Learning a Gaussian Process Prior for Automatically Generating Music Playlists

Part of Advances in Neural Information Processing Systems 14 (NIPS 2001)

Authors

John Platt, Christopher J. C. Burges, Steven Swenson, Christopher Weare, Alice Zheng

Abstract

This paper presents AutoDJ: a system for automatically generating mu- sic 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.