2022 •
E-Learning Course Recommender System Using Collaborative Filtering Models
Authors:
Kalyan Kumar Jena, Sourav Kumar Bhoi, Tushar Kanta Malik, Kshira Sagar Sahoo, N Z Jhanjhi, Sajal Bhatia, Fathi Amsaad
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 (...) 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.(Read More)
Kalyan Kumar Jena, Sourav Kumar Bhoi, Tushar Kanta Malik, Kshira Sagar Sahoo, N Z Jhanjhi, Sajal Bha (...)
Electronics ·
2022
N/A
We have placed cookies on your device to help make this website and the services we offer better. By using this site, you agree to the use of cookies. Learn more