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2008

Stochastic Relational Models for Large-scale Dyadic Data using MCMC

11 years 1 months ago
Stochastic Relational Models for Large-scale Dyadic Data using MCMC
Stochastic relational models (SRMs) [15] provide a rich family of choices for learning and predicting dyadic data between two sets of entities. The models generalize matrix factorization to a supervised learning problem that utilizes attributes of entities in a hierarchical Bayesian framework. Previously variational Bayes inference was applied for SRMs, which is, however, not scalable when the size of either entity set grows to tens of thousands. In this paper, we introduce a Markov chain Monte Carlo (MCMC) algorithm for equivalent models of SRMs in order to scale the computation to very large dyadic data sets. Both superior scalability and predictive accuracy are demonstrated on a collaborative filtering problem, which involves tens of thousands users and half million items. 1 Stochastic Relational Models Stochastic relational models (SRMs) [15] are generalizations of Gaussian process (GP) models [11] to the relational domain, where each observation is a dyadic datum, indexed by a pa...
Shenghuo Zhu, Kai Yu, Yihong Gong
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2008
Where NIPS
Authors Shenghuo Zhu, Kai Yu, Yihong Gong
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