Ontology Supported Hybrid Recommender System With Threshold Based Nearest Neighbourhood Approach

Abstract

Traditional e-learning systems lack the personalization feature to guide learners for selecting the most suitable courses needed. Choosing appropriate courses in the seminal years is important for a future learner who depends on such decisions, as selecting the wrong courses means a mismatch between learner's capability and personal interests. Therefore, a recommender system was developed to suggest and direct the students in selecting the appropriate courses. This study presents algorithms to personalize courses for scholars based on their interests to make learning effective and more productive. The hybrid methodology has been used to retrieve useful information and make accurate recommendations to help learners to increase their performance and improve their satisfaction level. The results suggest that a hybrid approach is better as it will enjoy all the advantages of the individual recommender systems and mitigate their limitations. A threshold-based nearest neighborhood approach will further strengthen the proposed system by finding a similar learner for targeted learners.