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.
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, 14(2), 704-717. https://doi.org/10.19139/soic-2310-5070-2181
Issue
Section
Research Articles
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