Abstract
Selecting the right learning content according to learners’ learning abilities and interests is the first and most important factor in achieving good learning performance. Based on the similarity between the course rating data in the Collaborative Filtering format (user, item, rating), and along with the development of Graph Neural Networks (GNN) in developing recommendation systems, we tried to develop a Collaborative Filtering (CF) model based on GNN architecture to recommend suitable courses to the learners. In this study, two CF models based on GNN, including Neural Graph Collaborative Filtering (NGCF) and Light Graph Convolutional Neural Networks (LightGCN), were experimentally compared with some traditional CF models such as Regularized Matrix Factorization (RMF), Light Matrix Factorization (LMF), and Neural Collaborative Filtering (NCF). The experimental results on the Coursera Course Review dataset using LightGCN give promising results in the precision, recall, and RMSE metrics.
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References
Thanh-Nhan, H.L., Nguyen, H.H., Thai-Nghe, N.: Methods for building course recommendation systems. In: 2016 Eighth International Conference on Knowledge and Systems Engineering (KSE), pp. 163–168 (2016)
Anupama, V., Elayidom, M.S.: Course recommendation system: collaborative filtering, machine learning and topic modelling. In: 2022 8th International Conference on Advanced Computing and Communication Systems (ICACCS), vol. 1, pp. 1459–1462 (2022)
Dien, T.T., Hoai, S., Thanh-Hai, N., Thai-Nghe, N.: Deep learning with data transformation and factor analysis for student performance prediction. Int. J. Adv. Comput. Sci. Appli. 11(8) (2020)
Wang, X., He, X., Wang, M., Feng, F., Chua, T.S.: Neural graph collaborative filtering. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM (2019)
Zhu, P., et al.: SI-news: integrating social information for news recommendation with attention-based graph convolutional network. Neurocomputing 494, 33–42 (2022)
He, X., Deng, K., Wang, X., Li, Y., Zhang, Y., Wang, M.: LightGCN. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM (2020)
Feng, L., Huang, J., Shu, S., An, B.: Regularized matrix factorization for multilabel learning with missing labels. IEEE Trans. Cybern. 52(5), 3710–3721 (2022)
Kula, M.: Metadata embeddings for user and item cold-start recommendations. arXiv preprint arXiv:1507.08439 (2015)
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web. International World Wide Web Conferences Steering Committee (2017)
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Tran, A.C., Tran, DT., Thai-Nghe, N., Dien, T.T., Nguyen, H.T. (2023). Course Recommendation Based on Graph Convolutional Neural Network. In: Fujita, H., Wang, Y., Xiao, Y., Moonis, A. (eds) Advances and Trends in Artificial Intelligence. Theory and Applications. IEA/AIE 2023. Lecture Notes in Computer Science(), vol 13925. Springer, Cham. https://doi.org/10.1007/978-3-031-36819-6_20
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DOI: https://doi.org/10.1007/978-3-031-36819-6_20
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