Eating for a Sustainable Planet: Personalized Sustainable Diet Recommendation via Constraint-Aware Decision-Making Modeling

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 across four dimensions. 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.

Dietary patterns are increasingly recognized as an important component of global sustainability transitions, with diets closely linked to environmental pressures, public health, and resource consumption. Although existing studies have established sustainable dietary guidelines at population scales, translating these principles into individual-level adoption remains challenging due to the diversity of personal preferences, cultural practices, and long-term eating habits. In this work, we develop an AI-based recommendation framework that models sustainable diet recommendation as a personalized decision-making problem, balancing individual preferences with broader sustainability considerations. We further construct a large-scale dataset containing approximately 150k recipes annotated with sustainability-related information and aligned with real-world user interactions. Experimental results show that the proposed system can generate more sustainable recommendations while remaining consistent with users’ existing preferences. More broadly, this work illustrates the potential of AI systems to support personalized sustainable consumption and contribute to future data-driven approaches for sustainable diet promotion and public health strategy development.