Abstract
In management education programmes today, students face a difficult time in choosing electives as the number of electives available are many. As the range and diversity of different elective courses available for selection have increased, course recommendation systems that help students in making choices about courses have become more relevant. In this paper we extend the concept of collaborative filtering approach to develop a course recommendation system. The proposed approach provides student an accurate prediction of the grade they may get if they choose a particular course, which will be helpful when they decide on selecting elective courses, as grade is an important parameter for a student while deciding on an elective course. We experimentally evaluate the collaborative filtering approach on a real life data set and show that the proposed system is effective in terms of accuracy.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Linden, G., Smith, B., York, J.: Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Computing 7(1), 76–80 (2003)
Miller, B., Albert, I., Lam, S., Konstan, J., Riedl, J.: Movielens unplugged: experiences with a recommender system on four mobile devices. In: The Proceedings of the Seventeenth Annual Human-Computer Interaction Conference (2003)
Ekdahl, M., Lindström, S., Svensson, C.: A Student Course Recommender. Master of Science Programme Thesis, Lulea University of Technology, Department of Computer Science and Electrical Engineering/Division of Computer Science and Networking (2002)
Simbi, P.: Course Recommender. Senior Thesis, Princeton University (2003)
Breese, J., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: The Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, pp. 43–52 (1998)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering of recommendation algorithms. In: The Proceedings of the Tenth International WWW Conference, Hong Kong (2001)
Murthy, S., Kasif, S., Salzberg, S., Beigel, R.: OC1: randomized induction of oblique decision trees. In: The Proceedings of the Eleventh National Conference on Artificial Intelligence, pp. 322–327 (1993)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc., San Francisco (1993)
Chen, C.M., Lee, H.M., Chen, Y.H.: Personalized e-learning system using item response theory. Computers and Education 44(3), 237–255 (2005)
Sandvig, J., Burke, R.: AACORN: A CBR recommender for academic advising. Technical Report TR05-015, DePaul University, Chicago, USA (2006)
Farzan, R., Brusilovsky, P.: Social navigation support in a course recommendation system. In: Wade, V.P., Ashman, H., Smyth, B. (eds.) AH 2006. LNCS, vol. 4018, pp. 91–100. Springer, Heidelberg (2006)
Bendakir, N., Aimeur, E.: Using association rules for course recommendation. In: Proceedings of the AAAI Workshop on Educational Data Mining, pp. 31–40 (2006)
O’Mahony, M.P., Smyth, B.: A recommender system for on-line course enrollment: an initial study. In: The Proceedings of the ACM Conference on Recommender Systems, pp. 133–136. ACM, New York (2007)
Zhang, X.: Civil Engineering professional courses collaborative recommendation system based on network. In: The Proceedings of the First International Conference on Information Science and Engineering (2009)
Liu, J., Wang, X., Liu, X., Yang, F.: Analysis and design of personalized recommendation system for university physical education. In: The Proceedings of the International Conference on Networking and Digital Society (2010)
Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using collaborative filtering to weave an information tapestry. Communications of ACM 35(12), 61–70 (1992)
Good, N., Schafer, J.B., Konstan, J.A., Borchers, A., Sarwar, B., Herlocker, J.L., Riedl, J.: Combining collaborative filtering with personal agents for better recommendations. In: The Proceedings of the Sixteenth National Conference on Artificial Intelligence and The Eleventh Innovative Applications of Artificial Intelligence Conference Innovative Applications of Artificial Intelligence, Orlando, Florida, United States, pp. 439–446 (1999)
Ali, K., Van Stam, W.: TiVo: making show recommendations using a distributed collaborative filtering architecture. In: The Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2004)
CDNow retail website, http://www.cdnow.com
Netflix, Inc., http://www.netflix.com
Resnick, P., Iakovou, N., Suchak, M., Bergstrom, P., Riedl, J.: GroupLens: an open architecture for collaborative filtering of netnews. In: The Proceedings of the ACM Conference on Computer Supported Cooperative Work, pp. 175–186. ACM, Chapel Hill (1994)
Konstan, J.A., Miller, B.N., Maltz, D., Herlocker, J.L., Gordon, L.R., Riedl, J.: GroupLens: Applying collaborative filtering to usenet news. Communications of the ACM 40(3), 77–87 (1997)
Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithm framework for performing collaborative filtering. In: The Proceedings of SIGIR, pp. 77–87. ACM, New York (1999)
Deshpande, M., Karypis, G.: Item-based top-N recommendation algorithms. ACM Transactions on Information Systems 22(1), 142–177 (2004)
Shardanand, U., Maes, P.: Social information filtering: algorithms for automating word of mouth. Human Factors in Computing Systems (1995)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ray, S., Sharma, A. (2011). A Collaborative Filtering Based Approach for Recommending Elective Courses. In: Dua, S., Sahni, S., Goyal, D.P. (eds) Information Intelligence, Systems, Technology and Management. ICISTM 2011. Communications in Computer and Information Science, vol 141. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19423-8_34
Download citation
DOI: https://doi.org/10.1007/978-3-642-19423-8_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-19422-1
Online ISBN: 978-3-642-19423-8
eBook Packages: Computer ScienceComputer Science (R0)