  Non-negative tensor factorization models enable predictive analysis on count
data. Among them, Bayesian Poisson-Gamma models can derive full posterior
distributions of latent factors and are less sensitive to sparse count data.
However, current inference methods for these Bayesian models adopt restricted
update rules for the posterior parameters. They also fail to share the update
information to better cope with the data sparsity. Moreover, these models are
not endowed with a component that handles the imbalance in count data values.
In this paper, we propose a novel variational auto-encoder framework called
VAE-BPTF which addresses the above issues. It uses multi-layer perceptron
networks to encode and share complex update information. The encoded
information is then reweighted per data instance to penalize common data values
before aggregated to compute the posterior parameters for the latent factors.
Under synthetic data evaluation, VAE-BPTF tended to recover the right number of
latent factors and posterior parameter values. It also outperformed current
models in both reconstruction errors and latent factor (semantic) coherence
across five real-world datasets. Furthermore, the latent factors inferred by
VAE-BPTF are perceived to be meaningful and coherent under a qualitative
analysis.
