A Recommender System with Uncertainty on the Example of Political Elections

  • Krzysztof Dyczkowski
  • Anna Stachowiak
Part of the Communications in Computer and Information Science book series (CCIS, volume 298)


The article presents a system of election recommendation in which both candidate’s and voter’s preferences can be described in an imprecise way. The model of the system is based on IF-set theory which can express hesitation or lack of knowledge. Similarity measures of IF-sets and linguistic quantifiers are used in the decision-making process.


recommender systems intuitionistic fuzzy sets (IF-sets) similarity of IF-sets linguistic quantifiers 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Jannach, D., Zanker, M., Felfernig, A., Friedrich, G.: Recommender Systems: An Introduction. Cambridge University Press (2010)Google Scholar
  2. 2.
    Vozalis, E., Margaritis, K.G.: Analysis of recommender systems algorithms. In: Proceedings of The Sixth Hellenic European Conference on Computer Mathematics and its Applications, HERCMA 2003 (2003)Google Scholar
  3. 3.
    Yager, R.R.: Fuzzy logic methods in recommender systems. Fuzzy Sets and Systems 136, 133–149 (2003)MathSciNetCrossRefMATHGoogle Scholar
  4. 4.
    Terán, L., Meier, A.: A Fuzzy Recommender System for eElections. In: Andersen, K.N., Francesconi, E., Grönlund, Å., van Engers, T.M. (eds.) EGOVIS 2010. LNCS, vol. 6267, pp. 62–76. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  5. 5.
    WWW: Center for Citizenship Education, http://www.ceo.org.pl/
  6. 6.
    WWW: Web service “Latarnik wyborczy” (Eng. Election Lighthouse), http://latarnik.nq.pl/
  7. 7.
    Atanassov, K.: Intuitionistic fuzzy sets. Fuzzy Sets and Systems 20, 87–96 (1986)MathSciNetCrossRefMATHGoogle Scholar
  8. 8.
    Atanassov, K.: Intuitionistic fuzzy sets: Theory and Applications. Springer (1999)Google Scholar
  9. 9.
    Pankowska, A., Wygralak, M.: On Hesitation Degrees in IF-Set Theory. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 338–343. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  10. 10.
    Szmidt, E., Kacprzyk, J.: Distances between intuitionistic fuzzy sets. Fuzzy Sets and Systems 114, 505–518 (2000)MathSciNetCrossRefMATHGoogle Scholar
  11. 11.
    Szmidt, E., Kacprzyk, J.: A Similarity Measure for Intuitionistic Fuzzy Sets and Its Application in Supporting Medical Diagnostic Reasoning. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 388–393. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  12. 12.
    Szmidt, E., Kacprzyk, J.: Analysis of Similarity Measures for Atanassov’s Intuitionistic Fuzzy Sets. In: Proccedings of IFSA-EUSFLAT 2009, pp. 1416–1421 (2009)Google Scholar
  13. 13.
    Szmidt, E., Kacprzyk, J.: Intuitionistic fuzzy sets two and three term representations in the context of a hausdorff distance. Acta Universitatis Matthiae Belii 19, 53–62 (2011)MathSciNetMATHGoogle Scholar
  14. 14.
    Yager, R.R.: Interpreting linguistically quantified propositions. International Journal of Intelligent Systems 9, 541–569 (1994)CrossRefMATHGoogle Scholar
  15. 15.
    Wygralak, M.: Cardinalities of fuzzy sets. Springer, Heidelberg (2003)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Krzysztof Dyczkowski
    • 1
  • Anna Stachowiak
    • 1
  1. 1.Faculty of Mathematics and Computer ScienceAdam Mickiewicz UniversityPoznańPoland

Personalised recommendations