A machine learning approach for personalized course recommendation systems for learners
*Anu ThomasCorresponding authoranuthomas.phd20@ljku.edu.inDepartment of Computer EngineeringLok Jagruti Kendra UniversityAhmedabad, Gujarat, 382210, India0009-0000-1915-8359View full profile → , Gayatri Pandigayatri.jain@ljinstitutes.edu.inDepartment of Computer Science and EngineeringSindhu Bhavan RoadNew L J Institute of Engineering and TechnologyAhmedabad, Gujarat, 380059, India0000-0002-3096-1836View full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Mar 2025
- Published Online:
- 30 Jun 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-2154
- Pages:
- 1–11
Abstract
Keywords
Subject Classifications
References
[1] S. Singh, R. Aggarwal, R. Bali, G. Bapat, and S. Mohite, “Transformative impact: Online learning evolution in higher education post-COVID-19 pandemic,” J. Inf. Optim. Sci., vol. 44, no. 8, pp. 1627–1647 (2023), doi: 10.47974/JIOS-1481.
[2] R. Burke, “Hybrid recommender systems: Survey and experiments,” User Modeling and User-Adapted Interaction, vol. 12, no. 4, pp. 331–370 (2002).
[3] S. J. Y. Weamie, V. Kolluru, A. B. B. Jallah, and Y. Challagundla, “Hybrid deep learning-based IoT intrusion detection: A comparative study of CNN, GRU, LSTM, and hybrid architectures,” Journal of Information and Optimization Sciences, vol. 46, no. 6, pp. 1983–1994 (2025), doi: 10.47974/JIOS-2027.
[4] M. Mali, D. Mishra, and M. Vijayalaxmi, “Bi-clustering based recommendation system,” Journal of Information and Optimization Sciences, vol. 45, no. 4, pp. 1029–1039 (2024), doi: 10.47974/JIOS-1625.
[5] X. He, L. Liao, H. Zhang, L. Nie, X. Hu, and T. S. Chua, “Neural collaborative filtering,” in Proc. 26th Int. Conf. on World Wide Web (WWW), pp. 173–182 (2017).
[6] X. Wang, X. He, M. Wang, F. Feng, and T.-S. Chua, “Neural graph collaborative filtering,” in Proc. 42nd Int. ACM SIGIR Conf. on Research and Development in Information Retrieval (SIGIR), Paris, France, pp. 165–174 (2019), doi: 10.1145/3331184.3331267.
[7] F. Xia, X. Xu, L. Chen, and S. Zhang, “Density-based spatial clustering of applications with noise for personalized recommendation,” Information Sciences, vol. 512, pp. 185–197 (2020).
[8] G. Adomavicius and A. Tuzhilin, “Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions,” IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 6, pp. 734–749 (2005).
[9] F. Ricci, L. Rokach, and B. Shapira, “Introduction to recommender systems,” in Recommender Systems Handbook, Boston, MA, USA: Springer, pp. 1–35 (2015), doi: 10.1007/978-1-4899-7637-6_1.
[10] W. Yuan, H. Wang, X. Yu, N. Liu, and Z. Li, “Attention-based context-aware sequential recommendation model,” Information Sciences, vol. 510, pp. 122–134 (2020), doi: 10.1016/j.ins.2019.09.007.
[11] D. Deepak, G. Gerard, and I. Trivedi, “A hybridized deep learning strategy for course recommendation,” International Journal of Adult Education and Technology, vol. 14, no. 1, pp. 1–16 (2023), doi: 10.4018/IJAET.321752.
[12] G. Pandi and K. P. Aggarwal, “Deep learning-based 3-D model for the cultural heritage sites in the state of Gujarat, India,” in Artificial Intelligence and Sustainable Computing (ICSISCET 2022), Singapore: Springer, (2023), doi: 10.1007/978-981-99-1431-9_59.
[13] Y. Zuo, S. Liu, Y. Zhou, and H. Liu, “TRAL: A tag-aware recommendation algorithm based on attention learning,” Applied Sciences, vol. 13, no. 2, Art. no. 814 (2023), doi: 10.3390/app13020814.
[14] J. Zhang, C. Li, and Z. Zhao, “Lightweight yet efficient: An external attentive graph convolutional network with positional prompts for sequential recommendation,” ACM Transactions on Information Systems, vol. 43, no. 3, Art. no. 79, pp. 1–25 (2025), doi: 10.1145/3719343.
