Understanding Music Playlists
This work addresses the need for better playlist understanding to enhance recommendation systems for music streaming users, but it is incremental as it focuses on validating existing hypotheses rather than introducing new methods.
The paper analyzed two playlist datasets to test common hypotheses about playlists, finding that deeper understanding is needed to improve prior information and machine learning algorithms for music recommendation systems.
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.