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.