Assessing the impact of AI-generated product names on e-commerce performance

This paper studies the impact of Large Language Model (LLM) technology on the e-commerce industry. This work conducts a detailed review of the current implementation level of LLM technologies in the e-commerce industry. Next, it analyzes the approaches to detecting AI-generated text and determines the limitations of their application. The proposed methodology defines the impact of LLM models on the e-commerce industry based on a comparative analysis between indicators of machine-generated texts and e-commerce product metrics. Applying this methodology to real data, one of the most relevant data collected after the release of ChatGPT, the results of statistical analyses show a positive correlation between the studied indicators. It is proved that this dependence is dynamic and changes over time. The obtained implicit indicators measure the influence of LLM technologies on the e-commerce domain. This influence is expected to grow, requiring further research.

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Publication Details

Journal
System research and information technologies
Published
2025-03-28
DOI
https://doi.org/10.20535/srit.2308-8893.2025.1.10
Primary Topic
Big Data and Business Intelligence
Type
article
Field-Weighted Citation Impact
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article

Assessing the impact of AI-generated product names on e-commerce performance

Oleksandr Bratus
System research and information technologies
Big Data and Business Intelligence
article

Assessing the impact of AI-generated product names on e-commerce performance

Oleksandr Bratus
article en

Abstract

This paper studies the impact of Large Language Model (LLM) technology on the e-commerce industry. This work conducts a detailed review of the current implementation level of LLM technologies in the e-commerce industry. Next, it analyzes the approaches to detecting AI-generated text and determines the limitations of their application. The proposed methodology defines the impact of LLM models on the e-commerce industry based on a comparative analysis between indicators of machine-generated texts and e-commerce product metrics. Applying this methodology to real data, one of the most relevant data collected after the release of ChatGPT, the results of statistical analyses show a positive correlation between the studied indicators. It is proved that this dependence is dynamic and changes over time. The obtained implicit indicators measure the influence of LLM technologies on the e-commerce domain. This influence is expected to grow, requiring further research.

System research and information technologies(1)
National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” (UA)
Openalex Percentile: Top 82%
Big Data and Business Intelligence
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