An Automated Recommender System for Course Selection

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.

Amer Al-Badarenah and Jamal Alsakran. “An Automated Recommender System for Course Selection”. International Journal of Advanced Computer Science and Applications (IJACSA) 7.3 (2016). http://dx.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},
doi = {10.14569/IJACSA.2016.070323},
url = {http://dx.doi.org/10.14569/IJACSA.2016.070323},
year = {2016},
publisher = {The Science and Information Organization},
volume = {7},
number = {3},
author = {Amer Al-Badarenah and Jamal Alsakran}
}