EGRec: A MOOCs Course Recommendation Model Based on Knowledge Graphs

EGRec: A MOOCs Course Recommendation Model Based on Knowledge Graphs | 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 EGRec: A MOOCs Course Recommendation Model Based on Knowledge Graphs Yuefeng Cen, Shuai Jiang, Wenxuan Cai, Gang Cen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6226722/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 Massive Open Online Courses (MOOCs) provide abundant learning resources but also overwhelm learners with their sheer volume, leading to challenges such as data sparsity and cold-start issues in conventional recommendation systems. To address these challenges, we propose EGRec, a novel course recommendation model that combines knowledge graphs and Heterogeneous Graph Attention Networks to improve recommendation precision, diversity, and relevance. By integrating multimodal data, EGRec captures intricate semantic relationships between courses and knowledge points, enabling personalized and context-sensitive recommendations. Extensive experiments on real MOOCs datasets demonstrate that EGRec significantly outperforms traditional models, highlighting its potential to enhance tailored learning experiences. Course Knowledge graph Recommender System Dynamic Subgraph Heterogeneous Information Network Personalized Learning Path Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 25 Apr, 2025 Reviews received at journal 23 Apr, 2025 Reviewers agreed at journal 23 Apr, 2025 Reviews received at journal 23 Apr, 2025 Reviews received at journal 23 Apr, 2025 Reviewers agreed at journal 23 Apr, 2025 Reviewers agreed at journal 23 Apr, 2025 Reviewers invited by journal 23 Apr, 2025 Submission checks completed at journal 22 Apr, 2025 First submitted to journal 12 Apr, 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. We do this by developing innovative software and high quality services for the global research community. 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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-6226722","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":442093538,"identity":"74cfa5fa-fe0e-4c25-bc9a-86ee5ffd1ecc","order_by":0,"name":"Yuefeng Cen","email":"","orcid":"","institution":"Zhejiang University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yuefeng","middleName":"","lastName":"Cen","suffix":""},{"id":442093539,"identity":"6fc30c97-d108-4ede-821d-2d9e641a456b","order_by":1,"name":"Shuai Jiang","email":"","orcid":"","institution":"Zhejiang University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Shuai","middleName":"","lastName":"Jiang","suffix":""},{"id":442093540,"identity":"ae40a4a5-2003-4fe8-ae70-8ea447479109","order_by":2,"name":"Wenxuan Cai","email":"","orcid":"","institution":"Zhejiang University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Wenxuan","middleName":"","lastName":"Cai","suffix":""},{"id":442093541,"identity":"38a45f63-bab9-400f-9c86-4f7d7530f857","order_by":3,"name":"Gang Cen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAm0lEQVRIiWNgGAWjYLCCDwY2dqTpYJxRkJZMmhZmng+HGBuIVi7vfvjYYxuDA8wM7IePbiBKi+GZtHTjHIM7fAw8aWk3iNMyg8dMOsfgGTODBI8ZsVr4v0lbGBxmbCBai7wED5s0A0laDHjSzCR7DNKS2Yj2i3z74WcSP/7Y2PGzHz5GpC0HoAw2opSDbWkgWukoGAWjYBSMWAAASOkp3TxAv3kAAAAASUVORK5CYII=","orcid":"","institution":"Zhejiang University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Gang","middleName":"","lastName":"Cen","suffix":""}],"badges":[],"createdAt":"2025-03-14 13:23:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6226722/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6226722/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80507898,"identity":"22812d9c-e240-48dc-aa3c-8b3cdac12545","added_by":"auto","created_at":"2025-04-14 06:03:23","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":924042,"visible":true,"origin":"","legend":"","description":"","filename":"EGRecAMOOCsCourseRecommendationModelBasedonKnowledgeGraphs.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6226722/v1_covered_1e259c4a-5e08-4c24-b1b3-70ef6335338b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eEGRec: A MOOCs Course Recommendation Model Based on Knowledge Graphs\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"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":"Course Knowledge graph, Recommender System, Dynamic Subgraph, Heterogeneous Information Network, Personalized Learning Path","lastPublishedDoi":"10.21203/rs.3.rs-6226722/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6226722/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Massive Open Online Courses (MOOCs) provide abundant learning resources but also overwhelm learners with their sheer volume, leading to challenges such as data sparsity and cold-start issues in conventional recommendation systems. 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