Bringing GRACE to Recommendation: Fine-Tuning for Sustainable and Accurate Personalization

• URL: http://arxiv.org/abs/2607.22341v1
• Date: Fri, 24 Jul 2026 14:17:37 GMT
• Title: Bringing GRACE to Recommendation: Fine-Tuning for Sustainable and Accurate Personalization
• Abstract summary: Growing concern about environmental sustainability has motivated green'' recommender systems that steer users toward more eco-friendly and healthier choices.<n>Many existing green recommendation approaches require training new models from scratch, incurring substantial computational and energy costs.<n>We propose GRACE (Green Recommendation via Adaptive Conflict-rEsolution), a fine-tuning framework that integrates item-level sustainability signals into pretrained recommendation models.
• Score: 57.58261252242669
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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