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LLMGreenRec is introduced, a multi-agent recommender system employing LLMs to promote sustainable consumption in e-commerce by deducing green-oriented user intents. The system uses collaborative analysis of user interactions and iterative prompt refinement to prioritize eco-friendly product recommendations. Experiments on benchmark datasets demonstrate LLMGreenRec's effectiveness in recommending sustainable products and fostering a responsible digital economy.
LLMGreenRec shows how LLMs can bridge the gap between user's green intentions and actual purchases, while simultaneously reducing the recommender system's carbon footprint.
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.
