Applying Recommended systems to books regarding user’s similarities and reader’s ratings
Autor(a) principal: | |
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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/10362/163667 |
Resumo: | Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science |
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Applying Recommended systems to books regarding user’s similarities and reader’s ratingsRecommended SystemsBig DataMachine LearningNatural Language ProcessingData AnalysisSDG 4 - Quality educationSDG 8 - Decent work and economic growthSDG 12 - Responsible production and consumptionSDG 13 - Climate actionSDG 17 - Partnerships for the goalsDomínio/Área Científica::Ciências Naturais::Ciências da Computação e da InformaçãoDissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceBooks are the key for knowledge, for entertainment, for widen the imagination. Are essential for individual growth and development, being described as the ladder of human progress. However, with the growth of the publishing business, the number of books offered to consumers are unmanageable. Becoming a problem for readers, which face the problem of how to choose or even discover a book they really will enjoy. The present work intends to detail the different recommendations techniques available today to identify the most accurate recommender system based on user similarities and previous ratings. Offering readers an efficient and quick way of discovering new books without being buried in options. The methodology adopted was the implementation and analysis of the most common and widely used recommender systems to conclude, based on the evaluation of the techniques, which is the most appropriate for this specific problem. Regarding Collaborative Filtering, the results stated that the best model was the Singular Value Decomposition with a Root Mean Square Error of 0.8. While, in Content-Based, the results stated that TF-IDF technique was better for extracting keywords and k-means was the ideal clustering algorithm for this specific problem. In conclusion, this Masters Project presents and compares the different algorithms applied to recommendation systems, finds the most suitable approach for the given problem, and offers a better understanding of the recommendations systems available nowadays.Henriques, Roberto André PereiraRUNMoralejo, Elena Nozal2024-02-16T15:14:44Z2024-01-292024-01-29T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/163667TID:203518497enginfo: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:RCAAP2024-03-11T05:48:14Zoai:run.unl.pt:10362/163667Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:59:48.723752Repositório 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 |
Applying Recommended systems to books regarding user’s similarities and reader’s ratings |
title |
Applying Recommended systems to books regarding user’s similarities and reader’s ratings |
spellingShingle |
Applying Recommended systems to books regarding user’s similarities and reader’s ratings Moralejo, Elena Nozal Recommended Systems Big Data Machine Learning Natural Language Processing Data Analysis SDG 4 - Quality education SDG 8 - Decent work and economic growth SDG 12 - Responsible production and consumption SDG 13 - Climate action SDG 17 - Partnerships for the goals Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação |
title_short |
Applying Recommended systems to books regarding user’s similarities and reader’s ratings |
title_full |
Applying Recommended systems to books regarding user’s similarities and reader’s ratings |
title_fullStr |
Applying Recommended systems to books regarding user’s similarities and reader’s ratings |
title_full_unstemmed |
Applying Recommended systems to books regarding user’s similarities and reader’s ratings |
title_sort |
Applying Recommended systems to books regarding user’s similarities and reader’s ratings |
author |
Moralejo, Elena Nozal |
author_facet |
Moralejo, Elena Nozal |
author_role |
author |
dc.contributor.none.fl_str_mv |
Henriques, Roberto André Pereira RUN |
dc.contributor.author.fl_str_mv |
Moralejo, Elena Nozal |
dc.subject.por.fl_str_mv |
Recommended Systems Big Data Machine Learning Natural Language Processing Data Analysis SDG 4 - Quality education SDG 8 - Decent work and economic growth SDG 12 - Responsible production and consumption SDG 13 - Climate action SDG 17 - Partnerships for the goals Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação |
topic |
Recommended Systems Big Data Machine Learning Natural Language Processing Data Analysis SDG 4 - Quality education SDG 8 - Decent work and economic growth SDG 12 - Responsible production and consumption SDG 13 - Climate action SDG 17 - Partnerships for the goals Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação |
description |
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data Science |
publishDate |
2024 |
dc.date.none.fl_str_mv |
2024-02-16T15:14:44Z 2024-01-29 2024-01-29T00:00:00Z |
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/10362/163667 TID:203518497 |
url |
http://hdl.handle.net/10362/163667 |
identifier_str_mv |
TID:203518497 |
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 |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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