Recommendation Model with Fusion of Heterogeneous Graph Neural Networks and Attention Mechanisms

Recommendation Model with Fusion of Heterogeneous Graph Neural Networks and Attention Mechanisms | 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 Recommendation Model with Fusion of Heterogeneous Graph Neural Networks and Attention Mechanisms Leona Whitman, Emiliano Reyes, Fletcher Hurst This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3878298/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 Traditional recommendation systems, particularly those based on collaborative filtering, have exhibited robust performance by harnessing the advantages of big data. In these systems, the essence lies in identifying users whose interests closely align with a target user and subsequently recommending items that align with their preferences, showcasing commendable effectiveness in delivering personalized suggestions. Traditional recommender algorithms based on Graph Neural Networks (GNNs) face limitations as they can only handle regular topological graphs composed of a single type of node. However, in contemporary networks, data is often not exclusively comprised of a singular node type. Additionally, conventional GNNs are constrained to incorporating only first-order neighbor features of nodes, lacking the ability to capture deeper structural relationships within the network. Consequently, when dealing with sparse datasets where nodes have very few neighbors, the recommendation quality of algorithms based on traditional GNNs significantly diminishes. To address the aforementioned limitations, this paper proposes a deep recommendation model that combines Graph Neural Networks (GNNs) with heterogeneous networks. By integrating GNNs with heterogeneous network structures, the aim is to overcome the challenges posed by single-node type limitations and the inability to capture deeper structural relationships in traditional recommendation algorithms. Information Retrieval and Management Recommendation GNN Attention Heterogeneous Feature Aggregation 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. 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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-3878298","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":268016076,"identity":"08851046-e84b-47f2-a33f-dc3c2961c56e","order_by":0,"name":"Leona Whitman","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABC0lEQVRIie3PsUrEMBjA8YDQLtFukiNn7xW+oxAdhHuVitAuCTg5K0Jv8A4fwJfIdOImBOtS95Y6iEOnznJCFHM9u7W91SH/5YOQH1+CkM32DzvYjifcHvjQDDjpJU5L/gwEgPY2k+wkqCVnsiFogLg36ftX8jaeHarqI9c6fnDvnuX6giBvfht2EpzG02VSYUyj40AkIB4XyikW5mEke5WdhHBG9lfKkJBRcQVC5udOjg0BIrrJpGaj74bEn5RriMGQQg8RghndbuGMcgfCDSkHt+Aookc/hpT8kookmMpMsXIMBPf9xXNVOqozNXPv45V5mD+Bl+uqqPWp782XnaQ/vPuKzWaz2fr6BVz2W1vPVxJFAAAAAElFTkSuQmCC","orcid":"","institution":"Missouri University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Leona","middleName":"","lastName":"Whitman","suffix":""},{"id":268016077,"identity":"451d0f7b-d081-4f06-9363-545c40cff421","order_by":1,"name":"Emiliano Reyes","email":"","orcid":"","institution":"Missouri University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Emiliano","middleName":"","lastName":"Reyes","suffix":""},{"id":268016078,"identity":"4c8b6070-950d-4ff2-beda-786bda019e4a","order_by":2,"name":"Fletcher Hurst","email":"","orcid":"","institution":"Missouri University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Fletcher","middleName":"","lastName":"Hurst","suffix":""}],"badges":[],"createdAt":"2024-01-19 09:37:23","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-3878298/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3878298/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49990800,"identity":"2256ff2e-5a64-492c-9fe3-c9a9ca28ab4a","added_by":"auto","created_at":"2024-01-22 18:44:08","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":446156,"visible":true,"origin":"","legend":"","description":"","filename":"RecommendationModelwithFusionofHeterogeneousGraphNeuralNetworksandAttentionMechanisms.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3878298/v1_covered_4808bdc9-8f0b-449f-8842-88922ca363ba.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eRecommendation Model with Fusion of Heterogeneous Graph Neural Networks and Attention Mechanisms\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Missouri University of Science and Technology","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":"Recommendation, GNN, Attention, Heterogeneous, Feature Aggregation","lastPublishedDoi":"10.21203/rs.3.rs-3878298/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3878298/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTraditional recommendation systems, particularly those based on collaborative filtering, have exhibited robust performance by harnessing the advantages of big data. In these systems, the essence lies in identifying users whose interests closely align with a target user and subsequently recommending items that align with their preferences, showcasing commendable effectiveness in delivering personalized suggestions. Traditional recommender algorithms based on Graph Neural Networks (GNNs) face limitations as they can only handle regular topological graphs composed of a single type of node. However, in contemporary networks, data is often not exclusively comprised of a singular node type. Additionally, conventional GNNs are constrained to incorporating only first-order neighbor features of nodes, lacking the ability to capture deeper structural relationships within the network. Consequently, when dealing with sparse datasets where nodes have very few neighbors, the recommendation quality of algorithms based on traditional GNNs significantly diminishes. 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