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].

Indeed, to the best of our knowledge, we lack forgotten over time (Wu and Humberman 2007). For example, a probabilistic framework to model and predict the popularity videos on YouTube or stories on Digg gain their popularity dynamics of individual items. The reason behind this is by striving for views or votes (Szabo and Huberman partly illustrated in Figure 1, suggesting that the dynamical 2010); papers increase their visibility by competing for citations processes governing individual items appear too noisy to be from new papers (Ren et al. 2010; Wang, Song, and amenable to quantification. Barabási 2013); tweets or Hashtags in Twitter become more In this paper, we model the stochastic popularity dynamics popular as being retweeted (Hong, Dan, and Davison 2011) using reinforced Poisson processes, capturing simultaneously and so do webpages as being attached by incoming hyperlinks three key ingredients: fitness of an item, characterizing (Ratkiewicz et al. 2010). An ability to predict the popularity its inherent competitiveness against other items; a general of individual items within a dynamically evolving system temporal relaxation function, corresponding to the aging not only probes our understanding of complex systems, in the ability to attract new attentions; and a reinforcement but also has important implications in a wide range of domains, mechanism, documenting the well-known "rich-get-richer" from marketing and traffic control to policy making phenomenon. The benefit of the proposed model is threefold: and risk management. Despite recent advances of empirical (1) It models the arrival process of individual attentions methods, we lack a general modeling framework to predict directly in contrast to relying on aggregated popularity the popularity of individual items within a complex evolving time series; (2) As a generative probabilistic model, it can be system.