  Deep neural network models represent the state-of-the-art methodologies for
natural language processing. Here we build on top of these methodologies to
incorporate temporal information and model how to review data changes with
time. Specifically, we use the dynamic representations of recurrent point
process models, which encode the history of how business or service reviews are
received in time, to generate instantaneous language models with improved
prediction capabilities. Simultaneously, our methodologies enhance the
predictive power of our point process models by incorporating summarized review
content representations. We provide recurrent network and temporal convolution
solutions for modeling the review content. We deploy our methodologies in the
context of recommender systems, effectively characterizing the change in
preference and taste of users as time evolves. Source code is available at [1].
