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

Shuang Zhou () and Yun Liu
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Shuang Zhou: Beijing Information Technology College
Yun Liu: Beijing Information Technology College

A chapter in Proceedings of the 2026 11th International Conference on Social Sciences and Economic Development (ICSSED 2026), 2026, pp 645-650 from Springer

Abstract: 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.

Keywords: Large Language Model; User Interest Modeling; Explain ability; User Interest Modeling (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6239-701-9_65

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DOI: 10.2991/978-94-6239-701-9_65

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