Modeling Infinite Topics on Social Behavior Data with Spatio-temporal Dependence.

The problem of modeling topics on user behavior data in social networks has been widely studied in social marketing and social emotion analysis, where latent topic models are commonly used as the solutions. The user behavior data are highly related in time and space, which demands new latent topic models that consider both temporal and spatial distances. However, existing topic models either fail to model these two factors simultaneously, or cannot handle the high order dependence among user behaviors. In this paper we present a new nonparametric Bayesian model Time and Space Dependent Chinese Restaurant Processes (TSD-CRP for short). TSD-CRP can auto-select the number of topics and model high-order temporal and spatial dependence behind user behavior data. Empirical results on real-world data sets demonstrate the effectiveness of the proposed method.