2008 BayesianProbabilisticMatrixFact

• (Salakhutdinov & Mnih, 2008) ⇒ Ruslan Salakhutdinov, and Andriy Mnih. (2008). “Bayesian Probabilistic Matrix Factorization Using Markov Chain Monte Carlo.” In: Proceedings of the 25th International Conference on Machine learning. ISBN:978-1-60558-205-4 doi:10.1145/1390156.1390267

Subject Headings: Bayesian Probabilistic Matrix Factorization

Cited By

• http://scholar.google.com/scholar?q=%222008%22+Bayesian+Probabilistic+Matrix+Factorization+Using+Markov+Chain+Monte+Carlo
• http://dl.acm.org/citation.cfm?id=1390156.1390267&preflayout=flat#citedby

Quotes

Abstract

Low-rank matrix approximation methods provide one of the simplest and most effective approaches to collaborative filtering. Such models are usually fitted to data by finding a MAP estimate of the model parameters, a procedure that can be performed efficiently even on very large datasets. However, unless the regularization parameters are tuned carefully, this approach is prone to overfitting because it finds a single point estimate of the parameters. In this paper we present a fully Bayesian treatment of the Probabilistic Matrix Factorization (PMF) model in which model capacity is controlled automatically by integrating over all model parameters and hyperparameters. We show that Bayesian PMF models can be efficiently trained using Markov chain Monte Carlo methods by applying them to the Netflix dataset, which consists of over 100 million movie ratings. The resulting models achieve significantly higher prediction accuracy than PMF models trained using MAP estimation.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2008 BayesianProbabilisticMatrixFact	Andriy Mnih Ruslan Salakhutdinov			Bayesian Probabilistic Matrix Factorization Using Markov Chain Monte Carlo				10.1145/1390156.1390267		2008