LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Goker, A.S.; He, D. (2003)
Languages: English
Types: Unknown
Subjects: ZA4050
World Wide Web is a source of information, and searches on the Web can be analyzed to detect patterns in Web users' search behaviors and information needs to effectively handle the users' subsequent needs. The rationale is that the information need of a user at a particular time point occurs in a particular context, and queries are derived from that need. In this paper, we discuss an extension of our personalization approach that was originally developed for a traditional bibliographic retrieval system but has been adapted and extended with a collaborative model for the Web retrieval environment. We start with a brief introduction of our personalization approach in a traditional information retrieval system. Then, based on the differences in the nature of documents, users and search tasks between traditional and Web retrieval environments, we describe our extensions of integrating collaboration in personalization in the Web retrieval environment. The architecture for the extension integrates machine learning techniques for the purpose of better modeling users' search tasks. Finally, a user-oriented evaluation of Web-based adaptive retrieval systems is presented as an important aspect of the overall strategy for personalization.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Bloedorn, E., Mani, I. and MacMillan, T.(1996). Representational issues in machine learning of user profiles. In AAAI Spring Symposium on Machine Learning in Information Access, pages 25--27.
    • Cool, C.,Spink, A. (2002). Issues of context in information retrieval, a special issue of Information Processing and Management, 38(5).
    • Coon, D. (1992). Introduction to Psychology. West Publisher.
    • Dix, A., Finlay, J., Abowd, G. and Beale, R. (1993). HumanComputer Interaction. Prentice-Hall
    • Edwards, P., Bayer, D., Green, C.L. and Payne,T.R. (1996). Experience with learning agents which manage internet-based information. In M.A. Hearst and H. Hirsh, editors, AAAI Spring Symposium on Machine Learning in Information Access, number SS-96-05, pages 31--40. AAAI Press.
    • Goker, A. and He, D. (2000). Analysing intranet logs to determine session boundaries for user-oriented learning. In AH2000: Proceedings of the International Conference on Adaptive Hypermedia and Adaptive Web-based Systems, pages 319--322, Trento, Italy. Springer-Verlag.
    • 6th International Symposium, ISMIS'91, pages 348--357.
    • Goker, A. (1997). Context Learning in Okapi. Journal of Documentation, 53(1):80--83.
    • Goker, A. (1999). Capturing information need by learning user context. In A. Rudstrom, editor, Proceedings of IJCAI99 workshop "Learning about Users", pages 21--28.
    • Han, S., Goker, A. and He, D. (2001). Web user search pattern analysis for modeling query topic changes. Proceedings of User Modeling for Context-Aware Applications, a workshop of the 8th International Conference on User Modeling.
    • He, D. and Goker, A. (2000). Detecting session boundaries from Web user logs. In Proceedings of the 22nd Annual Colloquium on IR Research IRSG 2000, pages 57--66, Cambridge, UK.
    • He, D., Goker, A. and Harper. D. (2002) Combining Evidence for Automatic Web Session Identification. Information Processing and Management, 38:727-742.
    • Hersh, W. and Over, P. (2000). SIGIR workshop on interactive retrieval at TREC and beyond. SIGIR Forum, 34(1), Fall 2000. http://www.acm.org/sigir/forum/F2000-TOC.html.
    • Jansen, Major B. J., Spink, A., Bateman, J. and Saracevic, T. (1998). Real Life Information Retrieval: A Study of User Queries on the Web. SIGIR FORUM, 32(1):5--17.
    • Matwin, S. and Kubat, M. (1996). The Role of Context in Concept Learning. In Proceedings of the ICML-96 workshop on Learning in Context-Sensitive Domains , pages 1-5, Bari Italy.
    • Mobasher, B., Cooley, R., and Srivastava, J. (2000). Automatic Personalization based on Web Usage Mining . Communications of ACM , 43(8):142--151.
    • Pazzani, M. and Billsus, D. (1997). Learning and revising user profiles: The identification of interesting Web sites. Machine Learning, 27:313--331.
    • Resnick, P. and Varian, H. (1997). Communications of the ACM, special issues on Recommender Systems. 40(3).
    • Robertson, S. and Sparck Jones, K. (1976). Relevance weighting of search terms. Journal of the American Society for Information Sciences , 27(3):129--146.
    • Robertson, S., Walker, S. and Beaulieu, M. (2000). Experimentation as a way of life: Okapi at TREC. Information Processing and Management, 36:95--108.
    • Spink, A., Wilson, T., Ellis, D. and Ford, N. (1998). Modeling Users' Successive Searches in Digital Environments. D-Lib Magazine.
    • Walker, S. and Hancock-Beaulieu, M. (1991). Okapi at City: An evaluation facility for interactive IR . Technical Report 6056, British Library R & D. Contributions by Goker A., McCluskey L., Palmer S.
  • No related research data.
  • No similar publications.

Share - Bookmark

Download from

Cite this article