Learning a Gaussian Process Prior for Automatically Generating Music Playlists
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.
Cite
Text
Platt et al. "Learning a Gaussian Process Prior for Automatically Generating Music Playlists." Neural Information Processing Systems, 2001.Markdown
[Platt et al. "Learning a Gaussian Process Prior for Automatically Generating Music Playlists." Neural Information Processing Systems, 2001.](https://mlanthology.org/neurips/2001/platt2001neurips-learning/)BibTeX
@inproceedings{platt2001neurips-learning,
title = {{Learning a Gaussian Process Prior for Automatically Generating Music Playlists}},
author = {Platt, John C. and Burges, Christopher J. C. and Swenson, Steven and Weare, Christopher and Zheng, Alice},
booktitle = {Neural Information Processing Systems},
year = {2001},
pages = {1425-1432},
url = {https://mlanthology.org/neurips/2001/platt2001neurips-learning/}
}