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Computer Science Colloquia

Monday, April 20, 2015
Sarah Masud Preum
Advisor: John A. Stankovic
Attending Faculty: Alf Weaver (Committee Chair), Yanjun Qi and Hongning Wang

10:30 AM in Rice Hall, Rm. 504

Ph.D. Qualifying Exam Presentation
MAPer: A Multi-scale Adaptive Personalized Model for Temporal Human Behavior Prediction

The primary objective of this research is to develop a novel predictive model to perform temporal modeling of individual user's behavior traits based on each person's past observed data. Individual-level human behavior patterns are possibly influenced by various temporal factors (e.g., lag, rhythm) and vary across temporal contexts (e.g., hour of the day, day of the week). Most of the existing forecasting models do not capture such multi-scale adaptive regularity of human behavior and/or lack interpretability due to relying on hidden variables. Hence, we extend the classic temporal autoregressive model by adding following components: (i) cycle components to represent the effect of behavior rhythm, (ii) a novel basis vector to ensure adaptive learning across multi-scale temporal contexts, and (iii) interaction parameters to capture interaction among multiple behaviors. Thus we build a multi-scale adaptive personalized (MAPer) model that captures temporal behavior dynamics of individual user while maintaining scalability and interpretability.