaction-INTERACT, pages 1058--1061. Springer, 2005.
[2]
R. Biedert, A. Dengel, G. Buscher, and A. Vartan. Reading and estimating gaze on smart phones. In Proc. of the Symposium on Eye Tracking Research and Applications, pages 385--388. ACM, 2012.
[3]
A. Broder. A taxonomy of web search. In ACM SIGIR Forum, pages 3--10. ACM, 2002.
[4]
G. Buscher, S. T. Dumais, and E. Cutrell. The good, the bad, and the random: an eye-tracking study of ad quality in web search. In Proc. of SIGIR, pages 42--49. ACM, 2010.
[5]
B. Carterette and R. Jones. Evaluating search engines by modeling the relationship between relevance and clicks. In Proc. of NIPS, pages 217--224, 2007.
[6]
O. Chapelle and Y. Zhang. A dynamic bayesian network +click model for web search ranking. In Proc. of WWW, pages 1--10. ACM, 2009.
[7]
A. T. Duchowski. Eye tracking methodology: Theory and practice, volume 373. Springer, 2007.
[8]
S. T. Dumais, G. Buscher, and E. Cutrell. Individual differences in gaze patterns for web search. In Proc. of IIiX, pages 185--194. ACM, 2010.
[9]
L. A. Granka, T. Joachims, and G. Gay. Eye-tracking analysis of user behavior in WWW search. In Proc. of SIGIR, pages 478--479. ACM, 2004.
[10]
Z. Guan and E. Cutrell. An eye tracking study of the effect of target rank on web search. In Proc. of SIGCHI, pages 417--420. ACM, 2007.
[11]
Q. Guo, H. Jin, D. Lagun, S. Yuan, and E. Agichtein. Mining touch interaction data on mobile devices to predict web search result relevance. In Proc. of SIGIR, pages 153--162. ACM, 2013.
[12]
Q. Guo, S. Yuan, and E. Agichtein. Detecting success in mobile search from interaction. In Proc. of SIGIR, pages 1229--1230. ACM, 2011.
[13]
J. Huang and A. Diriye. Web user interaction mining from touch-enabled mobile devices. Proc. of HCIR, 2012.
[14]
J. Huang, R. White, and G. Buscher. User see, user point: gaze and cursor alignment in web search. In Proc. of SIGCHI, pages 1341--1350. ACM, 2012.
[15]
J. Huang, R. W. White, G. Buscher, and K. Wang. Improving searcher models using mouse cursor activity. In Proc. of SIGIR, pages 195--204. ACM, 2012.
[16]
K. Järvelin and J. Kekäläinen. Cumulated gain-based evaluation of IR techniques. ACM Transactions on Information Systems (TOIS), 20(4):422--446, 2002.
[17]
T. Joachims, L. Granka, B. Pan, H. Hembrooke, and G. Gay. Accurately interpreting clickthrough data as implicit feedback. In Proc. of SIGIR, pages 154--161. ACM, 2005.
[18]
M. A. Just and P. A. Carpenter. A theory of reading: From eye fixations to comprehension. Psychological Review, 87:329--354, 1980.
[19]
M. Kamvar and S. Baluja. A large scale study of wireless search behavior: Google mobile search. In Proc. of SIGCHI, pages 701--709. ACM, 2006.
[20]
J. Kim, P. Thomas, R. Sankaranarayana, and T. Gedeon. Comparing scanning behaviour in web search on small and large screens. In Proc. of the Australasian Document Computing Symposium, pages 25--30. ACM, 2012.
[21]
D. Lagun and E. Agichtein. Viewser: enabling large-scale remote user studies of web search examination and interaction. In Proc. of SIGIR, pages 365--374. ACM, 2011.
[22]
D. Lagun and E. Agichtein. Re-examining search result snippet examination time for relevance estimation. In Proc. of SIGIR, pages 1141--1142. ACM, 2012.
[23]
L. Lorigo, M. Haridasan, H. Brynjarsdóttir, L. Xia, T. Joachims, G. Gay, L. Granka, F. Pellacini, and B. Pan. Eye tracking and online search: Lessons learned and challenges ahead. Journal of the American Society for Information Science and Technology, 59(7):1041--1052, 2008.
[24]
V. Navalpakkam, L. Jentzsch, R. Sayres, S. Ravi, A. Ahmed, and A. Smola. Measurement and modeling of eye-mouse behavior in the presence of nonlinear page layouts. In Proc. of WWW, pages 953--964. International World Wide Web Conferences Steering Committee, 2013.
[25]
RKG Digital Marketing Report, Q2 2013. Retrieved from http://www.rimmkaufman.com/blog/rkg-digital-marketing-report-q2--2013-released/10072013/, 2013.
[26]
Statcounter Global Stats. Retrieved from http://gs.statcounter.com/#mobile_vs_desktop-ww-monthly-201208--201308, 2014.
[27]
Tobii X Series Eye Trackers Product Description. Retrieved from http://www.tobii.com/Global/Analysis/Downloads/Product_Descriptions/Tobii_TX_Product_description.pdf.

