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Intelligent classification and personalized recommendation of E-commerce products based on machine learning
- 1 Computer Science and Engineering , Santa Clara University
- 2 Computer Science,Northeastern University
- 3 Electrical & Computer Engineering,New York University
- 4 Computer Information System, Colorado state university
- 5 Management Information Systems, University of Pittsburgh
* Author to whom correspondence should be addressed.
Abstract
With the rapid evolution of the Internet and the exponential proliferation of information, users encounter information overload and the conundrum of choice. Personalized recommendation systems play a pivotal role in alleviating this burden by aiding users in filtering and selecting information tailored to their preferences and requirements. This paper undertakes a comparative analysis between the operational mechanisms of traditional e-commerce commodity classification systems and personalized recommendation systems. It delineates the significance and application of personalized recommendation systems across e-commerce, content information, and media domains. Furthermore, it delves into the challenges confronting personalized recommendation systems in e-commerce, including data privacy, algorithmic bias, scalability, and the cold start problem. Strategies to address these challenges are elucidated. Subsequently, the paper outlines a personalized recommendation system leveraging the BERT model and nearest neighbor algorithm, specifically tailored to address the exigencies of the eBay e-commerce platform. The efficacy of this recommendation system is substantiated through manual evaluation, and a practical application operational guide and structured output recommendation results are furnished to ensure the system's operability and scalability.
Keywords
personalized recommendation system, E-commerce, data privacy, BERT model
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Cite this article
Xu,K.;Zhou,H.;Zheng,H.;Zhu,M.;Xin,Q. (2024). Intelligent classification and personalized recommendation of E-commerce products based on machine learning. Applied and Computational Engineering,64,147-153.
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The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 6th International Conference on Computing and Data Science
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