发表机构
University of California, San Diego; University of Illinois Urbana-Champaign（加州大学圣迭戈分校; 伊利诺伊大学厄巴纳-香槟分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出动态模型刻画信任感知健康推荐系统与建筑工人的交互，利用模型控制和强化学习设计个性化推荐策略，以平衡工人健康、生产力与信任。
AI 中文摘要
建筑工人面临疲劳、热应激及其他体力要求高的条件等工作场所风险，这些风险可能对其健康和安全产生负面影响。尽管监测这些风险很重要，但还需要及时且个性化的健康干预措施，以帮助防止对工人福祉和生产力的负面影响。为此，在本文中，我们提出了一个模型来捕捉信任感知的健康推荐系统与在健康和信任敏感性方面存在差异的工人之间的交互。具体而言，在我们提出的动态模型中，工人健康随时间演变，工人信任受到健康和推荐动态的共同影响，而信任反过来又影响对未来推荐的遵从度。基于该模型，我们刻画了推荐策略，包括基于健康的推荐触发阈值和推荐频率。我们通过基于模型的短时域控制和基于模型的强化学习两种方式来实现。随后，我们研究了如何针对不同工人调整推荐频率，以平衡其健康、生产力和信任。我们的研究结果为建筑工地及其他场景中个性化健康推荐策略的设计提供了见解。
英文摘要
Construction workers face workplace risks such as fatigue, heat stress, and other physically demanding conditions that can negatively affect their health and safety. Although monitoring these risks is important, timely and personalized health interventions are also needed to help prevent negative impacts on workers' well-being and productivity. To this end, in this paper, we propose a model to capture the interactions between a trust-aware health recommender system and workers who differ in health and trust sensitivity. Specifically, in our proposed dynamic model, worker health evolves over time, worker trust is affected by both health and recommendation dynamics, and trust in turn affects compliance with future recommendations. Given this model, we characterize the recommender policy, including a health-based recommendation triggering threshold and the recommendation frequency. We do so using both model-based short-horizon control and model-free reinforcement learning. We then investigate how recommendation frequencies are adjusted for different workers to balance their health, productivity, and trust. Our findings provide insight into the design of personalized health recommendation policies in construction workplaces and beyond.
