Modeling, predicting, and guiding users' temporal behaviors
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The increasing availability and granularity of temporal event data produced from user activities in online media, social networks and health informatics provide new opportunities and challenges to model and understand user behaviors. In addition to studying the macroscopic patterns on the population level, such type of data further enable us to investigate user interactions in a more fine-grained scale to address the "who will do what by when?" question with new exploratory and predictive models. On the other hand, these myriads of microscopic event data, such as publishing a post, forwarding a tweet, purchasing a product, checking in a place, often arise asynchronously and interdependently; hence they require new representing and analyzing methods far beyond those based on independent and identically distributed data models. In this dissertation, I present a novel probabilistic framework for modeling, learning, predicting, and guiding users’ temporal behaviors. Within the proposed framework, we introduce a pipeline of newly developed statistical models, state-of-the-arts learning algorithms to tackle several canonical problems in theory and practice, including: (1) provable nonparametric learning of temporal point processes, (2) a generic embedding framework for continuous-time evolving graphs, (3) scalable algorithms for predicting user activity levels, and (4) a stochastic differential equation framework for guiding users’ activities.