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Abstract:
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
froma large set of albums. This learned kernel is shown to be
more effective at predicting users' playlists than a reasonable
hand-designed kernel.
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