用大模型多智能体系统推荐绿色商品，兼顾环保与节能。
LLMGreenRec: LLM-Based Multi-Agent Recommender System for Sustainable E-Commerce
- 用大模型分析用户行为，动态识别绿色消费意图
- 在主流数据集上提升可持续商品推荐效果，减少无效交互
- 适合关注低碳电商与智能推荐的研究者
电子商务中日益增长的环保意识要求推荐系统不仅引导用户选择可持续产品，还需降低自身数字碳足迹。传统基于会话的系统为短期转化优化，难以捕捉用户对环保商品的细微意图，导致绿色意愿与行为脱节。为此，我们提出 LLMGreenRec，一种基于大语言模型的多智能体框架，通过协同分析用户交互与迭代提示优化，使专用智能体推断绿色导向的用户意图，并优先推荐环保产品。该意图驱动方法同时减少了不必要的交互与能耗。在基准数据集上的大量实验验证了其在推荐可持续商品方面的有效性，提供了一种推动负责任数字经济的稳健解决方案。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。