Applied and Computational Engineering

- The Open Access Proceedings Series for Conferences


Proceedings of the 6th International Conference on Computing and Data Science

Series Vol. 69 , 08 July 2024


Open Access | Article

Emerging synergies between large language models and machine learning in e-commerce recommendations

Xiaonan Xu * 1 , Yichao Wu 2 , Penghao Liang 3 , Yuhang He 4 , Han Wang 5
1 Independent Researcher, Northern Arizona University, Flagstaff, AZ, USA,86011
2 Computer Science, Northeastern University, Boston, MA,02115
3 Information Systems, Northeastern University, San Jose, CA,95110
4 Computer Science and Technology, Tianjin University of Technology, Tianjin,CN,300384
5 Financial Mathematics, University of Southern California, Los Angeles, USA,90089

* Author to whom correspondence should be addressed.

Applied and Computational Engineering, Vol. 69, 57-63
Published 08 July 2024. © 08 July 2024 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Xiaonan Xu, Yichao Wu, Penghao Liang, Yuhang He, Han Wang. Emerging synergies between large language models and machine learning in e-commerce recommendations. ACE (2024) Vol. 69: 57-63. DOI: 10.54254/2755-2721/69/20241512.

Abstract

This paper explores the integration of large language models (LLMs) into collaborative filtering algorithms to enhance recommendation systems in the e-commerce domain. The proposed approach combines user-based and item-based collaborative filtering with LLMs to improve recommendation accuracy and personalization. Specifically, the study introduces a novel framework called PALR, which leverages LLMs to refine user-item interactions and enrich item representations. PALR utilizes historical user behavior data, such as clicks, purchases, and ratings, to guide candidate retrieval and generate recommended items. This study highlights the importance of integrating LLMs into recommendation systems to deliver more accurate and personalized suggestions, ultimately improving user satisfaction and driving sales in e-commerce platforms.

Keywords

Large language models, collaborative filtering, recommendation systems, e-commerce

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Data Availability

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

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Volume Title
Proceedings of the 6th International Conference on Computing and Data Science
ISBN (Print)
978-1-83558-459-0
ISBN (Online)
978-1-83558-460-6
Published Date
08 July 2024
Series
Applied and Computational Engineering
ISSN (Print)
2755-2721
ISSN (Online)
2755-273X
DOI
10.54254/2755-2721/69/20241512
Copyright
08 July 2024
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated