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Non-negative tensor factorization models enable predictive analysis on countdata. Among them, Bayesian Poisson-Gamma models can derive full posteriordistributions of latent factors and are less sensitive to sparse count data.However, current inference methods for these Bayesian models adopt restrictedupdate rules for the posterior parameters. They also fail to share the updateinformation to better cope with the data sparsity. Moreover, these models arenot endowed with a component that handles the imbalance in count data values.In this paper, we propose a novel variational auto-encoder framework calledVAE-BPTF which addresses the above issues. It uses multi-layer perceptronnetworks to encode and share complex update information. The encodedinformation is then reweighted per data instance to penalize common data valuesbefore aggregated to compute the posterior parameters for the latent factors.Under synthetic data evaluation, VAE-BPTF tended to recover the right number oflatent factors and posterior parameter values. It also outperformed currentmodels in both reconstruction errors and latent factor (semantic) coherenceacross five real-world datasets. Furthermore, the latent factors inferred byVAE-BPTF are perceived to be meaningful and coherent under a qualitativeanalysis.

Variational Auto-encoder Based Bayesian Poisson Tensor Factorization for Sparse and Imbalanced Count Data