Collaborative Filtering in a Non-Uniform World: Learning with the Weighted Trace Norm

Part of Advances in Neural Information Processing Systems 23 (NIPS 2010)

Authors

Nathan Srebro, Russ R. Salakhutdinov

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.