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.

翻译：电子商务领域日益增长的环境意识要求推荐系统不仅引导用户选择可持续产品，还需最小化系统自身的数字碳足迹。传统基于会话的推荐系统虽针对短期转化率进行了优化，却常难以捕捉用户对环保选择的细微意图，导致绿色意愿与实际行动之间存在持续鸿沟。为解决这一问题，我们提出了LLMGreenRec——一种创新的多智能体框架，该框架利用大型语言模型（LLMs）来促进可持续消费。通过协同分析用户交互行为并进行迭代式提示优化，LLMGreenRec中的专业化智能体能够推断出以绿色为导向的用户意图，并优先推荐环保产品。值得注意的是，这种意图驱动的方法同时减少了不必要的交互操作与能源消耗。在基准数据集上进行的大量实验验证了LLMGreenRec在推荐可持续产品方面的有效性，证明其是培育负责任数字经济的一项稳健解决方案。