Playlist generation is an important task in music information retrieval. While previous work has treated a playlist
collection as an undifferentiated whole, we propose to build
playlist models which are tuned to specific categories or
dialects of playlists. Toward this end, we develop a general
class of flexible and scalable playlist models based upon
hypergraph random walks. To evaluate the proposed models, we present a large corpus of categorically annotated,
user-generated playlists. Experimental results indicate that
category-specific models can provide substantial improvements in accuracy over global playlist models.