Contrastive Learning based CrossDomain Recommendation via User Convergence

Contrastive Learning based CrossDomain Recommendation via User Convergence | 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 Article Contrastive Learning based CrossDomain Recommendation via User Convergence Rabia Khan, Naima Iltaf, Rabia Latif, Usman Zia, Nor Shahida Mohd Jamail This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6069866/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 Cold-start users have always been a challenging task to deal with in the paradigm of recommender systems. Providentially, the presence of these cold-start users in multiple domains has addressed the problem of their sparse presence in the target domain.The interactions of cold-start users in the source domain plays a pivotal role in predicting their interests in target domain.The question of paramount importance remains what to transfer and the manner to achieve it. Most recent advancements in this area mitigate the gap of two domains by using tags as a bridge to transfer knowledge. User convergence aligns user preferences across different domains. We propose a novel framework that includes the metadata of user and items to devise a neighbourhood based on similarity of preferences they make. The semantic similarity is drawn using SBERT model with cosine similarity. This technique empirically investigates the advantage of fusing metadata through graph neural network(GNN) for recommendation tasks. Particularly, we have fused the metadata and interaction information jointly to model a graphical structure. This helps in learning a user’s representation through hierarchical graph attention model that also incorporatespreferences of likeminded users, their behaviour and rating patterns 1 . This framework supplements the user-item ratings with embeddings generated from user’s and item’s metadata. The personalized preferences are further refined through contrastive learning. To bridge the semantic gap among two domains, a neural network is employed to learn a cross-domain mapping function. Our proposed algorithm seams the strength of GNNs with cross-domain paradigm to utilize the richness in metadatafor addressing sparsity. The combined advantages of GNNs, cross-domain and contrastive learning alleviated the issues of cold-start users by transferring user preferences from a source domain to a target domain 2 . Physical sciences/Mathematics and computing/Computer science Physical sciences/Mathematics and computing/Computational science Physical sciences/Engineering 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. 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-6069866","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":424243941,"identity":"5f2e271e-70d1-400b-966b-22d21d7eb00c","order_by":0,"name":"Rabia Khan","email":"","orcid":"","institution":"National University of Sciences and Technology","correspondingAuthor":false,"prefix":"","firstName":"Rabia","middleName":"","lastName":"Khan","suffix":""},{"id":424243942,"identity":"9e1d0448-40d3-45b9-84be-792496443dfa","order_by":1,"name":"Naima Iltaf","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYFACxgYGhgoIUwKIDYjUcoY0LSBdbaRoMWc/3Lrh57xt8vINzAdv8zDcMSaoxbInse1m77bbhhsOsCVb8zA8MyOoxeBAYtsN3m23EwwYeMykeRgO2xDWcv5h282/c24nyDfwfyNSy43Ettu8DbcTGA7wsIG0EOGwGw/bbsscA/rlMJux5RyDZ4S9b3A+/dnNNzW35eXbmx/eeFNxx7CBoB44YAabcIB4DTBAhpZRMApGwSgY9gAA99U/ZogR7Q8AAAAASUVORK5CYII=","orcid":"","institution":"National University of Sciences and Technology","correspondingAuthor":true,"prefix":"","firstName":"Naima","middleName":"","lastName":"Iltaf","suffix":""},{"id":424243943,"identity":"3dd2f1da-c981-44a7-8c03-b2f617afdb4d","order_by":2,"name":"Rabia Latif","email":"","orcid":"","institution":"Prince Sultan University","correspondingAuthor":false,"prefix":"","firstName":"Rabia","middleName":"","lastName":"Latif","suffix":""},{"id":424243944,"identity":"8958d38f-7da9-4b52-afc7-2d4352aa145f","order_by":3,"name":"Usman Zia","email":"","orcid":"","institution":"National University of Sciences and Technology","correspondingAuthor":false,"prefix":"","firstName":"Usman","middleName":"","lastName":"Zia","suffix":""},{"id":424243945,"identity":"5c6f7441-a171-47ba-a700-5ac5c443049c","order_by":4,"name":"Nor Shahida Mohd Jamail","email":"","orcid":"","institution":"Prince Sultan University","correspondingAuthor":false,"prefix":"","firstName":"Nor","middleName":"Shahida Mohd","lastName":"Jamail","suffix":""}],"badges":[],"createdAt":"2025-02-20 08:08:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6069866/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6069866/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101752960,"identity":"cf156b27-f749-48b6-a737-03bb53f4540a","added_by":"auto","created_at":"2026-02-03 10:38:28","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3534471,"visible":true,"origin":"","legend":"","description":"","filename":"TemplateforsubmissionstoScientificReports.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6069866/v1_covered_17860ce4-cd71-422a-8ddf-b4f1d0c6f72f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Contrastive Learning based CrossDomain Recommendation via User Convergence","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":"","lastPublishedDoi":"10.21203/rs.3.rs-6069866/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6069866/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCold-start users have always been a challenging task to deal with in the paradigm of recommender systems. Providentially, the presence of these cold-start users in multiple domains has addressed the problem of their sparse presence in the target domain.The interactions of cold-start users in the source domain plays a pivotal role in predicting their interests in target domain.The question of paramount importance remains what to transfer and the manner to achieve it. Most recent advancements in this area mitigate the gap of two domains by using tags as a bridge to transfer knowledge. User convergence aligns user preferences across different domains. We propose a novel framework that includes the metadata of user and items to devise a neighbourhood based on similarity of preferences they make. The semantic similarity is drawn using SBERT model with cosine similarity. This technique empirically investigates the advantage of fusing metadata through graph neural network(GNN) for recommendation tasks. Particularly, we have fused the metadata and interaction information jointly to model a graphical structure. This helps in learning a user’s representation through hierarchical graph attention model that also incorporatespreferences of likeminded users, their behaviour and rating patterns\u003csup\u003e1 \u003c/sup\u003e. This framework supplements the user-item ratings with embeddings generated from user’s and item’s metadata. The personalized preferences are further refined through contrastive learning. To bridge the semantic gap among two domains, a neural network is employed to learn a cross-domain mapping function. Our proposed algorithm seams the strength of GNNs with cross-domain paradigm to utilize the richness in metadatafor addressing sparsity. 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