[Paper Review] Intelligent Classification and Personalized Recommendation of E-commerce Products Based on Machine Learning

Kangming Xu, Huiming Zhou+3 more|arXiv (Cornell University)|Mar 28, 2024

E-commerce and Technology Innovations9 citations

TL;DR

The paper compares traditional e-commerce classification with personalized recommendation, and proposes a BERT-based nearest-neighbor system tailored to the eBay platform, validated by manual evaluation and accompanied by an operational guide.

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. 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.

Motivation & Objective

• Motivate the need to alleviate information overload in e-commerce through improved classification and personalized recommendations.
• Compare traditional commodity classification systems with personalized recommendation approaches in e-commerce, content, and media domains.
• Identify challenges in personalized recommendations (privacy, bias, scalability, cold start) and outline strategies to address them.
• Propose a practical, BERT-based nearest-neighbor recommendation framework tailored to the eBay platform.
• Provide an operational guide and structured output for deployment and scalability of the recommendation system.

Proposed method

• Leverage a BERT model to derive contextual representations of products and user queries.
• Apply a nearest-neighbor algorithm to provide personalized product recommendations based on learned representations.
• Address core challenges such as data privacy, algorithmic bias, scalability, and cold-start through proposed strategies.
• Offer a practical implementation framework with an operational guide and structured output formats for real-world deployment on e-commerce platforms (eBay).

Experimental results

Research questions

• RQ1 How can a BERT-based representations and nearest-neighbor search improve e-commerce product classification and personalization?
• RQ2 What strategies effectively mitigate privacy concerns, bias, scalability, and cold-start in an e-commerce personalized recommender?
• RQ3 How can the proposed system be adapted to the needs of the eBay platform and ensure operability and scalability?

Key findings

• The authors substantiate the efficacy of the proposed BERT-based nearest-neighbor recommender through manual evaluation.
• The paper provides a practical operational guide and structured output recommendations to support deployment.
• The approach is designed to address key challenges in e-commerce personalization, including privacy, bias, scalability, and cold-start.