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

arXiv: https://arxiv.org/abs/2608.24274 · HTML: https://mlsanity.com/paper/2608.24274/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 50 references that are in the map.
- First posted on arXiv 25 Aug 2026.

## Builds on (most cited first, 9 of 9)

- [Intent Contrastive Learning for Sequential Recommendation](https://mlsanity.com/paper/2202.02519/) (2022)
- [Generating Personalized Recipes from Historical User Preferences](https://mlsanity.com/paper/1909.00105/) (2019)
- [WorldCuisines: A Massive-Scale Benchmark for Multilingual and Multicultural Visual Question Answering on Global Cuisines](https://mlsanity.com/paper/2410.12705/) (2024)
- [Assessing Large Language Models on Climate Information](https://mlsanity.com/paper/2310.02932/) (2023)
- [AI for Global Climate Cooperation: Modeling Global Climate Negotiations, Agreements, and Long-Term Cooperation in RICE-N](https://mlsanity.com/paper/2208.07004/) (2022)
- [The World Wide recipe: A community-centred framework for fine-grained data collection and regional bias operationalisation](https://mlsanity.com/paper/2406.09496/) (2024)
- [What can large language models do for sustainable food?](https://mlsanity.com/paper/2503.04734/) (2025)
- [Multi-modal Food Recommendation using Clustering and Self-supervised Learning](https://mlsanity.com/paper/2406.18962/) (2024)
- [Bites of Tomorrow: Personalized Recommendations for a Healthier and Greener Plate](https://mlsanity.com/paper/2508.13870/) (2025)

## Abstract (arXiv)

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.