Stream Smarter, Not Harder: A Data-Driven Solution based on Recommendation Systems for Streaming Platforms through Power BI

Detalhes bibliográficos
Autor(a) principal: Brito, Adriana Gamboa Campos Calheiros de
Data de Publicação: 2024
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/163934
Resumo: Project Work presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics
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spelling Stream Smarter, Not Harder: A Data-Driven Solution based on Recommendation Systems for Streaming Platforms through Power BIBusiness IntelligenceMachine LearningRecommendation SystemsData VisualizationWord2vecNatural Language ProcessingSDG 4 - Quality educationSDG 8 - Decent work and economic growthSDG 9 - Industry, innovation and infrastructureDomínio/Área Científica::Ciências Naturais::Ciências da Computação e da InformaçãoProject Work presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsStreaming video platforms have gained remarkable popularity in recent years, offering users affordable access to a vast variety of entertainment content. Platforms such as Netflix and Spotify have fundamentally transformed the consumption of digital content, from films and TV programmes to music and podcasts, allowing instant access without the need for lengthy downloads, often through a simple subscription fee or freemium model. This revolution has reshaped the entertainment industry, establishing a dynamic global distribution channel that quickly connected creators to a massive audience. Nevertheless, the abundance of competitors and a large volume of content competing for viewers' attention has forced industry leaders to re-evaluate their content recommendation strategies. To battle for their market share in this competitive environment, these industry giants must leverage the power of data-driven insights through Artificial Intelligence and Machine Learning in order to select appealing content recommendations and ultimately foster deeper engagement. Therefore, the goal of this project was to create a complete Business Intelligence solution using the data of four streaming platforms and deliver an effective content-based recommendation system to boost audience level. Therefore, aimed of achieving this objective, a recommendation algorithm was applied to the different datasets and eight dashboards were created to display powerful insights of the four most popular streaming platforms worldwide.Santos, Vitor Manuel Pereira Duarte dosRUNBrito, Adriana Gamboa Campos Calheiros de2024-02-22T16:35:00Z2024-01-302024-01-30T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/163934TID:203526309enginfo: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:RCAAP2024-03-11T05:49:43Zoai:run.unl.pt:10362/163934Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:59:56.808909Repositório 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 Stream Smarter, Not Harder: A Data-Driven Solution based on Recommendation Systems for Streaming Platforms through Power BI
title Stream Smarter, Not Harder: A Data-Driven Solution based on Recommendation Systems for Streaming Platforms through Power BI
spellingShingle Stream Smarter, Not Harder: A Data-Driven Solution based on Recommendation Systems for Streaming Platforms through Power BI
Brito, Adriana Gamboa Campos Calheiros de
Business Intelligence
Machine Learning
Recommendation Systems
Data Visualization
Word2vec
Natural Language Processing
SDG 4 - Quality education
SDG 8 - Decent work and economic growth
SDG 9 - Industry, innovation and infrastructure
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
title_short Stream Smarter, Not Harder: A Data-Driven Solution based on Recommendation Systems for Streaming Platforms through Power BI
title_full Stream Smarter, Not Harder: A Data-Driven Solution based on Recommendation Systems for Streaming Platforms through Power BI
title_fullStr Stream Smarter, Not Harder: A Data-Driven Solution based on Recommendation Systems for Streaming Platforms through Power BI
title_full_unstemmed Stream Smarter, Not Harder: A Data-Driven Solution based on Recommendation Systems for Streaming Platforms through Power BI
title_sort Stream Smarter, Not Harder: A Data-Driven Solution based on Recommendation Systems for Streaming Platforms through Power BI
author Brito, Adriana Gamboa Campos Calheiros de
author_facet Brito, Adriana Gamboa Campos Calheiros de
author_role author
dc.contributor.none.fl_str_mv Santos, Vitor Manuel Pereira Duarte dos
RUN
dc.contributor.author.fl_str_mv Brito, Adriana Gamboa Campos Calheiros de
dc.subject.por.fl_str_mv Business Intelligence
Machine Learning
Recommendation Systems
Data Visualization
Word2vec
Natural Language Processing
SDG 4 - Quality education
SDG 8 - Decent work and economic growth
SDG 9 - Industry, innovation and infrastructure
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
topic Business Intelligence
Machine Learning
Recommendation Systems
Data Visualization
Word2vec
Natural Language Processing
SDG 4 - Quality education
SDG 8 - Decent work and economic growth
SDG 9 - Industry, innovation and infrastructure
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
description Project Work presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics
publishDate 2024
dc.date.none.fl_str_mv 2024-02-22T16:35:00Z
2024-01-30
2024-01-30T00:00:00Z
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/10362/163934
TID:203526309
url http://hdl.handle.net/10362/163934
identifier_str_mv TID:203526309
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
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