A Novel Approach to Balance Convenience and Nutrition in Meals With Long-Term Group Recommendations and Reasoning on Multimodal Recipes and its Implementation in BEACON

TL;DR

This paper proposes a data-driven, multimodal recipe recommendation system that balances nutritional value and convenience, utilizing contextual bandits and a novel recipe representation to personalize meal choices over time.

Contribution

It introduces goodness measures, a recipe conversion method to multimodal R3 format, and a prototype system called BEACON for personalized meal recommendations.

Findings

Preliminary results show promising learning performance.

The system effectively balances nutrition and convenience.

Recipe conversion to R3 format enhances multimodal representation.

Abstract

A common decision made by people, whether healthy or with health conditions, is choosing meals like breakfast, lunch, and dinner, comprising combinations of foods for appetizer, main course, side dishes, desserts, and beverages. Often, this decision involves tradeoffs between nutritious choices (e.g., salt and sugar levels, nutrition content) and convenience (e.g., cost and accessibility, cuisine type, food source type). We present a data-driven solution for meal recommendations that considers customizable meal configurations and time horizons. This solution balances user preferences while accounting for food constituents and cooking processes. Our contributions include introducing goodness measures, a recipe conversion method from text to the recently introduced multimodal rich recipe representation (R3) format, learning methods using contextual bandits that show promising preliminary results, and the prototype, usage-inspired, BEACON system.