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AI estimates product carbon footprints for greener e-commerce

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a method to estimate the carbon footprint of e-commerce products, even when labels are missing. This is achieved by using LLMs and semantic similarity to infer carbon footprints from a small set of assessed products. The system then re-ranks product recommendations to balance user engagement with carbon reduction, demonstrating that significant carbon savings are possible with minimal impact on user interest. AI

IMPACT Enables e-commerce platforms to integrate sustainability into recommendations, potentially shifting consumer behavior towards lower-carbon products.

RANK_REASON Academic paper detailing a novel methodology for carbon-aware e-commerce recommendations. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI estimates product carbon footprints for greener e-commerce

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Academic paper detailing a novel methodology for carbon-aware e-commerce recommendations. [lever_c_demoted from research: ic=1 ai=0.7]
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115 days old
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Jorgen Bergh ·

    Trading Engagement for Sustainability: Carbon-Aware Re-ranking for E-commerce Recommendations

    E-commerce recommender systems strongly influence which products users consider and purchase, yet sustainability signals such as Product Carbon Footprint (PCF) are almost never available at catalog scale. We study carbon-aware product recommendation in the realistic setting where…

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