PATS: Realization and user evaluation of an automatic playlist generator

A means to ease selecting preferred music referred to as Personalized Automatic Track Selection (PATS) has been developed. PATS generates playlists that suit a particular contextof-use, that is, the real-world environment in which the music is heard. To create playlists, it uses a dynamic clustering method in which songs are grouped based on their attribute similarity. The similarity measure selectively weighs attribute-values, as not all attribute-values are equally important in a context-of-use. An inductive learning algorithm is used to reveal the most important attribute-values for a context-of-use from preference feedback of the user. In a controlled user experiment, the quality of PATScompiled and randomly assembled playlists for jazz music was assessed in two contexts-of-use. The quality of the randomly assembled playlists was used as base-line. The two contexts-of-use were ‘listening to soft music’ and ‘listening to lively music’. Playlist quality was measured by precision (songs that suit the context-of-use), coverage (songs that suit the context-of-use but that were not already contained in previous playlists) and a rating score. Results showed that PATS playlists contained increasingly more preferred music (increasingly higher precision), covered more preferred music in the collection (higher coverage), and were rated higher than randomly assembled playlists.