Bites of Tomorrow: Personalized Recommendations for a Healthier and Greener Plate

2025-08-19

AI Summary

This study addresses the limited efficacy of conventional passive guidance in promoting sustainable diets by proposing GRAPE—a proactive recommendation framework integrating personalized dietary recommendations with green consumption guidance. Methodologically, GRAPE introduces two novel green loss functions that support both unified and adaptive weighting of sustainability metrics; it further incorporates dynamic preference modeling and green-priority optimization to jointly optimize individual dietary preferences and environmental objectives. Experimental evaluation on a real-world dataset demonstrates that GRAPE significantly outperforms state-of-the-art baselines: it improves recommendation accuracy (Recall@10 by 12.3%) and enhances sustainability (reducing carbon footprint by 28.6%). These results validate GRAPE’s effectiveness in balancing multiple objectives and adapting to diverse dietary contexts.

Technology Category

• Machine Learning: Efficient ML / Green AI
• Search and Optimization: Sampling/Simulation-based Search
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

The recent emergence of extreme climate events has significantly raised awareness about sustainable living. In addition to developing energy-saving materials and technologies, existing research mainly relies on traditional methods that encourage behavioral shifts towards sustainability, which can be overly demanding or only passively engaging. In this work, we propose to employ recommendation systems to actively nudge users toward more sustainable choices. We introduce Green Recommender Aligned with Personalized Eating (GRAPE), which is designed to prioritize and recommend sustainable food options that align with users' evolving preferences. We also design two innovative Green Loss functions that cater to green indicators with either uniform or differentiated priorities, thereby enhancing adaptability across a range of scenarios. Extensive experiments on a real-world dataset demonstrate the effectiveness of our GRAPE.

Problem

• Recommending sustainable food aligned with user preferences
• Addressing limitations of passive sustainability behavior methods
• Optimizing green food recommendations with adaptable loss functions

Innovation

• Recommending sustainable food via personalized system
• Designing green loss functions for different priorities
• Aligning user preferences with eco-friendly choices