TY - GEN
T1 - Audio-Based Sequential Music Recommendation
AU - Borges, Rodrigo
AU - Queiroz, Marcelo
N1 - Publisher Copyright:
© 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.
PY - 2023
Y1 - 2023
N2 - We propose an audio-based recommendation model designed to predict the upcoming track within a listening session, given the audio associated with the current track. Instead of relying on users' feedback, as most recommenders, the proposed model aims to learn intrinsic audio elements that can be leveraged in the context of sequential recommendation. The proposed model is evaluated using Mel-spectrogram and raw audio as input data and, in its best configuration, was able to predict almost 65% unseen transitions used in the evaluation phase, and 3.5% cold-start transitions, i.e. transitions from tracks that were never seen by the model.
AB - We propose an audio-based recommendation model designed to predict the upcoming track within a listening session, given the audio associated with the current track. Instead of relying on users' feedback, as most recommenders, the proposed model aims to learn intrinsic audio elements that can be leveraged in the context of sequential recommendation. The proposed model is evaluated using Mel-spectrogram and raw audio as input data and, in its best configuration, was able to predict almost 65% unseen transitions used in the evaluation phase, and 3.5% cold-start transitions, i.e. transitions from tracks that were never seen by the model.
KW - Audio Content
KW - Audio-Based music recommendation
KW - Gated Recurrent Unit
U2 - 10.23919/EUSIPCO58844.2023.10290094
DO - 10.23919/EUSIPCO58844.2023.10290094
M3 - Conference contribution
AN - SCOPUS:85178368015
T3 - European Signal Processing Conference
SP - 421
EP - 425
BT - 31st European Signal Processing Conference, EUSIPCO 2023 - Proceedings
PB - European Signal Processing Conference, EUSIPCO
T2 - European Signal Processing Conference
Y2 - 4 September 2023 through 8 September 2023
ER -