The profound impact of food on health necessitates advanced nutrition-oriented food recommendation services. Conventional methods often lack the crucial elements of personalization, explainability, and interactivity. While Large Language Models (LLMs) bring interpretability and explainability, their standalone use falls short of achieving true personalization. In this paper, we introduce ChatDiet, a novel LLM-powered framework designed specifically for personalized nutrition-oriented food recommendation chatbots. ChatDiet integrates personal and population models, complemented by an orchestrator, to seamlessly retrieve and process pertinent information. The personal model leverages causal discovery and inference techniques to assess personalized nutritional effects for a specific user, whereas the population model provides generalized information on food nutritional content. The orchestrator retrieves, synergizes and delivers the output of both models to the LLM, providing tailored food recommendations designed to support targeted health outcomes. The result is a dynamic delivery of personalized and explainable food recommendations, tailored to individual user preferences. Our evaluation of ChatDiet includes a compelling case study, where we establish a causal personal model to estimate individual nutrition effects. Our assessments, including a food recommendation test showcasing a 92\% effectiveness rate, coupled with illustrative dialogue examples, underscore ChatDiet's strengths in explainability, personalization, and interactivity.

翻译：食物对健康的深远影响促使我们需要更先进的营养导向食品推荐服务。传统方法往往缺乏个性化、可解释性和交互性等关键要素。尽管大型语言模型（LLM）带来了可解释性，但单独使用它们难以实现真正的个性化。本文介绍ChatDiet，一种专为个性化营养导向食品推荐聊天机器人设计的新型LLM驱动框架。ChatDiet整合了个人模型与群体模型，并辅以协调器，以无缝检索和处理相关信息。个人模型利用因果发现与推断技术评估特定用户的个性化营养效果，而群体模型则提供食物营养含量的通用信息。协调器检索、协同并整合两个模型的输出结果传递给LLM，从而提供针对特定健康目标的定制化食品推荐。最终实现动态传递符合个体用户偏好的个性化且可解释的食品推荐。我们对ChatDiet的评估包含一项引人注目的案例研究，其中建立了因果个人模型以评估个体营养效应。评估结果——包括展示92%有效率的食品推荐测试以及说明性对话示例——凸显了ChatDiet在可解释性、个性化和交互性方面的优势。