Skip to main content
Springer Nature Link
Log in

A Web Browsing Behavior Recording System

  • Conference paper
Knowledge-Based and Intelligent Information and Engineering Systems (KES 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6884))

Abstract

In this paper, we introduce a Web browsing behavior recording system for research. Web browsing behavior data can help us to provide sophisticated services for human activities, because the data must indicate characteristics of Web users. We discuss the necessity of the data with potential benefits, and develop a system for collecting the data as an add-on for Firefox. We also report some results of preliminary experiments to test its usefulness in analyses on human activities in this paper.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Aula, A., Khan, R.M., Guan, Z.: How does search behavior change as search becomes more difficult? In: Proceedings of the 28th International Conference on Human Factors in Computing Systems, CHI 2010, pp. 35–44. ACM, New York (2010), http://doi.acm.org/10.1145/1753326.1753333

    Google Scholar 

  2. Aula, A., Nordhausen, K.: Modeling successful performance in web searching. Journal of the American Society for Information Science and Technology 57(12), 1678–1693 (2006), http://dx.doi.org/10.1002/asi.20340

    Article  Google Scholar 

  3. Cheng, Z., Gao, B., Liu, T.Y.: Actively predicting diverse search intent from user browsing behaviors. In: Proceedings of the 19th International Conference on World Wide Web, WWW 2010, pp. 221–230. ACM, New York (2010), http://doi.acm.org/10.1145/1772690.1772714

    Google Scholar 

  4. Dou, Z., Song, R., Wen, J.R.: A large-scale evaluation and analysis of personalized search strategies. In: Proceedings of the 16th International Conference on World Wide Web, WWW 2007, pp. 581–590. ACM, New York (2007), http://doi.acm.org/10.1145/1242572.1242651

    Google Scholar 

  5. Druin, A., Foss, E., Hutchinson, H., Golub, E., Hatley, L.: Children’s roles using keyword search interfaces at home. In: Proceedings of the 28th International Conference on Human Factors in Computing Systems, CHI 2010, pp. 413–422. ACM, New York (2010), http://doi.acm.org/10.1145/1753326.1753388

    Google Scholar 

  6. Fu, X., Budzik, J., Hammond, K.J.: Mining navigation history for recommendation. In: Proceedings of the 5th International Conference on Intelligent User Interfaces, IUI 2000, pp. 106–112. ACM, New York (2000), http://doi.acm.org/10.1145/325737.325796

    Google Scholar 

  7. Gauch, S., Chaffee, J., Pretschner, A.: Ontology-based personalized search and browsing. Web Intelligence and Agent Systems 1(3-4), 219–234 (2003), http://iospress.metapress.com/content/D68RMJ5V6C897X3C

    Google Scholar 

  8. Guo, Q., Agichtein, E.: Ready to buy or just browsing?: detecting web searcher goals from interaction data. In: Proceeding of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2010, pp. 130–137. ACM, New York (2010), http://doi.acm.org/10.1145/1835449.1835473

    Google Scholar 

  9. Guo, Q., Agichtein, E.: Towards predicting web searcher gaze position from mouse movements. In: Proceedings of the 28th of the International Conference Extended Abstracts on Human Factors in Computing Systems, CHI EA 2010, pp. 3601–3606. ACM, New York (2010), http://doi.acm.org/10.1145/1753846.1754025

  10. Holub, M., Bielikova, M.: Estimation of user interest in visited web page. In: Proceedings of the 19th International Conference on World Wide Web, WWW 2010, pp. 1111–1112. ACM, New York (2010), http://doi.acm.org/10.1145/1772690.1772829

    Google Scholar 

  11. Hong, J.I., Landay, J.A.: Webquilt: a framework for capturing and visualizing the web experience. In: Proceedings of the 10th International Conference on World Wide Web, WWW 2001, pp. 717–724. ACM, New York (2001), http://doi.acm.org/10.1145/371920.372188

    Google Scholar 

  12. Huntington, P., Nicholas, D., Jamali, H.R.: Employing log metrics to evaluate search behaviour and success: case study BBC search engine. Journal of Information Science 33(5), 584–597 (2007), http://jis.sagepub.com/content/33/5/584.abstract

    Article  Google Scholar 

  13. Liu, C., White, R.W., Dumais, S.: Understanding web browsing behaviors through weibull analysis of dwell time. In: Proceeding of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2010, pp. 379–386. ACM, New York (2010), http://doi.acm.org/10.1145/1835449.1835513

    Google Scholar 

  14. Liu, J., Dolan, P., Pedersen, E.R.: Personalized news recommendation based on click behavior. In: Proceedings of the 14th International Conference on Intelligent User Interfaces, IUI 2010, pp. 31–40. ACM, New York (2010), http://doi.acm.org/10.1145/1719970.1719976

