  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. Such systems not only enhance user experience and
satisfaction but also furnish opportunities for businesses and platforms to
augment user engagement, sales, and advertising efficacy.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.
