Collaborative Ordinal Regression
Abstract
Ordinal regression has become an effective way of learning user preferences, but most research focuses on single regression problems. In this paper we introduce collaborative ordinal regression, where multiple ordinal regression tasks are handled simultaneously. Rather than modeling each task individually, we explore the dependency between ranking functions through a hierarchical Bayesian model and assign a common Gaussian Process (GP) prior to all individual functions. Empirical studies show that our collaborative model outperforms the individual counterpart in preference learning applications.
Cite
Text
Yu et al. "Collaborative Ordinal Regression." International Conference on Machine Learning, 2006. doi:10.1145/1143844.1143981Markdown
[Yu et al. "Collaborative Ordinal Regression." International Conference on Machine Learning, 2006.](https://mlanthology.org/icml/2006/yu2006icml-collaborative/) doi:10.1145/1143844.1143981BibTeX
@inproceedings{yu2006icml-collaborative,
title = {{Collaborative Ordinal Regression}},
author = {Yu, Shipeng and Yu, Kai and Tresp, Volker and Kriegel, Hans-Peter},
booktitle = {International Conference on Machine Learning},
year = {2006},
pages = {1089-1096},
doi = {10.1145/1143844.1143981},
url = {https://mlanthology.org/icml/2006/yu2006icml-collaborative/}
}