  Although latent factor models (e.g., matrix factorization) obtain good
performance in predictions, they suffer from several problems including
cold-start, non-transparency, and suboptimal recommendations. In this paper, we
employ text with side data to tackle these limitations. We introduce a hybrid
generative probabilistic model that combines a neural network with a latent
topic model, which is a four-level hierarchical Bayesian model. In the model,
each document is modeled as a finite mixture over an underlying set of topics
and each topic is modeled as an infinite mixture over an underlying set of
topic probabilities. Furthermore, each topic probability is modeled as a finite
mixture over side data. In the context of text, the neural network provides an
overview distribution about side data for the corresponding text, which is the
prior distribution in LDA to help perform topic grouping. The approach is
evaluated on several different datasets, where the model is shown to outperform
standard LDA and Dirichlet-multinomial regression (DMR) in terms of topic
grouping, model perplexity, classification and comment generation.
