Design and Implementation of Intelligent Shopping Recommendation System Based on AI

Bohan Wu

doi:10.54254/2755-2721/2025.PO26561

Applied and Computational EngineeringOpen access

Design and Implementation of Intelligent Shopping Recommendation System Based on AI

Research Article

Open Access

Design and Implementation of Intelligent Shopping Recommendation System Based on AI

Bohan Wu ^1*

¹ Faculty of Science and Technology, Beijing Normal-Hong Kong Baptist University, Zhuhai, China

^*Corresponding author: 951969788@qq.com

Published on 9 September 2025

ACE Vol.184

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-355-0

ISBN (Online): 978-1-80590-356-7

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Abstract

This paper presents the design and implementation of an Intelligent Shopping Recommendation System that addresses the increasing need for personalized product suggestions in modern e-commerce environments. The system combines a React-based responsive front-end with real-time multi-platform data crawling and advanced AI-driven recommendation generation. By integrating large language models such as Qwen and DeepSeek, the system supports both keyword-based search and conversational interaction, allowing users to refine their shopping needs through natural dialogue. Functional and performance tests confirm that the system delivers real-time, contextually relevant recommendations and stable search results across mainstream desktop environments and browsers. The architecture is modular and lightweight, leveraging serverless deployment and external AI services for scalability and maintainability. While the current implementation focuses on selected e-commerce platforms, future work will expand data sources and enhance personalization through user behavior modeling and continuous learning. The study demonstrates the feasibility and practical value of combining modern web technologies with AI capabilities to improve shopping efficiency and user satisfaction, providing a foundation for future intelligent retail solutions that adapt to evolving technologies and consumer expectations.

Keywords:

Intelligent Shopping Recommendation, E-commerce, Large Language Model, Conversational AI, Personalized Product Search

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References

[1]. Laudon, K. C., & Traver, C. G. (2021). E-commerce 2021: Business, Technology, and Society. 16th ed., Pearson.

[2]. Ricci, F., Rokach, L. and Shapira, B. (2015). Recommender Systems Handbook. [online] Boston, MA: Springer US. doi: https: //doi.org/10.1007/978-1-4899-7637-6.

[3]. Su, X. and Khoshgoftaar, T.M. (2009). A Survey of Collaborative Filtering Techniques. Advances in Artificial Intelligence, 2009, pp.1–19. doi: https: //doi.org/10.1155/2009/421425.

[4]. Zhang, S., Yao, L., Sun, A. and Tay, Y. (2019). Deep Learning Based Recommender System. ACM Computing Surveys, 52(1), pp.1–38. doi: https: //doi.org/10.1145/3285029.

[5]. Covington, P., Adams, J. and Sargin, E. (2016). Deep Neural Networks for YouTube Recommendations. Proceedings of the 10th ACM Conference on Recommender Systems - RecSys ’16, [online] pp.191–198. doi: https: //doi.org/10.1145/2959100.2959190.

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References

[1]. Laudon, K. C., & Traver, C. G. (2021). E-commerce 2021: Business, Technology, and Society. 16th ed., Pearson.

[2]. Ricci, F., Rokach, L. and Shapira, B. (2015). Recommender Systems Handbook. [online] Boston, MA: Springer US. doi: https: //doi.org/10.1007/978-1-4899-7637-6.

[3]. Su, X. and Khoshgoftaar, T.M. (2009). A Survey of Collaborative Filtering Techniques. Advances in Artificial Intelligence, 2009, pp.1–19. doi: https: //doi.org/10.1155/2009/421425.

[4]. Zhang, S., Yao, L., Sun, A. and Tay, Y. (2019). Deep Learning Based Recommender System. ACM Computing Surveys, 52(1), pp.1–38. doi: https: //doi.org/10.1145/3285029.

Cite this article

Wu,B. (2025). Design and Implementation of Intelligent Shopping Recommendation System Based on AI. Applied and Computational Engineering,184,1-7.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-CDS 2025 Symposium: Data Visualization Methods for Evaluation

ISBN: 978-1-80590-355-0(Print) / 978-1-80590-356-7(Online)

Editor: Marwan Omar, Elisavet Andrikopoulou

Conference website: https://2025.confcds.org/portsmouth.html

Conference date: 30 July 2025

Series: Applied and Computational Engineering

Volume number: Vol.184

ISSN: 2755-2721(Print) / 2755-273X(Online)

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