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Correlation Measures for Bipolar Rating Profiles

  • Fernando Monroy-Tenorio
  • 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)

Abstract

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.

Keywords

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

Notes

Acknowledgements

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.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Fernando Monroy-Tenorio
    • 1
  • 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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