The Development Trend of AI-Driven E-Commerce Personalized Recommendation Systems

DOI

10.2991/978-2-38476-585-0_27How to use a DOI?

Keywords

AI-Driven E-Commerce; Personalized Recommendation Systems; LLMs

Abstract

Artificial intelligence (AI) has become a common technology on e-commerce platforms, revolutionizing consumer behavior. One of the most effective technologies is product recommendation systems. AI systems leverage behavioral data, interaction history, and contextual information to analyze and predict user needs and enhance the user experience. Using content-based filtering, collaborative filtering, deep learning, and reinforcement learning techniques, these systems can identify customer preferences and provide timely and highly accurate recommendations. This reduces recommendation errors and fatigue, and ensures that recommended products align with user needs and expectations, ultimately increasing sales. Xiang notes that the Transformer architecture is a fundamental building block of large-scale language models (LLMs), widely used in industries including e-commerce. These models power everything from text understanding to recommendation engines, providing powerful technical capabilities and information retrieval and screening capabilities, thereby further enhancing user experience and streamlining service operations. This article explores the concept of recommendation systems and the development trends of AI-driven personalized recommendation systems in the e-commerce sector. Through a comprehensive analysis of relevant literature and research results, this article explains the current status of personalized recommendation systems in the e-commerce sector, analyzes the key role played by AI technology, and provides insights into future developments. This article provides a theoretical reference for the further development of personalized recommendation systems in the e-commerce industry.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Xiaoxi Zhang
PY  - 2026
DA  - 2026/06/18
TI  - The Development Trend of AI-Driven E-Commerce Personalized Recommendation Systems
BT  - Proceedings of the 2025 International Conference on Hybrid Commerce, Human Capital, and Economic Dynamics (ICHCH 2025)
PB  - Atlantis Press
SP  - 230
EP  - 236
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-2-38476-585-0_27
DO  - 10.2991/978-2-38476-585-0_27
ID  - Zhang2026
ER  -