Published: 2024-08-01
Leveraging Neural Matrix Factorization (NeuralMF) and Graph Neural Networks (GNNs) for Enhanced Personalization in E-Learning Systems
DOI: 10.35870/ijsecs.v4i2.2238
Downloads
Abstract
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
Keywords
Neural Matrix Factorization ; Graph Neural Networks ; Recommendation Systems ; E-learning; Personalization
Article Information
This article has been peer-reviewed and published in the International Journal Software Engineering and Computer Science (IJSECS). The content is available under the terms of the Creative Commons Attribution 4.0 International License.
-
Issue: Vol. 4 No. 2 (2024)
-
Section: Articles
-
Published: %750 %e, %2024
-
License: CC BY 4.0
-
Copyright: © 2024 Authors
-
DOI: 10.35870/ijsecs.v4i2.2238
AI Research Hub
This article is indexed and available through various AI-powered research tools and citation platforms. Our AI Research Hub ensures that scholarly work is discoverable, accessible, and easily integrated into the global research ecosystem. By leveraging artificial intelligence for indexing, recommendation, and citation analysis, we enhance the visibility and impact of published research.
Achmad Maezar Bayu Aji
Information Systems Study Program, Faculty of Information Technology, Universitas Nusa Mandiri, Central Jakarta City, Special Capital Region of Jakarta, Indonesia
Dewi Nurdiyanti
Faculty of Teacher Training and Education, Universitas Muhammadiyah Cirebon, Cirebon Regency, West Java Province, Indonesia
Hasan Basri
Islamic Religious Education Study Program, Faculty of Tarbiyah and Teacher Training, Universitas Islam Negeri Ar-Raniry, Banda Aceh City, Aceh Province, Indonesia
-
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.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Copyright Retention and Open Access License
Authors retain copyright of their work and grant the journal non-exclusive right of first publication under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license allows unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
2. Rights Granted Under CC BY 4.0
Under this license, readers are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, including commercial use
- No additional restrictions — the licensor cannot revoke these freedoms as long as license terms are followed
3. Attribution Requirements
All uses must include:
- Proper citation of the original work
- Link to the Creative Commons license
- Indication if changes were made to the original work
- No suggestion that the licensor endorses the user or their use
4. Additional Distribution Rights
Authors may:
- Deposit the published version in institutional repositories
- Share through academic social networks
- Include in books, monographs, or other publications
- Post on personal or institutional websites
Requirement: All additional distributions must maintain the CC BY 4.0 license and proper attribution.
5. Self-Archiving and Pre-Print Sharing
Authors are encouraged to:
- Share pre-prints and post-prints online
- Deposit in subject-specific repositories (e.g., arXiv, bioRxiv)
- Engage in scholarly communication throughout the publication process
6. Open Access Commitment
This journal provides immediate open access to all content, supporting the global exchange of knowledge without financial, legal, or technical barriers.