Nonlinear Adaptive Optimization of Multi-Modal Learning Paths Using Graph Convolutional Networks and Reinforcement Learning for Intelligent Educational Systems
This work is licensed under the Creative Commons Attribution 4.0 International License.
Wang, H., Zhao, M., Xie, X., Li, W., & Guo, M. (2019). Knowledge graph convolutional networks for recommender systems. The World Wide Web Conference, 3307-3313.https://doi.org/10.1145/3308558.3313417WangH.ZhaoM.XieX.LiW.GuoM. (2019). Knowledge graph convolutional networks for recommender systems. The World Wide Web Conference, 3307-3313.https://doi.org/10.1145/3308558.3313417Search in Google Scholar
He, X., Deng, K., Wang, X., Li, Y., Zhang, Y., & Wang, M. (2020). LightGCN: Simplifying and powering graph convolution network for recommendation. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 639-648.https://doi.org/10.1145/3397271.3401063HeX.DengK.WangX.LiY.ZhangY.WangM. (2020). LightGCN: Simplifying and powering graph convolution network for recommendation. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 639-648.https://doi.org/10.1145/3397271.3401063Search in Google Scholar
Yu, T., Zhao, Y., Huang, R., Liu, S., & Zhang, X. (2021). Chebyshev accelerated spectral clustering. Proceedings of the 14th ACM International Conference on Web Search and Data Mining, 247–255. https://doi.org/10.1145/3437963.3441758YuT.ZhaoY.HuangR.LiuS.ZhangX. (2021). Chebyshev accelerated spectral clustering.Proceedings of the 14th ACM International Conference on Web Search and Data Mining, 247-255.https://doi.org/10.1145/3437963.3441758Search in Google Scholar
Li, P., Qin, Z., Wang, X., & Metzler, D. (2019). Combining decision trees and neural networks for learning-to-rank in personal search. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2032-2040.https://doi.org/10.1145/3292500.3330681LiP.QinZ.WangX.MetzlerD. (2019). Combining decision trees and neural networks for learning-to-rank in personal search. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2032-2040.https://doi.org/10.1145/3292500.3330681Search in Google Scholar
Vaswani, A. (2017). Attention is all you need. Advances in Neural Information Processing Systems.https://arxiv.org/abs/1706.03762VaswaniA. (2017). Attention is all you need.Advances in Neural Information Processing Systems.https://arxiv.org/abs/1706.03762Search in Google Scholar
Wang, S., Hu, L., Wang, Y., He, X., Sheng, Q. Z., Orgun, M. A., & Yu, P. S. (2021). Graph learning-based recommender systems: A review. arXiv preprint arXiv:2105.06339. https://arxiv.org/abs/2105.06339WangS.HuL.WangY.HeX.ShengQ. Z.OrgunM. A.YuP. S. (2021). Graph learning-based recommender systems: A review.arXiv preprint arXiv:2105.06339.https://arxiv.org/abs/2105.06339Search in Google Scholar
Ma, J., Zhao, Z., Yi, X., Chen, J., Hong, L., & Chi, E. H. (2018). Modeling task relationships in multi-task learning with multi-gate mixture-of-experts. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 1930-1939. https://doi.org/10.1145/3219819.3220007MaJ.ZhaoZ.YiX.ChenJ.HongL.ChiE. H. (2018). Modeling task relationships in multi-task learning with multi-gate mixture-of-experts. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 1930-1939.https://doi.org/10.1145/3219819.3220007Search in Google Scholar
Ernst, D., & Louette, A. (2024). Introduction to reinforcement learning. En Feuerriegel, S., Hartmann, J., Janiesch, C., & Zschech, P. (Eds.), Advances in Computer, Signals and Systems (pp. 111-126).ErnstD.LouetteA. (2024). Introduction to reinforcement learning.En FeuerriegelS.HartmannJ.JanieschC.ZschechP. (Eds.), Advances in Computer, Signals and Systems (pp. 111-126).Search in Google Scholar
Kenton, J. D. M. W. C., & Toutanova, L. K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of NAACL-HLT, 1(2).https://arxiv.org/abs/1810.04805KentonJ. D. M. W. C.ToutanovaL. K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding.Proceedings of NAACL-HLT, 1(2).https://arxiv.org/abs/1810.04805Search in Google Scholar
Feng, Z., Guo, D., Tang, D., Duan, N., Feng, X., Gong, M., & Zhou, M. (2020). CodeBERT: A pre-trained model for programming and natural languages. arXiv preprint arXiv:2002.08155.https://arxiv.org/abs/2002.08155FengZ.GuoD.TangD.DuanN.FengX.GongM.ZhouM. (2020). Code BERT: A pre-trained model for programming and natural languages.arXiv preprint arXiv:2002.08155.https://arxiv.org/abs/2002.08155Search in Google Scholar
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770-778. https://doi.org/10.1109/CVPR.2016.90HeK.ZhangX.RenS.SunJ. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770-778.https://doi.org/10.1109/CVPR.2016.90Search in Google Scholar
