# Intelligent Classification and Personalized Recommendation of E-commerce Products Based on Machine Learning

arXiv: https://arxiv.org/abs/2403.19345 · HTML: https://mlsanity.com/paper/2403.19345/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 21 references that are in the map.
- First posted on arXiv 28 Mar 2024.

## Builds on (most cited first, 7 of 7)

- [E-BERT: A Phrase and Product Knowledge Enhanced Language Model for E-commerce](https://mlsanity.com/paper/2009.02835/) (2020)
- [Data Pipeline Training: Integrating AutoML to Optimize the Data Flow of Machine Learning Models](https://mlsanity.com/paper/2402.12916/) (2024)
- [Optimizing Portfolio Management and Risk Assessment in Digital Assets Using Deep Learning for Predictive Analysis](https://mlsanity.com/paper/2402.15994/) (2024)
- [The Random Forest Model for Analyzing and Forecasting the US Stock Market in the Context of Smart Finance](https://mlsanity.com/paper/2402.17194/) (2024)
- [Construction and application of artificial intelligence crowdsourcing map based on multi-track GPS data](https://mlsanity.com/paper/2402.15796/) (2024)
- [AI-Driven Anonymization: Protecting Personal Data Privacy While Leveraging Machine Learning](https://mlsanity.com/paper/2402.17191/) (2024)
- [A Protein Structure Prediction Approach Leveraging Transformer and CNN Integration](https://mlsanity.com/paper/2402.19095/) (2024)

## Built on by (most cited first, 2 of 2)

- [SeqUDA-Rec: Sequential User Behavior Enhanced Recommendation via Global Unsupervised Data Augmentation for Personalized Content Marketing](https://mlsanity.com/paper/2509.17361/) (2025)
- [Recommendation systems in e-commerce applications with machine learning methods](https://mlsanity.com/paper/2506.17287/) (2025)

## Abstract (arXiv)

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.