Comparison of machine learning algorithms and large language models for product categorization

Name: Comparison of machine learning algorithms and large language models for product categorization
Author: Yildiz, Olcay Taner, Zaval, Mounes, Ihsanoglu, Abdullah

İsim	Comparison of machine learning algorithms and large language models for product categorization
Yazar	Yildiz, Olcay Taner, Zaval, Mounes, Ihsanoglu, Abdullah
Basım Tarihi:	2024-01-01
Basım Yeri	- IEEE
Konu	E-commerce
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	979-835038896-1
Kayıt Numarası	a3e9769a-290b-4809-b7a9-f75b32fbe1b1
Lokasyon	Computer Science
Tarih	2024-01-01
Örnek Metin	This study explores the efficacy of traditional machine learning algorithms and Large Language Models (LLMs) in automating product categorization for online e-commerce platforms. By comparing these methodologies, we assess their performance in classifying a diverse range of product listings. Our findings indicate that for this context, LLMs offer similar performance in understanding and categorizing complex textual data to traditional machine learning techniques, suggesting that use of LLMs in this context may be unnecessary, and that the trade-off ultimately comes down to the operational costs and resource consumption of each model. This work contributes to the field by providing insights into the capabilities and limitations of current text categorization techniques in the context of rapidly expanding online marketplaces.
DOI	10.1109/SIU61531.2024.10600809

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Comparison of machine learning algorithms and large language models for product categorization

Yazar Yildiz, Olcay Taner, Zaval, Mounes, Ihsanoglu, Abdullah

Basım Tarihi 2024-01-01

Basım Yeri - IEEE

Konu E-commerce

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 979-835038896-1

Kayıt Numarası a3e9769a-290b-4809-b7a9-f75b32fbe1b1

Lokasyon Computer Science

Tarih 2024-01-01

Örnek Metin This study explores the efficacy of traditional machine learning algorithms and Large Language Models (LLMs) in automating product categorization for online e-commerce platforms. By comparing these methodologies, we assess their performance in classifying a diverse range of product listings. Our findings indicate that for this context, LLMs offer similar performance in understanding and categorizing complex textual data to traditional machine learning techniques, suggesting that use of LLMs in this context may be unnecessary, and that the trade-off ultimately comes down to the operational costs and resource consumption of each model. This work contributes to the field by providing insights into the capabilities and limitations of current text categorization techniques in the context of rapidly expanding online marketplaces.

DOI 10.1109/SIU61531.2024.10600809