HealthGenie: Empowering Users with Healthy Dietary Guidance through Knowledge Graph and Large Language Models

• URL: http://arxiv.org/abs/2504.14594v1
• Date: Sun, 20 Apr 2025 12:51:16 GMT
• Title: HealthGenie: Empowering Users with Healthy Dietary Guidance through Knowledge Graph and Large Language Models
• Authors: Fan Gao, Xinjie Zhao, Ding Xia, Zhongyi Zhou, Rui Yang, Jinghui Lu, Hang Jiang, Chanjun Park, Irene Li,
• Abstract summary: HealthGenie is an interactive system that combines the strengths of Large Language Models (LLMs) and Knowledge Graphs (KGs)<n>The system visualizes and highlights pertinent information, organized by defined categories, while offering detailed, explainable recommendation rationales.<n>Our evaluation, comprising a within-subject comparative experiment and an open-ended discussion, demonstrates that HealthGenie effectively supports users in obtaining personalized dietary guidance.
• Score: 19.722881088615647
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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