References
Adamu, S., Iorliam, A., & Asilkan, Ö. (2025). Exploring Explainability in Multi-Category Electronic Markets: A Comparison of Machine Learning and Deep Learning Approaches. Journal of Future Artificial Intelligence and Technologies, 1(4), 440–454. Alfaifi, Y. H. (2024). Recommender systems applications: Data sources, features, and challenges. Information, 15(10), 660. https://doi.org/10.3390/info15100660 Almahmood, R. J. K., & Tekerek, A. (2022). Issues and solutions in deep learning-enabled recommendation systems within the e-commerce field. Applied Sciences, 12(21), 11256. Ali, N. M., Alshahrani, A., Alghamdi, A. M., & Novikov, B. (2022). SmartTips: Online Products Recommendations System Based on Analyzing Customers Reviews. Applied Sciences, 12, 8823 Amosu, O. R., Kumar, P., Fadina, A., Ogunsuji, Y. M., Oni, S., Faworaja, O., & Adetula, K. (2024). Data-driven personalized marketing: deep learning in retail and E-commerce. World Journal of Advanced Research and Reviews, 23(02), 788–796. Benleulmi, M., Gasmi, I., Azizi, N., & Dey, N. (2025). Explainable AI and deep learning models for recommender systems: State of the art and challenges. Journal of Universal Computer Science, 31(4), 383. Bodduluri, K. C., Palma, F., Kurti, A., & Jusufi, I. (2024). Exploring the landscape of hybrid recommendation systems in e-commerce: A systematic literature review. IEEE Access, 12, 28273–28296. Chabane, N., Bouaoune, A., Tighilt, R., Abdar, M., Boc, A., Lord, E., ... & Makarenkov, V. (2022). Intelligent personalized shopping recommendation using clustering and supervised machine learning algorithms. Plos one, 17(12), e0278364 Dobrița, G. (2023). Adaptive Microservices for Dynamic E-commerce: Enabling Personalized Experiences through Machine Learning and Real-time Adaptation.Economic Insights – Trends and Challenges, 12(1), 95–103 Esmeli, R., & Gokce, A. (2025). An Analysis of Consumer Purchase Behavior Following Cart Addition in E-Commerce Utilizing Explainable Artificial Intelligence. Journal of Theoretical and Applied Electronic Commerce Research, 20(1), 28. Ezeife, C. I., & Karlapalepu, H. (2023). A survey of sequential pattern based e-commerce recommendation systems. Algorithms, 16(10), 467. He, Y., Du, Y., & Pu, X. (2025). Personalized Recommendation System of E-Commerce in the Digital Economy Era: Enhancing Social Connections with Graph Attention Networks. Applied Artificial Intelligence, 39(1), 2487417. Hassan, Y. G., Collins, A., Babatunde, G. O., Alabi, A. A., & Mustapha, S. D. (2023). AI- powered cyber-physical security framework for critical industrial IoT systems. Machine Learning, 27 Islam, M. R., Hossain, M., Alam, M., Khan, M. M., Rabbi, M. M. K., Rabby, M. F., ... & Tarafder, M. T. R. (2025). Leveraging Machine Learning for Insights and Predictions in Synthetic E-commerce Data in the USA: A Comprehensive Analysis. Journal of Ecohumanism, 4(2), 2394–2420. Jeong, E., Li, X., Kwon, A., Park, S., Li, Q., & Kim, J. (2024). A Multimodal Recommender System Using Deep Learning Techniques Combining Review Texts and Images. Applied Sciences, 14(20), 9206. Javeed, D., Saeed, M. S., Kumar, P., Jolfaei, A., Islam, S., & Islam, A. N. (2023). Federated learning-based personalized recommendation systems: An overview on security and privacy challenges. IEEE Transactions on Consumer Electronics, 70(1), 2618-2627. IJCSMT Jia, C., Juanatas, R., Portez, A., & Montaña, J. R. (2025). Machine Learning Models for Predicting Order Returns in Cross-Border E-Commerce. Economics and Management Innovation, 2(1), 10–18. Krishna, E. S. P., et al. (2025). Enhancing E-commerce Recommendations with Sentiment Analysis Using MLA-EDTCNet. Scientific Reports, 15, 6739. Li, C. (2024). A Personalized Product Recommendation System for E-Commerce Platforms Based on Artificial Intelligence and Image Processing Technologies. Traitement du Signal, 41(6). Liang, Y., Hu, Y., Luo, D., Zhu, Q., Chen, Q., & Wang, C. (2023). Distributed dynamic pricing strategy based on deep reinforcement learning approach in a presale mechanism. Sustainability, 15(13), 10480. Lin, J., Li, X., Yang, Y., Liu, L., Guo, W., Li, X., & Li, L. (2011, September). A context-aware recommender system for M-commerce applications. In International Conference on Active Media Technology (pp. 217-228). Berlin, Heidelberg: Springer Berlin Heidelberg. Liu, L. (2022). e-commerce Personalized