In fact, according background in automated recommendations of personalized to a recent meta-survey (Leme et al. 2021), almost meals and then discuss our problem formulation, key solution 40% of the population across high and low-and mediumincome components including data (recipe representation and countries do not adhere to their national food-based format conversion) and meal recommendation, and their dietary guidelines, often prioritizing convenience over nutrition evaluation. We then describe a prototype implementation of needs. Previous studies have shown that adhering the solution in the BEACON system along with the supported to a provided meal plan instead of a self-selected one reduces use cases and conclude with a discussion of practical the risk for adverse health conditions (Metz et al. considerations and avenues for future extensions.

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.