A University Recommender System based on Students Profiles
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Date
2023-06-10
Journal Title
Journal ISSN
Volume Title
Publisher
University of M'sila
Abstract
Although universities are known for their pursuit of knowledge and research, they do not fully
exploit the potential of the vast amounts of data they generate and collect. One consequence of this
is that future students face a daunting university selection process. This work aims to devise a
Recommender System (RS) that can automatically propose the best universities for students which
can simplify their selection process and raise their chances of admission.
To evaluate the effectiveness and feasibility of our proposed RS, we collect relevant data from
various sources. This data includes student profiles, academic qualifications, preferences, and
other factors. We analyze this data to evaluate the performance of our recommender system and
its ability to generate accurate and useful recommendations.
Our obtained results show that our University RS is in average effective in generating personalized
recommendations based on three powerful algorithms, namely, K-Nearest Neighbors (KNN),
Random Forest, and Support Vector Machine (SVM).
Description
Keywords
University Selection, Recommender Systems (RS), Machine Learning (ML), Data Processing.