Recommendation Based on Machine Learning Technology. Mobile Information Systems, 2022(1), 1761579 Mateos, P., & Bellogín, A. (2024). A systematic literature review of recent advances on context- aware recommender systems. Artificial Intelligence Review, 58, Article 20. https://doi.org/10.1007/s10462-024-10939-4 Matam, V. K., & Reddy, N. M. (2025). Overcoming the cold-start challenge in recommender systems: A novel two-stage framework. Open Computer Science, 15(1). https://www.degruyterbrill.com/document/doi/10.1515/comp-2025-0038/html Mettouris, C., Achilleos, A., Kapitsaki, G., & Papadopoulos, G. A. (2025). An MDD Framework Towards the Automated Development of Ubiquitous Context-Aware Recommender Systems for Commerce. SN Computer Science, 6(4), 1-31. Nagraj, S., & Palayyan, B. P. (2024). Personalized E-commerce based recommendation systems using deep-learning techniques. Int J Artif Intell, 2252(8938), 8938. Necula, S.-C., & Păvăloaia, V.-D. (2023). AI-driven recommendations: A systematic review of the state of the art in e-commerce. Applied Sciences, 13(9), 5531. Nguyen, D. N., Nguyen, V. H., Trinh, T., Ho, T., & Le, H. S. (2024). A personalized product recommendation model in e-commerce based on retrieval strategy. Journal of Open Innovation: Technology, Market, and Complexity, 10(2), 100303. Ojika, F. U., Owobu, W. O., Abieba, O. A., Esan, O. J., Ubamadu, B. C., & Daraojimba, A. I. (2023). Enhancing User Interaction through Deep Learning Models: A Data-Driven Approach to Improving Consumer Experience in E-Commerce. Park, M., & Oh, J. (2024). Enhancing e-commerce recommendation systems with multiple item purchase data: A Bidirectional Encoder Representations from Transformers-based approach. Applied Sciences, 14(16), 7255. Rahman, A., Haque, Z., & Ahammad, M. S. (2024). E-Commerce Product Recommendation System Using Machine Learning Algorithms. International Journal of Computer Science and Information Security , 22(3). Raji, M. A., Olodo, H. B., Oke, T. T., Addy, W. A., Ofodile, O. C., & Oyewole, A. T. (2024). E-commerce and consumer behavior: A review of AI-powered personalization and market trends. GSC Advanced Research and Reviews, 18(3), 66-77. https://doi.org/10.30574/gscarr.2024.18.3.0090 Raza, S., Rahman, M., Kamawal, S., Toroghi, A., Raval, A., Navah, F., & Kazemeini, A. (2024). A comprehensive review of recommender systems: Transitioning from theory to practice. arXiv preprint arXiv:2407.13699.https://arxiv.org/abs/2407.13699 IJCSMT Sakthi, B., & Sundar, D. (2024). An efficient attention-based hybridized deep learning network with deep RBM features for customer behavior prediction in digital marketing. Kybernetes Saleh, R. A., & Zeebaree, S. R. (2025). Artificial Intelligence in E-commerce and Digital Marketing: A Systematic Review of Opportunities, Challenges, and Ethical Implications. Asian Journal of Research in Computer Science, 18, 395–410. Sameena, S., Javali, G., Srilakshmi, N., Jhansi, M., & Sk, S. S. (2025). Personalized product recommendation system for e-commerce platforms. In ITM Web of Conferences (Vol. 74, p. 03012). EDP Sciences. Sreedhara, S. H., Kumar, V., & Salma, S. (2023). Efficient big data clustering using Adhoc Fuzzy C means and auto-encoder CNN. pp. 353–368 Swami, P. (2021). Personalization and Customization in Products and Services in E-commerce Using Big Data. In Big Data Technologies and Analytics (pp. 1–17). CRC Press. Tran, D. T., & Huh, J.-H. (2023). New machine learning model based on the time factor for e- commerce recommendation systems. The Journal of Supercomputing, 79, 6756–6801. Wang, Y., Han, X., & Zhang, X. (2025). AI-Driven Market Segmentation and Multi-Behavioral Sequential Recommendation for Personalized E-Commerce Marketing. Computer Simulation in Application, 3(1), 44-52. Xu, X., Wu, Y., Liang, P., He, Y., & Wang, H. (2024). Emerging synergies between large language models and machine learning in e-commerce recommendations. arXiv preprint arXiv:2403.02760. Xu, X., Wu, Y., Liang, P., He, Y., & Wang, H. (2024). Emerging Synergies Between Large Language Models and Machine Learning in E-commerce Recommendations. SPIE Proceedings Publications. Zeng, Q., Lin, L., Jiang, R., Huang, W., &Lin, D. (2025). NNEnsLeG: A novel approach for e- commerce payment fraud detection using ensemble learning and neural networks. Information Processing & Management, 62(1), 103916.
