  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.
