公平的代价：评估推荐系统中能源-公平-准确性权衡

AI 摘要

该研究评估了推荐系统中公平性干预的能源成本，比较了训练阶段和后处理阶段方法在多个模型、数据集和硬件上的表现。结果显示，公平性的绿色成本并不统一，后处理方法将成本转移到重复服务阶段，而训练阶段方法避免了重排序开销但效果因环境而异。研究呼吁将公平感知推荐评估为准确性、公平性和计算成本之间的三方权衡。

正文节选

What Price Fairness? Evaluating Energy – Fairness – Accuracy Trade-off in Recommender Systems Abstract. Fairness-aware recommender systems aim to mitigate systematic imbalances in recommendation outcomes, including how visibility, relevance, and opportunities are distributed among users, items, and providers. However, these systems are usually evaluated in terms of accuracy and fairness alone, while their computational and environmental costs remain largely invisible. This omission matters becau

发布时间：2026-09-09 12:00

抓取时间：2026-09-09 12:46

来源机构：arXiv