Group Crumb: Sharing Web Navigation by Visualizing Group Traces on the Web

  • Qing Wang
  • Gaoqiang Zheng
  • Ya Li
  • Huiyou Chang
  • Hongyang Chao
Conference paper


Although the sharing of Web navigation experiences can be useful, it is not supported by contemporary browsers. The Web has been constructed along the lines of a spatial metaphor, but with a flaw of not being able to share navigation experiences, that is, group traces, as is possible in a physical space. This paper shows that from the viewpoint of Information Foraging Theory, sharing Web navigation experiences among group members can increase their information foraging performance. To verify this, a simple prototype, the Group Crumb Prototype (GCP), has been designed. The GCP visualizes group Web traces by altering the appearance of links on a Web page according to their Group Crumb Scents, which are calculated from the recentness and times of group navigations to corresponding links. A longitudinal user study has been conducted to compare user performance and experience when surfing the Web with and without the aid of the GCP. Results show that making group navigation traces available on Web pages to group members increases their Web information foraging performance, promotes group collaboration, and enhances their Web browsing user experience as well.


Collection Active Group Awareness Group Visitation Bread Crumb Group Navigation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. AlMurtadha, Y. M., Sulaiman, M. N., Mustapha, N., & Udzir, N. I. (2010): ‘Mining Web Navigation Profiles For Recommendation System’, Information Technology Journal, 9(4), 790–796.CrossRefGoogle Scholar
  2. Amershi, S., & Morris, M. R. (2008) : ‘CoSearch: a system for co-located collaborative web search’, In Proceeding of the twenty-sixth annual SIGCHI conference on Human factors in computing systems (pp. 1647–1656).Google Scholar
  3. Anderson, J. R., & Pirolli, P. L. (1984) : ‘Spread of activation’, Journal of Experimental Psychology: Learning, Memory, and Cognition, 10(4), 791–798.CrossRefGoogle Scholar
  4. Anupam, V., Freire, J., Kumar, B., & Lieuwen, D. (2000): ‘Automating Web navigation with the WebVCR’, Computer Networks, 33(1–6), 503–517.CrossRefGoogle Scholar
  5. Bernstein, M., Bolter, J. D., Joyce, M., & Mylonas, E. (1991): ‘Architectures for volatile hypertext’, In Proceedings of the third annual ACM conference on Hypertext (pp. 243–260). San Antonio, Texas, United States: ACM. doi:10.1145/122974.122999CrossRefGoogle Scholar
  6. Bertel, S. (2001): Benutzerunterst\ützung im World Wide Web mit Hilfe r\äumlicher Konzepte, Hamburg, Department for Informatics, Universit \ät Hamburg.Google Scholar
  7. Borges, J., & Levene, M. (2008): ‘Mining Users’ Web Navigation Patterns and Predicting Their Next Step’, nato security through science series d-information and communication security, 15, 45.Google Scholar
  8. Brandenburg, J., Byerly, B., Dobridge, T., Lin, J., Rajan, D., & Roscoe, T. (1998): ‘Artefact: a framework for low-overhead Web-based collaborative systems’, In Proceedings of the 1998 ACM conference on Computer supported cooperative work (pp. 189–196). ACM New York, NY, USA.CrossRefGoogle Scholar
  9. Brush, A. B., Meyers, B. R., Scott, J., & Venolia, G. (2009): ‘Exploring awareness needs and information display preferences between coworkers’, In Proceedings of the 27 th international conference on Human factors in computing systems (pp. 2091–2094). Boston, MA, USA: ACM. doi:10.1145/1518701.1519018CrossRefGoogle Scholar
  10. Brusilovsky, P., Chavan, G., & Farzan, R. (2004): ‘Social adaptive navigation support for open corpus electronic textbooks’, In Adaptive Hypermedia and Adaptive Web-Based Systems (pp. 176–189).Google Scholar
  11. Catledge, L. D., & Pitkow, J. E. (1995): ‘Characterizing browsing strategies in the World-Wide web’, Computer Networks and ISDN Systems, 27(6), 1065–1073. doi:10.1016/0169-7552(95)00043-7CrossRefGoogle Scholar
