MAPer: A Multi-scale Adaptive Personalized Model for Temporal Human Behavior Prediction

The primary objective of this research is to develop a simple and interpretable predictive framework to perform temporal modeling of individual user's behavior traits based on each person's past observed traits/behavior. Individual-level human behavior patterns are possibly influenced by various temporal features (e.g., lag, cycle) and vary across temporal scales (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 or lack interpretability due to relying on hidden variables. Hence, we build a multi-scale adaptive personalized (MAPer) model that quantifies the effect of both lag and behavior cycle for predicting future behavior. MAper includes a novel basis vector to adaptively learn behavior patterns and capture the variation of lag and cycle across multi-scale temporal contexts. We also extend MAPer to capture the interaction among multiple behaviors to improve the prediction performance. We demonstrate the effectiveness of MAPer on four real datasets representing different behavior domains, including, habitual behavior collected from Twitter, need based behavior collected from search logs, and activities of daily living collected from a single resident and a multi-resident home. Experimental results indicate that MAPer significantly improves upon the state-of-the-art and baseline methods and at the same time is able to explain the temporal dynamics of individual-level human behavior.