A Recommender System Based on Hierarchical Clustering for Cloud e-Learning

Part of the Studies in Computational Intelligence book series (SCI, volume 737)


Cloud e-Learning (CeL) is a new paradigm for e-Learning, aiming towards using any possible learning object from the cloud in a smart way and generate a personalised learning path for individual learners. An issue that appears before the generation of the learning path through automated planning, is to filter a pool of resources that are relevant to the learners profile and desires in order to enhance their knowledge and skills at a higher cognitive level. In this paper, we present a Recommender System for Cloud e-Leaning (CeLRS) that uses hierarchical clustering to select the most appropriate resources and utilise a vector space model to rank these resources in order of relevance for any individual learner. We discuss the issues raised and we demonstrate how CeLRS works.


Intelligent e-learning Recommender systems Hierarchical clustering Personalised learning 


  1. 1.
    Buzydlowski, J.W., Lin, X., Zhang, M., Cassel, L.N.: A comparison of a hierarchical tree to an associative map interface for the selection of classification terms. Proc. Am. Soc. IST 50(1), 1–4 (2013)Google Scholar
  2. 2.
    Felder, R.M., Brent, R.: Understanding student differences. J. Eng. Educ. 94(1), 57–72 (2005)CrossRefGoogle Scholar
  3. 3.
    Fu, X., Budzik, J., Hammond, K.J.: Mining navigation history for recommendation. In: Proceedings of IC on Intelligent UI, pp. 106–112. ACM (2000)Google Scholar
  4. 4.
    Fung, B.C., Wang, K., Ester, M.: Hierarchical document clustering using frequent itemsets. In: Proceedings of IC on Data Mining, pp. 59–70. SIAM (2003)Google Scholar
  5. 5.
    Gomez-Uribe, C.A., Hunt, N.: The netflix recommender system: algorithms, business value, and innovation. ACM TMIS 6(4), 13 (2016)Google Scholar
  6. 6.
    Jabakji, A., Dag, H.: Improving item-based recommendation accuracy with user’s preferences on apache mahout. In: 2016 IEEE International Conference on Big Data (Big Data), pp. 1742–1749. IEEE (2016)Google Scholar
  7. 7.
    Li, X., Chang, S.K.: A personalized e-learning system based on user profile constructed using information fusion. In: DMS, pp. 109–114 (2005)Google Scholar
  8. 8.
    Linden, G., Smith, B., York, J.: recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)CrossRefGoogle Scholar
  9. 9.
    Lu, J.: Personalized e-learning material recommender system. In: IC on information technology for application, pp. 374–379 (2004)Google Scholar
  10. 10.
    Pireva, K., Kefalas, P., Cowling, A.: A Review of Automated Planning and its Application to Cloud e-Learning. Work in progress, Paper Submitted (2017)Google Scholar
  11. 11.
    Pireva, K., Kefalas, P., Stamatopoulou, I.: Representation of Learning Objects in Cloud e-Learning. Work in progress, Paper Submitted (2017)Google Scholar
  12. 12.
    Pireva, K., Kefalas, P.: The use of multi agent systems in cloud e-learning. In: Doctoral Student Conference on ICT, pp. 324–336 (2015)Google Scholar
  13. 13.
    Polettini, N.: The vector space model in information retrieval-term weighting problem. Entropy, 1–9 (2004)Google Scholar
  14. 14.
    Portugal, I., Alencar, P., Cowan, D.: The use of machine learning algorithms in recommender systems: a systematic review (2015). arXiv:1511.05263
  15. 15.
    Ricci, F., Rokach, L., Shapira, B.: Introduction to Recommender Systems Handbook. Springer (2011)Google Scholar
  16. 16.
    Rokach, L., Maimon, O.: Clustering Methods. In: Data Mining and Knowledge Discovery Handbook, pp. 321–352. Springer (2005)Google Scholar
  17. 17.
    Schafer, J.B., Konstan, J., Riedl, J.: Recommender systems in e-commerce. In: Proceedings of Conference on Electronic Commerce, pp. 158–166. ACM (1999)Google Scholar
  18. 18.
    Singh, V.K., Singh, V.K.: Vector space model: an information retrieval system. Int. J. Adv. Eng. Res. Stud. 141, 143 (2015)Google Scholar
  19. 19.
    Stern, D.H., Herbrich, R., Graepel, T.: Matchbox: large scale online bayesian recommendations. In: Proceedings of IC on WWW, pp. 111–120. ACM (2009)Google Scholar
  20. 20.
    Willett, P.: The porter stemming algorithm: then and now. Program 40(3), 219–223 (2006)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.South-East European Research CenterThessalonikiGreece
  2. 2.The University of Sheffield International FacultyThessalonikiGreece

Personalised recommendations