KAMC: Knowledge-Aware Meta-Concept Recommendation

KAMC: Knowledge-Aware Meta-Concept Recommendation | 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 KAMC: Knowledge-Aware Meta-Concept Recommendation Xianglin Wu, Haonan Jiang, Jingwei Zhang, Zezheng Wu, Xinghe Cheng, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4521552/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Sep, 2024 Read the published version in Discover Computing → Version 1 posted 16 You are reading this latest preprint version Abstract Massive Open Online Courses (MOOCs) are playing a key role in improving educational ways. Abundant learning resources make it difficult for online users to find suitable learning content. The current personalized service in the field of online education relies more on course recommendations. However, coarse-grained recommendations cannot help users discover the defects of their knowledge network effectively. In this paper, we propose a Knowledge-Aware Meta-Concept (KAMC) framework to provide fine-grained recommendation services. We innovatively incorporate Knowledge Graph (KG) into the field of educational recommendation to provide abundant auxiliary information. However, simply combining knowledge graphs with educational recommender systems cannot improve the performance of existing recommendation models, and may even weaken the performance of the models. Because the modeled KG ignores the enhancements on the user side and only considers the enhancements on the item side. We further propose to enrich the semantic representation of users with collaborative information in user-item interactions, and at the same time enrich the semantic representation of items with information in KG. Furthermore, to provide users with more accurate and fine-grained personalized recommendation services, we propose a user-based attention mechanism to capture users' fine-grained semantic information. Our method is experimentally validated on three real-world datasets. Experimental results show that the KAMC method outperforms the current state-of-the-art baseline methods. Knowledge-aware Attention Mechanism Meta-Concept Recommendation Embedding Propagation Knowledge Graph Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 16 Sep, 2024 Read the published version in Discover Computing → Version 1 posted Editorial decision: Revision requested 11 Jul, 2024 Reviews received at journal 11 Jul, 2024 Reviews received at journal 11 Jul, 2024 Reviews received at journal 10 Jul, 2024 Reviews received at journal 09 Jul, 2024 Reviews received at journal 04 Jul, 2024 Reviewers agreed at journal 03 Jul, 2024 Reviewers agreed at journal 02 Jul, 2024 Reviewers agreed at journal 01 Jul, 2024 Reviewers agreed at journal 01 Jul, 2024 Reviewers agreed at journal 01 Jul, 2024 Reviewers agreed at journal 01 Jul, 2024 Reviewers invited by journal 01 Jul, 2024 Editor assigned by journal 12 Jun, 2024 Submission checks completed at journal 07 Jun, 2024 First submitted to journal 03 Jun, 2024 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-4521552","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":315670409,"identity":"2ce6340a-e590-4bfa-86dc-5bae740632d6","order_by":0,"name":"Xianglin Wu","email":"","orcid":"","institution":"Guilin University of Electronic Technology","correspondingAuthor":false,"prefix":"","firstName":"Xianglin","middleName":"","lastName":"Wu","suffix":""},{"id":315670410,"identity":"22990b08-37ed-4929-8f7b-027c91c5e6bf","order_by":1,"name":"Haonan Jiang","email":"","orcid":"","institution":"Guilin University of Electronic 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