English(EN) Trading Engagement for Sustainability: Carbon-Aware Re-ranking for E-commerce Recommendations

人工智能估算产品碳足迹，助力绿色电子商务

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-03 07:34

研究人员开发了一种估算电子商务产品碳足迹的方法，即使在缺少标签的情况下也能实现。这是通过使用大型语言模型（LLMs）和语义相似性，从少量评估产品中推断碳足迹来实现的。然后，该系统重新排序产品推荐，以平衡用户参与度和碳减排，证明了在对用户兴趣影响极小的情况下实现显著碳节省是可能的。 AI

影响使电子商务平台能够将可持续性融入推荐，有可能将消费者行为转向低碳产品。

排序理由学术论文，详细介绍了面向电子商务推荐的碳感知新方法。[lever_c_demoted from research: ic=1 ai=0.7]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 学术论文，详细介绍了面向电子商务推荐的碳感知新方法。[lever_c_demoted from research: ic=1 ai=0.7]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, product, other; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: 119 days old; Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

完整方法见我们的编辑标准。

arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Jorgen Bergh · 2026-06-03 07:34

为可持续性牺牲交易参与度：电商推荐的碳感知重新排序

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…