Analyzing the Characteristics of Shared Playlists for Music Recommendation

The automated generation of music playlists – as supported by modern music services like last.fm or Spotify – represents a special form of music recommendation. When designing a “playlisting ” algorithm, the question arises which kind of quality criteria the generated playlists should fulfill and if there are certain characteristics like homogeneity, diversity or freshness that make the playlists generally more enjoyable for the listeners. In our work, we aim to obtain a better un-derstanding of such desired playlist characteristics in order to be able to design better algorithms in the future. The research approach chosen in this work is to analyze several thousand playlists that were created and shared by users on music platforms based on musical and meta-data features. Our first results for example reveal that factors like pop-ularity, freshness and diversity play a certain role for users when they create playlists manually. Comparing such user-generated playlists with automatically created ones more-over shows that today’s online playlisting services sometimes generate playlists which are quite different from user-created ones. Finally, we compare the user-created playlists with playlists generated with a nearest-neighbor technique from the research literature and observe even stronger differences. This last observation can be seen as another indication that the accuracy-based quality measures from the literature are probably not sufficient to assess the effectiveness of playlist-ing algorithms