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

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.

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Publication Details

Journal
Proceedings of the AAAI Conference on Artificial Intelligence
Published
2025-04-11
DOI
https://doi.org/10.1609/aaai.v39i11.33295
Primary Topic
Health, Environment, Cognitive Aging
Type
article
Field-Weighted Citation Impact
0.00

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article

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

J. Jing, Chunyan Miao, Yinan Zhang
Proceedings of the AAAI Conference on Artificial Intelligence
Health, Environment, Cognitive Aging
article

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

J. Jing, Chunyan Miao, Yinan Zhang
article en

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.

Proceedings of the AAAI Conference on Artificial IntelligenceVol. 39(11)
Nanyang Technological University (SG)
Nanyang Technological University
Openalex Percentile: Top 91%
Health, Environment, Cognitive Aging
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