Year: 2015
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Authors: Choi, Keunwoo, Fazekas, George, Sandler, Mark
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Award: G8217561489
OpenAlex ID: W2286606967