ARCHIVES
Original Article
Personalized E-Commerce Product Recommendation System Using Machine Learning
Mohd Fouzan Hussain1
Dr. Mohd Rafi Ahmed2
1Student, MCA, Deccan College of Engineering and Technology, Hyderabad, Telangana, India. 2Associate Professor, MCA, Deccan College of Engineering and Technology, Hyderabad, Telangana, India.
Published Online: September-October 2025
Pages: 41-46
Cite this article
↗ https://www.doi.org/10.59256/ijrtmr.20250505008
References
1. Y. Koren and R. Bell, “Advances in collaborative filtering for recommender systems,” IEEE Intell. Syst., vol. 34, no. 4, pp. 23–32, Jul.–Aug. 2019.
2. H. Wang, F. Zhang, J. Wang, M. Zhao, W. Li, X. Xie, and M. Guo, “RippleNet: Propagating user preferences on the knowledge graph for recommender systems,” ACM Trans. Inf. Syst., vol. 38, no. 4, pp. 1–23, Aug. 2020.
3. L. Sun, C. Ma, and X. Li, “A hybrid recommendation system based on deep learning for personalized e-commerce,” IEEE Access, vol. 7, pp. 175–188, Jan. 2019.
4. J. Zhang, C. Wang, and X. Chen, “Explainable recommendation with knowledge graphs for transparency in e-commerce,” Proc. IEEE ICDE, pp. 1210–1221, Apr. 2020.
5. S. Wang, L. Zhang, and X. Zhou, “Personalized recommendation based on attention mechanism and user behavior modeling,” IEEE Access, vol. 8, pp. 422–435, 2020.
6. Y. Xu, Z. Chen, and H. Wang, “Neural collaborative filtering with context-aware embeddings for recommender systems,” Proc. IEEE ICDM, pp. 520–529, Dec. 2019.
7. R. Yang, W. Huang, and Q. Li, “Sequential recommendation with transformer networks in e-commerce,” IEEE Trans. Neural Netw. Learn. Syst., vol. 32, no. 12, pp. 5162–5175, Dec. 2021.
8. M. Zhao, X. Xie, and H. Li, “DeepFM: A factorization-machine based neural network for CTR prediction,” Proc. AAAI/IEEE AIES, pp. 1725–1732, 2020.
9. T. Chen, S. Li, and Y. Luo, “Personalized product recommendation via multi-task learning with graph neural networks,” Proc. IEEE Big Data, pp. 1650–1659, Dec. 2020
10. K. Zhou, Y. Wang, and J. Lin, “Interactive recommender systems with reinforcement learning for e-commerce,” IEEE Trans. Knowl. Data Eng., vol. 34, no. 8, pp. 3760–3774, Aug. 2022.
11. H. Liu, Q. Gao, and Y. Yin, “Context-aware session-based recommendation using recurrent neural networks,” IEEE Access, vol. 8, pp. 4895–4908, 2020.
2. H. Wang, F. Zhang, J. Wang, M. Zhao, W. Li, X. Xie, and M. Guo, “RippleNet: Propagating user preferences on the knowledge graph for recommender systems,” ACM Trans. Inf. Syst., vol. 38, no. 4, pp. 1–23, Aug. 2020.
3. L. Sun, C. Ma, and X. Li, “A hybrid recommendation system based on deep learning for personalized e-commerce,” IEEE Access, vol. 7, pp. 175–188, Jan. 2019.
4. J. Zhang, C. Wang, and X. Chen, “Explainable recommendation with knowledge graphs for transparency in e-commerce,” Proc. IEEE ICDE, pp. 1210–1221, Apr. 2020.
5. S. Wang, L. Zhang, and X. Zhou, “Personalized recommendation based on attention mechanism and user behavior modeling,” IEEE Access, vol. 8, pp. 422–435, 2020.
6. Y. Xu, Z. Chen, and H. Wang, “Neural collaborative filtering with context-aware embeddings for recommender systems,” Proc. IEEE ICDM, pp. 520–529, Dec. 2019.
7. R. Yang, W. Huang, and Q. Li, “Sequential recommendation with transformer networks in e-commerce,” IEEE Trans. Neural Netw. Learn. Syst., vol. 32, no. 12, pp. 5162–5175, Dec. 2021.
8. M. Zhao, X. Xie, and H. Li, “DeepFM: A factorization-machine based neural network for CTR prediction,” Proc. AAAI/IEEE AIES, pp. 1725–1732, 2020.
9. T. Chen, S. Li, and Y. Luo, “Personalized product recommendation via multi-task learning with graph neural networks,” Proc. IEEE Big Data, pp. 1650–1659, Dec. 2020
10. K. Zhou, Y. Wang, and J. Lin, “Interactive recommender systems with reinforcement learning for e-commerce,” IEEE Trans. Knowl. Data Eng., vol. 34, no. 8, pp. 3760–3774, Aug. 2022.
11. H. Liu, Q. Gao, and Y. Yin, “Context-aware session-based recommendation using recurrent neural networks,” IEEE Access, vol. 8, pp. 4895–4908, 2020.
Related Articles
2025
Exploring Mathematical Concepts in Ramcharit Manas: A Unique Perspective on Navadha Bhakti
2025
ARMOIRE An Augmented Reality Fashion Try On
2025
Sign Vision AI powered sign language Recognition
2025
Drowzy Alert AI Powered Driver Fatigue Detection
2025
Beauty Care Shopping using 3D Modelling
2025
