用推荐系统主动引导用户选择更健康环保的饮食。
Bites of Tomorrow: Personalized Recommendations for a Healthier and Greener Plate
- 设计个性化饮食推荐系统GRAPE，结合用户偏好与可持续性。
- 提出两种绿色损失函数，适配不同优先级的环保指标。
- 在真实数据集上验证，显著提升可持续食品推荐效果。
极端气候事件的频发促使人们更加关注可持续生活。现有研究多依赖传统方法推动行为改变，但这些方法往往要求过高或参与度不足。本文提出通过推荐系统主动引导用户选择更可持续的食物。我们设计了与个性化饮食相匹配的绿色推荐系统（GRAPE），优先推荐符合用户动态偏好的可持续食品。同时，提出两种创新的绿色损失函数，分别适用于环保指标具有相同或不同优先级的场景，增强系统在多种情境下的适应性。在真实世界数据集上的大量实验表明，GRAPE在促进可持续饮食选择方面具有显著有效性。
原文摘要 · Abstract (English)
The recent emergence of extreme climate events has significantly raised awareness about sustainable living. In addition to developing energy-saving materials and technologies, existing research mainly relies on traditional methods that encourage behavioral shifts towards sustainability, which can be overly demanding or only passively engaging. In this work, we propose to employ recommendation systems to actively nudge users toward more sustainable choices. We introduce Green Recommender Aligned with Personalized Eating (GRAPE), which is designed to prioritize and recommend sustainable food options that align with users' evolving preferences. We also design two innovative Green Loss functions that cater to green indicators with either uniform or differentiated priorities, thereby enhancing adaptability across a range of scenarios. Extensive experiments on a real-world dataset demonstrate the effectiveness of our GRAPE.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。