On Some Approach to Integrating User Profiles in Document Retrieval System Using Bayesian Networks

  • Bernadetta Maleszka
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10449)


Information retrieval systems are more and more popular due to the information overload in the Internet. There are many problems that can cause this situation: user can not know his real information need when he submits a few words to the browser; these words can have many different meanings; user can expect different results depending on current context. To obtain satisfactory result, the retrieval system should save user profile and develop it. In this paper we propose a method for determining user profile based on content-based and collaborative filtering. Our approach uses Bayesian Networks to develop representative profile of the users’ group. We have performed some experiments to evaluate the quality of proposed method.


Document retrieval Knowledge integration Ontology-based user profile Bayesian network 



This research was partially supported by Polish Ministry of Science and Higher Education.


  1. 1.
    Billsus, D., Pazzani, M.J.: A hybrid user model for news story classification. In: Kay, J. (ed.) Proceedings of the Seventh International Conference on User Modeling, vol. 407, pp. 99–108. Springer, Heidelberg (1999). doi: 10.1007/978-3-7091-2490-1_10CrossRefGoogle Scholar
  2. 2.
    Bottcher, S.G., Dethlefsen, C.: Learning Bayesian networks with R. In: Proceedings of the 3rd International Workshop on Distributed Statistical Computing, DSC 2003 (2003)Google Scholar
  3. 3.
    Bouckaert, R.R.: Bayesian Network Classifiers in Weka for Version 3-5-7. University of Waikato (2007)Google Scholar
  4. 4.
    Druzdzel, M.J., Diez, F.J.: Combining Knowledge from different sources in causal probabilistic models. J. Mach. Learn. Res. 4, 295–316 (2003)MathSciNetzbMATHGoogle Scholar
  5. 5.
    Dahak, F., Boughanem, M., Balla, A.: A probabilistic model to exploit user expectations in XML information retrieval. Inf. Process. Manag. 53(1), 87–105 (2016)CrossRefGoogle Scholar
  6. 6.
    Ferchichi, A., Boulila, W., Farah, I.R.: Towards an uncertainty reduction framework for land-cover change prediction using possibility theory. Vietnam J. Comput. Sci. 4(3), 195–209 (2016)CrossRefGoogle Scholar
  7. 7.
    Giftodimos, E., Flach, P.A.: Hierarchical Bayesian networks: a probabilistic reasoning model for structured domains. In: Proceedings of the ICML-2002 Workshop on Development of Representations, pp. 23–30 (2002)Google Scholar
  8. 8.
    DesJardins, M., Rathod, P., Getoor, L.: Learning structured Bayesian networks: combining abstraction hierarchies and tree-structured conditional probability tables. Comput. Intell. 24(1), 1–22 (2008)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Maleszka, B.: A method for determining ontology-based user profile in document retrieval system. J. Intell. Fuzzy Syst. 32, 1253–1263 (2017)CrossRefGoogle Scholar
  10. 10.
    Mianowska, B., Nguyen, N.T.: A method for collaborative recommendation in document retrieval systems. In: Selamat, A., Nguyen, N.T., Haron, H. (eds.) ACIIDS 2013. LNCS, vol. 7803, pp. 168–177. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-36543-0_18CrossRefGoogle Scholar
  11. 11.
    Maleszka, B.: A method for determining representative of ontology-based user profile in personalized document retrieval systems. In: Nguyen, N.T., Trawiński, B., Fujita, H., Hong, T.-P. (eds.) ACIIDS 2016. LNCS, vol. 9621, pp. 202–211. Springer, Heidelberg (2016). doi: 10.1007/978-3-662-49381-6_20CrossRefGoogle Scholar
  12. 12.
    Maleszka, B.: A method for user profile learning in document retrieval system using Bayesian network. In: Nguyen, N.T., Tojo, S., Nguyen, L.M., Trawiński, B. (eds.) ACIIDS 2017. LNCS, vol. 10191, pp. 269–277. Springer, Cham (2017). doi: 10.1007/978-3-319-54472-4_26CrossRefGoogle Scholar
  13. 13.
    Stern, M.K., Beck, J.E., Woolf, B.P.: Naive Bayes classifiers for user modeling. In: Proceedings of the Conference on User Modeling (1999)Google Scholar
  14. 14.
    Pazzani, M.J., Billsus, D.: Content-based recommendation systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 325–341. Springer, Heidelberg (2007). doi: 10.1007/978-3-540-72079-9_10CrossRefGoogle Scholar
  15. 15.
    Pietranik, M., Nguyen, N.T.: A multi-attribute based framework for ontology aligning. Neurocomputing 146, 276–290 (2014)CrossRefGoogle Scholar
  16. 16.
    Waikato Environment for Knowledge Analysis. Machine Learning Group at the University of Waikato. Accessed 10 Mar 2017
  17. 17.
    Yu, K., Schwaighofer, A., Tresp, V., Ma, W.-Y., Zhang, H.J.: Collaborative ensemble learning: combining collaborative and content-based information filtering via hierarchical Bayes. In: Proceedings of UAI 2003, pp. 616–623 (2003)Google Scholar
  18. 18.
    Schiaffino, S.N., Amandi, A.: User profiling with case-based reasoning and Bayesian networks. In: Proceedings of International Joint Conference IBERAMIA-SBIA, pp. 12–21 (2000)Google Scholar
  19. 19.
    Wong, S.K.M., Butz, C.J.: A Bayesian approach to user profiling in information retrieval. Technol. Lett. 4(1), 50–56 (2000)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Faculty of Computer Science and ManagementWrocław University of Science and TechnologyWrocławPoland

Personalised recommendations