As music streaming services dominate the music industry, the playlist is becoming an increasingly crucial element of music consumption. Con- sequently, the music recommendation problem is often casted as a playlist generation prob- lem. Better understanding of the playlist is there- fore necessary for developing better playlist gen- eration algorithms. In this work, we analyse two playlist datasets to investigate some com- monly assumed hypotheses about playlists. Our findings indicate that deeper understanding of playlists is needed to provide better prior infor- mation and improve machine learning algorithms in the design of recommendation systems.
@article{arxiv.1511.07004,
title = {Understanding Music Playlists},
author = {Keunwoo Choi and George Fazekas and Mark Sandler},
journal= {arXiv preprint arXiv:1511.07004},
year = {2015}
}
Comments
International Conference on Machine Learning (ICML) 2015, Machine Learning for Music Discovery Workshop