Beyond Beats: A Recipe to Song Popularity? A machine learning approach

arXiv:2403.12079v1 Announce Type: cross
Abstract: Music popularity prediction has garnered significant attention in both industry and academia, fuelled by the rise of data-driven algorithms and streaming platforms like Spotify. This study aims to explore the predictive power of various machine learning models in forecasting song popularity using a dataset comprising 30,000 songs spanning different genres from 1957 to 2020. Methods: We employ Ordinary Least Squares (OLS), Multivariate Adaptive Regression Splines (MARS), Random Forest, and XGBoost algorithms to analyse song characteristics …

abstract academia algorithms arxiv attention beyond cs.ir cs.lg data data-driven dataset explore forecasting industry machine machine learning machine learning models music platforms power prediction predictive recipe song spotify stat.ap streaming study type