Fusion of Multimodal Data for Clothing Personalized Recommendation with Dynamic Preference Learning Framework

Fusion of Multimodal Data for Clothing Personalized Recommendation with Dynamic Preference Learning Framework | 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 Fusion of Multimodal Data for Clothing Personalized Recommendation with Dynamic Preference Learning Framework Ying Yuan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9212801/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract With the rapid expansion of e-commerce platforms, personalized clothing recommendation has emerged as a critical technology for improving user satisfaction and platform revenue. This study addresses the challenges of effectively integrating multimodal item information and capturing temporally evolving user preferences in clothing recommendation. We propose a Multimodal Fusion based Dynamic Preference Learning Framework (MF-DPL) that employs iterative cross-modal attention mechanisms and gated preference decomposition to construct a unified recommendation model. This approach successfully optimizes ranking accuracy in sparse implicit feedback scenarios, achieving substantial performance gains. Experimental results on the Amazon Clothing and Polyvore Outfits datasets show that MF-DPL improves Recall@10 by 8.2%-11.7% and Normalized Discounted Cumulative Gain (NDCG) @10 by 9.1%-12.4% compared to state of the art baselines. These findings provide practical guidance for multimodal personalized recommendation in fashion e-commerce and indicate new directions for dynamic preference modeling in trend sensitive domains. Personalized Recommendation Multimodal Fusion Dynamic Preference Modeling Clothing Recommendation Deep Learning Attention Mechanism Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 07 May, 2026 Reviews received at journal 05 May, 2026 Reviews received at journal 05 May, 2026 Reviewers agreed at journal 04 May, 2026 Reviewers agreed at journal 27 Apr, 2026 Reviewers invited by journal 27 Apr, 2026 Editor invited by journal 30 Mar, 2026 Editor assigned by journal 25 Mar, 2026 Submission checks completed at journal 25 Mar, 2026 First submitted to journal 24 Mar, 2026 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-9212801","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":633565278,"identity":"3eb15430-123c-44f3-b4c8-e98d7c75f683","order_by":0,"name":"Ying Yuan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIie3QPUvEMBjA8ScG4vLcdU0IqB8hUPAo5pu4RArt0luPDod3cNCpcKuDH+Im58IDdam43igITgo3yU1iBDdp6eiQ/xRCfnkDCIX+baU8i5C9wMGPpwB8BOlsrGpu2B2AFKMIq7KbnSccxxCzT99eURDbIQltS7oVpxsysLTX/SSbxYjEZ5ON0EVHUmCbOmiz+bqXuEuNkkSyjVo9rzyRRdywNQ2Q/FOjITTPXOhkHCn8KS6T5slfjP0SN0RU975Q9401qiae1F2uKv8W4wbeMn3MH+THl1xtMWX7Y3kVRf7H5GFpe8lFA3CCf6Zdz/Kfzv1e7DiwIBQKhULwDf/vU+TN+EQwAAAAAElFTkSuQmCC","orcid":"","institution":"Kaifeng University","correspondingAuthor":true,"prefix":"","firstName":"Ying","middleName":"","lastName":"Yuan","suffix":""}],"badges":[],"createdAt":"2026-03-24 13:53:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9212801/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9212801/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108804625,"identity":"62df92bc-c661-4e18-8053-477b4fa80861","added_by":"auto","created_at":"2026-05-08 15:22:23","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":645975,"visible":true,"origin":"","legend":"","description":"","filename":"YYDAS.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9212801/v1_covered_67675e72-09e1-4546-8301-6ad2d6f15471.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Fusion of Multimodal Data for Clothing Personalized Recommendation with Dynamic Preference Learning Framework","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"discover-applied-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Applied Sciences](https://link.springer.com/journal/42452)","snPcode":"42452","submissionUrl":"https://submission.springernature.com/new-submission/42452/3","title":"Discover Applied Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Personalized Recommendation, Multimodal Fusion, Dynamic Preference Modeling, Clothing Recommendation, Deep Learning, Attention Mechanism","lastPublishedDoi":"10.21203/rs.3.rs-9212801/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9212801/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWith the rapid expansion of e-commerce platforms, personalized clothing recommendation has emerged as a critical technology for improving user satisfaction and platform revenue. 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