Cross-Domain Recommendation Framework for Enhanced Personalization through Contrastive Learning

Cross-Domain Recommendation Framework for Enhanced Personalization through Contrastive Learning | 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 Cross-Domain Recommendation Framework for Enhanced Personalization through Contrastive Learning Rabia Khan, Naima Iltaf, Rabia Latif, Nor Shahida Mohd Jamail This is a preprint; it has not been peer reviewed by a journal.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-7315897/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract To tackle the persistent data sparsity issue in recommendation systems (RSs), cross-domain recommendation has emerged as an effective solution by leveraging data from richer domains to enhance performance in sparser ones. Our proposed framework not only addresses data limitations but also ensures that the recommendation process remains fair, transparent, and user-centered. By employing contrastive learning and multi-head attention, proposed framework creates uniform and unbiased user/item embeddings, counteracting issues of popularity bias and embedding inconsistencies. A critical aspect of this framework is its behaviour encoder, designed to learn user preferences through behavioural data such as clicks, reviews and sentiments ensuring a personalized experience for users even in domains with sparse data. To maintain fairness and transparency, the system adaptively balances user domain specific and domain shared features based on user preferences in target domain. Contrastive Learning Personalised Recommendation Collaborative Filtering Cold-start user Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 03 Oct, 2025 Reviews received at journal 25 Sep, 2025 Reviews received at journal 21 Sep, 2025 Reviews received at journal 17 Sep, 2025 Reviewers agreed at journal 14 Sep, 2025 Reviewers agreed at journal 10 Sep, 2025 Reviewers agreed at journal 10 Sep, 2025 Reviewers agreed at journal 08 Sep, 2025 Reviewers invited by journal 08 Sep, 2025 Editor assigned by journal 08 Sep, 2025 Submission checks completed at journal 08 Aug, 2025 First submitted to journal 07 Aug, 2025 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-7315897","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":515141767,"identity":"09736c2a-24b9-4f42-b70f-7db5e418c707","order_by":0,"name":"Rabia Khan","email":"data:image/png;base64,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","orcid":"","institution":"National University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Rabia","middleName":"","lastName":"Khan","suffix":""},{"id":515141768,"identity":"5c17dc0a-9cb7-4b44-82cb-28b798ead492","order_by":1,"name":"Naima Iltaf","email":"","orcid":"","institution":"National University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Naima","middleName":"","lastName":"Iltaf","suffix":""},{"id":515141769,"identity":"5c03c998-8371-4ead-8cb3-d495d5abfae8","order_by":2,"name":"Rabia Latif","email":"","orcid":"","institution":"Prince Sultan University","correspondingAuthor":false,"prefix":"","firstName":"Rabia","middleName":"","lastName":"Latif","suffix":""},{"id":515141770,"identity":"61888076-4e29-42fe-9026-e4384f492548","order_by":3,"name":"Nor Shahida Mohd Jamail","email":"","orcid":"","institution":"Prince Sultan University","correspondingAuthor":false,"prefix":"","firstName":"Nor","middleName":"Shahida Mohd","lastName":"Jamail","suffix":""}],"badges":[],"createdAt":"2025-08-07 07:41:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7315897/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7315897/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91390456,"identity":"1ebb4bec-c868-4ca7-8800-9824a422b6a0","added_by":"auto","created_at":"2025-09-16 03:48:53","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1686105,"visible":true,"origin":"","legend":"","description":"","filename":"snarticletemplate.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7315897/v1_covered_09bf386c-8432-4a69-b4b8-2201ee6c2ec0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cross-Domain Recommendation Framework for Enhanced Personalization through Contrastive Learning","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"the-journal-of-supercomputing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [The Journal of Supercomputing](https://www.springer.com/journal/11227)","snPcode":"11227","submissionUrl":"https://submission.nature.com/new-submission/11227/3","title":"The Journal of Supercomputing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Contrastive Learning, Personalised Recommendation, Collaborative Filtering, Cold-start user","lastPublishedDoi":"10.21203/rs.3.rs-7315897/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7315897/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"To tackle the persistent data sparsity issue in recommendation systems (RSs), cross-domain recommendation has emerged as an effective solution by leveraging data from richer domains to enhance performance in sparser ones. 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