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Comparison of Nature Inspired Algorithms Applied in Student Courses Recommendation

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Computational Collective Intelligence. Technologies and Applications (ICCCI 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7653))

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Abstract

In this paper we present comparison of several nature inspired algorithms applied in recommendation of student courses. Nature inspired algorithms proved to be very effective in solving many optimization problems, here we show that these techniques could be successfully used in solving the problem of prediction of final grades students receives on completing university courses is able to deliver good solutions. However to apply these algorithms we need special representation of the problem appropriate for each algorithm.

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References

  1. Abbass, H.A.: Marriage in Honey Bees Optimization (MBO): A Haplometrosis Polygynous Swarming Approach. In: The Congress on Evolutionary Computation, CEC 2001 Seoul, Korea, pp. 207–214 (2001)

    Google Scholar 

  2. Colorni, A., Dorigo, M., Maoli, F., Maniezzo, V., Righini, G., Trubian, M.: Heuristics from Nature for Hard Combinatorial Optimization Problems. International Transactions in Operational Research 3(1), 1–21 (1996)

    Article  MATH  Google Scholar 

  3. Grzegorczyk, A.: Student course recommendation in the enrolment systems. Master Thesis at Wrocław University of Technology, Faculty of Computer Science and Management, supervisor. J. Sobecki (2011)

    Google Scholar 

  4. Fealko, D.R.: Evaluating Particle Swarm Intelligence Techniques for Solving University Examination Timetabling Problems. A Ph.D. dissertation Graduate School of Computer and Information Sciences Nova Southwestern University (2005)

    Google Scholar 

  5. Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.: Evaluating Collaborative Filtering Recommender Systems. ACM Transactions on Information Systems 22, 5–53 (2004)

    Article  Google Scholar 

  6. Kennedy, Eberhart, Shi: Swarm Intelligence. Morgan Kaufmann division of Academic Press (2001)

    Google Scholar 

  7. Kobsa, A., Koenemann, J., Pohl, W.: Personalized Hypermedia Presentation Techniques for Improving Online Customer Relationships. The Knowledge Eng. Review 16(2), 111–155 (2001)

    Article  MATH  Google Scholar 

  8. Mateja, Ł.: Recommendation of the elective student courses. Master Thesis at Wrocław University of Technology, Faculty of Computer Science and Management, supervisor. J. Sobecki (2011)

    Google Scholar 

  9. Mehrabian, R., Lucas, C.: A novel numerical optimization algorithm inspired from weed colonization. Ecological Informatics 1(4) (2006)

    Google Scholar 

  10. Montaner, M., Lopez, B., De La Rosa, J.L.: A Taxonomy of Recommender Agents on the Internet. Artificial Intelligence Review 19, 285–330 (2003)

    Article  Google Scholar 

  11. Ricci, F., Rokach, L., Shapira, B., Kantor, P.: Recommender Systems Handbook. Springer (2011)

    Google Scholar 

  12. Shah-Hosseini, H.: The intelligent water drops algorithm: a nature-inspired swarm-based optimization algorithm. In: dos Santos, W.P. (ed.) Evolutionary Computation, p. 572. I-Tech, Vienna (2009)

    Google Scholar 

  13. Sobecki, J.: Ant colony metaphor applied in user interface recommendation. New Generation Computing 26(3), 277–293 (2008)

    Article  Google Scholar 

  14. Sobecki, J., Tomczak, J.M.: Student Courses Recommendation Using Ant Colony Optimization. In: Nguyen, N.T., Le, M.T., Świątek, J. (eds.) Intelligent Information and Database Systems. LNCS, vol. 5991, pp. 124–133. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  15. Teodorovic, D., Davidovic, T., Selmic, M.: Bee Colony Optimization: The Applications Survey (2012), Downloded June 2012 from http://www.mi.sanu.ac.rs/~tanjad/BCO-ACM-Trans-Ver2.pdf

  16. Ujjin, S., Bentley, P.: Particle Swarm Optimization Recommender System. In: Proc. IEEE Swarm Intelligence Symposium, pp. 124–131 (2003)

    Google Scholar 

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Sobecki, J. (2012). Comparison of Nature Inspired Algorithms Applied in Student Courses Recommendation. In: Nguyen, NT., Hoang, K., Jȩdrzejowicz, P. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2012. Lecture Notes in Computer Science(), vol 7653. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34630-9_29

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  • DOI: https://doi.org/10.1007/978-3-642-34630-9_29

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34629-3

  • Online ISBN: 978-3-642-34630-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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