Hypernetwork-Driven Trustworthy Recommendation

Hypernetwork-Driven Trustworthy 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PDF Method Article Hypernetwork-Driven Trustworthy Recommendation Yicheng Di, Song Shen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9159648/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In the context of rapidly advancing electronic technologies, a growing number of consumer electronic products have increasingly incorporated recommendation systems to improve overall user experience. Conventional recommendation approaches primarily rely on deep neural models to estimate user preferences for items, yet such methods often require extensive user data sharing, which may undermine user trust in the system outputs. To address this issue, federated learning has been introduced into recommendation frameworks to enable privacy-preserving and reliable recommendation processes. However, existing federated recommendation models typically depend on repeated access to user–item interaction data across clients to learn shared model parameters, which limits their practicality. To overcome these limitations, this work proposes a Hypernetwork-Driven Trustworthy Recommendation framework (HDTR), aiming to provide reliable recommendations while accommodating personalized user requirements. Specifically, hypernetworks are first employed to efficiently generate and initialize client-side recommendation models, where user preference representations are encoded as inputs to produce personalized parameterizations. Next, within the client model, item attribute embeddings are incorporated as a form of global contextual information, enriching the representation with additional semantic cues. Furthermore, attention residual blocks are introduced to adaptively capture the relative importance of different item attributes, thereby enhancing feature interaction modeling. Extensive experiments conducted on the Movielens1M, Hetrec-movielens, and Douban datasets demonstrate that the proposed approach consistently outperforms existing baselines, achieving improvements of approximately 4.31% in MAE, 4.01% in RMSE, and 3.70% in Accuracy, which verifies its effectiveness in both accuracy and robustness. International Relations Trustworthy recommendation Federated recommendation system Hypernet work Attention mechanism Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9159648","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Method Article","associatedPublications":[],"authors":[{"id":608277521,"identity":"8878de43-4647-4357-9f3d-fa3f255e3acc","order_by":0,"name":"Yicheng Di","email":"data:image/png;base64,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","orcid":"","institution":"School of Artificial Intelligence and Computer Science, Jiangnan University","correspondingAuthor":true,"prefix":"","firstName":"Yicheng","middleName":"","lastName":"Di","suffix":""},{"id":608277522,"identity":"35898e83-b69e-4350-ac6f-462c8559e92f","order_by":1,"name":"Song Shen","email":"","orcid":"","institution":"School of Artificial Intelligence and Computer Science, Jiangnan University","correspondingAuthor":false,"prefix":"","firstName":"Song","middleName":"","lastName":"Shen","suffix":""}],"badges":[],"createdAt":"2026-03-18 12:55:05","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9159648/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9159648/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105035041,"identity":"840fd577-a5f0-4509-a490-517aedf48783","added_by":"auto","created_at":"2026-03-20 07:25:20","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":407761,"visible":true,"origin":"","legend":"","description":"","filename":"dqrfnhkqgcksnnvdhbvmgwwmsrgttfbs.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9159648/v1_covered_3fb35a8f-6448-4ab5-89b4-8d06fd646173.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eHypernetwork-Driven Trustworthy Recommendation\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Trustworthy recommendation, Federated recommendation system, Hypernet work, Attention mechanism","lastPublishedDoi":"10.21203/rs.3.rs-9159648/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9159648/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn the context of rapidly advancing electronic technologies, a growing number of consumer electronic products have increasingly incorporated recommendation systems to improve overall user experience. Conventional recommendation approaches primarily rely on deep neural models to estimate user preferences for items, yet such methods often require extensive user data sharing, which may undermine user trust in the system outputs. To address this issue, federated learning has been introduced into recommendation frameworks to enable privacy-preserving and reliable recommendation processes. However, existing federated recommendation models typically depend on repeated access to user–item interaction data across clients to learn shared model parameters, which limits their practicality. To overcome these limitations, this work proposes a Hypernetwork-Driven Trustworthy Recommendation framework (HDTR), aiming to provide reliable recommendations while accommodating personalized user requirements. Specifically, hypernetworks are first employed to efficiently generate and initialize client-side recommendation models, where user preference representations are encoded as inputs to produce personalized parameterizations. Next, within the client model, item attribute embeddings are incorporated as a form of global contextual information, enriching the representation with additional semantic cues. Furthermore, attention residual blocks are introduced to adaptively capture the relative importance of different item attributes, thereby enhancing feature interaction modeling. Extensive experiments conducted on the Movielens1M, Hetrec-movielens, and Douban datasets demonstrate that the proposed approach consistently outperforms existing baselines, achieving improvements of approximately 4.31% in MAE, 4.01% in RMSE, and 3.70% in Accuracy, which verifies its effectiveness in both accuracy and robustness.\u003c/p\u003e","manuscriptTitle":"Hypernetwork-Driven Trustworthy Recommendation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-19 07:37:52","doi":"10.21203/rs.3.rs-9159648/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ba9a7449-ef8c-4206-8890-9bae148c3fb2","owner":[],"postedDate":"March 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":64726702,"name":"International Relations"}],"tags":[],"updatedAt":"2026-03-19T07:37:52+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-19 07:37:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9159648","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9159648","identity":"rs-9159648","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}