用可学习约束建模可持续饮食，兼顾个人偏好与环境影响
Eating for a Sustainable Planet: Personalized Sustainable Diet Recommendation via Constraint-Aware Decision-Making Modeling
- 将可持续性设为可学习的约束条件，而非用户偏好
- 在15万道食谱数据上验证，提升可持续选择率且不牺牲偏好
- 适合政策制定者和个性化营养推荐系统开发者
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维协同的结果。尽管群体级可持续性建模已广泛存在，实际落地仍依赖个体采纳。该过程常受个体差异阻碍，难以调和个人偏好与可持续要求。为此，本文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，将可持续性作为可学习约束嵌入，而非用户偏好。为系统评估该方法，我们构建了名为SusDiet的可持续饮食数据集，包含约15万道食谱，覆盖广泛可持续指标。实验结果表明，该方法在不降低个体偏好的前提下，显著促进更可持续的饮食选择。本研究建立了一个连接个体饮食行为与行星健康的框架，为未来可持续饮食干预与政策制定提供量化依据。
原文摘要 · Abstract (English)
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.
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