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Predicting User Activity Level In Point Processes With Mass Transport Equation
Yichen Wang · Xiaojing Ye · Hongyuan Zha · Le Song

Wed Dec 06 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #83

Point processes are powerful tools to model user activities and have a plethora of applications in social sciences. Predicting user activities based on point processes is a central problem. However, existing works are mostly problem specific, use heuristics, or simplify the stochastic nature of point processes. In this paper, we propose a framework that provides an unbiased estimator of the probability mass function of point processes. In particular, we design a key reformulation of the prediction problem, and further derive a differential-difference equation to compute a conditional probability mass function. Our framework is applicable to general point processes and prediction tasks, and achieves superb predictive and efficiency performance in diverse real-world applications compared to state-of-arts.

Author Information

Yichen Wang (Georgia Tech)
Xiaojing Ye (Georgia State University)
Hongyuan Zha (Georgia Tech)
Le Song (Ant Financial & Georgia Institute of Technology)

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