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Emerging synergies between large language models and machine learning in e-commerce recommendations
- 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.
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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Cite this article
Xu,X.;Wu,Y.;Liang,P.;He,Y.;Wang,H. (2024). Emerging synergies between large language models and machine learning in e-commerce recommendations. Applied and Computational Engineering,69,57-63.
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
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