Using Thematic Ontologies for User- and Group-Based Adaptive Personalization in Web Searching

  • Alexandros Paramythis
  • Florian König
  • Christian Schwendtner
  • Lex van Velsen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5811)

Abstract

This paper presents Prospector, an adaptive meta-search layer, which performs personalized re-ordering of search results. Prospector combines elements from two approaches to adaptive search support: (a) collaborative web searching; and, (b) personalized searching using semantic metadata. The paper focuses on the way semantic metadata and the users’ search behavior are utilized for user- and group- modeling, as well as on how these models are used to re-rank results returned for individual queries. The paper also outlines past evaluation activities related to Prospector, and discusses potential applications of the approach for the adaptive retrieval of multimedia documents.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Brin, S., Page, L.: The Anatomy of a Large-Scale Hypertextual Web Search Engine. Computer Networks and ISDN Systems 30(1-7), 107–117 (1998)CrossRefGoogle Scholar
  2. 2.
    Chirita, P.A., Nejdl, W., Paiu, R., Kohlschütter, C.: Using ODP Metadata to Personalize Search. In: Proceedings of the 28th ACM International SIGIR Conference on Research and Development in Information Retrieval, Salvador, Brazil. ACM, New York (2005)Google Scholar
  3. 3.
    Dell Zhang, Y.D.: Semantic, Hierarchical, Online Clustering of Web Search Results. In: Yu, J.X., Lin, X., Lu, H., Zhang, Y. (eds.) APWeb 2004. LNCS, vol. 3007, pp. 69–78. Springer, Heidelberg (2004)Google Scholar
  4. 4.
    Hamilton, N.: The mechanics of a deep net metasearch engine. In: Proceedings of the 12th International World Wide Web Conference, Budapest, Hungary (2003)Google Scholar
  5. 5.
    Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. Journal of the ACM 46(5), 604–632 (1999)MATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Lawrence, S.: Context in Web Search. IEEE Data Engineering Bulletin 23(3), 25–32 (2000)Google Scholar
  7. 7.
    Schwendtner, C., König, F., Paramythis, A.: Prospector: An adaptive front-end to the Google search engine. In: Proceedings of the 14th Workshop on Adaptivity and User Modeling in Interactive Systems (ABIS 2006), held in the context of Lernen-Wissensentdeckung-Adaptivität 2006 (LWA 2006), October 9-11, pp. 56–61. University of Hildesheim, Hildesheim (2006)Google Scholar
  8. 8.
    Smyth, B., Balfe, E., Briggs, P., Coyle, M., Freyne, J.: Collaborative Web Search. In: Proceedings of the 18th International Joint Conference on Artificial Intelligence (IJCAI 2003), Acapulco, Mexico, pp. 1417–1419. Morgan Kaufmann, San Francisco (2003)Google Scholar
  9. 9.
    Smyth, B., Freyne, J., Coyle, M., Briggs, P., Balfe, E.: I-SPY: Anonymous, Community-Based Personalization by Collaborative Web Search. In: Proceedings of the 23rd SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, Cambridge, UK, pp. 367–380. Springer, Heidelberg (2003)Google Scholar
  10. 10.
    Tanudjaja, F., Mui, L.: Persona: A Contextualized and Personalized Web Search. In: Proceedings of the 35th Annual Hawaii International Conference on System Sciences (HICSS 2002), Hilton Waikoloa Village, Island of Hawaii, vol. 3, p. 67 (9). IEEE Computer Society, Los Alamitos (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Alexandros Paramythis
    • 1
  • Florian König
    • 1
  • Christian Schwendtner
    • 1
  • Lex van Velsen
    • 2
  1. 1.Institute for Information Processing and Microprocessor Technology (FIM)Johannes Kepler UniversityLinzAustria
  2. 2.Department of Technical and Professional CommunicationUniversity of TwenteEnschedeThe Netherlands

Personalised recommendations