← Back to papers
2010 NIPS 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.

🌉 Interdisciplinary Bridge — Data Science & Analytics and Machine Learning
📈 Trend Setter — Recommender Systems
🧭 Keyword Pioneer — trace norm regularization
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning
🐣 Hot Topic Early Bird — collaborative filtering

Authors

Nathan Srebro , Ruslan Salakhutdinov

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 > Core Methods > Matrix Completion

Keywords

collaborative filtering matrix completion trace norm regularization weighted regularization non-uniform sampling weighted trace norm recommender system
Download PDF

Related papers

Link Discovery using Graph Feature Tracking 2010
Trading off Mistakes and Don't-Know Predictions 2010
A Novel Kernel for Learning a Neuron Model from Spike Train Data 2010
Decomposing Isotonic Regression for Efficiently Solving Large Problems 2010
Learning Kernels with Radiuses of Minimum Enclosing Balls 2010