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Stochastic Relational Models for Large-scale Dyadic Data using MCMC
Shenghuo Zhu · Kai Yu · Yihong Gong

Wed Dec 10 11:58 AM -- 11:59 AM (PST) @

Stochastic relational models provide a rich family of choices for learning and predicting dyadic data between two sets of entities. It generalizes matrix factorization to a supervised learning problem that utilizes attributes of objects in a hierarchical Bayesian framework. Previously empirical Bayesian inference was applied, which is however not scalable when the size of either object sets becomes tens of thousands. In this paper, we introduce a Markov chain Monte Carlo (MCMC) algorithm to scale the model to very large-scale dyadic data. Both superior scalability and predictive accuracy are demonstrated on a collaborative filtering problem, which involves tens of thousands users and a half million items.

Author Information

Shenghuo Zhu (NEC Laboratories America)
Kai Yu (Baidu)
Yihong Gong

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