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FairFRESH: A Neuro-Symbolic Framework for Fair and Explainable Household Food Recommendation

Kodur Sai Vinay Sathvik, Kakoli Bora, Tejaswini R. M · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.31506 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

我们推荐的食物组件通常服务一个用户一次。然而,在家庭中,成员共享一个有限的库存,并遵循不同的饮食规则,做出独立的建议倾向于资源冲突和饮食违规。我们制定家庭食品推荐作为有限的公平分配,并呈现FairFRESH,一个神经符号框架,在每个成员的门槛上学习,根据明确的福利目标分配食物,通过确定性过滤器强制执行饮食规则,并解释使用Shapley和Banzhaf优化属性的建议。理论上,我们展示了FairFRESH,一个神经符号框架,保证对每一个成员的饮食规则的遵守,为所有实用主义和平衡的福利目标建立了近似的保证,并解释了使用Shapley和Banzhaf定义的

Machine translation (Meta M2M100); the English original follows.

Abstract

Recommender systems typically serve one user at a time. However, in households, members share a finite pantry and follow different dietary rules, making independent recommendations prone to resource conflicts and dietary violations. We formulate household food recommendation as constrained fair division and present FairFRESH, a neuro-symbolic framework that learns per-member utilities, allocates foods under explicit welfare objectives, enforces dietary rules through a deterministic filter, and explains recommendations using Shapley and Banzhaf attributions. Theoretically, we show that no finite penalty guarantees compliance near a dietary-rule threshold, establish approximation guarantees for greedy allocation under utilitarian and smoothed Nash objectives in the stock-free model before filtering, and characterize the scale invariance of Nash welfare. We also use the minimax theorem to analyze rotating shared meals. On NHANES-derived data, the learned utility model achieves Recall@20 of 0.293, outperforming five learned recommenders (at most 0.244) but not a previous-day repetition heuristic (0.321). On held-out four-person households, Nash allocation reduces Gini disparity by 42% relative to conflict resolution while improving Recall@20 from 0.180 to 0.197. It comes within 2% of an upper bound on the smoothed Nash optimum, and rescaling one member's utilities changes at most 1.5% of assignments. All recommendations satisfy the encoded dietary rules, while attribution scores correlate with utility loss upon component removal (r = 0.76). In simulation, shared meals reduce pantry use by about one-third, and weekly rotation increases the worst-off member's utility by 38% over the best fixed meal.

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Topics: Fair division, Nash equilibrium, Best response, Computer science, Ranking (information retrieval), Resource allocation

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