Early Explorations of Recommender Systems for Physical Activity and Well-being

2025-08-11

AI Summary

Traditional recommender systems in wearable devices and health coaching tools face critical challenges—including poor user comprehension, low trust, and inadequate behavioral responsiveness—when deployed in embodied contexts where recommendations directly influence users’ physiological states, daily behaviors, and long-term health outcomes. Method: This study identifies the fundamental limitations of conventional recommendation logic in such settings and proposes the “tangible recommendation” conceptual framework, grounded in three design dimensions: trust and explainability, user-intent alignment, and consequence awareness. Using conceptual modeling and reflective design analysis, the framework is validated through real-world health intervention cases. Contribution/Results: The work establishes a theoretical foundation and provides actionable design principles for next-generation health recommender systems that support sustained health maintenance, behavioral consistency, and responsible personalization—thereby advancing human-centered, ethically grounded AI in digital health.

Technology Category

• Humans and AI: Intelligent User Interfaces
• Data Mining & Knowledge Management: Recommender Systems
• Intelligent Robots: Embodied AI

Application Category

• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems

Abstract

As recommender systems increasingly guide physical actions, often through wearables and coaching tools, new challenges arise around how users interpret, trust, and respond to this advice. This paper introduces a conceptual framework for tangible recommendations that influence users' bodies, routines, and well-being. We describe three design dimensions: trust and interpretation, intent alignment, and consequence awareness. These highlight key limitations in applying conventional recommender logic to embodied settings. Through examples and design reflections, we outline how future systems can support long-term well-being, behavioral alignment, and socially responsible personalization.

Problem

• How users interpret and trust activity recommendations
• Aligning recommender intent with user well-being goals
• Addressing consequences of embodied recommender systems

Innovation

• Conceptual framework for tangible recommendations
• Three design dimensions for embodied settings
• Support long-term well-being and alignment