Enhancing productiveness in E-commerce through machine learning: Challenges, Future Perspectives, and a MATLAB-Based Approach
1 Automation and Process Contol Engineer, Gist Limited, United Kingdom.
2 Business Administrator, Northern University, Boston USA.
3 Graduate research Assistant, University of Massachusetts, United States of America.
4 Graduate Assistant, Georgia State University, Atlanta, United States of America.
Review Article
World Journal of Advanced Research and Reviews, 2024, 23(02), 2120–2132
Publication history:
Received on 17 July 2024; revised on 24 August 2024; accepted on 26 August 2024
Abstract:
The rapid growth of e-commerce has created an urgent need for more productive and efficient systems to handle the increasing complexity of online transactions and consumer behaviour. Machine Learning (ML) offers a transformative approach to enhancing productiveness in e-commerce by optimizing processes such as personalized recommendations, dynamic pricing, customer segmentation, and supply chain management. This article explores the integration of ML-driven initiatives in e-commerce, addressing key challenges such as data privacy, algorithmic bias, and the computational demands of large-scale ML applications. A MATLAB-based approach is proposed for developing and implementing these ML models, leveraging MATLAB’s robust toolboxes for data analysis, model training, and system simulation. Through an extensive literature review, this study highlights the current state of ML in e-commerce, identifies existing challenges, and discusses future perspectives. The introduction of ML-driven strategies not only improves operational efficiency but also enhances customer satisfaction, ultimately driving productivity in the highly competitive e-commerce landscape. The findings of this article are expected to provide valuable insights for both academic researchers and industry professionals aiming to harness the full potential of ML in e-commerce.
Keywords:
Machine Learning; E-Commerce; Productiveness; MATLAB; Data Privacy; Algorithmic Bias
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Copyright information:
Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0