Automatic Music Recommendation Based on Acoustic Content and Implicit Listening Feedback


  • Rodrigo Carvalho Borges Universidade de São Paulo, São Paulo, São Paulo, Brasil,
  • Marcelo Gomes de Queiroz Universidade de São Paulo, São Paulo, São Paulo, Brasil,



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


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,

Marcelo Gomes de Queiroz, Universidade de São Paulo, São Paulo, São Paulo, Brasil,


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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: Acesso em: 29 maio. 2022.