Exploring the Depths of User Interest: How LLMs are Revolutionizing Personalized Shopping

Xin Guan

doi:10.54097/j6wgqx21

Authors

Xin Guan

DOI:

https://doi.org/10.54097/j6wgqx21

Keywords:

Large Language Models (LLMs), Personalized Recommendation, Intelligent E-Commerce, Natural Language Understanding, Multimodal Interaction, Dynamic Interest Modeling

Abstract

This paper explores the transformative impact of Large Language Models (LLMs) on personalized shopping, highlighting their revolutionary capabilities in natural language understanding, dynamic interest modeling, and multimodal interaction. It discusses how LLMs address the limitations of traditional recommendation systems by enabling semantic deconstruction of user queries, real-time strategy updates, and scenario-based solutions. The study reviews the evolution of LLMs and personalized recommendation systems, emphasizing breakthroughs in contextual reasoning and cross-modal feature fusion. Practical implementation strategies are proposed, including lightweight technology deployment, cross-departmental collaboration, and user-centric experience design. The paper concludes by underscoring the dual technical and commercial value of LLMs, advocating for future research in causal reasoning and multimodal evaluation to enhance algorithmic fairness and sustainability.

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References

[1] Chen, J., Liu, Z., Huang, X., et al. (2023). When large language models meet personalization: Perspectives of challenges and opportunities. World Wide Web, 27: 42.

[2] Editorial Office. (2018). Google open-sources the source code of the BERT model. Data Analysis and Knowledge Discovery, 2(11): 18.

[3] Bubeck, S., Chandrasekaran, V., Eldan, R., et al (2023). Sparks of artificial general intelligence: Early experiments with GPT-4. ArXiv, abs/2303.12712.

[4] Goldberg, D., Nichols, D., Oki, B.M., & Terry, D. (1992). Using collaborative filtering to weave an information tapestry. Communications of the ACM, 35(12): 61-70.

[5] Resnick, P., & Varian, H.R. (1997). Recommender systems. Communications of the ACM, 40(3): 56–58.

[6] Lu, W. (2007). Collaborative filtering algorithm and its application in personalized recommendation system. Beijing University of Posts and Telecommunications, 5.

[7] Zhou, F.Y., Jin, L.P., & Dong, J. (2017). A review of convolutional neural networks. Chinese Journal of Computers, 40(6): 1229–1251.

[8] Zhang, Y., Wu, J., Liu, J., et al. (2024) Towards efficient and scalable sharpness-aware minimization. arXiv. https://arxiv. org/ abs/2505.07105.

[9] Harte, J., et al. (2023). Leveraging large language models for sequential recommendation. RecSys '23: Proceedings of the 17th ACM Conference on Recommender Systems, 1096-1102.