A K-Nearest Neighbour Algorithm-Based Recommender System for the Dynamic Selection of Elective Undergraduate Courses
International Journal of Data Science and Analysis
Volume 5, Issue 6, December 2019, Pages: 128-135
Received: Oct. 15, 2019;
Accepted: Nov. 14, 2019;
Published: Nov. 21, 2019
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Adewale Opeoluwa Ogunde, Department of Computer Science, Redeemer’s University, Ede, Nigeria
Emmanuel Ajibade, Department of Computer Science, Redeemer’s University, Ede, Nigeria
The task of selecting a few elective courses from a variety of available elective courses has been a difficult one for many students over the years. In many higher institutions, guidance and counsellors or level advisers are usually employed to assist the students in picking the right choice of courses. In reality, these counsellors and advisers are most times overloaded with too many students to attend to, and sometimes they do not have enough time for the students. Most times, the academic strength of the student based on past results are not considered in the new choice of electives. Recommender systems implement advanced data analysis techniques to help users find the items of their interest by producing a predicted likeliness score or a list of top recommended items for a given active user. Therefore, in this work, a collaborative filtering-based recommender system that will dynamically recommend elective courses to undergraduate students based on their past grades in related courses was developed. This approach employed the use of the k-nearest Neighbour algorithm to discover hidden relationships between the related courses passed by students in the past and the currently available elective courses. Real-life students’ results dataset was used to build and test the recommendation model. The new model was found to outperform existing results in the literature. The developed system will not only improve the academic performance of students; it will also help reduce the workload on the level advisers and school counsellors.
Adewale Opeoluwa Ogunde,
A K-Nearest Neighbour Algorithm-Based Recommender System for the Dynamic Selection of Elective Undergraduate Courses, International Journal of Data Science and Analysis.
Vol. 5, No. 6,
2019, pp. 128-135.
B. Hana, Course Enrollment Recommender System. Proceedings of the 9th International Conference on Educational Data Mining, (2016), pp 312-317.
A. Al-Badarenah, J. Alsakran, An Automated Recommender System for Course. International Journal of Advanced Computer Science and Applications, Vol. 7, No. 3, (2016), pp 166-175.
K. D. Gupta, A Survey on Recommender System, International Journal of Applied Engineering Research, ISSN 0973-4562 Volume 14, Number 14, (2019) pp. 3274-3277.
P. Melville, V. Sindhwani, Recommender Systems, IBM T. J. Watson Research Center, Yorktown Heights, NY 10598, (2010), pp 1-18.
D. Siddharth, Weighted K Nearest Neighbour, CS 8751, (2009).
F. O. Isinkaye, Y. O. Folajimi, B. A. Ojokoh, Recommendation systems: Principles, methods and evaluation. Egyptian Informatics Journal, Vol. 16, (2015), pp 261-273.
Z. Xu, C. Chen, T. Lukasiewicz, Y. Miao, X. Meng, “Tag-Aware Personalized Recommendation using a Deep-Semantic Similarity Model with Negative Sampling,” Proc. 25th ACM International on Conference on Information and Knowledge Management, (2016), pp. 1921–1924.
A. M. Elkahky, Y. Song, X. He, “A Multi-View Deep Learning Approach for Cross Domain User Modeling in Recommendation Systems,” Proc. 24th International Conference on World Wide Web, (2015), pp. 278–288.
R. Sanjog, A. Sharma, A collaborative filtering based approach for recommending elective courses, Springer-Verlag Berlin Heidelberg, (2011), pp. 30-39.
G. K. Dziugaite, D. M. Roy, “Neural Network Matrix Factorization,” arXiv 103 preprint arXiv: 1511.06443, 2015.
X. He, L. Liao, H. Zhang, L. Y. Nie, X. Hu, T. S. Chua, “Neural Collaborative Filtering,” Proc. 26th International Conference on World Wide Web, (2017), pp. 173–182.
Y. Ouyang, W. Liu, W. Rong, Z. Xiong, “Autoencoder-Based Collaborative Fitering,” 21st International Conference on Neural Information Processing, (2014), pp. 284-291.
P. Covington, J. Adams, E. Sargin, “Deep Neural Networks for YouTube Recommendations,” Proc. 10th ACM Conference on Recommender Systems, (2016), pp. 191–198.
N. B. Samrit, A. Thomas, A Recommendation System for Prediction of Elective Subjects, International Journal for Research in Applied Science & Engineering Technology (IJRASET), Volume 5 Issue 4, (2017), pp 36-43.
C. K. Hsieh, L. Yang, Y. Cui, T. Y Lin, S. Belongie, D. Estrin, “Collaborative metric learning,” Proc. 26th International Conference on World Wide Web, (2017), pp. 193–201.
H. F. Unelsrød, Design and Evaluation of a Recommender System for Course Selection, Published Master of Science in Computer Science Project, Norwegian University of Science and Technology Department of Computer and Information Science, (2011), pp 1-43.
R. He, J. McAuley, “VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback,” Proc. 13th AAAI Conference on Artificial Intelligence, (2016), pp. 144–150.
B. Bai, Y. Fan, W. Tan, J. Zhang, “DLTSR: A Deep Learning Framework for Recommendation of Long-tail Web Services,” IEEE Transactions on Services Computing, (2017), pp. 1-11.
T. Donkers, B. Loepp, J. Ziegler, “Sequential User-Based Recurrent Neural Network Recommendations,” Proc. 11th ACM Conference on Recommender Systems, (2017), pp. 152–160.
A. O. Ogunde, J. O. Idialu, A recommender system for selecting potential industrial training organizations. Engineering Reports. 2019; e12046. https://doi.org/10.1002/eng2.12046.
P. P. Wadekar, Y. P. Pillai, M. U. Roy, N. Phadnis, Placement Predictor and Course Recommender System, International Research Journal of Engineering and Technology, Volume: 05 Issue: 03, (2018), pp 3960-3965.
S. Shahab, "Next Level: A Course Recommender System Based On Career Interests" (2019). Master's Projects. 684. DOI: https://doi.org/10.31979/etd.z4v2-k6gg.
H. Thanh-Nhan, H. Nguyen, N. Thai-Nghe, Methods for building course recommendation systems, Eighth International Conference on Knowledge and Systems Engineering (KSE), 2016, DOI: 10.1109/KSE.2016.7758047.