Collaborative Filtering in a Non-Uniform World: Learning with the Weighted Trace Norm
Abstract
We show that matrix completion with trace-norm regularization can be significantly hurt when entries of the matrix are sampled non-uniformly, but that a properly weighted version of the trace-norm regularizer works well with non-uniform sampling. We show that the weighted trace-norm regularization indeed yields significant gains on the highly non-uniformly sampled Netflix dataset.
Cite
Text
Srebro and Salakhutdinov. "Collaborative Filtering in a Non-Uniform World: Learning with the Weighted Trace Norm." Neural Information Processing Systems, 2010.Markdown
[Srebro and Salakhutdinov. "Collaborative Filtering in a Non-Uniform World: Learning with the Weighted Trace Norm." Neural Information Processing Systems, 2010.](https://mlanthology.org/neurips/2010/srebro2010neurips-collaborative/)BibTeX
@inproceedings{srebro2010neurips-collaborative,
title = {{Collaborative Filtering in a Non-Uniform World: Learning with the Weighted Trace Norm}},
author = {Srebro, Nathan and Salakhutdinov, Ruslan},
booktitle = {Neural Information Processing Systems},
year = {2010},
pages = {2056-2064},
url = {https://mlanthology.org/neurips/2010/srebro2010neurips-collaborative/}
}