发表机构
School of Advanced Interdisciplinary Sciences, University of Chinese Academy of Sciences; State Key Laboratory of AI Safety, Institute of Computing Technology; University of Chinese Academy of Sciences（中国科学院大学先进交叉科学学院; 中国科学院计算技术研究所人工智能安全国家重点实验室; 中国科学院大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对可持续饮食的个体适配难题，提出基于约束感知决策机制的个性化推荐模型，构建含15万份食谱的SusDiet数据集，验证其可在兼顾个人偏好的同时推动可持续饮食选择，为相关干预与政策提供定量依据。
AI 中文摘要
可持续饮食是营养充足、经济可负担、文化可接受与环境友好四大核心支柱间的多维度协同。尽管面向人群的可持续性建模已较为普及，但其实际落地依赖于个体层面的有效采纳，而个体异质性常阻碍这一转变，给可持续饮食需求与个人偏好的匹配带来巨大挑战。为解决该问题，我们提出一种基于约束感知决策机制的个性化可持续饮食推荐模型，该模型将可持续性通过可学习约束融入，而非作为用户偏好建模。为系统评估所提方法，我们构建了包含约15万份食谱的可持续饮食数据集SusDiet，该数据集覆盖广泛的可持续性指标。在该数据集上的实验结果表明，我们的方法在不损害个人偏好的前提下，能推动更多可持续选择。本研究建立了将个人饮食选择与地球健康相匹配的框架，为未来可持续饮食干预及可持续发展政策制定提供了定量证据。
英文摘要
A sustainable diet represents a multi-dimensional synergy among four essential pillars: nutrition adequacy, economic affordability, cultural acceptability, and environmental respect. Despite the prevalence of population-level sustainability modeling, practical implementation relies on effective individual-level adoption. This transition is often hindered by inter-individual heterogeneity, posing a formidable challenge in aligning sustainable diet requirements with individual preferences. To address this issue, we propose a personalized sustainable diet recommendation model based on a constraint-aware decision-making mechanism, where sustainability is incorporated through learnable constraints rather than modeled as user preferences. To systematically evaluate the proposed approach, we construct a sustainable diet dataset named SusDiet with about 150k recipes, characterized by broad coverage of sustainability indicators. Experimental results on this dataset show that our method promotes more sustainable choices without compromising individual preference. This work establishes a framework for aligning individual dietary choices with planetary health, offering quantitative evidence to guide future sustainable diet interventions and policy-making for sustainable development.
CommentsAccepted in ICML 2026, 24 pages, 7 figures
