Hyperml and deep interest network to build a recommender system for modatta: measuring the effectiveness of targeting users with new offers

Detalhes bibliográficos
Autor(a) principal: Lucas, Carolina Carvalho
Data de Publicação: 2021
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/140156
Resumo: Modatta aims at giving users control over their data and marketers the opportunity to target users with the right campaigns. Therefore, this paper proposes the use of Deep Learning to find users’ representations, so their data is kept private, but they can still learn about their interests. A Recommender System for predicting new interests is built to create a more complete representation of the users, which allows Modatta to find the offers that are of their most interest. After implementing the targeting strategy, this study shows how to evaluate the effectiveness of a campaign that could be conducted by Modatta.
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spelling Hyperml and deep interest network to build a recommender system for modatta: measuring the effectiveness of targeting users with new offersMachine learningDeep learningHyperbolic embeddingsData monetizationRecommender systemTargeted marketingCampaign effectivenessBusiness analysisDomínio/Área Científica::Ciências Sociais::Economia e GestãoModatta aims at giving users control over their data and marketers the opportunity to target users with the right campaigns. Therefore, this paper proposes the use of Deep Learning to find users’ representations, so their data is kept private, but they can still learn about their interests. A Recommender System for predicting new interests is built to create a more complete representation of the users, which allows Modatta to find the offers that are of their most interest. After implementing the targeting strategy, this study shows how to evaluate the effectiveness of a campaign that could be conducted by Modatta.Han, QiweiMoretti, RodrigoPinto (Modatta), EduardoRUNLucas, Carolina Carvalho2022-06-17T13:51:09Z2022-01-212021-02-142022-01-21T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/140156TID:202997383enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:17:16Zoai:run.unl.pt:10362/140156Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:49:35.483859Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Hyperml and deep interest network to build a recommender system for modatta: measuring the effectiveness of targeting users with new offers
title Hyperml and deep interest network to build a recommender system for modatta: measuring the effectiveness of targeting users with new offers
spellingShingle Hyperml and deep interest network to build a recommender system for modatta: measuring the effectiveness of targeting users with new offers
Lucas, Carolina Carvalho
Machine learning
Deep learning
Hyperbolic embeddings
Data monetization
Recommender system
Targeted marketing
Campaign effectiveness
Business analysis
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
title_short Hyperml and deep interest network to build a recommender system for modatta: measuring the effectiveness of targeting users with new offers
title_full Hyperml and deep interest network to build a recommender system for modatta: measuring the effectiveness of targeting users with new offers
title_fullStr Hyperml and deep interest network to build a recommender system for modatta: measuring the effectiveness of targeting users with new offers
title_full_unstemmed Hyperml and deep interest network to build a recommender system for modatta: measuring the effectiveness of targeting users with new offers
title_sort Hyperml and deep interest network to build a recommender system for modatta: measuring the effectiveness of targeting users with new offers
author Lucas, Carolina Carvalho
author_facet Lucas, Carolina Carvalho
author_role author
dc.contributor.none.fl_str_mv Han, Qiwei
Moretti, Rodrigo
Pinto (Modatta), Eduardo
RUN
dc.contributor.author.fl_str_mv Lucas, Carolina Carvalho
dc.subject.por.fl_str_mv Machine learning
Deep learning
Hyperbolic embeddings
Data monetization
Recommender system
Targeted marketing
Campaign effectiveness
Business analysis
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
topic Machine learning
Deep learning
Hyperbolic embeddings
Data monetization
Recommender system
Targeted marketing
Campaign effectiveness
Business analysis
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
description Modatta aims at giving users control over their data and marketers the opportunity to target users with the right campaigns. Therefore, this paper proposes the use of Deep Learning to find users’ representations, so their data is kept private, but they can still learn about their interests. A Recommender System for predicting new interests is built to create a more complete representation of the users, which allows Modatta to find the offers that are of their most interest. After implementing the targeting strategy, this study shows how to evaluate the effectiveness of a campaign that could be conducted by Modatta.
publishDate 2021
dc.date.none.fl_str_mv 2021-02-14
2022-06-17T13:51:09Z
2022-01-21
2022-01-21T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/140156
TID:202997383
url http://hdl.handle.net/10362/140156
identifier_str_mv TID:202997383
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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