Flexible and Hierarchical Prior for Bayesian Nonnegative Matrix Factorization
This work addresses the need for more robust NMF models in applications like recommendation systems, though it appears incremental as it builds on prior Bayesian NMF approaches.
The paper tackled the problem of improving prediction accuracy and avoiding overfitting in nonnegative matrix factorization (NMF) by introducing a Bayesian model with flexible and hierarchical priors, showing better performance on datasets like MovieLens 100K and 1M compared to existing Bayesian NMF methods.
In this paper, we introduce a probabilistic model for learning nonnegative matrix factorization (NMF) that is commonly used for predicting missing values and finding hidden patterns in the data, in which the matrix factors are latent variables associated with each data dimension. The nonnegativity constraint for the latent factors is handled by choosing priors with support on the nonnegative subspace. Bayesian inference procedure based on Gibbs sampling is employed. We evaluate the model on several real-world datasets including MovieLens 100K and MovieLens 1M with different sizes and dimensions and show that the proposed Bayesian NMF GRRN model leads to better predictions and avoids overfitting compared to existing Bayesian NMF approaches.