Cited By

View all
  • (2024)Web Search Engine Results Page Viewing Formats for Different Search TasksInternational Journal of Human–Computer Interaction10.1080/10447318.2024.2376358(1-16)Online publication date: 29-Jul-2024
  • (2024)A visual programming tool for mobile web augmentationKnowledge and Information Systems10.1007/s10115-023-02039-666:9(5631-5668)Online publication date: 23-May-2024
  • (2023)Screen Reading Regions in Social Media Comments: An Eye-Tracking Analysis of Visual Attention on SmartphonesProceedings of the 27th Pan-Hellenic Conference on Progress in Computing and Informatics10.1145/3635059.3635074(95-101)Online publication date: 24-Nov-2023
  • Show More Cited By

Index Terms

  1. Towards better measurement of attention and satisfaction in mobile search

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    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]

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 03 July 2014

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. search on mobile phone
    2. user attention and satisfaction
    3. viewport logging

    Qualifiers

    • Research-article

    Conference

    SIGIR '14
    Sponsor:

    Acceptance Rates

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

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)176
    • Downloads (Last 6 weeks)33
    Reflects downloads up to 30 Aug 2024

    Other Metrics

    Citations

    Cited By

    View all
    • (2024)Web Search Engine Results Page Viewing Formats for Different Search TasksInternational Journal of Human–Computer Interaction10.1080/10447318.2024.2376358(1-16)Online publication date: 29-Jul-2024
    • (2024)A visual programming tool for mobile web augmentationKnowledge and Information Systems10.1007/s10115-023-02039-666:9(5631-5668)Online publication date: 23-May-2024
    • (2023)Screen Reading Regions in Social Media Comments: An Eye-Tracking Analysis of Visual Attention on SmartphonesProceedings of the 27th Pan-Hellenic Conference on Progress in Computing and Informatics10.1145/3635059.3635074(95-101)Online publication date: 24-Nov-2023
    • (2023)An Intent Taxonomy of Legal Case RetrievalACM Transactions on Information Systems10.1145/362609342:2(1-27)Online publication date: 29-Sep-2023
    • (2023)An End-to-End Review of Gaze Estimation and its Interactive Applications on Handheld Mobile DevicesACM Computing Surveys10.1145/360694756:2(1-38)Online publication date: 15-Sep-2023
    • (2023)Scanning or Simply Unengaged in Reading? Opportune Moments for Pushed News Notifications and Their Relationship with Smartphone Users' Choice of News-reading ModesProceedings of the ACM on Human-Computer Interaction10.1145/36042687:MHCI(1-26)Online publication date: 13-Sep-2023
    • (2023)Detecting the Disengaged Reader - Using Scrolling Data to Predict Disengagement during ReadingLAK23: 13th International Learning Analytics and Knowledge Conference10.1145/3576050.3576078(585-591)Online publication date: 13-Mar-2023
    • (2023)Understanding Relevance Judgments in Legal Case RetrievalACM Transactions on Information Systems10.1145/356992941:3(1-32)Online publication date: 7-Feb-2023
    • (2023)A Passage-Level Reading Behavior Model for Mobile SearchProceedings of the ACM Web Conference 202310.1145/3543507.3583343(3236-3246)Online publication date: 30-Apr-2023
    • (2023)Behavior Modeling for Point of Interest SearchProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591955(1843-1847)Online publication date: 19-Jul-2023
    • Show More Cited By

    View Options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Get Access

    Login options

    Media

    Figures

    Other

    Tables

    Share

    Share

    Share this Publication link

    Share on social media