Scalable Bayesian factorization with high-dimensional side information using MCMC


An MCMC-based factorization method Macau that is able to scale to millions of features, Fu > 1,000,000 while the factorized matrix Y can have a million of rows (and/or columns). In contrast, the sampling-based Bayesian factorization methods Bayesian Matrix Factorization with Side Information (BMFSI)  and Bayesian Canonical PARAFAC with Features and Networks (BCPFN) have complexity O(Fu^3) because they require explicit computation and Cholesky decomposition of the covariance matrix of size Fu×Fu for linking the side information to the factorization.

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