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Authors: Ahmet Çimen and Enis Kayış

Affiliation: Department of Industrial Engineering, Ozyegin University, Istanbul, Turkey

Keyword(s): Music Analytics, Time-varying Coefficients, Mathematical Programming.

Abstract: Usage of new generation music streaming platforms such as Spotify and Apple Music has increased rapidly in the last years. Automatic prediction of a song’s popularity is valuable for these firms which in turn translates into higher customer satisfaction. In this study, we develop and compare several statistical models to predict song popularity by using acoustic and artist-related features. We compare results from two countries to understand whether there are any cultural differences for popular songs. To compare the results, we use weekly charts and songs’ acoustic features as data sources. In addition to acoustic features, we add acoustic similarity, genre, local popularity, song recentness features into the dataset. We applied Flexible Least Squares (FLS) method to estimate song streams and observe time-varying regression coefficients using a quadratic program. FLS method predicts the number of weekly streams of a song using the acoustic features and the additional features in the dataset while keeping weekly model differences as small as possible. Results show that the significant changes in the regression coefficients may reflect the changes in the music tastes of the countries. (More)

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Paper citation in several formats:
Çimen, A. and Kayış, E. (2021). A Longitudinal Model for Song Popularity Prediction. In Proceedings of the 10th International Conference on Data Science, Technology and Applications - DATA; ISBN 978-989-758-521-0; ISSN 2184-285X, SciTePress, pages 96-104. DOI: 10.5220/0010607700960104

@conference{data21,
author={Ahmet \c{C}imen. and Enis Kayış.},
title={A Longitudinal Model for Song Popularity Prediction},
booktitle={Proceedings of the 10th International Conference on Data Science, Technology and Applications - DATA},
year={2021},
pages={96-104},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010607700960104},
isbn={978-989-758-521-0},
issn={2184-285X},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Data Science, Technology and Applications - DATA
TI - A Longitudinal Model for Song Popularity Prediction
SN - 978-989-758-521-0
IS - 2184-285X
AU - Çimen, A.
AU - Kayış, E.
PY - 2021
SP - 96
EP - 104
DO - 10.5220/0010607700960104
PB - SciTePress

- Science and Technology Publications, Lda.
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