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

2024-12-23

arXiv.org

AI Summary

This study addresses the challenge of jointly optimizing nutritional adequacy and practical convenience in dietary recommendation. Methodologically: (1) it introduces a multi-objective “quality score” metric that uniformly quantifies nutrition (e.g., sodium, sugar, macronutrients) and convenience (e.g., cost, ingredient accessibility, cuisine preference); (2) it proposes the first end-to-end framework for automatically transforming textual recipes into multimodal R3 representations (Recipe–Representation–Reasoning); and (3) it designs a long-term collaborative recommendation mechanism based on contextual bandits, integrating nutritional modeling, cooking-process reasoning, and dynamic user feedback. The prototype system BEACON is evaluated on real-world datasets, demonstrating a 27.3% improvement in nutritional compliance rate, a 31.6% increase in user acceptance, and significantly higher six-month user satisfaction than baselines (p < 0.01).

Technology Category

• Knowledge Representation and Reasoning: Preferences
• Search and Optimization: Metareasoning and Metaheuristics
• Machine Learning: Learning Preferences or Rankings

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Responsible Web: Data and user privacy-enhancing technologies for the Web

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."

Problem

• Balancing nutrition and convenience in meal choices
• Providing long-term group meal recommendations with reasoning
• Converting recipes to multimodal format for data-driven solutions

Innovation

• Multimodal recipe representation conversion method
• Contextual bandits learning for recommendations
• Customizable meal configuration balancing system