FairFRESH: A Neuro-Symbolic Framework for Fair and Explainable Household Food Recommendation
[Sztuczna inteligencja] Autorzy: Kodur Sai Vinay Sathvik, Kakoli Bora, Tejaswini R. M
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 …
- Kategoria arxiv
- cs.AI
- Kategoria
- Sztuczna inteligencja