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

• URL: http://arxiv.org/abs/2406.13714v1
• Date: Wed, 19 Jun 2024 17:14:41 GMT
• Title: BEACON: Balancing Convenience and Nutrition in Meals With Long-Term Group Recommendations and Reasoning on Multimodal Recipes
• Authors: Vansh Nagpal, Siva Likitha Valluru, Kausik Lakkaraju, Biplav Srivastava,
• Abstract summary: We present a data-driven approach for the novel meal recommendation problem. Our contributions include a goodness measure, a recipe conversion method from text, and learning methods using contextual bandits.
• Score: 4.441718631975636
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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