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. 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.

9 pages

References in corpus (1)

• Matrix Completion With Noise

Cited by in corpus (3)

• Restricted strong convexity and weighted matrix completion: Optimal bounds with noise
• Concentration-Based Guarantees for Low-Rank Matrix Reconstruction
• Learning with the Weighted Trace-norm under Arbitrary Sampling Distributions