用知识图谱和大模型结合，给用户个性化饮食建议。
HealthGenie: Empowering Users with Healthy Dietary Guidance through Knowledge Graph and Large Language Models
- 融合知识图谱与大模型，实现可解释的对话式推荐
- 支持交互调整偏好，减少用户认知负担
- 适合关注健康饮食的普通用户与慢病管理人群
获取饮食建议常需应对复杂的健康知识并考虑个体状况。知识图谱（KG）提供结构化、可解释的营养信息，而大语言模型（LLM）擅长自然对话式推荐。本文提出 HealthGenie 系统，结合两者优势，为用户提供个性化饮食建议，并通过分层可视化实现快速直观的信息概览。用户输入后，系统先进行查询优化，从预构建的知识图谱中检索相关信息，再按预定义类别组织并高亮关键内容，同时提供详尽的可解释推荐理由。用户可交互调整偏好以细化建议。我们通过一项包含12名用户的组内对比实验和开放式讨论评估系统，结果表明，HealthGenie 能有效帮助用户基于健康状况获得个性化指导，显著降低交互成本与认知负荷。研究验证了 LLM-KG 融合在可解释与可视化决策支持中的潜力，并为未来融合对话式大模型与知识图谱的系统设计提供参考。
原文摘要 · Abstract (English)
Seeking dietary guidance often requires navigating complex professional knowledge while accommodating individual health conditions. Knowledge Graphs (KGs) offer structured and interpretable nutritional information, whereas Large Language Models (LLMs) naturally facilitate conversational recommendation delivery. In this paper, we present HealthGenie, an interactive system that combines the strengths of LLMs and KGs to provide personalized dietary recommendations along with hierarchical information visualization for a quick and intuitive overview. Upon receiving a user query, HealthGenie performs query refinement and retrieves relevant information from a pre-built KG. The system then visualizes and highlights pertinent information, organized by defined categories, while offering detailed, explainable recommendation rationales. Users can further tailor these recommendations by adjusting preferences interactively. Our evaluation, comprising a within-subject comparative experiment and an open-ended discussion, demonstrates that HealthGenie effectively supports users in obtaining personalized dietary guidance based on their health conditions while reducing interaction effort and cognitive load. These findings highlight the potential of LLM-KG integration in supporting decision-making through explainable and visualized information. We examine the system's usefulness and effectiveness with an N=12 within-subject study and provide design considerations for future systems that integrate conversational LLM and KG.
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