TCM_DiffPR: Graph Knowledge Diffusion Model for Traditional Chinese Medicine Personalized Recommendation

TCM_DiffPR: Graph Knowledge Diffusion Model for Traditional Chinese Medicine Personalized Recommendation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article TCM_DiffPR: Graph Knowledge Diffusion Model for Traditional Chinese Medicine Personalized Recommendation ChaoBo Zhang, Long Tan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6047566/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Oct, 2025 Read the published version in Journal of Intelligent Information Systems → Version 1 posted 10 You are reading this latest preprint version Abstract With the rapid development of modern Chinese medicine prescription recommendation technology based on artificial intelligence technology, the existing herb recommendation model lacks attention to the personalized attributes of the patient, ignoring the important factors of the clinical diagnosis and treatment process of Traditional Chinese medicine (TCM). To address this problem, we propose a novel Graph Knowledge Diffusion Model for Traditional Chinese Medicine Personalized Recommendation (TCM_DiffPR). Firstly, the patient's personalized attribute information is used to obtain symptom representations by means of ‘prompt fine-tuning’, and the prompt is enhanced by pre-training model with prompt-oriented comparative learning. Secondly, the algorithm combines the generative model with the data augmentation paradigm based on knowledge graph diffusion, which compensates for the misinterpretation of symptom preferences in the knowledge graph (KG) to achieve powerful knowledge learning. Finally, we introduce a collaborative knowledge graph convolution mechanism, which combines collaborative signals reflecting the symptom-herbal medication interaction patterns, to guide the knowledge graph diffusion process. In this study, experimental comparative analyses are conducted on two public datasets and a clinical dataset, and the results show that the present algorithm outperforms existing TCM prescription recommendation methods and provides an effective reference basis for clinical diagnosis and decision support. Personalized recommendation Prompt fine-tuning Herb recommendation Knowledge graph diffusion Clinical diagnosis and decision support Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 18 Oct, 2025 Read the published version in Journal of Intelligent Information Systems → Version 1 posted Editorial decision: Revision requested 28 Jul, 2025 Reviews received at journal 24 Jul, 2025 Reviewers agreed at journal 04 Jul, 2025 Reviewers agreed at journal 25 Jun, 2025 Reviews received at journal 23 Mar, 2025 Reviewers agreed at journal 07 Mar, 2025 Reviewers invited by journal 02 Mar, 2025 Editor assigned by journal 19 Feb, 2025 Submission checks completed at journal 19 Feb, 2025 First submitted to journal 17 Feb, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6047566","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":418076779,"identity":"72cb78e7-4599-411f-b9f6-5b103ba6d317","order_by":0,"name":"ChaoBo Zhang","email":"","orcid":"","institution":"Heilongjiang University","correspondingAuthor":false,"prefix":"","firstName":"ChaoBo","middleName":"","lastName":"Zhang","suffix":""},{"id":418076780,"identity":"d40a3704-7502-4531-b1dc-00b636aa4a4c","order_by":1,"name":"Long Tan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYHACxgNAQs6AnSEBSDMTVs8DxCAtxgbMpGpJ3ABRTIQWe/azBw583FGbvp2Z4ZkEQ4V1YgNQBL8tPHkJB2eeOZ67s5khTYLhTHpiA1CEgMNyDA7zth3L3XAYqIWx7XBigwSPAX4t/G/AWtINwFr+EaNFAmxLTQJESwMxWm68MTg4s+2AIdBhyRYJx9KN23hy8Gth788xfPCxrU7e4HhP4o0PNday/exn8GuBgsMgCxPAkclGjHogqANZeIBIxaNgFIyCUTDSAAAq+kVZjGHtagAAAABJRU5ErkJggg==","orcid":"","institution":"Heilongjiang University","correspondingAuthor":true,"prefix":"","firstName":"Long","middleName":"","lastName":"Tan","suffix":""}],"badges":[],"createdAt":"2025-02-17 11:38:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6047566/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6047566/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10844-025-00994-w","type":"published","date":"2025-10-18T15:57:16+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":93955936,"identity":"24218aff-49f2-4795-920b-4f00c97db78b","added_by":"auto","created_at":"2025-10-20 16:07:44","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5419726,"visible":true,"origin":"","legend":"","description":"","filename":"TCMDiffPRGraphKnowledgeDiffusionModelforTraditionalChineseMedicinePersonalizedRecommendationSpringernature.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6047566/v1_covered_bd2336b4-83db-42aa-9975-b8cba640cc02.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"TCM_DiffPR: Graph Knowledge Diffusion Model for Traditional Chinese Medicine Personalized Recommendation","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-intelligent-information-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jiis","sideBox":"Learn more about [Journal of Intelligent Information Systems](http://link.springer.com/journal/10844)","snPcode":"10844","submissionUrl":"https://submission.nature.com/new-submission/10844/3","title":"Journal of Intelligent Information Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Personalized recommendation, Prompt fine-tuning, Herb recommendation, Knowledge graph diffusion, Clinical diagnosis and decision support","lastPublishedDoi":"10.21203/rs.3.rs-6047566/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6047566/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"With the rapid development of modern Chinese medicine prescription recommendation technology based on artificial intelligence technology, the existing herb recommendation model lacks attention to the personalized attributes of the patient, ignoring the important factors of the clinical diagnosis and treatment process of Traditional Chinese medicine (TCM). 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