Collaborative-demographic hybrid for financial: product recommendation

Detalhes bibliográficos
Autor(a) principal: Pestana, Ana Silva
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/10362/113081
Resumo: Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
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spelling Collaborative-demographic hybrid for financial: product recommendationRecommendation SystemCollaborative FilteringDemographic FilteringHybrid FilteringMulticlass ClassificationMulti-Output RegressionInternship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsDue to the increased availability of mature data mining and analysis technologies supporting CRM processes, several financial institutions are striving to leverage customer data and integrate insights regarding customer behaviour, needs, and preferences into their marketing approach. As decision support systems assisting marketing and commercial efforts, Recommender Systems applied to the financial domain have been gaining increased attention. This thesis studies a Collaborative- Demographic Hybrid Recommendation System, applied to the financial services sector, based on real data provided by a Portuguese private commercial bank. This work establishes a framework to support account managers’ advice on which financial product is most suitable for each of the bank’s corporate clients. The recommendation problem is further developed by conducting a performance comparison for both multi-output regression and multiclass classification prediction approaches. Experimental results indicate that multiclass architectures are better suited for the prediction task, outperforming alternative multi-output regression models on the evaluation metrics considered. Withal, multiclass Feed-Forward Neural Networks, combined with Recursive Feature Elimination, is identified as the topperforming algorithm, yielding a 10-fold cross-validated F1 Measure of 83.16%, and achieving corresponding values of Precision and Recall of 84.34%, and 85.29%, respectively. Overall, this study provides important contributions for positioning the bank’s commercial efforts around customers’ future requirements. By allowing for a better understanding of customers’ needs and preferences, the proposed Recommender allows for more personalized and targeted marketing contacts, leading to higher conversion rates, corporate profitability, and customer satisfaction and loyalty.Castelli, MauroPinheiro, Flávio Luís PortasRUNPestana, Ana Silva2021-03-04T12:25:45Z2021-02-252021-02-25T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/113081TID:202660583enginfo: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:RCAAP2024-03-11T04:56:20Zoai:run.unl.pt:10362/113081Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:42:16.462066Repositório 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 Collaborative-demographic hybrid for financial: product recommendation
title Collaborative-demographic hybrid for financial: product recommendation
spellingShingle Collaborative-demographic hybrid for financial: product recommendation
Pestana, Ana Silva
Recommendation System
Collaborative Filtering
Demographic Filtering
Hybrid Filtering
Multiclass Classification
Multi-Output Regression
title_short Collaborative-demographic hybrid for financial: product recommendation
title_full Collaborative-demographic hybrid for financial: product recommendation
title_fullStr Collaborative-demographic hybrid for financial: product recommendation
title_full_unstemmed Collaborative-demographic hybrid for financial: product recommendation
title_sort Collaborative-demographic hybrid for financial: product recommendation
author Pestana, Ana Silva
author_facet Pestana, Ana Silva
author_role author
dc.contributor.none.fl_str_mv Castelli, Mauro
Pinheiro, Flávio Luís Portas
RUN
dc.contributor.author.fl_str_mv Pestana, Ana Silva
dc.subject.por.fl_str_mv Recommendation System
Collaborative Filtering
Demographic Filtering
Hybrid Filtering
Multiclass Classification
Multi-Output Regression
topic Recommendation System
Collaborative Filtering
Demographic Filtering
Hybrid Filtering
Multiclass Classification
Multi-Output Regression
description Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
publishDate 2021
dc.date.none.fl_str_mv 2021-03-04T12:25:45Z
2021-02-25
2021-02-25T00:00:00Z
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/10362/113081
TID:202660583
url http://hdl.handle.net/10362/113081
identifier_str_mv TID:202660583
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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