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

Fuente: arXiv
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Main Author: Said, Alan
Format: Preprint
Published: 2025
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author Said, Alan
author_facet Said, Alan
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2508_07980
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Early Explorations of Recommender Systems for Physical Activity and Well-being
Said, Alan
Human-Computer Interaction
Information Retrieval
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.
title Early Explorations of Recommender Systems for Physical Activity and Well-being
topic Human-Computer Interaction
Information Retrieval
url https://arxiv.org/abs/2508.07980