Skip to main content
SpringerLink
Log in

Exploiting User Interests to Characterize Navigational Patterns in Web Browsing Assistance

  • Published:
New Generation Computing Aims and scope Submit manuscript

Abstract

In order to be capable of exploiting context for pro-active information recommendation, agents need to extract and understand user activities based on their knowledge of the user interests. In this paper, we propose a novel approach for context-aware recommendation in browsing assistants based on the integration of user profiles, navigational patterns and contextual elements. In this approach, user profiles built using an unsupervised Web page clustering algorithm are used to characterize user ongoing activities and behavior patterns. Experimental evidence show that using longer-term interests to explain active browsing goals user assistance is effectively enhanced.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Acharyya, S. and Ghosh, J., “Context-sensitive modelling of Web-surfing behaviour using concept trees,” in Proc. of the Fifth Int. Workshop on Knowledge Discovery in the Web (WEBKDD’2003), pp. 1–8, 2003.

  2. Agrawal, R., Imielinski, T., and Swami, A., “Mining association rules between sets of items in large databases,” in Proc. of the 1993 ACM SIGMOD International Conf. on Management of Data, pp. 207–216, 1993.

  3. Bauer, T. and Leake, D., “WordSieve: A method for real-time context extraction,” in Modeling and Using Context, Int. and Interdisciplinary Conf. vol. 2116 of LNCS, Springer, pp. 30–44, 2001.

  4. Brown, P. and Jones, G., “Context-aware retrieval: Exploring a new environment for information retrieval and information filtering,” Personal and Ubiquitous Computing, 5, 4, pp. 253–263, 2001.

    Article  Google Scholar 

  5. Budzik, J., Bradshaw, S., Fu, X., and Hammond, K., “Supporting on-line resource discovery in the context of ongoing tasks with proactive software assistants,” Int. Journal of Human Computer Studies, 56, 1, pp. 47–74, 2002.

    Article  Google Scholar 

  6. Catledge, L. D. and Pitkow, J. E., “Characterizing browsing strategies in the World Wide Web,” Computer Networks and ISDN Systems, 27, 6, pp. 1065–1073, 1995.

    Article  Google Scholar 

  7. Chen, M.-S., Park, J. S., and Yu, P., “Efficient data mining for path traversal patterns,” IEEE Transactions on Knowledge and Data Engineering, 10, 2, pp. 209–221, 1998.

    Article  Google Scholar 

  8. Cooley, R., Mobasher, B., and Srivastava, J., “Grouping Web page references into transactions for mining World Wide Web browsing patterns,” in Proc. of the IEEE Knowledge and Data Engineering Exchange Workshop, pp. 2–9, 1997.

  9. Cooley, R., Mobasher, B., and Srivastava, J., “Data preparation for mining World Wide Web browsing patterns,” Knowledge and Information Systems, 1, 1, pp. 5–32, 1999.

    Google Scholar 

  10. Eirinaki, M., Vazirgiannis, M., and Varlamis, I., “SEWeP: Using site semantics and a taxonomy to enhance the web personalization process,” in Proc. of the 9th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD’03), pp. 99–108, 2003.

  11. Godoy, D. and Amandi, A., “A conceptual clustering approach for user profiling in personal information agents,” AI Communications, 19, 3, pp. 207–227, 2006.

    MATH  MathSciNet  Google Scholar 

  12. Godoy, D. and Amandi, A., “Learning browsing patterns for context-aware recommendation,” in Artificial Intelligence in Theory and Practice, IFIP 19th World Computer Congress, vol. 217, Springer, pp. 61–70, 2006.

  13. He, D. and Goker, A., “Detecting session boundaries from Web user logs,” in Proc. of the 22nd Annual Colloquium on IR Research, pp. 57–66, 2000.

  14. Lieberman, H., Fry, C., and Weitzman, L., “Exploring the web with reconnaissance agents,” Communications of the ACM, 44, 8, pp. 69–75, 2001.

    Article  Google Scholar 

  15. Michalski, R. S. and Stepp, R. E., “Learning from observation: Conceptual clustering,” in Machine Learning: An Artificial Intelligence Approach, Morgan Kaufmann, pp. 331–363, 1984.

  16. Mobasher, B., Dai, H., Luo, T., and Nakagawa, M., “Discovery and evaluation of aggregate usage profiles for Web personalization,” Data Mining and Knowledge Discovery, 6, 1, pp. 61–82, 2002.

    Article  MathSciNet  Google Scholar 

  17. Oberle, D., Berendt, B., Hotho, A., and Gonzalez, J., “Conceptual user tracking,” in Web Intelligence, 1st International Atlantic Web Intelligence Conference, vol. 2663 of LNCS, Springer, pp. 155–164, 2003.

  18. Perkowitz, M. and Etzioni, O., “Towards adaptive web sites: Conceptual framework and case study,” Artificial Intelligence, 118, 1-2, pp. 245–275, 2000.

    Article  MATH  Google Scholar 

  19. Ruthven, I., “and this set of words represents the user’s context...,” in Workshop on Information Retrieval in Context at SIGIR 2004, pp. 10, 2004.

  20. Sieg, A., Mobasher, B., and Burke, R., “Inferring user’s information context: Integrating user profiles and concept hierarchies,” in Proc. of the 2004 Meeting of the Int. Federation of Classification Societies, 2004.

  21. Srikant, R. and Agrawal, R., “Mining generalized association rules,” in Proc. of the 21th Int. Conf. on Very Large Data Bases, pp. 407–419, 1995.

Download references

Author information

Authors and Affiliations

  1. ISISTAN Research Institute, UNICEN University, Campus Universitario, Paraje Arroyo Seco, CP 7000, Tandil, Bs. As., Argentina

    Daniela Godoy & Analía Amandi

  2. CONICET, Consejo Nacional de Investigaciones Científicas y Técnicas, CP 1033, Capital Federal, Bs. As., Argentina

    Daniela Godoy & Analía Amandi

Authors
  1. Daniela Godoy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Analía Amandi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Daniela Godoy.

About this article

Cite this article

Godoy, D., Amandi, A. Exploiting User Interests to Characterize Navigational Patterns in Web Browsing Assistance. New Gener. Comput. 26, 259–275 (2008). https://doi.org/10.1007/s00354-008-0044-x

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00354-008-0044-x

Keywords

Search

Navigation