Advertisement

Mining Navigation Histories for User Need Recognition

  • Fabio Gasparetti
  • Alessandro Micarelli
  • Giuseppe Sansonetti
Part of the Communications in Computer and Information Science book series (CCIS, volume 434)

Abstract

The time spent using a web browser on a wide variety of tasks such as research activities, shopping or planning holidays is relevant. Web pages visited by users contain important hints about their interests, but empirical evaluations show that almost 40-50% of the elements of the web pages can be considered irrelevant w.r.t. the user interests driving the browsing activity. Moreover, pages might cover several different topics. For these reasons they are often ignored in personalized approaches. We propose a novel approach for selectively collecting text information based on any implicit signal that naturally exists through web browsing interactions. Our approach consists of three steps: (1) definition of a DOM-based representation of visited pages, (2) clustering of pages according with a tree edit distance measure and (3) exploiting the acquired evidence about the user behaviour to better filtering out irrelevant information and identify relevant text related to the current needs. A comparative evaluation shows the effectiveness of the proposed approach in retrieving additional web resources related to what the user is currently browsing

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Teevan, J., Dumais, S.T., Horvitz, E.: Personalizing search via automated analysis of interests and activities. In: SIGIR 2005: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 449–456. ACM Press, New York (2005)Google Scholar
  2. 2.
    Biancalana, C., Gasparetti, F., Micarelli, A., Sansonetti, G.: An approach to social recommendation for context-aware mobile services. ACM Trans. Intell. Syst. Technol. 4(1), 10:1–10:31 (2013)Google Scholar
  3. 3.
    Biancalana, C., Flamini, A., Gasparetti, F., Micarelli, A., Millevolte, S., Sansonetti, G.: Enhancing traditional local search recommendations with context-awareness. In: Konstan, J.A., Conejo, R., Marzo, J.L., Oliver, N. (eds.) UMAP 2011. LNCS, vol. 6787, pp. 335–340. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  4. 4.
    Gibson, D., Punera, K., Tomkins, A.: The volume and evolution of web page templates. In: Special Interest Tracks and Posters of the 14th International Conference on World Wide Web, WWW 2005, pp. 830–839. ACM, New York (2005)CrossRefGoogle Scholar
  5. 5.
    Sriram, S., Shen, X., Zhai, C.: A session-based search engine. In: Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2004, pp. 492–493. ACM, New York (2004)Google Scholar
  6. 6.
    Daoud, M., Tamine-Lechani, L., Boughanem, M., Chebaro, B.: A session based personalized search using an ontological user profile. In: Proceedings of the 2009 ACM Symposium on Applied Computing, SAC 2009, pp. 1732–1736. ACM, New York (2009)Google Scholar
  7. 7.
    Speretta, M., Gauch, S.: Personalized search based on user search histories. In: Proceeding of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence Proceedings, pp. 622–628 (September 2005)Google Scholar
  8. 8.
    Paranjpe, D.: Learning document aboutness from implicit user feedback and document structure. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management, CIKM 2009, pp. 365–374. ACM, New York (2009)Google Scholar
  9. 9.
    Gasparetti, F., Micarelli, A.: Exploiting web browsing histories to identify user needs. In: Proceedings of the 12th International Conference on Intelligent User Interfaces, IUI 2007, pp. 325–328. ACM, New York (2007)Google Scholar
  10. 10.
    Gasparetti, F., Micarelli, A., Sansonetti, G.: Exploiting web browsing activities for user needs identification. In: Proceedings of the 2014 International Conference on Computational Science and Computational Intelligence (CSCI 2014). IEEE Computer Society, Conference Publishing Services (March 2014)Google Scholar
  11. 11.
    Pirolli, P., Card, S.K.: Information foraging. Psychological Review 106(4), 643–675 (1999)CrossRefGoogle Scholar
  12. 12.
    Liu, B., Grossman, R., Zhai, Y.: Mining data records in web pages. In: Proceedings of the ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2003, pp. 601–606. ACM, New York (2003)Google Scholar
  13. 13.
    Chawathe, S.S.: Comparing hierarchical data in external memory. In: Proceedings of the 25th International Conference on Very Large Data Bases, VLDB 1999, pp. 90–101. Morgan Kaufmann Publishers Inc., San Francisco (1999)Google Scholar
  14. 14.
    Biancalana, C., Gasparetti, F., Micarelli, A., Sansonetti, G.: Social semantic query expansion. ACM Trans. Intell. Syst. Technol. 4, 60:1–60:43 (2013)Google Scholar
  15. 15.
    Biancalana, C., Gasparetti, F., Micarelli, A., Miola, A., Sansonetti, G.: Context-aware movie recommendation based on signal processing and machine learning. In: Proceedings of the 2nd Challenge on Context-Aware Movie Recommendation, CAMRA 2011, pp. 5–10. ACM, New York (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Fabio Gasparetti
    • 1
  • Alessandro Micarelli
    • 1
  • Giuseppe Sansonetti
    • 1
  1. 1.Roma Tre UniversityRomeItaly

Personalised recommendations