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)


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.


Recommender system Teaching and learning Fuzzy linguistic modeling 



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.


  1. 1.
    Alonso, S., Chiclana, F., Herrera, F., Herrera-Viedma, E., Alcalá-Fdez, J., Porcel, C.: A consistency-based procedure to estimating missing pairwise preference values. Int. J. Intell. Syst. 23, 155–175 (2008)CrossRefzbMATHGoogle Scholar
  2. 2.
    Burke, R., Felfernig, A., Göker, M.: Recommender systems: an overview. AI Mag. 32, 13–18 (2011)CrossRefGoogle Scholar
  3. 3.
    Dascalu, M., Bodea, C., Moldoveanu, A., Mohora, A., Lytras, M., Ordoñez de Pablos, P.: A recommender agent based on learning styles for better virtual collaborative learning experiences. Comput. Hum. Behav. 45, 243–253 (2015)Google Scholar
  4. 4.
    Goga, M., Kuyoro, S., Goga, N.: A recommender for improving the student academic performance. Procedia - Soc. Behav. Sci. 180, 1481–1488 (2015)CrossRefGoogle Scholar
  5. 5.
    Herrera, F., Martínez, L.: A 2-tuple fuzzy linguistic representation model for computing with words. IEEE Trans. Fuzzy Syst. 8(6), 746–752 (2000)CrossRefGoogle Scholar
  6. 6.
    Herrera, F., Martínez, L.: A model based on linguistic 2-tuples for dealing with multigranularity hierarchical linguistic contexts in multiexpert decision-making. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 31(2), 227–234 (2001)CrossRefGoogle Scholar
  7. 7.
    Kearsley, G.: Online Education: Learning and Teaching in Cyberspace. Wadsworth, Belmont (2000)Google Scholar
  8. 8.
    Mamat, N., Yusof, N.: Learning style in a personalized collaborative learning framework. Procedia - Soc. Behav. Sci. 103, 586–594 (2013)CrossRefGoogle Scholar
  9. 9.
    Massa, P., Avesani, P.: Trust metrics in recommender systems. In: Golbeck, J. (ed.) Computing with Social Trust, pp. 259–285. Springer, London (2009)CrossRefGoogle Scholar
  10. 10.
    Popovici, A., Mironov, C.: Students’ perception on using elearning technologies. Procedia - Soc. Behav. Sci. 180, 1514–1519 (2015)Google Scholar
  11. 11.
    Porcel, C., Herrera-Viedma, E.: Dealing with incomplete information in a fuzzy linguistic recommender system to disseminate information in university digital libraries. Knowl.-Based Syst. 23, 32–39 (2010)CrossRefGoogle Scholar
  12. 12.
    Tejeda-Lorente, A., Porcel, C., Peis, E., Sanz, R., Herrera-Viedma, E.: A quality based recommender system to disseminate information in a university digital library. Inf. Sci. 261, 52–69 (2014)CrossRefGoogle Scholar
  13. 13.
    Zadeh, L.: The concept of a linguistic variable and its applications to approximate reasoning. Part I, Inf. Sci. 8, 199–249 (1975). Part II, Inf. Sci. 8, 301–357 (1975). Part III, Inf. Sci. 9, 43–80 (1975)Google Scholar

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

Personalised recommendations