Cross-Domain Neural Collaborative Filtering forPersonalized Herbal Prescription Recommendation

Cross-Domain Neural Collaborative Filtering forPersonalized Herbal Prescription 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 Cross-Domain Neural Collaborative Filtering forPersonalized Herbal Prescription Recommendation Xin Dong, Kuo Yang, Wansong Zhang, Lei Zhang, Runshun Zhang, Juxian Tang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6728897/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Jan, 2026 Read the published version in Chinese Medicine → Version 1 posted 7 You are reading this latest preprint version Abstract Objective: Herbal prescriptions hold significant importance in Traditional Chinese Medicine (TCM) diagnosis and treatment, embodying millennia of clinical case summaries and wisdom. Despite numerous proposed methods for herbal prescription recommendation (HPR), significant challenges persist due to the lack of comprehensive clinical data, particularly regarding the relationships between symptoms and herbs. This scarcity poses considerable hurdles for effective HPR modeling. Methods: In this study, we introduced a novel herbal prescription recommendation framework with cross-domain learning and neural collaborative filtering (termed PresRecCD). The cross-domain learning mechanism is introduced to learn the noise-reduced cross-domain features of herbs and symptoms in the unified space, which alleviated the sparsity of data, and the neural collaborative filtering is utilized to carry out prescription recommendations. Results: Comprehensive experiments demonstrate the superiority of the proposed PresRecCD model over the SOTA model. The effectiveness of each module in PresRecCD and model robustness are validated by the ablation and hyper-parameter tuning experiments, respectively. The case study based on network pharmacology further validates the effectiveness of our approach, particularly its scientific rigor and feasibility at the molecular mechanism level. Conclusion: This study contributes to enhancing the performance of the HPR model, ultimately benefiting the efficiency and precision of clinical treatment. Herbal prescription recommendation Cross-domain learning Neural collaborative filtering Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 28 Jan, 2026 Read the published version in Chinese Medicine → Version 1 posted Editorial decision: Revision requested 19 Sep, 2025 Reviews received at journal 23 Jun, 2025 Reviewers agreed at journal 05 Jun, 2025 Reviewers invited by journal 03 Jun, 2025 Editor assigned by journal 25 May, 2025 Submission checks completed at journal 23 May, 2025 First submitted to journal 22 May, 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. We do this by developing innovative software and high quality services for the global research community. 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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-6728897","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":465873261,"identity":"81dfa444-7059-4d7c-a2a0-56e3c1c8a0e9","order_by":0,"name":"Xin Dong","email":"","orcid":"","institution":"Beijing Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Dong","suffix":""},{"id":465873262,"identity":"c8002581-67e3-41d9-9b01-b62b94ad3a91","order_by":1,"name":"Kuo 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Despite numerous proposed methods for herbal prescription recommendation (HPR), significant challenges persist due to the lack of comprehensive clinical data, particularly regarding the relationships between symptoms and herbs. This scarcity poses considerable hurdles for effective HPR modeling.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethods: In this study, we introduced a novel herbal prescription recommendation framework with cross-domain learning and neural collaborative filtering (termed PresRecCD). The cross-domain learning mechanism is introduced to learn the noise-reduced cross-domain features of herbs and symptoms in the unified space, which alleviated the sparsity of data, and the neural collaborative filtering is utilized to carry out prescription recommendations.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults:\u0026nbsp;Comprehensive experiments demonstrate the superiority of the proposed PresRecCD\u0026nbsp;model over the SOTA model. 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