An Intelligent Personalized Service for Conference Participants

  • Marco Degemmis
  • Pasquale Lops
  • Pierpaolo Basile
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4203)


This paper presents the integration of linguistic knowledge in learning semantic user profiles able to represent user interests in a more effective way with respect to classical keyword-based profiles. Semantic profiles are obtained by integrating a naïve Bayes approach for text categorization with a word sense disambiguation strategy based on the WordNet lexical database (Section 2). Semantic profiles are exploited by the “conference participant advisor” service in order to suggest papers to be read and talks to be attended by a conference participant. Experiments on a real dataset show the effectiveness of the service (Section 3).


Text Categorization Word Sense Disambiguation Conference Participant Intelligent Personalize Semantic User 
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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  1. 1.
    Bloedhorn, S., Hotho, A.: Boosting for text classification with semantic features. In: Proc. of 10th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, Mining for and from the Semantic Web Workshop, pp. 70–87 (2004)Google Scholar
  2. 2.
    Guarino, N., Masolo, C., Vetere, G.: Content-based access to the web. IEEE Intelligent Systems 14(3), 70–80 (1999)CrossRefGoogle Scholar
  3. 3.
    Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the Fourteenth Int. Joint Conf. on Artificial Intelligence, pp. 1137–1145. Morgan Kaufmann, San Mateo (1995)Google Scholar
  4. 4.
    Leacock, C., Chodorow, M.: Combining local context and WordNet similarity for word sense identification. In: Fellbaum, C. (ed.), pp. 305–332. MIT Press, Cambridge (1998)Google Scholar
  5. 5.
    Magnini, B., Strapparava, C.: Improving user modelling with content-based techniques. In: Proc. 8th Int. Conf. User Modeling, pp. 74–83. Springer, Heidelberg (2001)Google Scholar
  6. 6.
    McCallum, A., Nigam, K.: A comparison of event models for naive bayes text classification. In: Proceedings of the AAAI/ICML-1998 Workshop on Learning for Text Categorization, pp. 41–48. AAAI Press, Menlo Park (1998)Google Scholar
  7. 7.
    Miller, G.: Wordnet: An on-line lexical database. International Journal of Lexicography 3(4) (1990) (special Issue)Google Scholar
  8. 8.
    Mooney, R.J., Roy, L.: Content-based book recommending using learning for text categorization. In: Proceedings of the 5th ACM Conference on Digital Libraries, San Antonio, US, pp. 195–204. ACM Press, New York (2000)CrossRefGoogle Scholar
  9. 9.
    Scott, S., Matwin, S.: Text classification using wordnet hypernyms. In: COLING-ACL Workshop on usage of WordNet in NLP Systems, pp. 45–51 (1998)Google Scholar
  10. 10.
    Sebastiani, F.: Machine learning in automated text categorization. ACM Computing Surveys 34(1) (2002)Google Scholar
  11. 11.
    Theobald, M., Schenkel, R., Weikum, G.: Exploting structure, annotation, and ontological knowledge for automatic classification of xml data. In: Proceedings of International Workshop on Web and Databases, pp. 1–6 (2004)Google Scholar
  12. 12.
    Witten, I., Bell, T.: The zero-frequency problem: Estimating the probabilities of novel events in adaptive text compression. IEEE Transactions on Information Theory 37(4) (1991)Google Scholar
  13. 13.
    Yao, Y.Y.: Measuring retrieval effectiveness based on user preference of documents. Journal of the American Society for Information Science 46(2), 133–145 (1995)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Marco Degemmis
    • 1
  • Pasquale Lops
    • 1
  • Pierpaolo Basile
    • 1
  1. 1.Dipartimento di InformaticaUniversità di BariBariItaly

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