  A common, yet regular, decision made by people, whether healthy or with any
health condition, is to decide what to have in meals like breakfast, lunch, and
dinner, consisting of a combination of foods for appetizer, main course, side
dishes, desserts, and beverages. However, often this decision is seen as a
trade-off between nutritious choices (e.g., low salt and sugar) or convenience
(e.g., inexpensive, fast to prepare/obtain, taste better). In this preliminary
work, we present a data-driven approach for the novel meal recommendation
problem that can explore and balance choices for both considerations while also
reasoning about a food's constituents and cooking process. Beyond the problem
formulation, our contributions also include a goodness measure, a recipe
conversion method from text to the recently introduced multimodal rich recipe
representation (R3) format, and learning methods using contextual bandits that
show promising results.
