Correlation Measures for Bipolar Rating Profiles

  • Fernando Monroy-TenorioEmail author
  • Ildar Batyrshin
  • Alexander Gelbukh
  • Valery Solovyev
  • Nailya Kubysheva
  • Imre Rudas
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 648)


We introduce new correlation measures for measuring similarity and association of rating profiles obtained from bipolar rating scales. Instead of the measurement based approach when the user’s rating is considered as a number measured in ordinal, interval or ratio scales we use model based approach when user’s rating is modeled by bipolar score function that can be nonlinear. This approach can use different models of preferences for different users. The values of utility function can be adjusted in machine learning procedure to obtain better solutions on the output of recommender or decision making system. We show that Pearson’s correlation coefficient often used for measuring similarity between bipolar rating profiles in recommender systems has some drawbacks. New correlation measures proposed in the paper have not these drawbacks. These measures are obtained using general methods of construction of association measures from similarity measures on sets with involutive operation. Proposed measures can be used in recommender systems, in opinion mining and in sociological research for analysis of possible relationships between opinions of users and ratings of items.


Rating scale Bipolar scale Recommender system Opinion mining Sentient analysis Correlation Association measure 



The paper is supported in parts by the projects 20171344 of SIP IPN, 240844 and 283778 of CONACYT, 15-01-06456 of RFBR and by the Russian Government Program of Competitive Growth of Kazan Federal University.


  1. 1.
    Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)CrossRefGoogle Scholar
  2. 2.
    Batyrshin, I.: Constructing time series shape association measures: Minkowski distance and data standardization. In: 1st BRICS Countries Congress on Computational Intelligence, BRICS-CCI 2013, pp. 204–212. IEEE (2013).
  3. 3.
    Batyrshin, I.Z.: Association measures on [0,1]. J. Intell. Fuzzy Syst. 29(3), 1011–1020 (2015)CrossRefMathSciNetzbMATHGoogle Scholar
  4. 4.
    Batyrshin, I.Z.: On definition and construction of association measures. J. Intell. Fuzzy Sys. 29(6), 2319–2326 (2015)CrossRefMathSciNetzbMATHGoogle Scholar
  5. 5.
    Batyrshin, I. Monroy-Tenorio, F., Gelbukh, A., Solovyev, V., Kubysheva, N.: Bipolar rating scales: a survey and novel correlation measures based on nonlinear bipolar scoring functions. Acta Polytech. Hung. (2017)Google Scholar
  6. 6.
    Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, pp. 43–52. Morgan Kaufmann Publishers Inc. (1998)Google Scholar
  7. 7.
    Dubois, D., Prade, H.: Bipolar representations in reasoning, knowledge extraction and decision processes. In: International Conference on Rough Sets and Current Trends in Computing, pp. 15–26. Springer, Heidelberg (2006)Google Scholar
  8. 8.
    Grabisch, M., Marichal, J.-L., Mesiar, R., Pap, E.: Aggregation Functions. Cambridge University Press, Cambridge (2009)CrossRefzbMATHGoogle Scholar
  9. 9.
    Herrera, F., Herrera-Viedma, E.: Linguistic decision analysis: steps for solving decision problems under linguistic information. Fuzzy Sets Syst. 115(1), 67–82 (2000)CrossRefMathSciNetzbMATHGoogle Scholar
  10. 10.
    Hjermstad, M.J., Fayers, P.M., Haugen, D.F., et al.: Studies comparing numerical rating scales, verbal rating scales, and visual analogue scales for assessment of pain intensity in adults: a systematic literature review. J. Pain Symptom Manag. 41(6), 1073–1093 (2011)CrossRefGoogle Scholar
  11. 11.
    Jang, J.S.R., Sun, C.T., Mizutani, E.: Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence. Prentice Hall, Upper Saddle River (1997)Google Scholar
  12. 12.
    Likert, R.: A technique for the measurement of attitudes. Arch. Psychol. 22(140), 5–55 (1932)Google Scholar
  13. 13.
    Osgood, C.E.: The nature and measurement of meaning. Psychol. Bull. 49(3), 197–237 (1952)CrossRefGoogle Scholar
  14. 14.
    Pfanzagl, J.: Theory of Measurement. Physica, Heidelberg (1971)CrossRefzbMATHGoogle Scholar
  15. 15.
    Poria, S., Gelbukh, A., Cambria, E., Hussain, A., Huang, G.: EmoSenticSpace: a novel framework for affective commonsense reasoning. Knowl.-Based Syst. 69, 108–123 (2014)CrossRefGoogle Scholar
  16. 16.
    Ricci, F., Rokach, L., Shapira, B., Kantor, P.B.: Recommender Systems Handbook. Springer, Heidelberg (2011)CrossRefzbMATHGoogle Scholar
  17. 17.
    Shardanand, U., Maes, P.: Social information filtering: algorithms for automating “word of mouth”. In: Proceedings of SIGCHI conference on Human Factors in Computing Systems, pp. 210–217. ACM Press/Addison-Wesley Publishing (1995)Google Scholar
  18. 18.
    Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning—I. Inf. Sci. 8(3), 199–249 (1975)CrossRefMathSciNetzbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Fernando Monroy-Tenorio
    • 1
    Email author
  • Ildar Batyrshin
    • 1
  • Alexander Gelbukh
    • 1
  • Valery Solovyev
    • 2
  • Nailya Kubysheva
    • 2
  • Imre Rudas
    • 3
  1. 1.Centro de Investigación en Computación (CIC)Instituto Politécnico NacionalMexico CityMexico
  2. 2.Kazan Federal UniversityKazanRussian Federation
  3. 3.Ódudu UniversityBudapestHungary

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