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

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Intelligent Distributed Computing XI (IDC 2017)

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

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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.

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    ACM CCS,


  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. Felder, R.M., Brent, R.: Understanding student differences. J. Eng. Educ. 94(1), 57–72 (2005)

    Article  Google Scholar 

  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. 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. Gomez-Uribe, C.A., Hunt, N.: The netflix recommender system: algorithms, business value, and innovation. ACM TMIS 6(4), 13 (2016)

    Google Scholar 

  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. 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. Linden, G., Smith, B., York, J.: recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)

    Article  Google Scholar 

  9. Lu, J.: Personalized e-learning material recommender system. In: IC on information technology for application, pp. 374–379 (2004)

    Google Scholar 

  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. Pireva, K., Kefalas, P., Stamatopoulou, I.: Representation of Learning Objects in Cloud e-Learning. Work in progress, Paper Submitted (2017)

    Google Scholar 

  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. Polettini, N.: The vector space model in information retrieval-term weighting problem. Entropy, 1–9 (2004)

    Google Scholar 

  14. Portugal, I., Alencar, P., Cowan, D.: The use of machine learning algorithms in recommender systems: a systematic review (2015). arXiv:1511.05263

  15. Ricci, F., Rokach, L., Shapira, B.: Introduction to Recommender Systems Handbook. Springer (2011)

    Google Scholar 

  16. Rokach, L., Maimon, O.: Clustering Methods. In: Data Mining and Knowledge Discovery Handbook, pp. 321–352. Springer (2005)

    Google Scholar 

  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. 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. 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. Willett, P.: The porter stemming algorithm: then and now. Program 40(3), 219–223 (2006)

    Article  Google Scholar 

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Correspondence to Krenare Pireva .

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Pireva, K., Kefalas, P. (2018). A Recommender System Based on Hierarchical Clustering for Cloud e-Learning. In: Ivanović, M., Bădică, C., Dix, J., Jovanović, Z., Malgeri, M., Savić, M. (eds) Intelligent Distributed Computing XI. IDC 2017. Studies in Computational Intelligence, vol 737. Springer, Cham.

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  • Print ISBN: 978-3-319-66378-4

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