Design and Effectiveness Evaluation of an E-commerce Recommendation System Based on Large Language Models

DOI

10.2991/978-94-6239-701-9_65How to use a DOI?

Keywords

Large Language Model; User Interest Modeling; Explain ability; User Interest Modeling

Abstract

Traditional e-commerce recommendation systems primarily rely on collaborative filtering, matrix factorization, or deep neural networks. This leads to significant shortcomings in scenarios such as cold starts, long-tail product recommendations, interest expansion, and alleviating information cocoons. In recent years, large language models (LLMs) have made remarkable progress in semantic understanding, knowledge reasoning, and generative expression, presenting new opportunities for e-commerce recommendation systems. This paper analyzes the typical architecture and limitations of traditional recommendation systems, as well as the current status and development trends of LLMs in the recommendation domain. It proposes a solution for an e-commerce recommendation system that integrates LLMs. This validates the feasibility and effectiveness of the proposed solution. Finally, the paper summarizes the limitations of the research and outlines future research directions.

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.

Download article (PDF)

TY  - CONF
AU  - Shuang Zhou
AU  - Yun Liu
PY  - 2026
DA  - 2026/07/30
TI  - Design and Effectiveness Evaluation of an E-commerce Recommendation System Based on Large Language Models
BT  - Proceedings of the 2026 11th International Conference on Social Sciences and Economic Development (ICSSED 2026)
PB  - Atlantis Press
SP  - 645
EP  - 650
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-701-9_65
DO  - 10.2991/978-94-6239-701-9_65
ID  - Zhou2026
ER  -