FairFRESH: A Neuro-Symbolic Framework for Fair and Explainable Household Food Recommendation
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...
| Authors | Kodur Sai Vinay Sathvik, Kakoli Bora, Tejaswini R. M |
|---|---|
| Primary category | cs.AI |
| Published | 22 October 2026 |
| Last updated at source | 22 October 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 23 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.