Reviews: Deep Dynamic Poisson Factorization Model

This papers introduces the deep dynamic Poisson factorization model, a model that builds on PF to allow for temporal dependencies. In contrast to previous works on dynamic PF, this paper uses a simplified version of a recurrent neural network to allow for long-term dependencies. Inference is carried out via variational inference, with an extra step to maximize over the neural network parameters. The paper reports experiments on 5 real-world datasets. Overall, I found the idea potentially interesting, but I still think the paper needs some improvements in its execution before it can be accepted in a conference like NIPS.