将营养科学融入推荐系统，确保购物清单符合人体能量与蛋白需求。
Physics-Informed Neuro-Symbolic Recommender System: A Dual-Physics Approach for Personalized Nutrition
- 用知识图谱对齐商品与权威营养数据，构建可解释推荐基础。
- 训练时引入可微热力学损失，使推荐向量具备营养合理性。
- 推理阶段用模拟退火优化商品组合，严格满足用户每日蛋白和热量目标。
传统电商推荐系统主要优化用户参与度和购买概率，常忽视人体健康所需的严格生理约束。标准协同过滤算法对这些硬性限制视而不见，频繁推荐无法满足每日总能量消耗和宏量营养素平衡要求的商品组合。为解决这一脱节问题，本文提出一种物理信息神经符号推荐系统，通过双层架构将营养科学直接嵌入推荐流程。框架首先利用句级编码器构建语义知识图谱，严格对齐商品与权威营养数据；训练阶段引入可微热力学正则化项，确保学习到的隐向量体现营养合理性而非单纯流行度；推理阶段则采用模拟退火与弹性数量优化，生成严格符合用户蛋白质与卡路里目标的离散购物清单。
原文摘要 · Abstract (English)
Traditional e-commerce recommender systems primarily optimize for user engagement and purchase likelihood, often neglecting the rigid physiological constraints required for human health. Standard collaborative filtering algorithms are structurally blind to these hard limits, frequently suggesting bundles that fail to meet specific total daily energy expenditure and macronutrient balance requirements. To address this disconnect, this paper introduces a Physics-Informed Neuro-Symbolic Recommender System that integrates nutritional science directly into the recommendation pipeline via a dual-layer architecture. The framework begins by constructing a semantic knowledge graph using sentence-level encoders to strictly align commercial products with authoritative nutritional data. During the training phase, an implicit physics regularizer applies a differentiable thermodynamic loss function, ensuring that learned latent embeddings reflect nutritional plausibility rather than simple popularity. Subsequently, during the inference phase, an explicit physics optimizer employs simulated annealing and elastic quantity optimization to generate discrete grocery bundles that strictly adhere to the user's protein and caloric targets.
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