[15] N. Lal, M. Singh, S. Pandey, and A. Solanki, “A proposed ranked clustering approach for unstructured data from dataspace using VSM,” in Proc. Int. Conf. on Computer Science and Applications (ICCSA), pp. 80–86 (2020), doi: 10.1109/ICCSA50381.2020.00024.
[16] G. Zheng, “DRN: A deep reinforcement learning framework for news recommendation,” in Proc. The Web Conference (WWW ’18), Lyon, France, pp. 167–176 (2018), doi: 10.1145/3178876.3185994.
[17] D. Kim, “Convolutional matrix factorization for document context-aware recommendation,” in Proc. ACM Conference on Recommender Systems (RecSys), Boston, MA, USA, pp. 233–240 (2016), doi: 10.1145/2959100.2959165.
[18] S. Zhang, L. Yao, A. Sun, and Y. Tay, “Deep learning based recommender system: A survey and new perspectives,” ACM Computing Surveys, vol. 52, no. 1, pp. 1–38 (2019).
[19] S. Renaud-Deputter, T. Xiong, and S. Wang, “Combining collaborative filtering and clustering for implicit recommender system,” in Proc. IEEE Int. Conf. on Advanced Information Networking and Applications (AINA), Barcelona, Spain, pp. 748–755 (2013), doi: 10.1109/AINA.2013.65.
[20] J. Huang, Z. Jia, and P. Zuo, “Improved collaborative filtering personalized recommendation algorithm based on k-means clustering and weighted similarity on the reduced item space,” Mathematical Modelling and Control, vol. 3, no. 1, pp. 39–49 (2023), doi: 10.3934/mmc.2023004.
[21] S. Wang, “Graph learning based recommender systems: A review,” in Proc. Int. Joint Conf. on Artificial Intelligence (IJCAI), Montreal, Canada, pp. 4644–4652 (2021), doi: 10.24963/ijcai.2021/630.
[22] A. Gandomi and M. Haider, “Beyond the hype: Big data concepts, methods, and analytics,” International Journal of Information Management, vol. 35, no. 2, pp. 137–144 (2015).
[23] J. Alanya-Beltran, “Personalized learning recommendation system in e-learning platforms using collaborative filtering and machine learning,” in Proc. 2024 Int. Conf. on Advances in Computing, Communication and Applied Informatics (ACCAI), Chennai, India, pp. 1–5 (2024), doi: 10.1109/ACCAI61061.2024.10602322.
[24] L. Lei, “Research on personalized education recommendation algorithm based on artificial intelligence,” in Proc. Int. Conf. on Applied Physics and Computing (ICAPC), Ottawa, ON, Canada, pp. 531–535 (2023), doi: 10.1109/ICAPC61546.2023.00104.
[25] R. L. Ulloa-Cazarez, “Prediction of online students’ performance by means of genetic programming,” Applied Artificial Intelligence, vol. 32, no. 9–10, pp. 858–881 (2018), doi: 10.1080/08839514.2018.1508839.
[26] Y. Chen, X. Li, J. Liu, and Z. Ying, “Recommendation system for adaptive learning,” Appl. Psychol. Meas., vol. 42, no. 1, pp. 24–41 (2018), doi: 10.1177/0146621617697959.
[27] S. K. Banihashem, “Modeling teachers’ and students’ attitudes, emotions, and perceptions in blended education: Towards post-pandemic education,” Int. J. Manage. Educ., vol. 21, no. 2, Art. no. 100803 (2023), doi: 10.1016/j.ijme.2023.100803.
[28] M. Fu, “A deep reinforcement learning recommender system with multiple policies for recommendations,” IEEE Trans. Ind. Informat., vol. 19, no. 2, pp. 2049–2061 (Feb. 2023), doi: 10.1109/TII.2022.3209290.
[29] S. Latifi, D. Jannach, and A. Ferraro, “Sequential recommendation: A study on transformers, nearest neighbors and sampled metrics,” Inf. Sci., vol. 609, pp. 660–678 (2022), doi: 10.1016/j.ins.2022.07.079.
[30] G. Behl, “Optimizing agile adoption in virtual learning environments: A framework for classification and prioritization,” J. Inf. Optim. Sci., vol. 45, no. 6, pp. 1705–1716 (2024), doi: 10.47974/JIOS-1706.




