Elevating E-commerce Customer Experience: A Machine Learning-Driven Recommendation System
Keywords:
E-commerce, Customer experience, Recommendation system, Machine learning, Collaborative filtering, Textual clustering, User engagement
Abstract
In the era of e-commerce, providing an exceptional customer experience is pivotal for online businesses. This paper introduces a comprehensive machine learning-based recommendation system meticulously crafted to enhance the customer experience on e-commerce platforms. Our system employs a multifaceted approach, incorporating product popularity analysis, model-based collaborative filtering, and textual clustering, to address a spectrum of user profiles and business contexts. It excels in delivering personalized product recommendations, effectively tackling the challenges associated with attracting and retaining new customers, as well as guiding businesses in their nascent stages of online presence. By harnessing diverse methodologies, this system not only optimizes the customer journey but also offers a versatile framework for future research endeavors aimed at continuously refining and adapting to the dynamic e-commerce landscape.References
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\end{thebibliography}
\bibitem{Guven2020}
H. Guven,
\emph{Industry 4.0 and marketing 4.0: in perspective of digitalization and E-Commerce},
In Agile Business Leadership Methods for Industry 4.0 (pp. 25-46). Emerald Publishing Limited, 2020.
\bibitem{Signori2019}
P. Signori, I. Gozzo, D. J. Flint, T. Milfeld, and B. Satinover Nichols,
\emph{Sustainable customer experience: Bridging theory and practice},
The Synergy of Business Theory and Practice: Advancing the practical application of scholarly research, 131-174, 2019.
\bibitem{Loukili2023}
M. Loukili, F. Messaoudi, M. E. Ghazi, and H. Azirar,
\emph{Predicting Future Sales: A Machine Learning Algorithm Showdown},
In The International Conference on Artificial Intelligence and Smart Environment (pp. 26-31). Cham: Springer Nature Switzerland, 2023. doi: 10.1007/978-3-031-48465-0\_4
\bibitem{Robillard2009}
M. Robillard, R. Walker, and T. Zimmermann,
\emph{Recommendation systems for software engineering},
IEEE software, 27(4), 80-86, 2009.
\bibitem{Chandra2022}
S. Chandra, S. Verma, W. M. Lim, S. Kumar, and N. Donthu,
\emph{Personalization in personalized marketing: Trends and ways forward},
Psychology \& Marketing, 39(8), 1529-1562, 2022.
\bibitem{Vijayakumar2023}
H. Vijayakumar,
\emph{Revolutionizing Customer Experience with AI: A Path to Increase Revenue Growth Rate},
In 2023 15th International Conference on Electronics, Computers and Artificial Intelligence (ECAI) (pp. 1-6). IEEE, 2023.
\bibitem{Loukili2024}
M. Loukili, F. Messaoudi, and H. Azirar,
\emph{E-Payment Fraud Detection in E-Commerce using Supervised Learning Algorithms},
In: Y. Maleh, J. Zhang, and A. Hansali (Eds.), Advances in Emerging Financial Technology and Digital Money. CRC Press, pp. 27-35, 2024. doi: 10.1201/9781032667478-3
\bibitem{Mason2023}
T. Mason, and S. Jarvis,
\emph{Omnichannel retail: How to build winning stores in a digital world},
Kogan Page Publishers, 2023.
\bibitem{LoukiliIJAI2023}
M. Loukili, F. Messaoudi, and M. E. Ghazi,
\emph{Machine learning based recommender system for e-commerce},
IAES International Journal of Artificial Intelligence, 12(4), 1803-1811, 2023. doi: 10.11591/ijai.v12.i4
\bibitem{Smith2017}
B. Smith, and G. Linden,
\emph{Two decades of recommender systems at Amazon.com},
IEEE Internet Computing, 21(3), 12-18, 2017.
\bibitem{Ko2022}
H. Ko, S. Lee, Y. Park, and A. Choi,
\emph{A survey of recommendation systems: recommendation models, techniques, and application fields},
Electronics, 11(1), 141, 2022.
\bibitem{Kshetri2014}
N. Kshetri,
\emph{The emerging role of Big Data in key development issues: Opportunities, challenges, and concerns},
Big Data \& Society, 1(2), 2053951714564227, 2014.
