Automatic Music Recommendation Based on Acoustic Content and Implicit Listening Feedback

Autores

  • Rodrigo Carvalho Borges Universidade de São Paulo, São Paulo, São Paulo, Brasil, rcborges@ime.usp.br http://orcid.org/0000-0001-6920-3576
  • Marcelo Gomes de Queiroz Universidade de São Paulo, São Paulo, São Paulo, Brasil, mqz@ime.usp.br

DOI:

https://doi.org/10.5216/mh.v18i1.53569

Palavras-chave:

Music recommendation systems, Cold start, Collaborative filtering, Content-based recommendation, Codeword bernoulli average model, Vector quantization

Resumo

Recommending music automatically isn’t simply about finding songs similar to what a user is accustomed to listen, but also about suggesting potentially interesting pieces that bear no obvious relationships to a user listen- ing history. This work addresses the problem known as “cold start”, where new songs with no user listening history are added to an existing dataset, and proposes a probabilistic model for inference of users listening interest on newly added songs based on acoustic content and implicit listening feedback. Experiments using a dataset of selected Bra- zilian popular music show that the proposed method compares favorably to alternative statistical models. 

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Biografia do Autor

Rodrigo Carvalho Borges, Universidade de São Paulo, São Paulo, São Paulo, Brasil, rcborges@ime.usp.br

Marcelo Gomes de Queiroz, Universidade de São Paulo, São Paulo, São Paulo, Brasil, mqz@ime.usp.br

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Publicado

2018-06-19

Como Citar

CARVALHO BORGES, R.; GOMES DE QUEIROZ, M. Automatic Music Recommendation Based on Acoustic Content and Implicit Listening Feedback. Revista Música Hodie, Goiânia, v. 18, n. 1, p. 31–43, 2018. DOI: 10.5216/mh.v18i1.53569. Disponível em: https://www.revistas.ufg.br/musica/article/view/53569. Acesso em: 27 jun. 2022.

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