Modeling Playlist Dialects

Brian McFee

Center for Jazz Studies at Columbia University

Tuesday, October 1, 2013
12:30 p.m., Conference Room 5A

Abstract:

A core component of streaming online radio services is the playlist generation algorithm. Playlist generation algorithms can be viewed as probabilistic models over ordered song sequences, and algorithms can be optimized and evaluated according to the likelihood of observed, real playlists. While previous studies have treated playlist collections as an undifferentiated whole, we propose to build models which are tuned to specific categories or contexts. Toward this end, we develop a general class of flexible and scalable models based upon hyper-graph random walks. To evaluate the proposed models, we present a large corpus of categorically annotated, user-generated playlists over the Million Song Dataset. Experimental results indicate that category-specific models can provide substantial improvements in accuracy over global playlist models.

(This is joint work with Gert Lanckriet at UCSD.)

Bio:

Brian McFee is a postdoctoral research scholar in the Center for Jazz Studies at Columbia University. He received his PhD in Computer Science and Engineering from the University of California, San Diego in 2012. In 2010, he was a recipient of the Qualcomm Innovation Fellowship.