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LLMs for product classification in e-commerce: A zero-shot comparative study of GPT and claude models
Open accessAbstract
In the rapidly evolving e-commerce landscape, efficient and accurate product classification is essential for enhancing customer experience and streamlining operations. Traditional product classification methods, which depend heavily on labeled data and manual effort, struggle with scalability and adaptability to diverse product categories. This study explores the transformative potential of large language models (LLMs) for zero-shot product classification in e-commerce, addressing the challenge of automating product categorization without prior labeled training data. We evaluate the performance of four state-of-the-art LLMs — GPT-4o, GPT-4o mini, Claude 3.5 Sonnet, and Claude 3.5 Haiku — on a diverse dataset of 248 product categories, each containing 20 samples, structured into 8 subsets. Each model performs zero-shot classification, assigning products to predefined categories without prior exposure. Our findings reveal significant variations in classification accuracy across models, with certain LLMs demonstrating superior scalability and adaptability for real-world e-commerce applications. Based on these insights, we developed an API software to integrate the top-performing models into e-commerce systems, enhancing automation and efficiency. This study underscores the transformative role of LLMs in revolutionizing e-commerce workflows and recommends their adoption for scalable, intelligent product classification.
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“LLMs such as GPT-4 [79] and PaLM [80] are trained on massive datasets of text from books, web content, and dialogue corpora. These models exhibit emergent capabilities in natural language understanding, question answering, summarization, dialogue coherence, and even symbolic reasoning [81-83].”
From AI Agents vs. Agentic AI: A Conceptual taxonomy, applications and challenges (2026) · cites this paper as [83]
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Cites
Espejel, J. L., Ettifouri, E. H., Alassan, M. S. Y., Chouham, E. M., & Dahhane, W. (2023) · Natural Language Processing Journal · doi:10.1016/j.nlp.2023.100032 · Cited by 148
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Roumeliotis, K. I., Tselikas, N. D., & Nasiopoulos, D. Κ. (2024) · Natural Language Processing Journal · doi:10.1016/j.nlp.2024.100056 · Cited by 127
Self-citationFang, C., Li, X., Fan, Z., Xu, J., Nag, K., Körpeoğlu, E., Kumar, S., & Achan, K. (2024) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3626772.3661357 · Cited by 75
Applying Machine Learning to Text Segmentation for Information Retrieval
Huang, X., Peng, F., Schuurmans, D., Cercone, N. J., & Robertson, S. E. (2003) · Information Retrieval · doi:10.1023/a:1026028229881 · Cited by 73
The impact of using biased performance metrics on software defect prediction research
Yao, J., & Shepperd, M. (2021) · Information and Software Technology · doi:10.1016/j.infsof.2021.106664 · Cited by 70
LLMs-based machine translation for E-commerce
Gao, D., Chen, K., Chen, B., Dai, H.-F., Jin, L., Jiang, W., Ning, W., Yu, S., Xuan, Q., Cai, X., et al. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125087 · Cited by 44
Haque, M. A., Nahin, K. H., Nirob, J. H., Ahmed, M. K., Singh, N. S. S., Paul, L. C., Algarni, A. D., ELAffendi, M. A., & Ateya, A. A. (2024) · Results in Engineering · doi:10.1016/j.rineng.2024.103106 · Cited by 44
Chen, J., Ma, L., Li, X., Thakurdesai, N., Xu, J., Cho, J. H. D., Nag, K., Körpeoğlu, E., Kumar, S., & Achan, K. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2305.09858 · Cited by 43
Liu, Y., Zhang, W., Chen, Y., Zhang, Y., Bai, H., Feng, F., Cui, H., Li, Y., & Che, W. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.643 · Cited by 41
EcomGPT: Instruction-Tuning Large Language Models with Chain-of-Task Tasks for E-commerce
Li, Y., Ma, S., Wang, X., Huang, S., Jiang, C., Zheng, H.-T., Xie, P., Huang, F., & Jiang, Y. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i17.29820 · Cited by 38
Cost-efficient prompt engineering for unsupervised entity resolution in the product matching domain
Nananukul, N., Sisaengsuwanchai, K., & Kejriwal, M. (2024) · Discover Artificial Intelligence · doi:10.1007/s44163-024-00159-8 · Cited by 36
Leveraging Large Language Models to Enhance Personalized Recommendations in E-commerce
Xu, W. X., Xiao, J., & Chen, J. (2024) · International Conference on Electrical, Communication, and Computer Engineering (ICECCE) · doi:10.1109/icecce63537.2024.10823618 · Cited by 34
Evaluation of Machine Translation Quality through the Metrics of Error Rate and Accuracy
Munková, D., Hájek, P., Munk, M., & Skalka, J. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.04.142 · Cited by 27
Roumeliotis, K. I., Tselikas, N. D., & Nasiopoulos, D. Κ. (2023) · Sustainability · doi:10.3390/su151512015 · Cited by 26
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Palen-Michel, C., Wang, R., Zhang, Y., Yu, D., Xu, C., & Wu, Z. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2408.12779 · Cited by 22
Emerging synergies between large language models and machine learning in e-commerce recommendations
