电子商务推荐系统研究综述
A Review of Research on E-Commerce Recommender Systems
DOI: 10.12677/ecl.2025.14124216, PDF,    科研立项经费支持
作者: 曹俊伟, 李军祥*, 李玉璐：上海理工大学管理学院，上海
关键词: 电子商务；推荐系统；协同过滤；深度学习；冷启动；E-Commerce； Recommendation System； Collaborative Filtering； Deep Learning； Cold Start
摘要: 随着互联网技术的飞速发展和电子商务规模的不断扩大，信息过载问题日益严重。推荐系统作为解决信息过载问题的有效工具，已成为电子商务平台提升用户体验、增加销售额和增强用户粘性的核心技术。本文旨在对电子商务推荐系统的研究现状进行系统性综述。首先介绍了推荐系统在电子商务中的重要性及其基本架构，其次详细梳理和分析了基于协同过滤、基于内容、基于知识以及混合推荐等主流推荐算法的基本原理、优势与局限性，然后介绍了用于训练模型的主流电子商务平台的数据集，再探讨了推荐系统在实战中面临的关键挑战如冷启动、数据稀疏性等问题；最后展望了电子商务推荐系统未来的发展趋势，包括深度学习与大模型的深度融合、多目标与序列化推荐、可解释性与公平性、跨域与联邦学习推荐，以及多模态信息融合等方向。
Abstract: With the rapid development of Internet technology and the continuous expansion of the e-commerce scale, the information overload problem has become increasingly severe. As an effective tool to alleviate the information overload problem, recommendation systems have become a core technology for e-commerce platforms to enhance user experience, increase sales, and strengthen user stickiness. This paper aims to provide a systematic review of the current research status of e-commerce recommendation systems. It first introduces the importance and fundamental architecture of recommendation systems in e-commerce. Subsequently, it provides a detailed organization and analysis of the basic principles, advantages, and limitations of mainstream recommendation algorithms, including collaborative filtering-based, content-based, knowledge-based, and hybrid recommendation approaches. Then, it introduces mainstream e-commerce platform datasets commonly used for training models. Furthermore, it discusses key challenges faced by recommendation systems in practical applications, such as the cold-start problem and data sparsity. Finally, the paper prospects the future development trends of e-commerce recommendation systems, including the deep integration of deep learning and large models, multi-objective and sequential recommendation, explainability and fairness, cross-domain and federated learning-based recommendation, and multi-modal information fusion.
文章引用：曹俊伟, 李军祥, 李玉璐. 电子商务推荐系统研究综述[J]. 电子商务评论, 2025, 14(12): 3087-3097. https://doi.org/10.12677/ecl.2025.14124216

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