Property-Based Interest Propagation in Ontology-Based User Model

  • Federica Cena
  • Silvia Likavec
  • Francesco Osborne
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7379)


We present an approach for propagation of user interests in ontology-based user models taking into account the properties declared for the concepts in the ontology. Starting from initial user feedback on an object, we calculate user interest in this particular object and its properties and further propagate user interest to other objects in the ontology, similar or related to the initial object. The similarity and relatedness of objects depends on the number of properties they have in common and their corresponding values. The approach we propose can support finer recommendation modalities, considering the user interest in the objects, as well as in singular properties of objects in the recommendation process. We tested our approach for interest propagation with a real adaptive application and obtained an improvement with respect to IS-A-propagation of interest values.


Recommender System Semantic Similarity Domain Ontology User Feedback User Interest 
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.
    Abel, F., Araújo, S., Gao, Q., Houben, G.-J.: Analyzing Cross-System User Modeling on the Social Web. In: Auer, S., Díaz, O., Papadopoulos, G.A. (eds.) ICWE 2011. LNCS, vol. 6757, pp. 28–43. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  2. 2.
    Adomavicius, G., Manouselis, N., Kwon, Y.: Multi-criteria recommender systems. In: Recommender Systems Handbook, pp. 769–803 (2011)Google Scholar
  3. 3.
    Aroyo, L., Dolog, P., Houben, G.-J., Kravcik, M., Naeve, A., Nilsson, M., Wild, F.: Interoperability in personalized adaptive learning. Educational Technology & Society 9(2), 4–18 (2006)Google Scholar
  4. 4.
    Brusilovsky, P., Millán, E.: User Models for Adaptive Hypermedia and Adaptive Educational Systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 3–53. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  5. 5.
    Cantador, I., Bellogín, A., Castells, P.: A multilayer ontology-based hybrid recommendation model. AI Communications 21(2-3), 203–210 (2008)MathSciNetzbMATHGoogle Scholar
  6. 6.
    Carmagnola, F., Cena, F., Console, L., Cortassa, O., Gena, C., Goy, A., Torre, I., Toso, A., Vernero, F.: Tag-based user modeling for social multi-device adaptive guides. User Modeling and User-Adapted Interaction 18, 497–538 (2008)CrossRefGoogle Scholar
  7. 7.
    Cena, F., Likavec, S., Osborne, F.: Propagating User Interests in Ontology-Based User Model. In: Pirrone, R., Sorbello, F. (eds.) AI*IA 2011. LNCS, vol. 6934, pp. 299–311. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  8. 8.
    Heckmann, D., Schwartz, T., Brandherm, B., Schmitz, M., von Wilamowitz-Moellendorff, M.: Gumo – The General User Model Ontology. In: Ardissono, L., Brna, P., Mitrović, A. (eds.) UM 2005. LNCS (LNAI), vol. 3538, pp. 428–432. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    IJntema, W., Goossen, F., Frasincar, F., Hogenboom, F.: Ontology-based news recommendation. In: 2010 EDBT/ICDT Workshops. ACM Int. Conf. Proc. Series. ACM (2010)Google Scholar
  10. 10.
    Jiang, J., Conrath, D.: Semantic similarity based on corpus statistics and lexical taxonomy. In: International Conference on Research in Computational Linguistics, pp. 19–33 (1997)Google Scholar
  11. 11.
    Kobsa, A., Koenemann, J., Pohl, W.: Personalized hypermedia presentation techniques for improving online customer relationship. The Knowledge Engineering Review 16(2), 111–155 (2001)zbMATHCrossRefGoogle Scholar
  12. 12.
    Lin, D.: An information-theoretic definition of similarity. In: 15th International Conference on Machine Learning ICML 1998, pp. 296–304. Morgan Kaufmann Publishers Inc. (1998)Google Scholar
  13. 13.
    Middleton, S.E., Shadbolt, N.R., De Roure, D.C.: Ontological user profiling in recommender systems. ACM Transactions on Information Systems 22, 54–88 (2004)CrossRefGoogle Scholar
  14. 14.
    O’Sullivan, D., Smyth, B., Wilson, D.C.: Preserving recommender accuracy and diversity in sparse datasets. Int. Journal on Artificial Intelligence Tools 13(1), 219–235 (2004)CrossRefGoogle Scholar
  15. 15.
    PIEMONTE Team: Interacting with a social web of smart objects for enhancing tourist experiences. In: ENTER 2012 Conference, Helsingborg (2012)Google Scholar
  16. 16.
    Pirró, G., Euzenat, J.: A Feature and Information Theoretic Framework for Semantic Similarity and Relatedness. In: Patel-Schneider, P.F., Pan, Y., Hitzler, P., Mika, P., Zhang, L., Pan, J.Z., Horrocks, I., Glimm, B. (eds.) ISWC 2010, Part I. LNCS, vol. 6496, pp. 615–630. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  17. 17.
    Rada, R., Mili, H., Bicknell, E., Blettner, M.: Development and application of a metric on semantic nets. IEEE Trans. on Systems Management and Cybernetics 19(1), 17–30 (1989)CrossRefGoogle Scholar
  18. 18.
    Razmerita, L., Angehrn, A., Maedche, A.: Ontology-based User Modeling for Knowledge Management Systems. In: Brusilovsky, P., Corbett, A.T., de Rosis, F. (eds.) UM 2003. LNCS, vol. 2702, pp. 213–217. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  19. 19.
    Resnik, P.: Semantic similarity in a taxonomy: An information-based measure and its application to problems of ambiguity in natural language. Journal of Artificial Intelligence Research 11, 95–130 (1999)zbMATHGoogle Scholar
  20. 20.
    Salton, G., McGill, M.: Introduction to Modern Information Retrieval. McGraw-Hill Book Company (1984)Google Scholar
  21. 21.
    Schein, A.I., Popescul, A., Ungar, L.H., Pennock, D.M.: Methods and metrics for cold-start recommendations. In: 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2002, pp. 253–260. ACM (2002)Google Scholar
  22. 22.
    Sieg, A., Mobasher, B., Burke, R.: Web search personalization with ontological user profiles. In: 16th ACM Conference on Information and Knowledge Management, CIKM 2007, pp. 525–534. ACM (2007)Google Scholar
  23. 23.
    Smyth, B.: Case-Based Recommendation. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 342–376. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  24. 24.
    Tversky, A.: Features of similarity. Psychological Review 84(4), 327–352 (1977)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Federica Cena
    • 1
  • Silvia Likavec
    • 1
  • Francesco Osborne
    • 1
  1. 1.Dipartimento di InformaticaUniversità di TorinoTorinoItaly

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