Using Bibliometrics and Fuzzy Linguistic Modeling to Deal with Cold Start in Recommender Systems for Digital Libraries

  • Alvaro Tejeda-Lorente
  • Juan Bernabé-Moreno
  • Carlos Porcel
  • Enrique Herrera-Viedma
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 643)

Abstract

Every recommender system approach suffers the cold start problem to a greater or lesser extent. To soften this impact, the more common solution is to find the way of populating users profiles either using hybrid approach or finding external data sources. In this paper, we present a fuzzy linguistic approach that using bibliometrics aids to soft or remove the necessity of interaction of users providing them with personalized profiles built beforehand, thus reducing the cold start problem. To prove the effectiveness of the system, we conduct a test involving some researchers, aiming to build their profiles automatically. The results obtained proved to be satisfactory for the researchers.

Keywords

Recommender system Cold start Fuzzy linguistic modeling Digital library 

Notes

Acknowledgments

This paper has been developed with the FEDER financing under Projects TIN2013-40658-P and TIN2016-75850-R

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Alvaro Tejeda-Lorente
    • 1
  • Juan Bernabé-Moreno
    • 1
  • Carlos Porcel
    • 2
  • Enrique Herrera-Viedma
    • 1
  1. 1.Department of Computer Science and Artificial IntelligenceUniversity of GranadaGranadaSpain
  2. 2.Department of Computer ScienceUniversity of JaénJaénSpain

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