$HealthGenie:$ A Knowledge-Driven LLM Framework for Tailored Dietary Guidance
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3746252.3761479.pdf
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1.86 MB
Format
Adobe PDF
Checksum (MD5)
70a06fc74de85f12ed3ca8106c744f6a
Author(s)
Gao, Fan
Zhao, Xinjie
Xia, Ding
Zhou, Zhongyi
Yang, Rui
Lu, Jinghui
Jiang, Hang
Park, Chanjun
Li, Irene
Date Issued
November 10, 2025
Publisher
ACM|Proceedings of the 34th ACM International Conference on Information and Knowledge Management
Citation
Fan Gao, Xinjie Zhao, Ding Xia, Zhongyi Zhou, Rui Yang, Jinghui Lu, Hang Jiang, Chanjun Park, and Irene Li. 2025. HealthGenie: A Knowledge-Driven LLM Framework for Tailored Dietary Guidance. In Proceedings of the 34th ACM International Conference on Information and Knowledge Management (CIKM '25). Association for Computing Machinery, New York, NY, USA, 6639–6643.
Version
Final published version
Abstract
Seeking dietary guidance often requires navigating complex nutritional knowledge while considering individual health needs. To address this, we present HealthGenie, an interactive platform that leverages the interpretability of knowledge graphs (KGs) and the conversational power of large language models (LLMs) to deliver tailored dietary recommendations alongside integrated nutritional visualizations for fast, intuitive insights. Upon receiving a user query, HealthGenie performs intent refinement and maps user's needs to a curated nutritional knowledge graph. The system then retrieves and visualizes relevant subgraphs, while offering detailed, explainable recommendations. Users can interactively adjust preferences to further tailor results. A within-subject study and quantitative analysis show that HealthGenie reduces cognitive load and interaction effort while supporting personalized, health-aware decision-making.
Description
CIKM ’25, Seoul, Republic of Korea
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1145/3746252.3761479
