Collaborative filtering in a non-uniform world: Learning with the weighted trace norm
N Srebro, RR Salakhutdinov - Advances in neural …, 2010 - proceedings.neurips.cc
Advances in neural information processing systems, 2010•proceedings.neurips.cc
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.
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.
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.
proceedings.neurips.cc