Singh, H., & Waks, E. (2022). Splitting indistinguishable photons: Using linear optics to exceed the limit of photon blockade. arXiv preprint arXiv:2201.04555.https://arxiv.org/abs/2201.04555SinghH.WaksE. (2022). Splitting indistinguishable photons: Using linear optics to exceed the limit of photon blockade.arXiv preprint arXiv:2201.04555.https://arxiv.org/abs/2201.04555Search in Google Scholar
Zhong, L., Wei, Y., Yao, H., Deng, W., Wang, Z., & Tong, M. (2020). Review of deep learning-based personalized learning recommendation. Proceedings of the 2020 11th International Conference on E-Education, E-Business, E-Management, and E-Learning.https://doi.org/10.1145/3377571.3377615ZhongL.WeiY.YaoH.DengW.WangZ.TongM. (2020). Review of deep learning-based personalized learning recommendation.Proceedings of the 2020 11th International Conference on E-Education, E-Business, E-Management, and E-Learning.https://doi.org/10.1145/3377571.3377615Search in Google Scholar
Liu, X. (2024). Construction of a personalized recommendation service model for online learning resources. Advances in Computer, Signals and Systems.LiuX. (2024). Construction of a personalized recommendation service model for online learning resources.Advances in Computer, Signals and Systems.Search in Google Scholar
Ma, Y., Ouyang, R., Long, X., Gao, Z., Lai, T., & Fan, C. (2023). DORIS: Personalized course recommendation system based on deep learning. PLOS ONE, 18(6), e0284687.https://doi.org/10.1371/journal.pone.0284687MaY.OuyangR.LongX.GaoZ.LaiT.FanC. (2023). DORIS: Personalized course recommendation system based on deep learning. PLOS ONE, 18(6), e0284687.https://doi.org/10.1371/journal.pone.0284687Search in Google Scholar
Babu, B. V., Aravinth, S. S., Gowthami, K., Navyasri, P., Jivitha, A., & Yasaswini, T. (2024, abril). Enhancing personalized learning experiences by leveraging deep learning for content understanding in e-learning recommender systems. 2024 International Conference on Computing and Data Science (ICCDS), 1-6. IEEE.https://doi.org/10.1109/ICCDS.2024.00012BabuB. V.AravinthS. S.GowthamiK.NavyasriP.JivithaA.YasaswiniT. (2024, abril). Enhancing personalized learning experiences by leveraging deep learning for content understanding in e-learning recommender systems. 2024 International Conference on Computing and Data Science (ICCDS), 1-6. IEEE. https://doi.org/10.1109/ICCDS.2024.00012Search in Google Scholar
Qiu, B. et al. (2024). A comprehensive study on personalized learning recommendation in e -learning systems. IEEE Access, 12, 100446-100482.https://doi.org/10.1109/ACCESS.2024.3245678QiuB. (2024). A comprehensive study on personalized learning recommendation in e -learning systems. IEEE Access, 12, 100446-100482.https://doi.org/10.1109/ACCESS.2024.3245678Search in Google Scholar
Li, B., & Cuison, L. (2024). Design and implementation of a personalized course recommendation system for MOOCs based on deep learning. Academic Journal of Computing & Information Science, 7(7), 90–97.https://doi.org/10.25236/ajcis.2024.070712LiB.CuisonL. (2024). Design and implementation of a personalized course recommendation system for MOOCs based on deep learning. Academic Journal of Computing & Information Science, 7(7), 90-97.https://doi.org/10.25236/ajcis.2024.070712Search in Google Scholar
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Philip, S. Y. (2020). A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems, 32(1), 4–24.https://doi.org/10.1109/TNNLS.2020.2978386WuZ.PanS.ChenF.LongG.ZhangC.PhilipS. Y. (2020). A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems, 32(1), 4-24.https://doi.org/10.1109/TNNLS.2020.2978386Search in Google Scholar
Devlin, J. (2018). BERT: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.https://arxiv.org/abs/1810.04805DevlinJ. (2018). BERT: Pre-training of deep bidirectional transformers for language understanding.arXiv preprint arXiv:1810.04805.https://arxiv.org/abs/1810.04805Search in Google Scholar
Hochreiter, S. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780. https://doi.org/10.1162/neco.1997.9.8.1735HochreiterS. (1997). Long short-term memory. Neural Computation, 9(8), 1735-1780.https://doi.org/10.1162/neco.1997.9.8.1735Search in Google Scholar
Hu, T., Xia, Y., & Luo, J. (2019). To return or to explore: Modelling human mobility and dynamics in cyberspace. The World Wide Web Conference, 705-716.https://doi.org/10.1145/3308558.3313632HuT.XiaY.LuoJ. (2019). To return or to explore: Modelling human mobility and dynamics in cyberspace. The World Wide Web Conference, 705-716.https://doi.org/10.1145/3308558.3313632Search in Google Scholar
Stair, N. H., & Evangelista, F. A. (2021). Simulating many-body systems with a projective quantum eigensolver. PRX Quantum, 2(3), 030301.https://doi.org/10.1103/PRX Quantum.2.030301StairN. H.EvangelistaF. A. (2021). Simulating many-body systems with a projective quantum eigensolver. PRXQuantum, 2(3), 030301.https://doi.org/10.1103/PRXQuantum.2.030301Search in Google Scholar