  12. Charnov, E. L. (1976): ‘Optimal foraging, the marginal value theorem’, Theoretical population biology, 9(2), 129–136.CrossRefGoogle Scholar
  13. Cockburn A., & Mckenzie B. (2001): ‘What do web users do? An empirical analysis of web use’, International Journal of Human-Computer Studies, 54, 903–922.MATHCrossRefGoogle Scholar
  14. Deline, R., Czerwinski, M., & Robertson, G. (2005): ‘Easing Program Comprehension by Sharing Navigation Data’, Visual Languages and Human-Centric Computing, IEEE Symposium on (Vol. 0, pp 241–248). Los Alamitos, CA, USA: IEEE Computer Society. doi: Scholar
  15. Diamadis, E. T., & Polyzos, G. C. (2004): ‘Efficient cooperative searching on the Web: system design and evaluation’, International journal of human-computer studies, 61(5), 699–724.CrossRefGoogle Scholar
  16. Dieberger, A., Dourish, P., Höök, K., Resnick, P., & Wexelblat, A. (2000, November): ‘Social navigation: techniques for building more usable systems’, interactions, 7, 36–45.CrossRefGoogle Scholar
  17. Ebbinghaus, H. (1913): Memory: A contribution to experimental psychology. Teachers College, Columbia University.Google Scholar
  18. Fidel, R., Bruce, H., Pejtersen, A. M., Dumais, S., Grudin, J., & Poltrock, S. (2000): ‘Collaborative information retrieval’, The New Review of Information Behaviour Research, 1(1), 235–247.Google Scholar
  19. Freyne, J., & Smyth, B. (2006): ‘Cooperating search communities’, In Adaptive Hypermedia and Adaptive Web-Based Systems (pp. 101–110).Google Scholar
  20. Fu, W., & Pirolli, P. (2007): ‘SNIF-ACT: a cognitive model of user navigation on the world wide web’, Hum.-Comput. Interact., 22(4), 355–412.Google Scholar
  21. Georgia, A. D., & Dieberger, A. (1997): ‘Supporting Social Navigation on the World Wide Web’, Retrieved from
  22. Graham, T. C. N. (1997): ‘Groupscape: Integrating synchronous groupware and the world wide web’, In Proceedings of the IFIP TC13 Interantional Conference on Human-Computer Interaction (pp. 547–554). Chapman & Hall, Ltd. London, UK, UK.Google Scholar
  23. Grcar, M., Mladenic, D., & Grobelnik, M. (2005): ‘User profiling for interest-focused browsing history’, In Workshop on End User Aspects of the Semantic Web, ESWC-2005, Heraklion.Google Scholar
  24. Greenberg, S., & Roseman, M. (1996): ‘GroupWeb: a WWW browser as real time groupware’, In Conference companion on Human factors in computing systems: common ground (pp. 271–272). Vancouver, British Columbia, Canada: ACM. doi:10.1145/257089.257317CrossRefGoogle Scholar
  25. Hansen, P., & Järvelin, K. (2005): ‘Collaborative information retrieval in an information-intensive domain’, Information Processing and Management, 41(5), 1101–1119.CrossRefGoogle Scholar
  26. Hill, W. C., Hollan, J. D., Wroblewski, D., & McCandless, T. (1992): ‘Edit wear and read wear’, Proceedings of the SIGCHI conference on Human factors in computing systems, CHI ’92 (p 3–9). New York, NY, USA: ACM. doi:10.1145/142750.142751Google Scholar
  27. Large, A., Beheshti, J., & Rahman, T. (2002): ‘Gender differences in collaborative web searching behavior: an elementary school study’, Information Processing & Management, 38(3), 427–443.CrossRefGoogle Scholar
  28. Marshall, C. C., & Shipman III, F. M. (1993): ‘Searching for the missing link: discovering implicit structure in spatial hypertext’, In Proceedings of the fifth ACM conference on Hypertext (p.230).Google Scholar
  29. McFadden, D. (1974): ‘Conditional logit analysis of qualitative choice behavior’, Frontiers in econometrics, 8, 105–142.Google Scholar
  30. McFadden, D. et al. (1978): ‘Modelling the choice of residential location’, Spatial interaction theory and planning models, 25, 75–96.Google Scholar
