Trading Engagement for Sustainability: Carbon-Aware Re-ranking for E-commerce Recommendations
Researchers have developed a method to estimate product carbon footprints for e-commerce recommendations, even when labels are missing. This is achieved by inferring carbon data using semantic similarity and LLM prompting. A post-hoc re-ranking strategy then balances predicted user engagement with estimated carbon impact, demonstrating significant carbon reductions with minimal loss in engagement across various product categories. AI
IMPACT Enables e-commerce platforms to integrate sustainability into product recommendations, potentially influencing consumer purchasing decisions towards lower-carbon options.