Using Lexical Semantic Relation and Multi-attribute Structures for User Profile Adaptation

  • Agnieszka Indyka-Piasecka
  • Piotr Jacewicz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8397)

Abstract

This contribution presents a new approach to the representation of user interests and preferences at information retrieval process. The adaptive user profile includes both interests given explicitly by the user, as a query, and also preferences expressed by the valuation of relevance of retrieved documents, so to express field independent translation between terminology used by user and terminology accepted in some field of knowledge. Building, modifying, expanding by semantically related terms and using procedures for the profile are presented. Experiments concerning the profile, as a personalization mechanism of Web retrieval system, are presented and discussed.

Keywords

Information Retrieval User Modeling Relevant Document User Profile Query Expansion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Ambrosini, L., Cirillo, V., Micarelli, A.: A Hybrid Architecture for User-Adapted Information Filtering on the World Wide Web. In: Proc. of the 6th Int. Conf. on User Modeling, pp. 59–62. Springer (1997)Google Scholar
  2. 2.
    Asnicar, F., Tasso, C.: ifWeb: A Prototype of User Model-Based Intelligent Agent for Document Filtering and Navigation in the World Wide Web. In: Proc. of the Workshop Adaptive Systems and User Modeling on the World Wide Web, UM 1997. Springer (1997)Google Scholar
  3. 3.
    Billsus, D., Pazzani, M.: A Hybrid User Model for News Story Classification. In: Proc. of the 7th Int. Conf. on User Modeling, UM 1999, Banff, Canada, pp. 99–108. Springer (1999)Google Scholar
  4. 4.
    Benaki, E., Karkaletsis, A., Spyropoulos, D.: User Modeling in WWW: The UMIE Prototype. In: Proc. of the 6th Int. Conf. on User Modeling, pp. 55–58. Springer (1997)Google Scholar
  5. 5.
    Bhatia, S.J.: Selection of Search Terms Based on User Profile. Comm. of the ACM (1992)Google Scholar
  6. 6.
    Bull, S.: See Yourself Write: A Simple Student Model to Make Students Think. In: Proc. of the 6th Int. Conf. on User Modeling, pp. 315–326. Springer (1997)Google Scholar
  7. 7.
    Collins, J.A., Greer, J.E., Kumar, V.S., McCalla, G.I., Meagher, P., Tkatch, R.: Inspectable User Models for Just–In Time Workplace Training. In: Proc. of the 6th Int. Conf. on User Modeling, pp. 327–338. Springer (1997)Google Scholar
  8. 8.
    Daniłowicz, C.: Modelling of user preferences and needs in Boolean retrieval systems. Information Processing and Management 30(3), 363–378 (1994)CrossRefGoogle Scholar
  9. 9.
    Davies, N.J., Weeks, R., Revett, M.C.: Information Agents for World Wide Web. In: Nwana, H.S., Azarmi, N. (eds.) Software Agents and Soft Computing: Towards Enhancing Machine Intelligence. LNCS (LNAI), vol. 1198, pp. 79–99. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  10. 10.
    Goldberg, J.L.: CDM: An Approach to Learning in Text Categorization. International Journal on Artificial Intelligence Tools 5(1 and 2), 229–253 (1996)CrossRefGoogle Scholar
  11. 11.
    Indyka-Piasecka, A., Piasecki, M.: Adaptive Translation between User’s Vocabulary and Internet Queries. In: Kłopotek, M.A., Wierzchoń, S.T., Trojanowski, K. (eds.) Proc. of the IIS IPWM 2003. ASC, vol. 22, pp. 149–157. Springer, Heidelberg (2003)Google Scholar
  12. 12.
    Danilowicz, C., Indyka-Piasecka, A.: Dynamic User Profiles Based on Boolean Formulas. In: Orchard, B., Yang, C., Ali, M. (eds.) IEA/AIE 2004. LNCS (LNAI), vol. 3029, pp. 779–787. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  13. 13.
    Jeapes, B.: Neural Intelligent Agents. Online & CDROM Rev. 20(5), 260–262 (1996)CrossRefGoogle Scholar
  14. 14.
    Maglio, P.P., Barrett, R.: How to Build Modeling Agents to Support Web Searchers. In: Proc. of the 6th Int. Conf. on User Modeling, pp. 5–16. Springer (1997)Google Scholar
  15. 15.
    Moukas, A., Zachatia, G.: Evolving a Multi-agent Information Filtering Solution in Amalthaea. In: Proc. of the Conference on Agents, Agents 1997. ACM Press (1997)Google Scholar
  16. 16.
    Qiu, Y.: Automatic Query Expansion Based on a Similarity Thesaurus. PhD. Thesis (1996)Google Scholar
  17. 17.
    Salton, G., Bukley, C.: Term-Weighting Approaches in Automatic Text Retrieval. Information Processing & Management 24(5), 513–523 (1988)CrossRefGoogle Scholar
  18. 18.
    Seo, Y.W., Zhang, B.T.: A Reinforcement Learning Agent for Personalised Information Filtering. In: Int. Conf. on the Intelligent User Interfaces, pp. 248–251. ACM (2000)Google Scholar
  19. 19.
    Voorhees, E.M.: Implementing Agglomerative Hierarchic Clustering Algorithms for Use in Document Retrieval. Inf. Processing & Management 22(6), 465–476 (1986)CrossRefGoogle Scholar
  20. 20.
    Indyka-Piasecka, A.: Using multi-attribute structures and significance term evaluation for user profile adaptation. In: Jędrzejowicz, P., Nguyen, N.T., Hoang, K. (eds.) ICCCI 2011, Part I. LNCS, vol. 6922, pp. 336–345. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  21. 21.
    Piasecki, M., Szpakowicz, S., Broda, B.: A Wordnet from the Ground Up. Oficyna Wydawnicza Politechniki Wrocławskiej (2009)Google Scholar
  22. 22.
    Fellbaum, C. (ed.): WordNet – An Electronic Lexical Database. The MIT Press (1998)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Agnieszka Indyka-Piasecka
    • 1
  • Piotr Jacewicz
    • 1
  1. 1.Institute of InformaticsWrocław University of TechnologyPoland

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