LLMGreenRec: LLM-Based Multi-Agent Recommender System for Sustainable E-Commerce
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866912961994424320 |
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| author | Nguyen, Hao N. Nguyen, Hieu M. Van Nguyen, Son Hanh, Nguyen Thi |
| author_facet | Nguyen, Hao N. Nguyen, Hieu M. Van Nguyen, Son Hanh, Nguyen Thi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_11025 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | LLMGreenRec: LLM-Based Multi-Agent Recommender System for Sustainable E-Commerce Nguyen, Hao N. Nguyen, Hieu M. Van Nguyen, Son Hanh, Nguyen Thi Multiagent Systems Information Retrieval 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. |
| title | LLMGreenRec: LLM-Based Multi-Agent Recommender System for Sustainable E-Commerce |
| topic | Multiagent Systems Information Retrieval |
| url | https://arxiv.org/abs/2603.11025 |