Collaborative Filtering in a Non-Uniform World: Learning with the Weighted Trace Norm
Machine Learning
2010-02-16 v1
Abstract
We show that matrix completion with trace-norm regularization can be significantly hurt when entries of the matrix are sampled non-uniformly. We introduce a weighted version of the trace-norm regularizer that works well also with non-uniform sampling. Our experimental results demonstrate that the weighted trace-norm regularization indeed yields significant gains on the (highly non-uniformly sampled) Netflix dataset.
Cite
@article{arxiv.1002.2780,
title = {Collaborative Filtering in a Non-Uniform World: Learning with the Weighted Trace Norm},
author = {Ruslan Salakhutdinov and Nathan Srebro},
journal= {arXiv preprint arXiv:1002.2780},
year = {2010}
}
Comments
9 pages