AI 中文总结
为应对绿色推荐系统训练成本高、推理延迟大的问题，提出GRACE微调框架，将项目级可持续性信号融入预训练推荐模型，引入可微近似并采用梯度投影机制平衡目标冲突，实验证明其能提升可持续性推荐结果并保持准确性。
AI 中文摘要
对环境可持续性（如减少碳排放和资源使用）和公共卫生的日益关注推动了“绿色”推荐系统的发展，引导用户做出更环保、更健康的选择。然而，许多现有绿色推荐方法需从头训练新模型，成本高昂。基于重排的方法在推理时增加排序阶段，增加延迟和计算成本。本文提出GRACE框架，将项目级可持续性信号整合到预训练推荐模型中。因绿色值通常离散不可微，GRACE引入可微近似以直接优化绿色标准。为平衡可持续性和个性化质量，GRACE采用梯度投影机制减轻微调期间绿色目标与准确性目标之间的冲突。实验表明GRACE能改善面向可持续性的推荐结果，同时通过可控的偏好锚定更新机制保持推荐准确性。
英文摘要
Growing concern about environmental sustainability (e.g., reducing carbon emissions and resource use) and public health has motivated ``green'' recommender systems that steer users toward more eco-friendly and healthier choices. However, many existing green recommendation approaches require training new models from scratch, incurring substantial computational and energy costs. Reranking-based methods, meanwhile, introduce an additional sorting stage at inference, increasing latency and computational cost. In this work, we propose GRACE (Green Recommendation via Adaptive Conflict-rEsolution), a fine-tuning framework that integrates item-level sustainability signals (e.g., eco-scores or health indices) into pretrained recommendation models. Since these green values are usually discrete and non-differentiable, existing methods often rely on pairwise comparisons to promote greener items. GRACE instead introduces a differentiable approximation that enables direct optimization of the green criterion. To balance sustainability and personalization quality, GRACE further employs a gradient projection mechanism to mitigate conflicts between the green objective and the accuracy objective during fine-tuning. Experiments on real-world datasets demonstrate that GRACE improves sustainability-oriented recommendation outcomes while generally preserving recommendation accuracy through a controllable preference-anchored update mechanism.
