Rising environmental awareness in e-commerce necessitates recommender systems that not only guide users to sustainable products but also minimize their own digital carbon footprints. Traditional session-based systems, optimized for short-term conversions, often fail to capture nuanced user intents for eco-friendly choices, perpetuating a gap between green intentions and actions. To tackle this, we introduce LLMGreenRec, a novel multi-agent framework that leverages Large Language Models (LLMs) to promote sustainable consumption. Through collaborative analysis of user interactions and iterative prompt refinement, LLMGreenRec's specialized agents deduce green-oriented user intents and prioritize eco-friendly product recommendations. Notably, this intent-driven approach also reduces unnecessary interactions and energy consumption. Extensive experiments on benchmark datasets validate LLMGreenRec's effectiveness in recommending sustainable products, demonstrating a robust solution that fosters a responsible digital economy.
@article{arxiv.2603.11025,
title = {LLMGreenRec: LLM-Based Multi-Agent Recommender System for Sustainable E-Commerce},
author = {Hao N. Nguyen and Hieu M. Nguyen and Son Van Nguyen and Nguyen Thi Hanh},
journal= {arXiv preprint arXiv:2603.11025},
year = {2026}
}
Comments
Accepted to the Proceedings of the Conference on Digital Economy and Fintech Innovation (DEFI 2025). To appear in IEEE Xplore