  31. Morris, M. R., & Horvitz, E. (2007): ‘SearchTogether: an interface for collaborative web search’, In Proceedings of the 20th annual ACM symposium on User interface software and technology (p. 12).Google Scholar
  32. Morris, M. R. (2008): ‘A survey of collaborative web search practices’, In Proceeding of the twenty-sixth annual SIGCHI conference on Human factors in computing systems (pp. 1657–1660). Florence, Italy: ACM. doi:10.1145/1357054.1357312CrossRefGoogle Scholar
  33. Morris, M. R., Teevan, J., & Bush, S. (2008): ‘Enhancing collaborative web search with personalization: groupization, smart splitting, and group hit-highlighting’, In Proceedings of the ACM 2008 conference on Computer supported cooperative work (pp. 481–484). San Diego, CA, USA: ACM. doi:10.1145/1460563.1460640CrossRefGoogle Scholar
  34. Obendorf, H., Weinreich, H., Herder, E., & Mayer, M. (2007): ‘Web page revisitation revisited: implications of a long-term click-stream study of browser usage’, In Proceedings of the SIGCHI conference on Human factors in computing systems (pp. 597–606). San Jose, California, USA: ACM. doi:10.1145/1240624.1240719CrossRefGoogle Scholar
  35. Pickens, J., Golovchinsky, G., Shah, C., Qvarfordt, P., & Back, M. (2008): ‘Algorithmic mediation for collaborative exploratory search’, In Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval (pp. 315–322).Google Scholar
  36. Pirolli, P. L. T. (2007): Information Foraging Theory: Adaptive Interaction with Information (1st ed.). Oxford University Press, USA.Google Scholar
  37. Stanton, N. A., & Baber, C. (1994): ‘The Myth of Navigating in Hypertext: How a" Bandwagon" Has Lost Its Course!’, Journal of educational multimedia and hypermedia.Google Scholar
  38. Sun, J., Wang, X., Shen, D., Zeng, H., & Chen, Z. (2006): ‘Mining clickthrough data for collaborative web search’, In Proceedings of the 15th international conference on World Wide Web (pp. 947–948). Edinburgh, Scotland: ACM. doi:10.1145/1135777.1135958CrossRefGoogle Scholar
  39. Tan, B., Shen, X., & Zhai, C. (2006): ‘Mining long-term search history to improve search accuracy’, In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 718–723). Philadelphia, PA, USA: ACM. doi:10.1145/1150402.1150493CrossRefGoogle Scholar
  40. Teevan, J., Dumais, S. T., & Horvitz, E. (2005): ‘Personalizing search via automated analysis of interests and activities’, In Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval (p. 456).Google Scholar
  41. Ting, I. H., Clark, L., & Kimble, C. (2009): ‘Identifying web navigation behaviour and patterns automatically from clickstream data’, International Journal of Web Engineering and Technology, 5(4), 398–426.CrossRefGoogle Scholar
  42. Twidale, M. B., Nichols, D. M., & Paice, C. D. (1997): ‘Browsing is a collaborative process’, Information Processing & Management, 33(6), 761–783.CrossRefGoogle Scholar
  43. Weinreich, H., Obendorf, H., Herder, E., & Mayer, M. (2006): ‘Off the beaten tracks: exploring three aspects of web navigation’, In Proceedings of the 15th international conference on World Wide Web (pp. 133–142). Edinburgh, Scotland: ACM. doi:10.1145/1135777.1135802CrossRefGoogle Scholar
  44. Wexelblat, A., & Maes, P. (1999): ‘Footprints: history-rich tools for information foraging’, In Proceedings of the SIGCHI conference on Human factors in computing systems: the CHI is the limit, CHI ’99 (pp. 270–277). New York, NY, USA: ACM. doi:10.1145/302979.303060CrossRefGoogle Scholar
  45. Yeh, P. J., Chen, B. H., Lai, M. C., & Yuan, S. M. (1996): ‘Synchronous navigation control for distance learning on the Web’, Computer Networks and ISDN Systems, 28(7–11), 1207–1218.Google Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • Qing Wang
    • 1
  • Gaoqiang Zheng
    • 1
  • Ya Li
    • 1
  • Huiyou Chang
    • 1
  • Hongyang Chao
    • 1
  1. 1.School of SoftwareSun Yat-sen UniversityGuangzhouChina

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