E-Learning Course Recommender System Using Collaborative Filtering Models Article Swipe
Kalyan Kumar Jena
,
Sourav Kumar Bhoi
,
Tushar Kanta Malik
,
Kshira Sagar Sahoo
,
N. Z. Jhanjhi
,
Sajal Bhatia
,
Fathi Amsaad
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/electronics12010157
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/electronics12010157
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.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/electronics12010157
- https://www.mdpi.com/2079-9292/12/1/157/pdf?version=1672311230
- OA Status
- gold
- Cited By
- 112
- References
- 38
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4313316862
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4313316862Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/electronics12010157Digital Object Identifier
- Title
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E-Learning Course Recommender System Using Collaborative Filtering ModelsWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-12-29Full publication date if available
- Authors
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Kalyan Kumar Jena, Sourav Kumar Bhoi, Tushar Kanta Malik, Kshira Sagar Sahoo, N. Z. Jhanjhi, Sajal Bhatia, Fathi AmsaadList of authors in order
- Landing page
-
https://doi.org/10.3390/electronics12010157Publisher landing page
- PDF URL
-
https://www.mdpi.com/2079-9292/12/1/157/pdf?version=1672311230Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2079-9292/12/1/157/pdf?version=1672311230Direct OA link when available
- Concepts
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Recommender system, Computer science, Collaborative filtering, Machine learning, Artificial intelligence, Singular value decomposition, Ranking (information retrieval), Data miningTop concepts (fields/topics) attached by OpenAlex
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112Total citation count in OpenAlex
- Citations by year (recent)
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2025: 63, 2024: 32, 2023: 17Per-year citation counts (last 5 years)
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38Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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