Personalized recognition system in online shopping by using deep learning

Manjula Devarakonda Venkata, Prashanth Donda, N. B. Madhavi, Pavitar Parkash Singh, A. Azhagu, Jaisudhan Pazhani, Shaik Rehana Banu
{"title":"Personalized recognition system in online shopping by using deep learning","authors":"Manjula Devarakonda Venkata, Prashanth Donda, N. B. Madhavi, Pavitar Parkash Singh, A. Azhagu, Jaisudhan Pazhani, Shaik Rehana Banu","doi":"10.4108/eetiot.4810","DOIUrl":null,"url":null,"abstract":"This study presents an effective monitoring system to watch the Buying Experience across multiple shop interactions based on the refinement of the information derived from physiological data and facial expressions. The system's efficacy in recognizing consumers' emotions and avoiding bias based on age, race, and evaluation gender in a pilot study. The system's data has been compared to the outcomes of conventional video analysis. The study's conclusions indicate that the suggested approach can aid in the analysis of consumer experience in a store setting.","PeriodicalId":506477,"journal":{"name":"EAI Endorsed Transactions on Internet of Things","volume":"1 4","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"EAI Endorsed Transactions on Internet of Things","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4108/eetiot.4810","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

This study presents an effective monitoring system to watch the Buying Experience across multiple shop interactions based on the refinement of the information derived from physiological data and facial expressions. The system's efficacy in recognizing consumers' emotions and avoiding bias based on age, race, and evaluation gender in a pilot study. The system's data has been compared to the outcomes of conventional video analysis. The study's conclusions indicate that the suggested approach can aid in the analysis of consumer experience in a store setting.
利用深度学习的在线购物个性化识别系统
本研究基于从生理数据和面部表情中提取的信息的改进,提出了一种有效的监测系统,用于在多次商店互动中观察购买体验。在一项试点研究中,该系统有效识别了消费者的情绪,避免了基于年龄、种族和评价性别的偏见。该系统的数据与传统的视频分析结果进行了比较。研究结论表明,建议的方法有助于分析消费者在商店环境中的体验。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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