Collaborative Filtering Recommendation model Based on Graph Neural Network and Attention Mechanism

Collaborative Filtering Recommendation model Based on Graph Neural Network and Attention Mechanism | 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 Collaborative Filtering Recommendation model Based on Graph Neural Network and Attention Mechanism Matt Dahl, Vessela Ivan, Dora Patko, Steve Georgi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1971133/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 With the popularity and development of the internet and mobile terminals, people can access a lot of information through them every day. Recommender systems have become one of the important technologies for various online platforms that aim to predict whether a user will interact with an item or not. Among them, collaborative filtering-based models have made effective progress in learning user and item representations by modeling historical user-item interactions. Recently, models based on GCN have been effective in recommendation, and the main function of GCN models is to improve the embedding representation of users and items by iteratively aggregating feature information from neighbors using graph connectivity to extract additional information. However, in previous works, dividing users into subgraphs without intersection only leads to a partial loss of information, ignoring the potential connections that may exist between different groups of users; and because only users are divided, the influence of commodity factors on the purchase outcome at the time of purchase is ignored in the learning process. Based on the above considerations, in this paper we propose a message-passing recommendation model. The model uses intersects users and items in separate subgraphs and uses an optimized attention mechanism to obtain the final node embedding to optimize the embedding representation by introducing multiple embedding propagation layers that encode higher-order connectivity relationships. We conduct extensive experiments to evaluate the proposed model. The results show that our model can effectively improve the performance of the recommendation. Recommendation Graph Neural Network Attention Mechanism Full Text 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. 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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-1971133","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":129604365,"identity":"4478ae7a-5ad0-49ad-ab49-b4fc7aa1aa72","order_by":0,"name":"Matt Dahl","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYLACHoYDMmDGByBmYyeonpmxAaiFh4GNgYFxBkgLMylamHnAAgQ06LafP/7gTc0dHgb5HuPPNr+2yfMxMzB++JiDW4vZmWTGxjnHngFt4TGTzu27bdjGzMAsOXMbHi0HkhmbedgOg7Uw5/bcZgRqYWPmxafl/GOgln9gLcafLXtu2xPWcgNoC28bWIuBNMOP24lEaHlsOHNu32EeNra0MsnehtvJbcyMzfj9cj7xwYc33w7L8TMf3vzhx5/btvPbmw9++IhHCxywgQjGNjDZQIR6OPhDiuJRMApGwSgYKQAALYFMyyqI2qYAAAAASUVORK5CYII=","orcid":"","institution":"Technical University of Sofia","correspondingAuthor":true,"prefix":"","firstName":"Matt","middleName":"","lastName":"Dahl","suffix":""},{"id":129604366,"identity":"339dfd7c-e296-43eb-a415-6993e11ba4b1","order_by":1,"name":"Vessela Ivan","email":"","orcid":"","institution":"Technical University of Sofia","correspondingAuthor":false,"prefix":"","firstName":"Vessela","middleName":"","lastName":"Ivan","suffix":""},{"id":129604367,"identity":"07c6449c-9b99-491d-a55e-46fa131907de","order_by":2,"name":"Dora Patko","email":"","orcid":"","institution":"Technical University of Sofia","correspondingAuthor":false,"prefix":"","firstName":"Dora","middleName":"","lastName":"Patko","suffix":""},{"id":129604368,"identity":"e70ac8b3-43bd-452b-a0b2-b75062809c43","order_by":3,"name":"Steve Georgi","email":"","orcid":"","institution":"Technical University of Sofia","correspondingAuthor":false,"prefix":"","firstName":"Steve","middleName":"","lastName":"Georgi","suffix":""}],"badges":[],"createdAt":"2022-08-17 13:33:55","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false,"coiExplicitlySet":false},"doi":"10.21203/rs.3.rs-1971133/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1971133/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":25556270,"identity":"6fa40026-a04b-4bfb-a84b-ea57b4e2f6ee","added_by":"auto","created_at":"2022-08-23 16:37:44","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":404203,"visible":true,"origin":"","legend":"","description":"","filename":"CollaborativeFilteringRecommendationmodelBasedonGraphNeuralNetworkandAttentionMechanism1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1971133/v1_covered.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eCollaborative Filtering Recommendation model Based on Graph Neural Network and Attention Mechanism\u003c/p\u003e","fulltext":[{"header":"Full Text","content":"This preprint is available for \u003ca href='/article/rs-1971133/latest.pdf' target='_blank'\u003edownload as a PDF\u003c/a\u003e."},{"header":"Declarations","content":"The authors declare no competing interests."}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":" Technical University of Sofia","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"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, Graph Neural Network, Attention Mechanism","lastPublishedDoi":"10.21203/rs.3.rs-1971133/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1971133/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWith the popularity and development of the internet and mobile terminals, people can access a lot of information through them every day. Recommender systems have become one of the important technologies for various online platforms that aim to predict whether a user will interact with an item or not. Among them, collaborative filtering-based models have made effective progress in learning user and item representations by modeling historical user-item interactions. Recently, models based on GCN have been effective in recommendation, and the main function of GCN models is to improve the embedding representation of users and items by iteratively aggregating feature information from neighbors using graph connectivity to extract additional information. However, in previous works, dividing users into subgraphs without intersection only leads to a partial loss of information, ignoring the potential connections that may exist between different groups of users; and because only users are divided, the influence of commodity factors on the purchase outcome at the time of purchase is ignored in the learning process. Based on the above considerations, in this paper we propose a message-passing recommendation model. The model uses intersects users and items in separate subgraphs and uses an optimized attention mechanism to obtain the final node embedding to optimize the embedding representation by introducing multiple embedding propagation layers that encode higher-order connectivity relationships. We conduct extensive experiments to evaluate the proposed model. 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