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An Advertising Real-Time Intelligent and Scalable Framework for Profiling Customers Emotions

2022Conference object Open access
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dc.contributor.authorAlves, Leandro
dc.contributor.authorOliveira, Pedro
dc.contributor.authorHenriques, João
dc.contributor.authorBernardo, Marco V.
dc.contributor.authorWanzeller, Cristina
dc.contributor.authorCaldeira, Filipe
dc.date.accessioned2023-07-04T09:35:38Z
dc.date.available2023-07-04T09:35:38Z
dc.date.issued2022
dc.date.updated2023-06-09T13:39:06Z
dc.description.abstractThe advertising industry is continuously looking up for effective ways to communicate to customers to impact their purchasing. Usually, profiling them is a time-consuming offline activity. Therefore, it becomes necessary to reduce costs and time to address consumers’ needs. This work proposes a scalable framework enabled by a Machine Learning (ML) model to profile customers to identify their emotions to help to drive campaigns. A multi-platform mobile application continuously profiles consumers crossing the front stores. Profiling customers according to their age and hair color, the color of their eyes, and emotions (e.g. happiness, sadness, disgust, fear) will help companies to make the most suitable advertisement (e.g. to predict whether the advertising tones on the front store are the adequate ones). All that data are made available in web portal dashboards, wherein subscribers can take their analysis. Such results from the analysis data help them to identify tendencies regarding the culture and age, and drive companies to fit front stores accordingly (e.g. to discover the right tones for the season). This framework can help to develop new innovative cost-effective business models at scale by driving in real-time the advertisements to a huge number of consumers to maximize their impact and centralizing the data collected from a large number of stores to design future campaigns.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1007/978-3-031-14859-0_5pt_PT
dc.identifier.isbn9783031148583
dc.identifier.isbn9783031148590
dc.identifier.issn2194-5357
dc.identifier.issn2194-5365
dc.identifier.slugcv-prod-3037670
dc.identifier.urihttp://hdl.handle.net/10400.19/7843
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringer International Publishingpt_PT
dc.subjectMachine Learningpt_PT
dc.subjectCognitive servicespt_PT
dc.subjectIoTpt_PT
dc.subjectAdvertisingpt_PT
dc.subjectScalabilitypt_PT
dc.titleAn Advertising Real-Time Intelligent and Scalable Framework for Profiling Customers Emotionspt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.endPage68pt_PT
oaire.citation.startPage55pt_PT
oaire.citation.titleDiTTEt 2022: New Trends in Disruptive Technologies, Tech Ethics and Artificial Intelligencept_PT
person.familyNameMenoita Henriques
person.familyNameBernardo
person.familyNameWanzeller Guedes de Lacerda
person.familyNameCaldeira
person.givenNameJoão Pedro
person.givenNameMarco
person.givenNameAna Cristina
person.givenNameFilipe
person.identifierhttps://scholar.google.pt/citations?user=AExQrJwAAAAJ
person.identifierlXPmBvYAAAAJ
person.identifier.ciencia-idBB15-BFE2-17AA
person.identifier.ciencia-idE617-3633-CC0F
person.identifier.ciencia-idE81F-11C0-E77C
person.identifier.ciencia-idCB11-8109-AB1D
person.identifier.orcid0000-0001-7380-9511
person.identifier.orcid0000-0003-0046-8685
person.identifier.orcid0000-0001-7558-2330
person.identifier.scopus-author-id36023210300
rcaap.cv.cienciaidCB11-8109-AB1D | Filipe Caldeira
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublication9b3258cd-a3d1-46f9-bc04-2bdd99d87014
relation.isAuthorOfPublication8acab082-f157-4aa0-b092-54f156430c35
relation.isAuthorOfPublicationb353121e-fa46-43fe-b4c0-5e9848084d17
relation.isAuthorOfPublicatione845705e-5b0b-4f70-9c53-c472ffd768d1
relation.isAuthorOfPublication.latestForDiscoverye845705e-5b0b-4f70-9c53-c472ffd768d1

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