基于生成模型的广告文本改进方法

Kh.V. Lipianina-Honcharenko, I.R. Kit
{"title":"基于生成模型的广告文本改进方法","authors":"Kh.V. Lipianina-Honcharenko, I.R. Kit","doi":"10.31498/2225-6733.46.2023.288087","DOIUrl":null,"url":null,"abstract":"The article is dedicated to the development of a new method of improvement advertising texts based on the use of generative models. Advertising content plays a crucial role in modern marketing, as it fosters a brand's interaction with the audience and attracts new customers. However, creating effective advertising content often presents a challenge. Generative models open up new opportunities for creating advertising texts. They can be used to automate the process of content creation, providing a high level of originality and creativity. Specifically, this article focuses on the use of GPT series models for generating advertising texts. The first task within this research is to study the theoretical basis of generative models and their capabilities for text creation. The authors conduct a detailed analysis of the main principles of these models' operation, as well as their potential use in the context of advertising text. Further, the article describes a method of collecting and preparing input data for training generative models. Since the quality of output texts heavily depends on the quality of input data, this stage is important for the successful application of generative models. Next, the authors develop an algorithm for training the generative model based on the collected data. They describe the process of selecting optimal hyperparameters for the model, which is vital to achieve the best results. The approach presented in this article is a significant contribution to developing new methods for optimizing advertising texts. It has considerable potential for use in the marketing sphere, where there is a need to quickly and effectively generate large volumes of content. At the same time, the research results may be useful for further scientific studies in this field","PeriodicalId":31573,"journal":{"name":"Visnik Priazovs''kogo Derzgavnogo Tehnicnogo Universitetu Seria Tehnicni Nauki","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The method of improving advertising texts based on the use of generative models\",\"authors\":\"Kh.V. Lipianina-Honcharenko, I.R. Kit\",\"doi\":\"10.31498/2225-6733.46.2023.288087\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The article is dedicated to the development of a new method of improvement advertising texts based on the use of generative models. Advertising content plays a crucial role in modern marketing, as it fosters a brand's interaction with the audience and attracts new customers. However, creating effective advertising content often presents a challenge. Generative models open up new opportunities for creating advertising texts. They can be used to automate the process of content creation, providing a high level of originality and creativity. Specifically, this article focuses on the use of GPT series models for generating advertising texts. The first task within this research is to study the theoretical basis of generative models and their capabilities for text creation. The authors conduct a detailed analysis of the main principles of these models' operation, as well as their potential use in the context of advertising text. Further, the article describes a method of collecting and preparing input data for training generative models. Since the quality of output texts heavily depends on the quality of input data, this stage is important for the successful application of generative models. Next, the authors develop an algorithm for training the generative model based on the collected data. They describe the process of selecting optimal hyperparameters for the model, which is vital to achieve the best results. The approach presented in this article is a significant contribution to developing new methods for optimizing advertising texts. It has considerable potential for use in the marketing sphere, where there is a need to quickly and effectively generate large volumes of content. At the same time, the research results may be useful for further scientific studies in this field\",\"PeriodicalId\":31573,\"journal\":{\"name\":\"Visnik Priazovs''kogo Derzgavnogo Tehnicnogo Universitetu Seria Tehnicni Nauki\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-06-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Visnik Priazovs''kogo Derzgavnogo Tehnicnogo Universitetu Seria Tehnicni Nauki\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.31498/2225-6733.46.2023.288087\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Visnik Priazovs''kogo Derzgavnogo Tehnicnogo Universitetu Seria Tehnicni Nauki","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.31498/2225-6733.46.2023.288087","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

本文致力于开发一种基于生成模型的改进广告文本的新方法。广告内容在现代营销中起着至关重要的作用,因为它促进了品牌与受众的互动,吸引了新的客户。然而,创造有效的广告内容往往是一个挑战。生成模型为广告文本的创作提供了新的机会。它们可用于自动化内容创建过程,提供高水平的原创性和创造力。具体来说,本文关注的是使用GPT系列模型生成广告文本。本研究的第一个任务是研究生成模型的理论基础及其文本创建能力。作者详细分析了这些模式运行的主要原理,以及它们在广告文本语境中的潜在用途。此外,本文还描述了一种收集和准备用于训练生成模型的输入数据的方法。由于输出文本的质量在很大程度上取决于输入数据的质量,因此这个阶段对于生成模型的成功应用非常重要。接下来,作者开发了一种基于收集数据的生成模型训练算法。他们描述了为模型选择最优超参数的过程,这对获得最佳结果至关重要。本文提出的方法对开发优化广告文本的新方法有重要贡献。它在需要快速有效地生成大量内容的营销领域具有相当大的潜力。同时,研究结果对该领域的进一步科学研究也有一定的参考价值
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The method of improving advertising texts based on the use of generative models
The article is dedicated to the development of a new method of improvement advertising texts based on the use of generative models. Advertising content plays a crucial role in modern marketing, as it fosters a brand's interaction with the audience and attracts new customers. However, creating effective advertising content often presents a challenge. Generative models open up new opportunities for creating advertising texts. They can be used to automate the process of content creation, providing a high level of originality and creativity. Specifically, this article focuses on the use of GPT series models for generating advertising texts. The first task within this research is to study the theoretical basis of generative models and their capabilities for text creation. The authors conduct a detailed analysis of the main principles of these models' operation, as well as their potential use in the context of advertising text. Further, the article describes a method of collecting and preparing input data for training generative models. Since the quality of output texts heavily depends on the quality of input data, this stage is important for the successful application of generative models. Next, the authors develop an algorithm for training the generative model based on the collected data. They describe the process of selecting optimal hyperparameters for the model, which is vital to achieve the best results. The approach presented in this article is a significant contribution to developing new methods for optimizing advertising texts. It has considerable potential for use in the marketing sphere, where there is a need to quickly and effectively generate large volumes of content. At the same time, the research results may be useful for further scientific studies in this field
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
审稿时长
12 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信