UTH-SAD: User Trust-Based Hypergraph Decomposition Model for Shilling Attack Detection

UTH-SAD: User Trust-Based Hypergraph Decomposition Model for Shilling Attack Detection | 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 UTH-SAD: User Trust-Based Hypergraph Decomposition Model for Shilling Attack Detection Zhengli Zhai, Cheng Xu, Yang Li, Shunqi Su This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7343977/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 Recommendation systems play a crucial role in enhancing user engagement across e-commerce and social platforms. However, these systems are increasingly vulnerable to shilling attacks, where adversaries manipulate recommendations by injecting fake profiles. Traditional detection methods, relying on manual feature engineering or conventional graph models, struggle to capture sophisticated attack patterns due to evolving adversarial strategies and the complexity of user-item interactions. To address these limitations, we propose a novel method called user trust-based hypergraph decomposition model for shilling attack detection (UTH-SAD). This method integrates user trust relationships, rating prediction errors, and hypergraph spectral features to provide robust shilling attack detection. By leveraging multi-granularity hypergraph analysis, UTH-SAD models higher-order user-item relationships, capturing intricate interactions that traditional methods fail to detect. The method combines trust relationships derived from shared rating behaviors, spectral features from hypergraph analysis, and rating prediction errors to identify malicious profiles effectively. This approach not only enhances the detection accuracy of shilling attacks but also provides insights into the complex interactions within recommendation systems, making it a promising solution for securing recommendation systems against evolving adversarial strategies. Extensive experiments conducted on MovieLens and Amazon demonstrate that UTH-SAD achieves superior performance. Shilling attack Fake profile Trust relationship Hypergraph spectral feature Full Text Additional Declarations No competing interests reported. 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. 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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-7343977","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":504066841,"identity":"fc2fe6e1-96ef-4cd7-b4c0-545c1d2a43f2","order_by":0,"name":"Zhengli Zhai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAqElEQVRIiWNgGAWjYFAC5sYHEEYC0VoYmw0YGAxI09ImQZoW+f6DbRVv2/4w8LPnGDD83EGMHQ0H227ObTNgkOx5Y8DYe4YILcyMjW23eYFaDG7kGDAzthGhhQ2orBikxZ5oLTxsjG3MYFskiNUiwcPYLDnnnDGPxJlnBQd7idEi33/44Ic3ZXJy/O3JGx/8JEYLxHVgxMBwgFgNUPWjYBSMglEwCnABAK6cLlKdfkdqAAAAAElFTkSuQmCC","orcid":"","institution":"Qingdao University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Zhengli","middleName":"","lastName":"Zhai","suffix":""},{"id":504066842,"identity":"991e17ac-a656-47e1-89c9-654fd5b66aa9","order_by":1,"name":"Cheng Xu","email":"","orcid":"","institution":"Qingdao University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Cheng","middleName":"","lastName":"Xu","suffix":""},{"id":504066843,"identity":"ca79917b-d2aa-44df-9c72-44e9d35d2bd3","order_by":2,"name":"Yang Li","email":"","orcid":"","institution":"Qingdao University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Li","suffix":""},{"id":504066844,"identity":"c5297a44-796e-4d49-96cf-8d1b16cd0480","order_by":3,"name":"Shunqi Su","email":"","orcid":"","institution":"Qingdao University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Shunqi","middleName":"","lastName":"Su","suffix":""}],"badges":[],"createdAt":"2025-08-11 08:23:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7343977/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7343977/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93637084,"identity":"a7a65f69-3a54-4eb6-9e3a-6e05cfb2a5c2","added_by":"auto","created_at":"2025-10-16 01:38:41","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":972905,"visible":true,"origin":"","legend":"","description":"","filename":"UTHSADmanuscipt.doc.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7343977/v1_covered_6164fa8e-7fd0-48ae-a445-de0009193d14.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"UTH-SAD: User Trust-Based Hypergraph Decomposition Model for Shilling Attack Detection","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Shilling attack, Fake profile, Trust relationship, Hypergraph spectral feature","lastPublishedDoi":"10.21203/rs.3.rs-7343977/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7343977/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRecommendation systems play a crucial role in enhancing user engagement across e-commerce and social platforms. However, these systems are increasingly vulnerable to shilling attacks, where adversaries manipulate recommendations by injecting fake profiles. Traditional detection methods, relying on manual feature engineering or conventional graph models, struggle to capture sophisticated attack patterns due to evolving adversarial strategies and the complexity of user-item interactions. To address these limitations, we propose a novel method called user trust-based hypergraph decomposition model for shilling attack detection (UTH-SAD). This method integrates user trust relationships, rating prediction errors, and hypergraph spectral features to provide robust shilling attack detection. By leveraging multi-granularity hypergraph analysis, UTH-SAD models higher-order user-item relationships, capturing intricate interactions that traditional methods fail to detect. 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