CHS: Small: Balancing Individual and Group Needs in Personalized Adaptive Systems for Improved Health

过去十年见证了智能系统的激增，这些系统能够在现代生活的许多方面提供个性化的用户体验。个性化自适应系统，如个性化搜索、社交媒体过滤和电子商务推荐系统，已经显示出提高生产力和享受的潜力。然而，通过迎合对个人的直接偏好和不强调集体需求，这些系统也导致了诸如智力隔离之类的新兴社会问题。该项目旨在了解如何减少当前个性化适应系统中的盲点，并将直接解决个性化适应系统中的两个关键挑战：如何平衡1)短期和长期需求/偏好以及2)群体中多个个体的需求。具体来说，这个项目研究了如何使用个性化的适应性健康系统来增加和维持身体活动。美国三分之二的成年人受到超重和肥胖的影响，久坐行为是主要原因。除了解决公共卫生问题外，该项目开发的技术还将推进人类行为科学的理论。从计划系统中产生的经验数据可以揭示人们社会比较过程和反应的动态性。为了应对上述挑战，该团队将研究新的参与者建模算法，专门设计用于模拟动态参与者特征。此外，该研究将通过开发利用这些参与者模型的算法来适应用户群体而不是个人的交互式体验，从而为体验管理的文献做出贡献。该方法在引导设计理论、算法创新和健康行为科学方面进行创新，协同推进科学进步。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.