Realization and User Evaluation of an Automatic Playlist Generator

Abstract

An automatic music playlist generator called PATS (Personalized Automatic Track Selection) creates playlists that aim at suiting a particular listening situation. It uses dynamic clustering in which songs are grouped based on a weighted attribute-value similarity measure. An inductive learning algorithm is used to reveal the weights for attribute-values using user preference feedback. In a controlled user experiment, the quality of PATS-generated and randomly assembled playlists for jazz music was assessed in two listening situations. The two listening situations were “listening to soft music” and “listening to lively music.” Playlist quality was measured by precision (songs that suit the listening situation), coverage (songs that suit the listening situation 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.