A longitudinal model for song popularity prediction

Name: A longitudinal model for song popularity prediction
Author: Çimen, Ahmet, Kayış, Enis

İsim	A longitudinal model for song popularity prediction
Yazar	Çimen, Ahmet, Kayış, Enis
Basım Tarihi:	2021
Basım Yeri	- SciTePress
Konu	Mathematical programming, Music analytics, Time-varying coefficients
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-989-758-521-0
Kayıt Numarası	b5614697-9185-4d50-938c-14453cae764f
Lokasyon	Industrial Engineering
Tarih	2021
Örnek Metin	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.
DOI	10.5220/0010607700960104

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A longitudinal model for song popularity prediction

Yazar Çimen, Ahmet, Kayış, Enis

Basım Tarihi 2021

Basım Yeri - SciTePress

Konu Mathematical programming, Music analytics, Time-varying coefficients

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-989-758-521-0

Kayıt Numarası b5614697-9185-4d50-938c-14453cae764f

Lokasyon Industrial Engineering

Tarih 2021

Örnek Metin 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.

DOI 10.5220/0010607700960104