Xu, X., Wu, Y., Liang, P., He, Y., & Wang, H. (2024) · Applied and Computational Engineering · doi:10.54254/2755-2721/69/20241512 · Cited by 20
Generative-AI in E-Commerce: Use-Cases and Implementations
Ghaffari, S., Yousefimehr, B., & Ghatee, M. (2024) · CSI International Symposium on Artificial Intelligence and Signal Processing · doi:10.1109/aisp61396.2024.10475266 · Cited by 18
LLaSA: Large Language and E-Commerce Shopping AssistantPreprint
Zhang, S., Peng, B., Zhao, X., Hu, B., Zhu, Y., Zeng, Y., & Hu, X. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2408.02006 · Cited by 18
Roumeliotis, K. I., Tselikas, N. D., & Nasiopoulos, D. Κ. (2024) · Software · doi:10.3390/software3010004 · Cited by 16
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Soviero, B., Kuhn, D., Salle, A., & Moreira, V. P. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-56066-8_1 · Cited by 15
Relation labeling in product knowledge graphs with large language models for e-commerce
Chen, J., Ma, L., Li, X., Xu, J., Cho, J. H. D., Nag, K., Körpeoğlu, E., Kumar, S., & Achan, K. (2024) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-024-02274-5 · Cited by 15
Hi-Gen: Generative Retrieval For Large-Scale Personalized E-commerce SearchPreprint
Wu, Y., Feng, Y., Wang, J., Zhou, W., Ye, Y., Xiao, R., & Xiao, J. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2404.15675 · Cited by 15
Wang, H., & Na, T. (2023) · ACM SIGIR Forum · doi:10.1145/3642979.3643007 · Cited by 14
Leveraging Large Language Models for Enhanced Product Descriptions in eCommercePreprint
Zhou, J., Liu, B., Hong, J. N. A. Y., Lee, K.-C., & Wen, M. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2310.18357 · Cited by 13
E-Commerce Product Categorization with LLM-based Dual-Expert Classification Paradigm
Cheng, Z., Zhang, W., Chou, C.-C., Jau, Y.-Y., Pathak, A., Gao, P., & Batur, U. (2024) · Proceedings of the 1st Workshop on Customizable NLP: Progress and Challenges in Customizing NLP for a Domain, Application, Group, or Individual (CustomNLP4U) · doi:10.18653/v1/2024.customnlp4u-1.22 · Cited by 12
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D’Asaro, F., Luca, S. D., Bongiovanni, L., Rizzo, G. F., Papadopoulos, S., Schinas, M., & Koutlis, C. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125108 · Cited by 11
Adapting the GPT engine for proactive customer insight extraction in product development
Shahin, M., Chen, F. F., Maghanaki, M., & Hosseinzadeh, A. (2024) · Manufacturing Letters · doi:10.1016/j.mfglet.2024.09.164 · Cited by 10
Deep Learning-based Sentiment Analysis of Amazon Product Reviews
Chhabra, D., Sah, S., Rani, R., Bisla, N., Tomar, A., & Sharma, A. (2024) · International Conference on Trends in Engineering Systems and Technologies (ICTEST) · doi:10.1109/ictest60614.2024.10576180 · Cited by 10
A Large Language Model and Qualitative Comparative Analysis-Based Study of Trust in E-Commerce
Davoodi, L., & Mezei, J. (2024) · Applied Sciences · doi:10.3390/app142110069 · Cited by 9
Rahman, M., Khatoonabadi, S., Abdellatif, A., & Shihab, E. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2409.01382 · Cited by 8
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Nguyen, C., Carrion, D., & Badawy, M. K. (2024) · medRxiv · doi:10.1101/2024.11.16.24317414 · Cited by 6
Oh, S., Yoon, J., Chung, Y., Cho, Y., Shim, H., & Kwon, O. N. (2024) · The Mathematical Education · doi:10.63311/mathedu.2024.63.3.549 · Cited by 6
Comparison of Machine Learning Algorithms and Large Language Models for Product Categorization
İhsanoğlu, A., Zaval, M., & Yıldız, O. T. (2024) · Signal Processing and Communications Applications Conference (SIU) · doi:10.1109/siu61531.2024.10600809 · Cited by 4
Scaling Use-case Based Shopping using LLMs
Farfade, S., Vernekar, S. S., Chaoji, V., & Mukherjee, R. (2024) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3616855.3635748 · Cited by 3
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- Wang, 2024, Leveraging large language models for context-aware product discovery in E-commerce search systems, J. Knowl. Learn. Sci. Technol., 3, 300
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“These models exhibit emergent capabilities in natural language understanding, question answering, summarization, dialogue coherence, and even symbolic reasoning [81–83].”
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Toudas, K. S., Roumeliotis, K. I., Nasiopoulos, D. Κ., & Georgakopoulos, G. (2026) · Information Systems Frontiers · doi:10.1007/s10796-026-10742-2 · Cited by 6
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Metadata
- Authors
- Roumeliotis, K. I., Tselikas, N. D., & Nasiopoulos, D. K.
- Affiliations
- University of Peloponnese (GR)
- Corresponding author
- Roumeliotis, Konstantinos I.
- Venue
- Natural Language Processing Journal
- Publisher
- Elsevier BV
- Published
- 2025-06
- Volume
- 11
- Pages
- 100142
- ISSN
- 2949-7191 (ISSN-L)
- OpenAlex
- W4408935345
- Cited by
- 14
- References
- 37
- Citations by year
- 2026: 8 · 2025: 6
- FWCI
- 5.62
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Natural Language Processing Journal (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Keywords
- product classification, e-commerce, large language models, zero-shot classification, GPT-4o, Claude 3.5 Sonnet, model comparison
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