Deep Learning Based Personalized Recommendation Systems for E-Commerce Platforms

Deep Learning Based Personalized Recommendation Systems for E-Commerce Platforms | 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 Deep Learning Based Personalized Recommendation Systems for E-Commerce Platforms Ayomide owolabi, Jagadeesh Sundaramoorthy This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8913353/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 Due to the faster development of e-commerce platform, the information overload can be considered overbearing, which allows promoting the need of the personalized recommendation systems that can provide the most precise and context-oriented product recommendations. Despite the showed strong predictive performance of deep learning techniques in comparison to the traditional method of recommendation, the current survey studies tend to offer partial analysis, constrained methodology, or out of date scopes that do not represent in detail the recent advances in architectures. The current investigation is a systematic review of e-commerce personalized recommendation systems based on deep learning, formulated in IEEE style, and summarizing the works published since 2018. Having used a structured literature selection protocol in the context of the large scholarly databases, the thematic classification and comparative analysis of convolutional neural networks, recurrent and sequential networks, Transformer-based models, graph neural networks, and hybrid models were conducted. The analysis discusses these methods in various aspects such as prediction accuracy, scalability, interpretability, sensitivity to data sparsity, fairness factor, and deployment practicability. The results suggest that deep learning models are always superior to traditional methods that model more and intricate user-item interactions and fusion with multi-modal data sources. Nonetheless, Explainability, cold-start treatment, and computability efficiency and ethical transparency continue to be problematic. The main contribution of the review is that it forms the coherent analytical framework, which consolidates the latest achievements, sets the trade-offs that are yet to be resolved, and outlines the research directions in terms of explainable, data-efficient, fair, and trustworthy recommenders at the next-generation e-commerce platforms. Nuclear Physics Deep learning Personalized recommendation systems E-commerce platforms Neural collaborative filtering Recommender systems User behavior modeling Explainable AI 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-8913353","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":593618650,"identity":"aef5bf71-3d67-4c9c-948b-13787dfd406f","order_by":0,"name":"Ayomide owolabi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYLCCBDYQmdjA8AFIsbGTooVxBkgLM1HWgLUkMDDzgGhCWvhnJB/d8KDMJpqfPbnxs82vbfJ8zAyMHz7m4NYicSMt7UbCubTcmT0Pm6Vz+24btjEzMEvO3IbHmhs5ZjcS2w7nbgCSzLk9txmBWtiYefFokYdo+Z+7H6TFsue2PUEtBhAtB3I3SAC1MPy4nUhQi+GZZyC/JOfOOPOwWbK34XZyGzNjM16/yB1PPnbzR5ldbn97+sMPP/7ctp3f3nzww0d83hdIQOIwtoHJBjzqgYD/ADLvD37Fo2AUjIJRMDIBABtAWUqsAw1LAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0004-2049-7891","institution":"Ladoke Akintola University Of Technology Ogbomosho","correspondingAuthor":true,"prefix":"","firstName":"Ayomide","middleName":"","lastName":"owolabi","suffix":""},{"id":593618651,"identity":"3327acba-92ba-4289-b5cf-46789a433535","order_by":1,"name":"Jagadeesh Sundaramoorthy","email":"","orcid":"","institution":"Infosys Ltd.","correspondingAuthor":false,"prefix":"","firstName":"Jagadeesh","middleName":"","lastName":"Sundaramoorthy","suffix":""}],"badges":[],"createdAt":"2026-02-19 02:48:04","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-8913353/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8913353/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103506606,"identity":"8d5543e5-96cb-4e2e-a76c-7311a9728cf2","added_by":"auto","created_at":"2026-02-26 13:38:01","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":687015,"visible":true,"origin":"","legend":"","description":"","filename":"jaga2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8913353/v1_covered_fd8ee3ce-4200-439f-b4a1-1815682a44b6.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eDeep Learning Based Personalized Recommendation Systems for E-Commerce Platforms\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"infoyse ltd","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":"Deep learning, Personalized recommendation systems, E-commerce platforms, Neural collaborative filtering, Recommender systems, User behavior modeling, Explainable AI","lastPublishedDoi":"10.21203/rs.3.rs-8913353/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8913353/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDue to the faster development of e-commerce platform, the information overload can be considered overbearing, which allows promoting the need of the personalized recommendation systems that can provide the most precise and context-oriented product recommendations. Despite the showed strong predictive performance of deep learning techniques in comparison to the traditional method of recommendation, the current survey studies tend to offer partial analysis, constrained methodology, or out of date scopes that do not represent in detail the recent advances in architectures. The current investigation is a systematic review of e-commerce personalized recommendation systems based on deep learning, formulated in IEEE style, and summarizing the works published since 2018. Having used a structured literature selection protocol in the context of the large scholarly databases, the thematic classification and comparative analysis of convolutional neural networks, recurrent and sequential networks, Transformer-based models, graph neural networks, and hybrid models were conducted. The analysis discusses these methods in various aspects such as prediction accuracy, scalability, interpretability, sensitivity to data sparsity, fairness factor, and deployment practicability. The results suggest that deep learning models are always superior to traditional methods that model more and intricate user-item interactions and fusion with multi-modal data sources. Nonetheless, Explainability, cold-start treatment, and computability efficiency and ethical transparency continue to be problematic. The main contribution of the review is that it forms the coherent analytical framework, which consolidates the latest achievements, sets the trade-offs that are yet to be resolved, and outlines the research directions in terms of explainable, data-efficient, fair, and trustworthy recommenders at the next-generation e-commerce platforms.\u003c/p\u003e","manuscriptTitle":"Deep Learning Based Personalized Recommendation Systems for E-Commerce Platforms","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-24 14:45:04","doi":"10.21203/rs.3.rs-8913353/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"483e8806-a25f-446a-a418-4e3ff8a1aec7","owner":[],"postedDate":"February 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":63166854,"name":"Nuclear Physics"}],"tags":[],"updatedAt":"2026-02-24T14:45:04+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-24 14:45:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8913353","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8913353","identity":"rs-8913353","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}