The past decade has witnessed a surge of intelligent systems capable of providing personalized user experiences in many aspects of modern life. Personalized adaptive systems such as personalized search, social media filtering, and e-commerce recommendation systems have demonstrated potentials to improve productivity and enjoyment. However, by catering to the immediate preferences on individuals and de-emphasizing collective needs, these systems also contributed to emergent social issues such as intellectual isolation. This project seeks to understand how to reduce the current blind spots in personalized adaptive systems and will directly address two key challenges in personalized adaptive systems: how to balance 1) short- and long-term needs/preferences and 2) needs of multiple individuals in a group.

Specifically, this project investigates how to increase and sustain physical activity using personalized adaptive systems for health. Two thirds of the adult population in the U.S. are affected by overweight and obesity, with sedentary behavior as a primary cause. In addition to address a public health issue, the technology developed in this project will advance theories in human behavior science. The empirical data generated from the planned system can shed light on the dynamic nature of people's social comparison process and reactions. To address the aforementioned challenges, the team will investigate novel participant modeling algorithms, specifically designed to model dynamic participant characteristics. Additionally, the research will contribute to the literature of experience management by developing algorithms that exploit those participant models to adapt interactive experiences to groups of users rather than individuals. The approach is innovative in bootstrapping design theory, algorithmic innovation, and health behavior science in a synergistic way to make scientific advancement.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

National Science Foundation (NSF)
Division of Information and Intelligent Systems (IIS)
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Tonya Smith-Jackson
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Drexel University
United States
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