MIGP uses integer servings and goal deviations to produce feasible, practical meal plans that outperform post-hoc rounding of continuous solutions in 66% of cases while always succeeding.
Delighting Palates with AI: Reinforcement Learning’s Triumph in Crafting Personalized Meal Plans with High User Acceptance
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.AI 1years
2026 1verdicts
ACCEPT 1representative citing papers
citing papers explorer
-
Mixed Integer Goal Programming for Personalized Meal Optimization with User-Defined Serving Granularity
MIGP uses integer servings and goal deviations to produce feasible, practical meal plans that outperform post-hoc rounding of continuous solutions in 66% of cases while always succeeding.