Hyperml and deep interest network to build a recommender system for modatta: targeting customers for campaign offers in a two-sided market

Detalhes bibliográficos
Autor(a) principal: Macedo, Patrícia Alexandra Cravo
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/10362/140149
Resumo: This project studies two Deep Learning approaches, aiming to learn representations using embeddings, as well as get more insights about users, by deploying a Recommender System. After wards, it will allow Modatta to provide users with personalized offers based on their interests. Choosing the right users is critical for the success of a campaign offer. Therefore, it’s necessary to identify a user-base making sure that ,not only marketers will target their offer for those that are going to accept the campaign, but also users will get the offers they need and desire.
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spelling Hyperml and deep interest network to build a recommender system for modatta: targeting customers for campaign offers in a two-sided marketMachine learningDeep learningRecommender systemsHyperbolic embeddingsData monetizationCustomer targetingPersonalized offersBusiness analysisDomínio/Área Científica::Ciências Sociais::Economia e GestãoThis project studies two Deep Learning approaches, aiming to learn representations using embeddings, as well as get more insights about users, by deploying a Recommender System. After wards, it will allow Modatta to provide users with personalized offers based on their interests. Choosing the right users is critical for the success of a campaign offer. Therefore, it’s necessary to identify a user-base making sure that ,not only marketers will target their offer for those that are going to accept the campaign, but also users will get the offers they need and desire.Han, QiweiRUNMacedo, Patrícia Alexandra Cravo2022-06-17T12:17:57Z2022-01-212021-12-162022-01-21T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/140149TID:202972097enginfo: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:RCAAP2024-03-11T05:17:15Zoai:run.unl.pt:10362/140149Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:49:35.158739Repositório 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: targeting customers for campaign offers in a two-sided market
title Hyperml and deep interest network to build a recommender system for modatta: targeting customers for campaign offers in a two-sided market
spellingShingle Hyperml and deep interest network to build a recommender system for modatta: targeting customers for campaign offers in a two-sided market
Macedo, Patrícia Alexandra Cravo
Machine learning
Deep learning
Recommender systems
Hyperbolic embeddings
Data monetization
Customer targeting
Personalized offers
Business analysis
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
title_short Hyperml and deep interest network to build a recommender system for modatta: targeting customers for campaign offers in a two-sided market
title_full Hyperml and deep interest network to build a recommender system for modatta: targeting customers for campaign offers in a two-sided market
title_fullStr Hyperml and deep interest network to build a recommender system for modatta: targeting customers for campaign offers in a two-sided market
title_full_unstemmed Hyperml and deep interest network to build a recommender system for modatta: targeting customers for campaign offers in a two-sided market
title_sort Hyperml and deep interest network to build a recommender system for modatta: targeting customers for campaign offers in a two-sided market
author Macedo, Patrícia Alexandra Cravo
author_facet Macedo, Patrícia Alexandra Cravo
author_role author
dc.contributor.none.fl_str_mv Han, Qiwei
RUN
dc.contributor.author.fl_str_mv Macedo, Patrícia Alexandra Cravo
dc.subject.por.fl_str_mv Machine learning
Deep learning
Recommender systems
Hyperbolic embeddings
Data monetization
Customer targeting
Personalized offers
Business analysis
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
topic Machine learning
Deep learning
Recommender systems
Hyperbolic embeddings
Data monetization
Customer targeting
Personalized offers
Business analysis
Domínio/Área Científica::Ciências Sociais::Economia e Gestão
description This project studies two Deep Learning approaches, aiming to learn representations using embeddings, as well as get more insights about users, by deploying a Recommender System. After wards, it will allow Modatta to provide users with personalized offers based on their interests. Choosing the right users is critical for the success of a campaign offer. Therefore, it’s necessary to identify a user-base making sure that ,not only marketers will target their offer for those that are going to accept the campaign, but also users will get the offers they need and desire.
publishDate 2021
dc.date.none.fl_str_mv 2021-12-16
2022-06-17T12:17:57Z
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/10362/140149
TID:202972097
url http://hdl.handle.net/10362/140149
identifier_str_mv TID:202972097
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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