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.

翻译：传统电子商务推荐系统主要优化用户参与度和购买可能性，往往忽略了人体健康所需的严格生理约束。标准协同过滤算法在结构上无法感知这些硬性限制，经常推荐无法满足特定每日总能量消耗和宏量营养素平衡需求的商品组合。为解决这一脱节问题，本文提出一种基于物理信息的神经符号推荐系统，通过双层架构将营养科学直接整合到推荐流程中。该框架首先利用句子级编码器构建语义知识图谱，将商业产品与权威营养数据进行严格对齐。在训练阶段，隐式物理正则化器应用可微分热力学损失函数，确保学习到的潜在嵌入反映营养合理性而非简单流行度。随后在推理阶段，显式物理优化器采用模拟退火和弹性数量优化方法，生成严格遵循用户蛋白质与热量目标的离散食品组合。