Journal
KDD'15: PROCEEDINGS OF THE 21ST ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING
Volume -, Issue -, Pages 9-18Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/2783258.2783373
Keywords
Large-Scale; Distributed; Matrix Factorization; MCMC; Stochastic Gradient; Bayesian Inference
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Funding
- NSF [IIS-1216045]
- Amazon AWS in Education Grant award
- Yahoo
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Despite having various attractive qualities such as high prediction accuracy and the ability to quantify uncertainty and avoid over fitting, Bayesian Matrix Factorization has not been widely adopted because of the prohibitive cost of inference. In this paper, we propose a scalable distributed Bayesian matrix factorization algorithm using stochastic gradient MCMC. Our algorithm, based on Distributed Stochastic Gradient Langevin Dynamics, can not only match the prediction accuracy of standard MCMC methods like Gibbs sampling, but at the same time is as fast and simple as stochastic gradient descent. In our experiments, we show that our algorithm can achieve the same level of prediction accuracy as Gibbs sampling an order of magnitude faster. We also show that our method reduces the prediction error as fast as distributed stochastic gradient descent, achieving a 4.1% improvement in RMSE for the Netflix dataset and an 1.8% for the Yahoo music dataset.
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