[论文解读] 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 Innovations被引用 9

一句话总结

论文将传统电子商务分类与个性化推荐进行比较，并提出一个面向 eBay 平台的基于 BERT 的最近邻系统，通过人工评估进行验证，并附带一个操作指南。

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.

研究动机与目标

• 激发通过改进分类和个性化推荐减轻电子商务中的信息过载的需求。
• 在电子商务、内容和媒体领域，比较传统商品分类系统与个性化推荐方法。
• 识别个性化推荐中的挑战（隐私、偏见、可扩展性、冷启动）并概述应对策略。
• 提出一个实用的、面向 eBay 平台的基于 BERT 的最近邻推荐框架。
• 提供一个用于部署和扩展推荐系统的操作指南与结构化输出。

提出的方法

• 利用 BERT 模型获取产品与用户查询的上下文表示。
• 应用最近邻算法基于学习到的表示提供个性化产品推荐。
• 通过提出的策略解决核心挑战，如数据隐私、算法偏见、可扩展性和冷启动。
• 提供一个实用的实现框架，包含操作指南和面向真实世界部署在电子商务平台（eBay）上的结构化输出格式。

实验结果

研究问题

• RQ1 基于 BERT 的表征和最近邻搜索如何提升电子商务的商品分类和个性化？
• RQ2 哪些策略在电子商务个性化推荐中有效缓解隐私问题、偏见、可扩展性和冷启动？
• RQ3 提出的系统如何适应 eBay 平台的需求，确保可操作性和可扩展性？

主要发现

• 作者通过人工评估证明了所提出的基于 BERT 的最近邻推荐器的有效性。
• 该论文提供一个实用的操作指南和结构化输出建议以支持部署。
• 该方法旨在解决电子商务个性化中的关键挑战，包括隐私、偏见、可扩展性和冷启动。