HYBRID METHOD USING NON-NEGATIVE MATRIX FACTORIZATION AND KEYWORD-BASED FILTERING FOR RECOMMENDER SYSTEM IN MOOCS
Abstract
Massive Open Online Courses (MOOCs), introduced by Dave Cormier in 2008, have revolutionized education by providing widespread access to open and participatory online learning. While MOOCs offer broad access and flexibility in learning, users often encounter challenges in selecting appropriate courses. This leads to high dropout rates. To address this issue, this research develops a recommendation system employing the Weighted Hybrid method that combines Non-Negative Matrix Factorization (NMF) and Keyword-Based Filtering (KBF). The primary objective of the research is to enhance the accuracy of course recommendations on MOOCs. The findings of this study demonstrate that the Weighted Hybrid method, integrating NMF and KBF, successfully attained a Mean Average Precision (MAP) of 0.1963. This figure signifies an improvement compared to the MAP value of 0.1855 achieved in prior research. This method effectively addresses challenges such as cold start and sparsity, while also improving scalability. Consequently, the Weighted Hybrid approach holds promise for improving the quality of recommendations, enhancing the user's learning experience, and potentially reducing dropout rates in MOOCs.
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