综述机器学习在电商推荐中的应用与挑战
Recommendation systems in e-commerce applications with machine learning methods
- 系统梳理2013-2025年38篇文献，分析推荐方法演进
- 对比协同过滤、内容推荐与混合模型的优劣
- 适合关注电商推荐技术发展的研究者与工程师
电商平台日益依赖推荐系统以提升用户体验、留住客户并推动销售。将机器学习方法融入推荐系统显著提升了其效率、个性化和可扩展性。本文旨在揭示电商推荐系统的当前趋势，识别主要挑战，并评估各类机器学习方法（包括协同过滤、基于内容的过滤和混合模型）的有效性。通过系统文献回顾（SLR），分析了2013至2025年间38篇相关文献，对所用方法进行了比较评估，以确定其在应对电商实际问题中的性能与效果。
原文摘要 · Abstract (English)
E-commerce platforms are increasingly reliant on recommendation systems to enhance user experience, retain customers, and, in most cases, drive sales. The integration of machine learning methods into these systems has significantly improved their efficiency, personalization, and scalability. This paper aims to highlight the current trends in e-commerce recommendation systems, identify challenges, and evaluate the effectiveness of various machine learning methods used, including collaborative filtering, content-based filtering, and hybrid models. A systematic literature review (SLR) was conducted, analyzing 38 publications from 2013 to 2025. The methods used were evaluated and compared to determine their performance and effectiveness in addressing e-commerce challenges.
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