E-commerce Intelligent Recommendation Optimization and Personalized Marketing Strategy Based on Big Model

E-commerce Intelligent Recommendation Optimization and Personalized Marketing Strategy Based on Big Model | 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 E-commerce Intelligent Recommendation Optimization and Personalized Marketing Strategy Based on Big Model Hong Peng, Xiaoliang Jin, Qiao Huang, Supeng Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7430857/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 This paper proposes an e-commerce recommendation model integrating large-scale representation learning with personalized marketing strategies. Leveraging multimodal fusion of text, image, and category data, semantic alignment, and dynamic ranking, the framework incorporates optimized fusion selection, task-specific weighting, and reinforcement learning – based marketing decision-making. Multi-task learning jointly models clicks, conversions, and other behaviors, with weight tuning verified through comparative experiments. A cold-start evaluation is included to assess adaptability for new users and items. Experimental results show CTR improving from 6.45% to 9.17%, CVR from 1.89% to 3.25%, and NDCG@10 reaching 0.832, confirming enhanced accuracy, robustness, and user value. CCS CONCEPTS Applied computing ~ Electronic commerce ~ commerce infrastructure Theoretical Computer Science Big model Multimodal fusion Recommender system Personalized marketing Ranking optimization 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. 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-7430857","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":503983683,"identity":"a0ce371e-abed-437e-940b-44bb98dbef9c","order_by":0,"name":"Hong Peng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYJACZhDBxt588MEHMINYLfw8x5INZ4C0MBOrRXJGjpk0D5yLB5iz9x5+XVBxx27DmQNm0ja/tsnzMTMwfviYg1uLZc+5NOsZZ54lbzjekGyd23fbsI2ZgVly5jbcWgxu5JgZ87YdTjY4c+Dg7dye24xALWzMvPi03H8D1XIjsUHasue2PWEtN3iMHwO12EnOSGaSZvhxO5GgFsueHDNmnjOHE4CBzGzY23A7uY2ZsRmvX8zZzxh/5qk4bM/G3v/xwY8/t23ntzcf/PARn8OAcScBpBMbQDzGNjDZgFs9RAszKJnYQ7h/8CoeBaNgFIyCEQoAloFVkukp9ZAAAAAASUVORK5CYII=","orcid":"","institution":"Beijing Tianyuan Dike Network Technology Co., Ltd,Beijing","correspondingAuthor":true,"prefix":"","firstName":"Hong","middleName":"","lastName":"Peng","suffix":""},{"id":503983684,"identity":"fc416197-edb8-4e09-ae31-166e1ffe43fb","order_by":1,"name":"Xiaoliang Jin","email":"","orcid":"","institution":"Beijing Tianyuan Dike Network Technology Co., Ltd,Beijing","correspondingAuthor":false,"prefix":"","firstName":"Xiaoliang","middleName":"","lastName":"Jin","suffix":""},{"id":503983685,"identity":"eb8f5fe4-642b-4367-8893-7d920676327c","order_by":2,"name":"Qiao Huang","email":"","orcid":"","institution":"Beijing Tianyuan Dike Network Technology Co., Ltd,Beijing","correspondingAuthor":false,"prefix":"","firstName":"Qiao","middleName":"","lastName":"Huang","suffix":""},{"id":503983686,"identity":"4f19b253-5067-4e2e-80d0-726728929a5a","order_by":3,"name":"Supeng Liu","email":"","orcid":"","institution":"Beijing Tianyuan Dike Network Technology Co., Ltd,Beijing","correspondingAuthor":false,"prefix":"","firstName":"Supeng","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-08-22 05:16:02","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-7430857/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7430857/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89786760,"identity":"36ea3d1e-eef8-4a3b-9e4a-7178b31ce426","added_by":"auto","created_at":"2025-08-25 04:31:06","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":629351,"visible":true,"origin":"","legend":"","description":"","filename":"8.19HongPengICIDC2025.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7430857/v1_covered_6180b636-e6e6-447c-917f-61cd64ee7b8a.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eE-commerce Intelligent Recommendation Optimization and Personalized Marketing Strategy Based on Big Model\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Big model, Multimodal fusion, Recommender system, Personalized marketing, Ranking optimization","lastPublishedDoi":"10.21203/rs.3.rs-7430857/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7430857/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper proposes an e-commerce recommendation model integrating large-scale representation learning with personalized marketing strategies. 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