Shufflenetv2: An Effective Technique for Recommendation System in E-Learning by User Preferences
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-36402-0_16
Reference21 articles.
1. Bhaskaran, S., Marappan, R., Santhi, B.: Design and analysis of a cluster-based intelligent hybrid recommendation system for e-learning applications. Mathematics 9(2), 197 (2021)
2. Ali, S., Hafeez, Y., Humayun, M., Jamail, N.S.M., Aqib, M., Nawaz, A.: Enabling recommendation system architecture in virtualized environment for e-learning. Egyptian Informatics Journal 23(1), 33–45 (2022)
3. Ghosh, S., Roy, S., Sen, S.: An efficient recommendation system on e-learning platform by query lattice optimization. In: Data Management, Analytics and Innovation: Proceedings of ICDMAI 2020, Vol. 1, pp. 73–86. Springer Singapore (2021)
4. Rahhali, M., Oughdir, L., Jedidi, Y., Lahmadi, Y., El Khattabi, M.Z.: E-learning recommendation system based on cloud computing. In: WITS 2020: Proceedings of the 6th International Conference on Wireless Technologies, Embedded, and Intelligent Systems, pp. 89–99. Springer Singapore (2022)
5. Amane, M., Aissaoui, K., Berrada, M.: A multi-agent and content-based course recommender system for university e-learning platforms. In: Digital Technologies and Applications: Proceedings of ICDTA 21, Fez, Morocco, pp. 663–672. Springer International Publishing, Cham (2021)
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