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A Learning Web Platform Based on a Fuzzy Linguistic Recommender System to Help Students to Learn Recommendation Techniques

  • Carlos Porcel
  • Maria Jesús Lizarte
  • Juan Bernabé-Moreno
  • Enrique Herrera-Viedma
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9375)

Abstract

The rapid advances in Web technologies are promoting the development of new pedagogic models based on virtual teaching. To achieve this personalized services are necessary to provide the users with relevant information, according to their preferences and needs. Recommender systems can be used in an academic environment to improve and assist users in their teaching-learning processes. In this paper we propose a fuzzy linguistic recommender system to facilitate learners the access to e-learning resources interesting for them. By suggesting didactic resources according to the learner’s specific needs, a relevance-guided learning is encouraged, influencing directly the teaching-learning process. We propose the combination of the relevance degree of a resource for a user with its quality in order to generate more profitable and accurate recommendations. In addition to that, we present a computer-supported learning system to teach students the principles and concepts of recommender systems.

Keywords

Recommender system Teaching and learning Fuzzy linguistic modeling 

Notes

Acknowledgments

This paper has been developed with the financing of Projects UJA2013/08/41, TIN2013-40658-P, TIC5299, TIC-5991, TIN2012-36951 co-financed by FEDER and TIC6109.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Carlos Porcel
    • 1
  • Maria Jesús Lizarte
    • 2
  • Juan Bernabé-Moreno
    • 2
  • Enrique Herrera-Viedma
    • 2
  1. 1.Departament of Computer ScienceUniversity of JaénJaénSpain
  2. 2.Department of Computer Science and Artificial IntelligenceUniversity of GranadaGranadaSpain

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