智能推荐兼顾营养与便利，支持长期饮食规划
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
- 基于多模态食谱的个性化推荐框架
- 通过上下文强化学习实现营养与便利的动态平衡
- 适用于关注健康饮食的用户及慢性病管理人群
人们在选择早餐、午餐、晚餐等餐食时，常需权衡营养（如盐糖含量、营养成分）与便利性（如成本、可得性、菜系类型、食材来源）。本文提出一种数据驱动的餐食推荐方法，支持自定义餐食配置与长期时间范围规划，兼顾用户偏好、食物成分与烹饪过程。贡献包括引入合理性度量、将文本食谱转换为近期提出的多模态丰富食谱（R3）格式的转换方法、采用上下文老虎机的学习策略（初步结果表现良好），以及原型系统BEACON的实现。
原文摘要 · Abstract (English)
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.
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