E-Learning Course Recommender System Using Collaborative Filtering Models

• DOI: 10.3390/electronics12010157
• Publication Date: 2022-12-30T08:14:28Z
• Authors: Kalyan Kumar Jena; Sourav Kumar Bhoi; Tushar Kanta Malik; Kshira Sagar Sahoo; N Z Jhanjhi; Sajal Bhatia; Fathi Amsaad
• Keywords: recommender system; Computer Sciences; recommender system; machine intelligence; collaborative filtering; KNN; SVD; NCF; KNN; 02 engineering and technology; machine intelligence; NCF; Datavetenskap (datalogi); collaborative filtering; 0202 electrical engineering, electronic engineering, information engineering; SVD; Systemvetenskap, informationssystem och informatik; Information Systems

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