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NIPS
2001

Learning a Gaussian Process Prior for Automatically Generating Music Playlists

13 years 5 months ago
Learning a Gaussian Process Prior for Automatically Generating Music Playlists
This paper presents AutoDJ: a system for automatically generating music playlists based on one or more seed songs selected by a user. AutoDJ uses Gaussian Process Regression to learn a user preference function over songs. This function takes music metadata as inputs. This paper further introduces Kernel Meta-Training, which is a method of learning a Gaussian Process kernel from a distribution of functions that generates the learned function. For playlist generation, AutoDJ learns a kernel from a large set of albums. This learned kernel is shown to be more effective at predicting users' playlists than a reasonable hand-designed kernel.
John C. Platt, Christopher J. C. Burges, S. Swenso
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2001
Where NIPS
Authors John C. Platt, Christopher J. C. Burges, S. Swenson, C. Weare, A. Zheng
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