NudgeRank: Digital Algorithmic Nudging for Personalized Health
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Abstract (arXiv)
In this paper we describe NudgeRank, an innovative digital algorithmic nudging system designed to foster positive health behaviors on a population-wide scale. Utilizing a novel combination of Graph Neural Networks augmented with an extensible Knowledge Graph, this Recommender System is operational in production, delivering personalized and context-aware nudges to over 1.1 million care recipients daily. This enterprise deployment marks one of the largest AI-driven health behavior change initiatives, accommodating diverse health conditions and wearable devices. Rigorous evaluation reveals statistically significant improvements in health outcomes, including a 6.17% increase in daily steps and 7.61% more exercise minutes. Moreover, user engagement and program enrollment surged, with a 13.1% open rate compared to baseline systems' 4%. Demonstrating scalability and reliability, NudgeRank operates efficiently on commodity compute resources while maintaining automation and observability standards essential for production systems.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 8 of its 31 references that are in the map. First posted on arXiv 16 Jul 2024.
Builds on
8 of 31 references.
References · 8
- 2017Graph Attention Networkscited by 8,923
- 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,138
- 2019KGAT: Knowledge Graph Attention Network for Recommendationcited by 343
- 2018RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systemscited by 138
- 2019Knowledge Graph Convolutional Networks for Recommender Systemscited by 121
- 2019Personalized HeartSteps: A Reinforcement Learning Algorithm for Optimizing Physical Activitycited by 70
- 2019IntentGC: a Scalable Graph Convolution Framework Fusing Heterogeneous Information for Recommendationcited by 19
- 2019An End-to-End Neighborhood-based Interaction Model for Knowledge-enhanced Recommendationcited by 8
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Citing papers per year: 2025: 2; 2026: 1.
3 papers.