\bibitem{Wibowo2020}
A. Wibowo, S. C. Chen, U. Wiangin, Y. Ma, and A. Ruangkanjanases,
\emph{Customer behavior as an outcome of social media marketing: The role of social media marketing activity and customer experience},
Sustainability, 13(1), 189, 2020.
\bibitem{Reinartz2019}
W. Reinartz, N. Wiegand, and M. Imschloss,
\emph{The impact of digital transformation on the retailing value chain},
International Journal of Research in Marketing, 36(3), 350-366, 2019.
\bibitem{Sun2020}
H. Sun, M. Fan, and Y. Tan,
\emph{An empirical analysis of seller advertising strategies in an online marketplace},
Information Systems Research, 31(1), 37-56, 2020.
\bibitem{Metsai2021}
A. I. Metsai, I. M. Tabakis, K. Karamitsios, K. Kotrotsios, P. Chatzimisios, G. Stalidis, and K. Goulianas,
\emph{Customer Journey: Applications of AI and Machine Learning in E-Commerce},
In Interactive Mobile Communication, Technologies and Learning (pp. 123-132). Cham: Springer International Publishing, 2021.
\bibitem{Bharadiya2023}
J. P. Bharadiya,
\emph{Machine Learning and AI in Business Intelligence: Trends and Opportunities},
International Journal of Computer (IJC), 48(1), 123-134, 2023.
\bibitem{Han2022}
C. Han, P. Castells, P. Gupta, X. Xu, and V. Salaka,
\emph{Addressing Cold Start in Product Search via Empirical Bayes},
In Proceedings of the 31st ACM International Conference on Information \& Knowledge Management (pp. 3141-3151), 2022.
\bibitem{Fayyaz2020}
Z. Fayyaz, M. Ebrahimian, D. Nawara, A. Ibrahim, and R. Kashef,
\emph{Recommendation systems: Algorithms, challenges, metrics, and business opportunities},
Applied Sciences, 10(21), 7748, 2020.
\bibitem{ElYoubi2023}
R. El Youbi, F. Messaoudi, and M. Loukili,
\emph{Deep Learning for Dynamic Content Adaptation: Enhancing User Engagement in E-commerce},
In The International Conference on Artificial Intelligence and Smart Environment (pp. 160-165). Cham: Springer Nature Switzerland, 2023. doi: 10.1007/978-3-031-48465-0\_21
\bibitem{LoukiliICIT2023}
M. Loukili, F. Messaoudi, and M. E. Ghazi,
\emph{Personalizing Product Recommendations using Collaborative Filtering in Online Retail: A Machine Learning Approach},
In 2023 International Conference on Information Technology (ICIT) (pp. 19-24). IEEE, 2023. doi: 10.1109/ICIT58056.2023.10226042
\bibitem{Loukili2022}
M. Loukili, and F. Messaoudi,
\emph{Machine Learning, Deep Neural Network and Natural Language Processing Based Recommendation System},
In International Conference on Advanced Intelligent Systems for Sustainable Development (pp. 65-76). Cham: Springer Nature Switzerland, 2022. doi: 10.1007/978-3-031-26384-2\_7
\bibitem{Heckel2017}
R. Heckel, M. Vlachos, T. Parnell, and C. Dünner,
\emph{Scalable and interpretable product recommendations via overlapping co-clustering},
In 2017 IEEE 33rd International Conference on Data Engineering (ICDE) (pp. 1033-1044). IEEE, 2017.
\bibitem{Huang2023}
X. Huang, J. Lian, Y. Lei, J. Yao, D. Lian, and X. Xie,
\emph{Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations},
arXiv preprint arXiv:2308.16505, 2023.
\bibitem{LoukiliIJS2024}
M. Loukili, F. Messaoudi, and M. E. Ghazi,
\emph{Enhancing Customer Retention through Deep Learning and Imbalanced Data Techniques},
Iraqi Journal of Science, 2853-2866, 2024. doi: 10.24996/ijs.2024.65.5.39
\end{thebibliography}
Published
2025-05-26
How to Cite
El Youbi, R., Messaoudi, F., Loukili, M., & El Ghazi, M. (2025). Elevating E-commerce Customer Experience: A Machine Learning-Driven Recommendation System. Statistics, Optimization & Information Computing. https://doi.org/10.19139/soic-2310-5070-2181
Issue
Section
Research Articles
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