Enhancing Hybrid Course Recommendation with Weighted Voting Ensemble Learning

Authors

  • Kyawt Kyawt San University of Information Technology
  • Hlaing Hlaing Win University of Information Technology
  • Khin Ei Ei Chaw University of Information Technology

DOI:

https://doi.org/10.62411/faith.3048-3719-55

Keywords:

Course Recommendation, Ensemble Learning, Hybrid Approach, ARHR, NDCG

Abstract

Course recommendation aims to find suitable and attractive courses for students based on their needs, playing a significant role in the curricula-variable system. However, with the abundant available courses, students often face cognitive overload when selecting the most appropriate ones. This research proposes a course recommendation system called the Enhanced Hybrid Course Recommender to address this challenge. This system uses an ensemble learning approach to combine and leverage the power of multiple machine learning classifiers, including Random Forest, Naive Bayes, and Support Vector Machine. By utilizing TF-IDF vectorization for text data transformation and label encoding for target label compatibility, this experiment significantly enhances recommendation precision and relevance, easing students' decision-making process and improving the overall quality of course recommendations. A hybrid approach is applied to improve the recommendation quality by combining predictions from all three classifiers through weighted voting. This ensemble method improves overall robustness and accuracy. This approach not only mitigates the cognitive overload faced by students but also significantly improves the quality of recommendations. Our hybrid model represents a substantial advancement in personalized course recommendation technology by demonstrating superior performance across key evaluation metrics such as accuracy, precision, recall, F1-score, ARHR, and NDCG.

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Author Biographies

Kyawt Kyawt San, University of Information Technology

Deep Learning and Information System (DLIS) Research lab, University of Information Technology, Yangon, Myanmar

Hlaing Hlaing Win, University of Information Technology

Deep Learning and Information System (DLIS) Research lab, University of Information Technology, Yangon, Myanmar

Khin Ei Ei Chaw, University of Information Technology

Deep Learning and Information System (DLIS) Research lab, University of Information Technology, Yangon, Myanmar

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Published

2025-01-20

How to Cite

[1]
K. K. San, H. H. Win, and K. E. E. Chaw, “Enhancing Hybrid Course Recommendation with Weighted Voting Ensemble Learning”, J. Fut. Artif. Intell. Tech., vol. 1, no. 4, pp. 337–347, Jan. 2025.

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