# BEACON: Balancing Convenience and Nutrition in Meals With Long-Term Group Recommendations and Reasoning on Multimodal Recipes

arXiv: https://arxiv.org/abs/2406.13714 · HTML: https://mlsanity.com/paper/2406.13714/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 28 references that are in the map.
- First posted on arXiv 19 Jun 2024.

## Builds on (most cited first, 3 of 3)

- [RecipeQA: A Challenge Dataset for Multimodal Comprehension of Cooking Recipes](https://mlsanity.com/paper/1809.00812/) (2018)
- [ULTRA: A Data-driven Approach for Recommending Team Formation in Response to Proposal Calls](https://mlsanity.com/paper/2201.05646/) (2022)
- [Promoting Research Collaboration with Open Data Driven Team Recommendation in Response to Call for Proposals](https://mlsanity.com/paper/2309.09404/) (2023)

## Abstract (arXiv)

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.