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

TL;DR

This paper introduces a data-driven meal recommendation system that balances nutrition and convenience, leveraging multimodal recipe representations and contextual bandits for improved decision-making.

Contribution

It presents a novel formulation for meal recommendations that considers both nutrition and convenience, along with a recipe conversion method and learning algorithms using contextual bandits.

Findings

Promising results in balancing nutrition and convenience.

Effective recipe conversion to multimodal representations.

Novel contextual bandit learning approach for meal recommendations.

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.