@inproceedings{0f58dc0d12da47b19f9ad9b0348ff82d,
title = "Adapting Collaborative Filtering to Personalized Audio Production",
abstract = "Recommending media objects to users typically requires users to rate existing media objects so as to understand their preferences. The number of ratings required to produce good suggestions can be reduced through collaborative filtering. Collaborative filtering is more difficult when prior users have not rated the same set of media objects as the current user or each other. In this work, we describe an approach to applying prior user data in a way that does not require users to rate the same media objects and that does not require imputation (estimation) of prior user ratings of objects they have not rated. This approach is applied to the problem of finding good equalizer settings for music audio and is shown to greatly reduce the number of ratings the current user must make to find a good equalization setting.",
author = "Bongjun Kim and Bryan Pardo",
note = "Funding Information: This research was supported by grant 1116384 from the US National Science Foundation. Publisher Copyright: {\textcopyright} HCOMP 2014. All rights reserved.; 2nd AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2014 ; Conference date: 02-11-2014 Through 04-11-2014",
year = "2014",
month = nov,
day = "5",
language = "English (US)",
series = "Proceedings of the 2nd AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2014",
publisher = "AAAI Press",
pages = "32--33",
editor = "Bigham, {Jeffrey P.} and David Parkes",
booktitle = "Proceedings of the 2nd AAAI Conference on Human Computation and Crowdsourcing, HCOMP 2014",
}