E-Learning Course Recommender System Using Collaborative Filtering Models

Authors

Kalyan Kumar Jena, Parala Maharaja Engineering College, India
Sourav Kumar Bhoi, Parala Maharaja Engineering College, India
Tushar Kanta Malik, Parala Maharaja Engineering College, India
Kshira Sagar Sahoo, SRM University, India and Umeå University, Sweden
N. Z. Jhanjhi, SCS Taylor’s University, Malaysia
Sajal Bhatia, Sacred Heart UniversityFollow
Fathi Amsaad, Wright State University

Document Type

Peer-Reviewed Article

Publication Date

2023

Abstract

e-Learning is a sought-after option for learners during pandemic situations. In e-Learning platforms, there are many courses available, and the user needs to select the best option for them. Thus, recommender systems play an important role to provide better automation services to users in making course choices. It makes recommendations for users in selecting the desired option based on their preferences. This system can use machine intelligence (MI)-based techniques to carry out the recommendation mechanism. Based on the preferences and history, this system is able to know what the users like most. In this work, a recommender system is proposed using the collaborative filtering mechanism for e-Learning course recommendation. This work is focused on MI-based models such as K-nearest neighbor (KNN), Singular Value Decomposition (SVD) and neural network–based collaborative filtering (NCF) models. Here, one lakh of Coursera’s course review dataset is taken from Kaggle for analysis. The proposed work can help learners to select the e-Learning courses as per their preferences. This work is implemented using Python language. The performance of these models is evaluated using performance metrics such as hit rate (HR), average reciprocal hit ranking (ARHR) and mean absolute error (MAE). From the results, it is observed that KNN is able to perform better in terms of higher HR and ARHR and lower MAE values as compared to other models.

Comments

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.

DOI

10.3390/electronics12010157

Recommended Citation

Jena, K. K., Bhoi, S. K., Malik, T. K., Sahoo, K. S., Jhanjhi, N. Z., Bhatia, S., & Amsaad, F. (2023). E-learning course recommender system using collaborative filtering models. Electronics, 12(1), 157. Doi.org/10.3390/electronics12010157

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.