1 resultado para dating recommendation
em DRUM (Digital Repository at the University of Maryland)
Filtro por publicador
- ABACUS. Repositorio de Producción Científica - Universidad Europea (1)
- Acceda, el repositorio institucional de la Universidad de Las Palmas de Gran Canaria. España (3)
- AMS Tesi di Laurea - Alm@DL - Università di Bologna (3)
- Applied Math and Science Education Repository - Washington - USA (1)
- Aquatic Commons (7)
- ArchiMeD - Elektronische Publikationen der Universität Mainz - Alemanha (1)
- Archimer: Archive de l'Institut francais de recherche pour l'exploitation de la mer (2)
- Archive of European Integration (253)
- Aston University Research Archive (15)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (6)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (BDPI/USP) (8)
- Bioline International (1)
- BORIS: Bern Open Repository and Information System - Berna - Suiça (135)
- Brock University, Canada (1)
- Bulgarian Digital Mathematics Library at IMI-BAS (2)
- CaltechTHESIS (1)
- Cambridge University Engineering Department Publications Database (1)
- CentAUR: Central Archive University of Reading - UK (29)
- Center for Jewish History Digital Collections (2)
- Chinese Academy of Sciences Institutional Repositories Grid Portal (36)
- Cochin University of Science & Technology (CUSAT), India (1)
- Comissão Econômica para a América Latina e o Caribe (CEPAL) (2)
- CORA - Cork Open Research Archive - University College Cork - Ireland (1)
- Cornell: DigitalCommons@ILR (1)
- Corvinus Research Archive - The institutional repository for the Corvinus University of Budapest (1)
- Dalarna University College Electronic Archive (1)
- Deakin Research Online - Australia (28)
- Digital Commons - Michigan Tech (1)
- DigitalCommons - The University of Maine Research (1)
- DigitalCommons@The Texas Medical Center (5)
- DigitalCommons@University of Nebraska - Lincoln (1)
- DRUM (Digital Repository at the University of Maryland) (1)
- Duke University (2)
- Düsseldorfer Dokumenten- und Publikationsservice (1)
- Glasgow Theses Service (1)
- Harvard University (6)
- Helda - Digital Repository of University of Helsinki (2)
- Illinois Digital Environment for Access to Learning and Scholarship Repository (1)
- Indian Institute of Science - Bangalore - Índia (4)
- INSTITUTO DE PESQUISAS ENERGÉTICAS E NUCLEARES (IPEN) - Repositório Digital da Produção Técnico Científica - BibliotecaTerezine Arantes Ferra (6)
- Instituto Politécnico do Porto, Portugal (1)
- Massachusetts Institute of Technology (1)
- Ministerio de Cultura, Spain (6)
- National Center for Biotechnology Information - NCBI (2)
- Nottingham eTheses (5)
- Publishing Network for Geoscientific & Environmental Data (171)
- QUB Research Portal - Research Directory and Institutional Repository for Queen's University Belfast (43)
- Queensland University of Technology - ePrints Archive (67)
- Repositório Científico do Instituto Politécnico de Lisboa - Portugal (1)
- Repositorio de la Universidad del Pacífico - PERU (1)
- Repositório digital da Fundação Getúlio Vargas - FGV (1)
- Repositório Institucional da Universidade de Aveiro - Portugal (2)
- Repositório Institucional UNESP - Universidade Estadual Paulista "Julio de Mesquita Filho" (24)
- RUN (Repositório da Universidade Nova de Lisboa) - FCT (Faculdade de Cienecias e Technologia), Universidade Nova de Lisboa (UNL), Portugal (1)
- School of Medicine, Washington University, United States (9)
- Universidad de Alicante (1)
- Universidad del Rosario, Colombia (1)
- Universidad Politécnica de Madrid (8)
- Universidade Estadual Paulista "Júlio de Mesquita Filho" (UNESP) (1)
- Universidade Federal do Pará (2)
- Universitat de Girona, Spain (2)
- Université de Montréal (1)
- Université de Montréal, Canada (1)
- University of Michigan (15)
- University of Queensland eSpace - Australia (28)
- University of Southampton, United Kingdom (1)
- University of Washington (1)
- WestminsterResearch - UK (2)
Resumo:
There are hundreds of millions of songs available to the public, necessitating the use of music recommendation systems to discover new music. Currently, such systems account for only the quantitative musical elements of songs, failing to consider aspects of human perception of music and alienating the listener’s individual preferences from recommendations. Our research investigated the relationships between perceptual elements of music, represented by the MUSIC model, with computational musical features generated through The Echo Nest, to determine how a psychological representation of music preference can be incorporated into recommendation systems to embody an individual’s music preferences. Our resultant model facilitates computation of MUSIC factors using The Echo Nest features, and can potentially be integrated into recommendation systems for improved performance.