Applying Recommended systems to books regarding user’s similarities and reader’s ratings

Detalhes bibliográficos
Autor(a) principal: Moralejo, Elena Nozal
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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spelling 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
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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