    Google Scholar 

  15. Matthijs, N., Radlinski, F.: Personalizing web search using long term browsing history. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, WSDM 2011, pp. 25–34. ACM, New York (2011), http://doi.acm.org/10.1145/1935826.1935840

    Google Scholar 

  16. Shahabi, C., Chen, Y.S.: An adaptive recommendation system without explicit acquisition of user relevance feedback. Distributed and Parallel Databases 14(2), 173–192 (2003), http://dx.doi.org/10.1023/A:1024888710505

    Article  Google Scholar 

  17. Shen, X., Tan, B., Zhai, C.: Context-sensitive information retrieval using implicit feedback. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2005, pp. 43–50. ACM, New York (2005), http://doi.acm.org/10.1145/1076034.1076045

  18. Shen, X., Tan, B., Zhai, C.: Implicit user modeling for personalized search. In: Proceedings of the 14th ACM International Conference on Information and Knowledge Management, CIKM 2005, pp. 824–831. ACM, New York (2005), http://doi.acm.org/10.1145/1099554.1099747

    Google Scholar 

  19. Sugiyama, K., Hatano, K., Yoshikawa, M.: Adaptive Web search based on user profile constructed without any effort from users. In: Proceedings of the 13th International Conference on World Wide Web, WWW 2004, pp. 675–684. ACM, New York (2004), http://doi.acm.org/10.1145/988672.988764

    Google Scholar 

  20. Takashita, T., Abe, Y., Itokawa, T., Kitasuka, T., Aritsugi, M.: Design and implementation of a system for finding appropriate tags to photos in Flickr from Web browsing behaviour. Int. J. Web and Grid Services 7(1), 75–90 (2011), http://dx.doi.org/10.1504/IJWGS.2011.038385

    Article  Google Scholar 

  21. Takashita, T., Itokawa, T., Kitasuka, T., Aritsugi, M.: Extracting user preference from Web browsing behaviour for spam filtering. Int. J. Advanced Intelligence Paradigms 1(2), 126–138 (2008), http://dx.doi.org/10.1504/IJAIP.2008.024769

    Article  Google Scholar 

  22. Takashita, T., Itokawa, T., Kitasuka, T., Aritsugi, M.: A spam filtering method learning from web browsing behavior. In: Lovrek, I., Howlett, R., Jain, L. (eds.) KES 2008, Part II. LNCS (LNAI), vol. 5178, pp. 774–781. Springer, Heidelberg (2008), http://dx.doi.org/10.1007/978-3-540-85565-1_96

  23. Takashita, T., Itokawa, T., Kitasuka, T., Aritsugi, M.: Tag recommendation for flickr using web browsing behavior. In: Taniar, D., Gervasi, O., Murgante, B., Pardede, E., Apduhan, B. (eds.) ICCSA 2010. LNCS, vol. 6017, pp. 412–421. Springer, Heidelberg (2010), http://dx.doi.org/10.1007/978-3-642-12165-4_33

    Chapter  Google Scholar 

  24. Teevan, J., Dumais, S.T., Horvitz, E.: 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, SIGIR 2005, pp. 449–456. ACM, New York (2005), http://doi.acm.org/10.1145/1076034.1076111

    Chapter  Google Scholar 

  25. White, R.W., Morris, D.: Investigating the querying and browsing behavior of advanced search engine users. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2007, pp. 255–262. ACM, New York (2007), http://doi.acm.org/10.1145/1277741.1277787

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science and Electrical Engineering, Kumamoto University, Kumamoto, 860-8555, Japan

    Hayato Ohmura, Teruaki Kitasuka & Masayoshi Aritsugi

Authors
  1. Hayato Ohmura
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Teruaki Kitasuka
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Masayoshi Aritsugi
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Institute of Integrated Sensor Systems, University of Kaiserslautern, Erwin-Schroedinger-str. 12, 67663, Kaiserslautern, Germany

    Andreas König

  2. Knowledge-Based Systems Group, Department of Computer Science, University of Kaiserslautern, P.O. Box 3049, 67653, Kaiserslautern, Germany

    Andreas Dengel

  3. School of Business, University of Applied Sciences Northwestern Switzerland, Riggenbachstr. 16, 4600, Olten, Switzerland

    Knut Hinkelmann

  4. Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, 599-8531, Sakai, Osaka, Japan

    Koichi Kise

  5. KES International, P.O. Box 2115, BN43 9AF, Shoreham-by-sea, UK

    Robert J. Howlett

  6. University of South Australia, Adelaide, 5095, Mawson Lakes, SA, Australia

    Lakhmi C. Jain

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ohmura, H., Kitasuka, T., Aritsugi, M. (2011). A Web Browsing Behavior Recording System. In: König, A., Dengel, A., Hinkelmann, K., Kise, K., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2011. Lecture Notes in Computer Science(), vol 6884. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23866-6_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-23866-6_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23865-9

  • Online ISBN: 978-3-642-23866-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics