Skip to main content

2018 | OriginalPaper | Buchkapitel

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

verfasst von : Krenare Pireva, Petros Kefalas

Erschienen in: Intelligent Distributed Computing XI

Verlag: Springer International Publishing

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

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.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat 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) 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)
2.
Zurück zum Zitat Felder, R.M., Brent, R.: Understanding student differences. J. Eng. Educ. 94(1), 57–72 (2005)CrossRef Felder, R.M., Brent, R.: Understanding student differences. J. Eng. Educ. 94(1), 57–72 (2005)CrossRef
3.
Zurück zum Zitat Fu, X., Budzik, J., Hammond, K.J.: Mining navigation history for recommendation. In: Proceedings of IC on Intelligent UI, pp. 106–112. ACM (2000) Fu, X., Budzik, J., Hammond, K.J.: Mining navigation history for recommendation. In: Proceedings of IC on Intelligent UI, pp. 106–112. ACM (2000)
4.
Zurück zum Zitat 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) 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)
5.
Zurück zum Zitat Gomez-Uribe, C.A., Hunt, N.: The netflix recommender system: algorithms, business value, and innovation. ACM TMIS 6(4), 13 (2016) Gomez-Uribe, C.A., Hunt, N.: The netflix recommender system: algorithms, business value, and innovation. ACM TMIS 6(4), 13 (2016)
6.
Zurück zum Zitat 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) 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)
7.
Zurück zum Zitat Li, X., Chang, S.K.: A personalized e-learning system based on user profile constructed using information fusion. In: DMS, pp. 109–114 (2005) Li, X., Chang, S.K.: A personalized e-learning system based on user profile constructed using information fusion. In: DMS, pp. 109–114 (2005)
8.
Zurück zum Zitat Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)CrossRef Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)CrossRef
9.
Zurück zum Zitat Lu, J.: Personalized e-learning material recommender system. In: IC on information technology for application, pp. 374–379 (2004) Lu, J.: Personalized e-learning material recommender system. In: IC on information technology for application, pp. 374–379 (2004)
10.
Zurück zum Zitat Pireva, K., Kefalas, P., Cowling, A.: A Review of Automated Planning and its Application to Cloud e-Learning. Work in progress, Paper Submitted (2017) Pireva, K., Kefalas, P., Cowling, A.: A Review of Automated Planning and its Application to Cloud e-Learning. Work in progress, Paper Submitted (2017)
11.
Zurück zum Zitat Pireva, K., Kefalas, P., Stamatopoulou, I.: Representation of Learning Objects in Cloud e-Learning. Work in progress, Paper Submitted (2017) Pireva, K., Kefalas, P., Stamatopoulou, I.: Representation of Learning Objects in Cloud e-Learning. Work in progress, Paper Submitted (2017)
12.
Zurück zum Zitat Pireva, K., Kefalas, P.: The use of multi agent systems in cloud e-learning. In: Doctoral Student Conference on ICT, pp. 324–336 (2015) Pireva, K., Kefalas, P.: The use of multi agent systems in cloud e-learning. In: Doctoral Student Conference on ICT, pp. 324–336 (2015)
13.
Zurück zum Zitat Polettini, N.: The vector space model in information retrieval-term weighting problem. Entropy, 1–9 (2004) Polettini, N.: The vector space model in information retrieval-term weighting problem. Entropy, 1–9 (2004)
14.
Zurück zum Zitat Portugal, I., Alencar, P., Cowan, D.: The use of machine learning algorithms in recommender systems: a systematic review (2015). arXiv:1511.05263 Portugal, I., Alencar, P., Cowan, D.: The use of machine learning algorithms in recommender systems: a systematic review (2015). arXiv:​1511.​05263
15.
Zurück zum Zitat Ricci, F., Rokach, L., Shapira, B.: Introduction to Recommender Systems Handbook. Springer (2011) Ricci, F., Rokach, L., Shapira, B.: Introduction to Recommender Systems Handbook. Springer (2011)
16.
Zurück zum Zitat Rokach, L., Maimon, O.: Clustering Methods. In: Data Mining and Knowledge Discovery Handbook, pp. 321–352. Springer (2005) Rokach, L., Maimon, O.: Clustering Methods. In: Data Mining and Knowledge Discovery Handbook, pp. 321–352. Springer (2005)
17.
Zurück zum Zitat Schafer, J.B., Konstan, J., Riedl, J.: Recommender systems in e-commerce. In: Proceedings of Conference on Electronic Commerce, pp. 158–166. ACM (1999) Schafer, J.B., Konstan, J., Riedl, J.: Recommender systems in e-commerce. In: Proceedings of Conference on Electronic Commerce, pp. 158–166. ACM (1999)
18.
Zurück zum Zitat Singh, V.K., Singh, V.K.: Vector space model: an information retrieval system. Int. J. Adv. Eng. Res. Stud. 141, 143 (2015) Singh, V.K., Singh, V.K.: Vector space model: an information retrieval system. Int. J. Adv. Eng. Res. Stud. 141, 143 (2015)
19.
Zurück zum Zitat Stern, D.H., Herbrich, R., Graepel, T.: Matchbox: large scale online bayesian recommendations. In: Proceedings of IC on WWW, pp. 111–120. ACM (2009) Stern, D.H., Herbrich, R., Graepel, T.: Matchbox: large scale online bayesian recommendations. In: Proceedings of IC on WWW, pp. 111–120. ACM (2009)
20.
Zurück zum Zitat Willett, P.: The porter stemming algorithm: then and now. Program 40(3), 219–223 (2006)CrossRef Willett, P.: The porter stemming algorithm: then and now. Program 40(3), 219–223 (2006)CrossRef
Metadaten
Titel
A Recommender System Based on Hierarchical Clustering for Cloud e-Learning
verfasst von
Krenare Pireva
Petros Kefalas
Copyright-Jahr
2018
DOI
https://doi.org/10.1007/978-3-319-66379-1_21