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In this paper, we introduce a probabilistic model for learning nonnegativematrix factorization (NMF) that is commonly used for predicting missing valuesand finding hidden patterns in the data, in which the matrix factors are latentvariables associated with each data dimension. The nonnegativity constraint forthe latent factors is handled by choosing priors with support on thenonnegative subspace. Bayesian inference procedure based on Gibbs sampling isemployed. We evaluate the model on several real-world datasets includingMovieLens 100K and MovieLens 1M with different sizes and dimensions and showthat the proposed Bayesian NMF GRRN model leads to better predictions andavoids overfitting compared to existing Bayesian NMF approaches.

Flexible and Hierarchical Prior for Bayesian Nonnegative Matrix Factorization