Original scientific paper
https://doi.org/10.32985/ijeces.15.4.5
DHM-OCR: A Deep Hybrid Model for Online Course Recommendation and Sustainable Development of Education
Sagar Mekala
orcid.org/0000-0003-2909-500X
; Department of Computer Science and Engineering, CVR College of Engineering, Telangana, Hyderabad, India
*
Padma TNS
; Assistant Professor, Department of CSE- Data Science, Sreenidhi Institute of Science and Technology, Hyderabad, India
Rama Rao Tandu
; Department of Computer Science and Engineering, CVR College of Engineering, Telangana, Hyderabad, India
* Corresponding author.
Abstract
In recent years, there has been an increase in online education resources to help learners improve their skills. However, it is difficult to select the right course from available online education resources due to the demands and needs of learners with different knowledge domains. To solve this problem, an online course recommendation model has the important factor of enhancing learner's knowledge. Many existing recommendation systems (RS) use collaborative filtering (CF) to recommend courses to learners. The major problems with the Collaborative Filtering Recommendation System (CFRS) are the sparse preferences and the scalability of the data. According to the similarity of items, many recommendation models are proposed and developed, but none of these provide suggestions to users without their associations or preferences. We propose a deep hybrid model-online course recommendation (DHM-OCR) that uses high-level learner behavior and course objective features. We demonstrate the improvements and efficiency of the model for suggesting online e-learning courses. According to the analysis and evaluation results, it seems that our DHM-OCR outperforms the parallel research recommendation system. Experimental findings from online course data reveal that the suggested model and approach significantly improve classification accuracy and training efficiency, particularly limited available data.
Keywords
Recommendation System; Convolutional Neural Network; Content Based Recommendation System; Collaborative Filtering Recommendation System; Deep Hybrid Model; Ranking; Recurrent Neural Networks; Similarity; Preferences;
Hrčak ID:
315580
URI
Publication date:
28.3.2024.
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