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Research Article | Open Access |

An Automated Recommender System for Course Selection

Author 1: Amer Al-Badarenah Author 2: Jamal Alsakran
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 7, No. 3 · Published 2016 · Cited by 80

DOI: 10.14569/IJACSA.2016.070323

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Abstract

Most of electronic commerce and knowledge management` systems use recommender systems as the underling tools for identifying a set of items that will be of interest to a certain user. Collaborative recommender systems recommend items based on similarities and dissimilarities among users’ preferences. This paper presents a collaborative recommender system that recommends university elective courses to students by exploiting courses that other similar students had taken. The proposed system employs an association rules mining algorithm as an underlying technique to discover patterns between courses. Experiments were conducted with real datasets to assess the overall performance of the proposed approach.

Keywords

collaborative recommendation association rule mining data mining recommender system course selection

How to Cite this Article

Al-Badarenah, A., & Alsakran, J. (2016). An Automated Recommender System for Course Selection. International Journal of Advanced Computer Science and Applications, 7(3). https://doi.org/10.14569/IJACSA.2016.070323

Al-Badarenah, Amer, and Jamal Alsakran. "An Automated Recommender System for Course Selection." International Journal of Advanced Computer Science and Applications, vol. 7, no. 3, 2016, https://doi.org/10.14569/IJACSA.2016.070323.

@article{Al-Badarenah2016,
  title     = {An Automated Recommender System for Course Selection},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {7},
  number    = {3},
  year      = {2016},
  publisher = {The Science and Information Organization},
  author    = {Amer Al-Badarenah and Jamal Alsakran},
  doi       = {10.14569/IJACSA.2016.070323},
  url       = {https://doi.org/10.14569/IJACSA.2016.070323}
}

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Volume 17 No. 11, November 2026
Paper Submission Due October 25, 2026
Review Notification November 15, 2026
Publication Date November 30, 2026
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