doi: 10.18178/ijiet.2026.16.4.2581
Content-Based Personalized Course Recommendation in e-Learning Ecosystems: A TF-IDF and Similarity Measures Approach
Fatima Ezzahraa El Habti1,*
,
Mohamed Chrayah2 ,
Mustafa Hiri1 ,
Noura Aknin1
- 1Laboratory of Information Technologies and Systems Modeling (TIMS), Faculty of Sciences of Tetouan, Abdelmalek Essaadi University Morocco, Tetouan, Morocco
- 2Laboratory of Information Technologies and Systems Modeling (TIMS), National School of Applied Sciences of Tetouan, Abdelmalek Essaadi University Morocco, Tetouan, Morocco
- Manuscript receivedJuly 25, 2025
- revisedSeptember 3, 2025
- acceptedNovember 17, 2025
- publishedApril 22, 2026
Abstract
The rapid expansion of online education platforms has posed significant challenges for learners in identifying courses aligned with their goals and interests. This paper proposes a novel content-based Course Recommender System (CRS) tailored for e-learning ecosystems, specifically addressing cold-start scenarios without user history. The innovation lies in integrating Term Frequency–Inverse Document Frequency (TF-IDF) and Count Vectorization with Cosine and Jaccard Similarity measures to create a balanced framework that optimizes accuracy, recall, and diversity, with Jaccard enhancing exploratory recommendations as validated by statistical analysis. Evaluated on a dataset of 3682 Udemy courses across diverse subjects (Business, Graphic Design, Musical Instruments, Web Development), the system’s performance was assessed using Precision@k, Recall@k, Mean Average Precision (MAP), Mean Reciprocal Rank (MRR), diversity index, and accuracy metrics. Results show the TF-IDF with cosine similarity model achieving 99.98% accuracy at top-10 recommendations, while Jaccard-based models enhance diversity (diversity index score of 0.85), confirming the approach’s scalability and robustness. These findings contribute to personalized course discovery in large-scale e-learning environments.
Keywords
- e-learning
- course recommender system
- Term Frequency–Inverse Document Frequency (TF-IDF)
- cosine similarity
- Jaccard similarity
How to Cite
Fatima Ezzahraa El Habti, Mohamed Chrayah, Mustafa Hiri, and Noura Aknin, "Content-Based Personalized Course Recommendation in e-Learning Ecosystems: A TF-IDF and Similarity Measures Approach," International Journal of Information and Education Technology, vol. 16, no. 4, pp. 1122-1133, 2026. https://doi.org/10.18178/ijiet.2026.16.4.2581
Copyright & License
Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).