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

TL;DR

This paper introduces GRAPE, a personalized recommendation system that actively encourages sustainable eating choices by aligning with user preferences and utilizing innovative green loss functions, demonstrated to be effective through real-world experiments.

Contribution

The paper presents a novel personalized recommendation system for sustainable eating, incorporating green loss functions for better adaptability and effectiveness.

Findings

GRAPE effectively promotes sustainable food choices.

Green Loss functions improve recommendation relevance.

Experimental results validate the system's effectiveness.

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.