A collaborative filtering method for music recommendation

Detalhes bibliográficos
Autor(a) principal: Manso, João Pedro Real
Data de Publicação: 2020
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/99079
Resumo: Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
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spelling A collaborative filtering method for music recommendationRecommender SystemsMusic Recommender SystemsCollaborative FilteringK-nearest NeighborsDissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsThe present dissertation focuses on proposing and describing a collaborative filtering approach for Music Recommender Systems. Music Recommender Systems, which are part of a broader class of Recommender Systems, refer to the task of automatically filtering data to predict the songs that are more likely to match a particular profile. So far, academic researchers have proposed a variety of machine learning approaches for determining which tracks to recommend to users. The most sophisticated among them consist, often, on complex learning techniques which can also require considerable computational resources. However, recent research studies proved that more simplistic approaches based on nearest neighbors could lead to good results, often at much lower computational costs, representing a viable alternative solution to the Music Recommender System problem. Throughout this thesis, we conduct offline experiments on a freely-available collection of listening histories from real users, each one containing several different music tracks. We extract a subset of 10 000 songs to assess the performance of the proposed system, comparing it with a Popularity-based model approach. Furthermore, we provide a conceptual overview of the recommendation problem, describing the state-of-the-art methods, and presenting its current challenges. Finally, the last section is dedicated to summarizing the essential conclusions and presenting possible future improvements.Castelli, MauroRUNManso, João Pedro Real2020-06-09T07:56:40Z2020-06-022020-06-02T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/99079TID:202485110enginfo: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:RCAAP2024-03-11T04:46:10Zoai:run.unl.pt:10362/99079Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:39:07.493513Repositório 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 A collaborative filtering method for music recommendation
title A collaborative filtering method for music recommendation
spellingShingle A collaborative filtering method for music recommendation
Manso, João Pedro Real
Recommender Systems
Music Recommender Systems
Collaborative Filtering
K-nearest Neighbors
title_short A collaborative filtering method for music recommendation
title_full A collaborative filtering method for music recommendation
title_fullStr A collaborative filtering method for music recommendation
title_full_unstemmed A collaborative filtering method for music recommendation
title_sort A collaborative filtering method for music recommendation
author Manso, João Pedro Real
author_facet Manso, João Pedro Real
author_role author
dc.contributor.none.fl_str_mv Castelli, Mauro
RUN
dc.contributor.author.fl_str_mv Manso, João Pedro Real
dc.subject.por.fl_str_mv Recommender Systems
Music Recommender Systems
Collaborative Filtering
K-nearest Neighbors
topic Recommender Systems
Music Recommender Systems
Collaborative Filtering
K-nearest Neighbors
description Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
publishDate 2020
dc.date.none.fl_str_mv 2020-06-09T07:56:40Z
2020-06-02
2020-06-02T00:00:00Z
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/10362/99079
TID:202485110
url http://hdl.handle.net/10362/99079
identifier_str_mv TID:202485110
dc.language.iso.fl_str_mv eng
language eng
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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)
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instacron:RCAAP
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