2010
NeurIPS
NeurIPS 2010
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, 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.
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Interdisciplinary Bridge
- Artificial Intelligence and Data Science & Analytics and Mathematics & Optimization
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Trend Setter
- Matrix Completion
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Hot Topic Early Bird
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Authors
Topics
Machine Learning > Core Methods > Representation Learning
Machine Learning > Optimization & Theory > Optimization
Data Science & Analytics > Applications > Recommender Systems
Machine Learning > Core Methods > Matrix Factorization
Machine Learning > Application Areas > Recommender Systems
Machine Learning > Core Methods > Matrix Completion
Mathematics & Optimization > Optimization > Matrix Completion