Profiling Users with Tag-enhanced Spherical Metric Learning for Recommendation

Profiling Users with Tag-enhanced Spherical Metric Learning for 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 Profiling Users with Tag-enhanced Spherical Metric Learning for Recommendation Yanchao Tan, Hang Lv, Xinyi Huang, Guofang Ma, Chaochao Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4742092/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Mar, 2025 Read the published version in International Journal of Machine Learning and Cybernetics → Version 1 posted 13 You are reading this latest preprint version Abstract With the growing complex interactions between users and items on the Internet, the difficulty of representing them has increased in Recommender Systems (RSs). Existing methods propose to capture the complex user-item interactions that implicitly associate a user with other like-minded ones. However, the learned embeddings cannot efficiently capture the similar patterns underlying users’ behaviors. Moreover, the implicitly retrieved patterns cannot be readily accurate and interpretable. To this end, we propose to leverage both users' interacted items and item tags, so as to advocate for tag-enhanced recommendations. Specifically, we propose an adaptive tag selection mechanism to ensure the quality of tag representations, so as to profile users and learn tag-enhanced representations of users/items. Additionally, we introduce a spherical optimization strategy to fully exploit the increased representation capacity of the tag-enhanced recommendation framework. We conduct numerous experiments on four real-world datasets, where our proposed tag-enhanced framework for recommendation can bring consistent performance gains and averagely achieve a 12.54% improvement regarding both Recall and NDCG metrics. Recommender System Tag-enhanced Metric Learning User Profiling Spherical Optimization Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 07 Mar, 2025 Read the published version in International Journal of Machine Learning and Cybernetics → Version 1 posted Editorial decision: Revision requested 04 Nov, 2024 Reviews received at journal 20 Oct, 2024 Reviewers agreed at journal 25 Sep, 2024 Reviews received at journal 22 Aug, 2024 Reviews received at journal 01 Aug, 2024 Reviewers agreed at journal 29 Jul, 2024 Reviewers agreed at journal 24 Jul, 2024 Reviewers agreed at journal 23 Jul, 2024 Reviewers agreed at journal 21 Jul, 2024 Reviewers invited by journal 21 Jul, 2024 Editor assigned by journal 18 Jul, 2024 Submission checks completed at journal 15 Jul, 2024 First submitted to journal 15 Jul, 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-4742092","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":336938424,"identity":"9c813d2e-9d27-418e-a9fd-f85228eeabfa","order_by":0,"name":"Yanchao Tan","email":"","orcid":"","institution":"Fuzhou University","correspondingAuthor":false,"prefix":"","firstName":"Yanchao","middleName":"","lastName":"Tan","suffix":""},{"id":336938425,"identity":"3d147130-c6cb-4687-8040-19a74bea9149","order_by":1,"name":"Hang Lv","email":"","orcid":"","institution":"Fuzhou University","correspondingAuthor":false,"prefix":"","firstName":"Hang","middleName":"","lastName":"Lv","suffix":""},{"id":336938426,"identity":"304f90c7-e1f9-4822-9a2f-d26be139e22b","order_by":2,"name":"Xinyi Huang","email":"","orcid":"","institution":"Fuzhou University","correspondingAuthor":false,"prefix":"","firstName":"Xinyi","middleName":"","lastName":"Huang","suffix":""},{"id":336938427,"identity":"295228cf-f01d-45d8-97ff-f4f35fc2cb54","order_by":3,"name":"Guofang Ma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYNACGyBmb2AwgPASiNGSBsQ8B0jWIgFXSUCLbvvxaxIfEuzy5COfPyj88ucwAz97jgHDzx24tZidySmTnJGQXGx4OyHBWLbtMINkzxsDxt4zeLQcyEmT5v3BnLhxdsIBY8mGwwwGN3IMmBnb8Gg5/yZNmiehPnHjzIMNxhJAh9kT1HIj/RhQy+HE+RLMDIYf2IC2SBDU8obZckbC8cQNPGkMxoxt6TwSZ54VHOzF67D0hzc+JFQnzm8//szwxx9rOf725I0PfuLRAoxCSAQaHGBgM+YBckGcA/g0ABPKAzAl38DA/PAHfqWjYBSMglEwQgEAEC9VxGRKTZMAAAAASUVORK5CYII=","orcid":"","institution":"Zhejiang Gongshang University","correspondingAuthor":true,"prefix":"","firstName":"Guofang","middleName":"","lastName":"Ma","suffix":""},{"id":336938428,"identity":"71bc091f-8fb8-4b21-9924-074e4dba977a","order_by":4,"name":"Chaochao Chen","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Chaochao","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2024-07-15 10:10:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4742092/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4742092/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s13042-025-02584-2","type":"published","date":"2025-03-07T15:58:41+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":78190533,"identity":"4dd4ecc3-9822-457a-9c5a-d4786cab2df8","added_by":"auto","created_at":"2025-03-10 19:49:55","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3854928,"visible":true,"origin":"","legend":"","description":"","filename":"IJMLC2024UTRec.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4742092/v1_covered_78ba8d3a-dd17-413e-911f-43d2e1da2949.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Profiling Users with Tag-enhanced Spherical Metric Learning for Recommendation","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"international-journal-of-machine-learning-and-cybernetics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jmlc","sideBox":"Learn more about [International Journal of Machine Learning and Cybernetics](http://actavetscand.biomedcentral.com/)","snPcode":"13042","submissionUrl":"https://submission.nature.com/new-submission/13042/3","title":"International Journal of Machine Learning and Cybernetics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Recommender System, Tag-enhanced, Metric Learning, User Profiling, Spherical Optimization","lastPublishedDoi":"10.21203/rs.3.rs-4742092/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4742092/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"With the growing complex interactions between users and items on the Internet, the difficulty of representing them has increased in Recommender Systems (RSs). 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