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

We show that matrix completion with tracenorm regularization can be significantly hurt when entries of the matrix are sampled nonuniformly. 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.

• Type: Journal
• Year: 2010
• Where: CORR
• Authors: Ruslan Salakhutdinov, Nathan Srebro
• Keywords: CORR 2010, Education, Matrix Completion, Tracenorm Regularization, Weighted Trace-norm Regularization