公平的代价是什么？评估推荐系统中的能量-公平-准确性权衡

中文摘要 AI 辅助

公平感知推荐系统旨在缓解推荐结果中的系统性失衡，包括可见性、相关性和机会在用户、物品和提供者之间的分布不均。然而，这些系统通常仅以准确性和公平性来评估，其计算成本和环境成本在很大程度上仍不可见。这一遗漏至关重要，因为公平性干预可能以不同方式影响推荐的成本。训练时方法修改模型优化，后处理方法在推理时增加计算量，而两者都可能依赖于模型、数据集、硬件和部署环境。我们考察推荐中的提供者侧公平性是否伴随可测量的绿色成本。我们比较了多种模型、两个数据集和两种硬件设置下的处理中、图级重加权和后处理干预措施。我们分别测量了训练和推理阶段的推荐质量、提供者侧曝光度和能耗。我们的结果表明，公平性的绿色成本并不统一，后处理将成本转移到重复服务中，而处理中和图级方法避免了重排序开销，但在模型、数据集和硬件之间差异显著。研究结果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。

英文摘要

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 because fairness interventions may affect the cost of recommendation in different ways. Training-time methods modify model optimization, post-processing methods add computation at inference time, and both may depend on the model, dataset, hardware, and deployment setting. We examine whether provider-side fairness in recommendation comes with a measurable green cost. We compare in-processing, graph-level reweighting and post-processing interventions across multiple models, two datasets, and two hardware settings. We measure recommendation quality, provider-side exposure, and energy consumption separately across training and inference stages. Our results show that the green cost of fairness is not uniform, post-processing shifts cost to repeated serving, while in-processing and graph-level methods avoid re-ranking overhead but vary substantially across models, datasets, and hardware. Findings call for evaluating fairness-aware recommendation as a three-way trade-off between accuracy, fairness, and computational cost.

发表机构

• Johannes Kepler University Linz（林茨约翰内斯·开普勒大学）
• ISISTAN, CONICET-UNCPBA（ISISTAN，阿根廷国家科学研究委员会-内乌肯国立大学）

机构由 AI 辅助整理，请以论文原文为准。

补充信息

DOI

10.1145/3773078.3841290