Leveraging Neural Matrix Factorization (NeuralMF) and Graph Neural Networks (GNNs) for Enhanced Personalization in E-Learning Systems
DOI:
https://doi.org/10.35870/ijsecs.v4i2.2238Keywords:
Neural Matrix Factorization, Graph Neural Networks, Recommendation Systems, E-learning; PersonalizationAbstract
This study investigates the application of a combined approach utilizing Neural Matrix Factorization (NeuralMF++) and Graph Neural Networks (GNNs) to enhance personalization in e-learning recommendation systems. The primary objective is to address significant challenges commonly encountered in recommendation systems, such as data sparsity and the cold start problem, where new users or items need prior interaction history. NeuralMF++ leverages neural networks in matrix factorization to capture complex non-linear interactions between users and content. GNNs model intricate relationships between users and items within a graph structure. Experimental results demonstrate a substantial improvement in recommendation accuracy, measured by metrics such as Hit Ratio (HR) and Normalized Discounted Cumulative Gain (NDCG). Additionally, the proposed model exhibits greater efficiency in training time than traditional methods, achieving this without compromising recommendation quality. User feedback from several universities involved in this research indicates high satisfaction with the recommendations provided, suggesting that the model effectively adapts recommendations to align with evolving user preferences. Thus, this study asserts that integrating NeuralMF++ and GNNs presents significant potential for broad application in e-learning platforms, offering substantial benefits in personalization and system efficiency
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Zhao, X., Zeng, W., & He, Y. (2021). Collaborative filtering via factorized neural networks. Applied Soft Computing, 109, 107484. https://doi.org/10.1016/J.ASOC.2021.107484
Fan, W., Ma, Y., Li, Q., He, Y., Zhao, Y., Tang, J., & Yin, D. (2019). Graph neural networks for social recommendation. In The World Wide Web Conference (pp. 417-426). https://doi.org/10.1145/3308558.3313488
Liu, S. (2020). Enhancing graph neural networks for recommender systems. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1421-1424). https://doi.org/10.1145/3397271.3401456
Liu, Z., Yang, L., Fan, Z., Peng, H., & Yu, P. S. (2021). Federated social recommendation with graph neural network. ACM Transactions on Intelligent Systems and Technology (TIST), 13(1), 1-24. https://doi.org/10.1145/3501815
Bonet, E. R., Nguyen, D. M., & Deligiannis, N. (2020). Temporal collaborative filtering with graph convolutional neural networks. In 2020 25th International Conference on Pattern Recognition (ICPR) (pp. 4736-4742). https://doi.org/10.1109/ICPR48806.2021.9413200
Chen, Y., Tang, X., Qi, X., Li, C. G., & Xiao, R. (2020). Learning graph normalization for graph neural networks. Neurocomputing, 493, 613-625. https://doi.org/10.1016/j.neucom.2022.01.003
Chizari, N., Shoeibi, N., & Moreno-García, M. (2022). A comparative analysis of bias amplification in graph neural network approaches for recommender systems. ArXiv. https://doi.org/10.3390/electronics11203301
Chen, K., Liu, S., Zhu, T., Zheng, T., Zhang, H., Feng, Z., Ye, J., & Song, M. (2023). Improving expressivity of GNNs with subgraph-specific factor embedded normalization. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 1-10). https://doi.org/10.1145/3580305.3599388
Tolety, V. B. P., & Prasad, E. V. (2023). Graph neural networks for e-learning recommendation systems. International Journal on Recent and Innovation Trends in Computing and Communication, 11(9S), 120-126. https://doi.org/10.17762/ijritcc.v11i9s.7395
Kurt, S. E., Yan, J., Sukumaran-Rajam, A., Pandey, P., & Sadayappan, P. (2023). Communication optimization for distributed execution of graph neural networks. In 2023 IEEE International Parallel and Distributed Processing Symposium (IPDPS) (pp. 512-523). https://doi.org/10.1109/IPDPS54959.2023.00058
Batatia, I., Schaaf, L. L., Chen, H., Cs'anyi, G., Ortner, C., & Faber, F. A. (2023). Equivariant matrix function neural networks. ArXiv. https://doi.org/10.48550/arXiv.2310.10434
Yang, H., Yan, X., Dai, X., Chen, Y., & Cheng, J. (2020). Self-enhanced GNN: Improving graph neural networks using model outputs. In 2021 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). https://doi.org/10.1109/IJCNN52387.2021.9533748
Fang, Z., Zhang, Z., Song, G., Zhang, Y., Li, D., Hao, J., & Wang, X. (2022). Invariant factor graph neural networks. In 2022 IEEE International Conference on Data Mining (ICDM) (pp. 933-938). https://doi.org/10.1109/ICDM54844.2022.00110
Liu, C., Wu, J., Liu, W., & Hu, W. (2021). Enhancing graph neural networks by a high-quality aggregation of beneficial information. Neural Networks, 142, 20-33. https://doi.org/10.1016/j.neunet.2021.04.025
Aboagye, E. O., & Gao, J. (2018). Computational intelligence strategies for effective collaborative decisions. In 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) (pp. 776-782). https://doi.org/10.1109/IEMCON.2018.8615069
Chen, C., Zhang, M., Zhang, Y., Liu, Y., & Ma, S. (2020). Efficient Neural Matrix Factorization without Sampling for Recommendation. ACM Transactions on Information Systems (TOIS), 38(2), 1-28. https://doi.org/10.1145/3373807
Ong, K., Ng, K. W., & Haw, S. (2021). Neural matrix factorization++ based recommendation system. F1000Research, 10, 1079. https://doi.org/10.12688/f1000research.73240.1
Qian, T., Liang, Y., & Li, Q. (2022). Attribute Graph Neural Networks for Strict Cold Start Recommendation. IEEE Transactions on Knowledge and Data Engineering, 34(8), 3597-3610. https://doi.org/10.1109/tkde.2020.3038234
Xiao, T., & Shen, H. (2019). Neural variational matrix factorization for collaborative filtering in recommendation systems. Applied Intelligence, 1-12. https://doi.org/10.1007/s10489-019-01469-6
Guo, W., Feng, S., Li, Y., & Wang, Y. (2021). Enhancing matrix factorization-based recommender systems using graph neural networks. IEEE Transactions on Knowledge and Data Engineering. https://doi.org/10.1109/TKDE.2021.3081660
Guo, W., & Wang, Y. (2021). A graph neural network-based mechanism for social recommendations in industrial IoT environments. IEEE Transactions on Industrial Informatics, 17(8), 5674-5683. https://doi.org/10.1109/TII.2021.3059704
Ying, R., He, R., Chen, K., Eksombatchai, P., Hamilton, W. L., & Leskovec, J. (2018). Graph convolutional neural networks for web-scale recommender systems. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 974-983. https://doi.org/10.1145/3219819.3219890
Xi, W., Li, L., Zhu, Z., & Yang, H. (2020). Graph factorization machines: A graph-based approach to cross-domain recommendation. IEEE Access, 8, 138610-138620. https://doi.org/10.1109/ACCESS.2020.3012117.
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