Advertisement

Discovering of Users’ Interests Evolution Patterns for Learning Goals Recommendation

  • Witold Abramowicz
  • Jacek Małyszko
  • Dawid Grzegorz Węckowski
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 90)

Abstract

Building and utilizing users’ models to the end of better customized education products, especially in e-learning, is beneficial for both users and companies they work for. However, there is no general solution enhancing users’ choices concerning their long–term learning goals. In this paper, we introduce a method, that trough analysis of time–variable user models provides a structured collection of information on common patterns of users’ interests changes. Based on that, predictions can be made to reveal possible future changes in the fields of users’ interests. We argue, that such predictions can be helpful in learning goals recommendation.

Keywords

lifelong learning recommender systems collaborative filtering e-learning 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Zaíane, O.R.: Building a recommender agent for e-learning systems. In: Proceedings of the International Conference on Computers in Education, ICCE 2002, pp. 55–59. IEEE Computer Society, Washington, DC (2002)CrossRefGoogle Scholar
  2. 2.
    Kelly, D.: Dual Perceptions of HRD: Issues for Policy: SME’s, Other Constituencies, and the Contested Definitions of Human Resource Development (2001)Google Scholar
  3. 3.
    Sterbini, A., Temperini, M.: Adaptive construction and delivery of web-based learning paths. In: Proceedings of the 39th IEEE International Conference on Frontiers in Education Conference, FIE 2009, pp. 1223–1228. IEEE Press, Piscataway (2009)Google Scholar
  4. 4.
    Abramowicz, W., Bukowska, E., Kaczmarek, M., Starzecka, M.: Semantic-enabled efficient and scalable retrieval of experts. In: International Conference on Information, Process, and Knowledge Management, eKNOW 2011 (2011)Google Scholar
  5. 5.
    Drachsler, H., Hummel, H., Koper, R.: Recommendations for learners are different: Applying memory-based recommender system techniques to lifelong learning. In: Proceedings of the EC-TEL Conference, Crete, Greece, Citeseer (2007)Google Scholar
  6. 6.
    Cacheda, F., Carneiro, V., Fernández, D., Formoso, V.: Comparison of collaborative filtering algorithms: Limitations of current techniques and proposals for scalable, high-performance recommender systems. ACM Trans. Web 5, 2:1–2:33 (2011)CrossRefGoogle Scholar
  7. 7.
    Resnick, P., Varian, H.R.: Recommender systems. Commun. ACM 40, 56–58 (1997)CrossRefGoogle Scholar
  8. 8.
    Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: Grouplens: an open architecture for collaborative filtering of netnews. In: Proceedings of the 1994 ACM Conference on Computer Supported Cooperative Work, CSCW 1994, pp. 175–186. ACM, New York (1994)CrossRefGoogle Scholar
  9. 9.
    Sarwar, B., Karypis, G., Konstan, J., Reidl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, WWW 2001, pp. 285–295. ACM, New York (2001)Google Scholar
  10. 10.
    Treerattanapitak, K., Jaruskulchai, C.: Membership enhancement with exponential fuzzy clustering for collaborative filtering. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds.) ICONIP 2010, Part I. LNCS, vol. 6443, pp. 559–566. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  11. 11.
    Abramowicz, W.: Filtrowanie Informacji. Wydawnictwo Akademii Ekonomicznej w Poznaniu (2008)Google Scholar
  12. 12.
    Liu, N.N., Zhao, M., Xiang, E., Yang, Q.: Online evolutionary collaborative filtering. In: Proceedings of the Fourth ACM Conference on Recommender Systems, RecSys 2010, pp. 95–102. ACM, New York (2010)Google Scholar
  13. 13.
    Hijikata, Y., Shimizu, T., Nishida, S.: Discovery-oriented collaborative filtering for improving user satisfaction. In: Proceedings of the 14th International Conference on Intelligent User Interfaces, IUI 2009, pp. 67–76. ACM, New York (2009)Google Scholar
  14. 14.
    Santos, O., Boticario, J., Baldiris, S., Moreno, G., Huerva, D., Fabregat, R.: Recommender Systems for Lifelong Learning inclusive scenarios. In: Workshop on Recommender Systems, Citeseer, Citeseer, p. 45 (2008)Google Scholar
  15. 15.
    Janssen, J., Tattersall, C., Waterink, W., van den Berg, B., van Es, R., Bolman, C., Koper, R.: Self-organising navigational support in lifelong learning: How predecessors can lead the way. Comput. Educ. 49, 781–793 (2007)CrossRefGoogle Scholar
  16. 16.
    Wang, T.I., Wang, K.T., Huang, Y.M.: Using a style-based ant colony system for adaptive learning. Expert Systems with Applications 34(4), 2449–2464 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Witold Abramowicz
    • 1
  • Jacek Małyszko
    • 1
  • Dawid Grzegorz Węckowski
    • 1
  1. 1.Poznań University of EconomicsPoznańPoland

Personalised recommendations