Time-aware Graph Flashback Network for Next Location Recommendation

Time-aware Graph Flashback Network for Next Location 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 Time-aware Graph Flashback Network for Next Location Recommendation Junheng Gao, Wei Liu, Shangsong Liang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6252855/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 May, 2025 Read the published version in Journal of Intelligent Information Systems → Version 1 posted 9 You are reading this latest preprint version Abstract Next Point-of-Interest (POI) recommendation predicts a user’s next likely destination based on historical check-ins, enhancing trip planning and location discovery. Current models, including sequence-based and graph-based approaches, often lack adaptability to temporal variations in relationships and treat graph construction as an isolated pre-training step, limiting their effectiveness. To address these challenges, we propose the Time-aware Graph Flashback Network (TGFN), introducing a Spatial-Temporal Knowledge Graph (STKG) that captures dynamic, time-evolving POI relationships. Our Time-TransH model learns both temporal edge variations and core feature representations, enabling real-time updates through weighted convolutions on location nodes and neighbors. By integrating relationship learning in an end-to-end framework, TGFN ensures accurate node representations. Experiments on real-world datasets show that TGFN significantly outperforms existing methods, achieving higher accuracy across multiple metrics. Point-of-Interest Recommendation Knowledge Graph Dynamic Graph Convolution Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 24 May, 2025 Read the published version in Journal of Intelligent Information Systems → Version 1 posted Editorial decision: Revision requested 01 Apr, 2025 Reviews received at journal 01 Apr, 2025 Reviewers agreed at journal 26 Mar, 2025 Reviews received at journal 26 Mar, 2025 Reviewers agreed at journal 26 Mar, 2025 Reviewers invited by journal 25 Mar, 2025 Editor assigned by journal 21 Mar, 2025 Submission checks completed at journal 20 Mar, 2025 First submitted to journal 18 Mar, 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. 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-6252855","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":437029879,"identity":"a9e1235d-1350-43d4-97e5-00a536762e12","order_by":0,"name":"Junheng Gao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYLACxgYJBgNmBjaGD0AOGzspWhhngLQwE6eFgcEApJgHxCOkRX5G7sHHlTss7Lazs197bPNrmzwfMwPjh485uLUY3MhLNjx7RiJ5ZzNPuXFu323DNmYGZsmZ2/Bokcgxk2xsk0g2OMyTJp3bc5sRqIWNmRePFvkZOeY/4Vose27bE9TCcCPHjBGoxc7gMPsxaYYftxMJajE48y5ZsvGMRALQFjbJ3obbyW3MjM14/SLfnnvwY+OOOnuD88efSfz4c9t2fnvzwQ8f8TmMARwXDIkNDDwGDIxtIDYomvACiBZ7Bgb2BwwMfwgoHgWjYBSMghEJAHqRT1fngux4AAAAAElFTkSuQmCC","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":true,"prefix":"","firstName":"Junheng","middleName":"","lastName":"Gao","suffix":""},{"id":437029880,"identity":"bc24dcbb-0a15-4c6d-8869-c3a9fa3eab4e","order_by":1,"name":"Wei Liu","email":"","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Liu","suffix":""},{"id":437029881,"identity":"35b021d0-e642-4e6c-87bc-270cf15d91ab","order_by":2,"name":"Shangsong Liang","email":"","orcid":"","institution":"Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Shangsong","middleName":"","lastName":"Liang","suffix":""}],"badges":[],"createdAt":"2025-03-18 11:53:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6252855/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6252855/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10844-025-00950-8","type":"published","date":"2025-05-24T15:57:12+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83460076,"identity":"5b352e89-28db-4ae7-ae5f-ec19c4c4f5d9","added_by":"auto","created_at":"2025-05-26 16:09:56","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1252700,"visible":true,"origin":"","legend":"","description":"","filename":"NextLocationRecommendation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6252855/v1_covered_1a734f90-f5c2-44b0-b5fc-c842e8c34a4d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Time-aware Graph Flashback Network for Next Location 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":"journal-of-intelligent-information-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jiis","sideBox":"Learn more about [Journal of Intelligent Information Systems](http://link.springer.com/journal/10844)","snPcode":"10844","submissionUrl":"https://submission.nature.com/new-submission/10844/3","title":"Journal of Intelligent Information Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Point-of-Interest, Recommendation, Knowledge Graph, Dynamic Graph Convolution","lastPublishedDoi":"10.21203/rs.3.rs-6252855/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6252855/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNext Point-of-Interest (POI) recommendation predicts a user\u0026rsquo;s next likely destination based on historical check-ins, enhancing trip planning and location discovery. Current models, including sequence-based and graph-based approaches, often lack adaptability to temporal variations in relationships and treat graph construction as an isolated pre-training step, limiting their effectiveness. To address these challenges, we propose the Time-aware Graph Flashback Network (TGFN), introducing a Spatial-Temporal Knowledge Graph (STKG) that captures dynamic, time-evolving POI relationships. Our Time-TransH model learns both temporal edge variations and core feature representations, enabling real-time updates through weighted convolutions on location nodes and neighbors. By integrating relationship learning in an end-to-end framework, TGFN ensures accurate node representations. Experiments on real-world datasets show that TGFN significantly outperforms existing methods, achieving higher accuracy across multiple metrics.\u003c/p\u003e","manuscriptTitle":"Time-aware Graph Flashback Network for Next Location Recommendation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-02 10:47:19","doi":"10.21203/rs.3.rs-6252855/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-01T17:41:51+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-01T15:31:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"119345787310668444000182697355395777817","date":"2025-03-26T11:01:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-26T05:22:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"118135345623018335723586437046504874581","date":"2025-03-26T05:15:39+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-03-26T02:58:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-21T22:20:34+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-21T02:51:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Intelligent Information Systems","date":"2025-03-18T11:42:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-intelligent-information-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jiis","sideBox":"Learn more about [Journal of Intelligent Information Systems](http://link.springer.com/journal/10844)","snPcode":"10844","submissionUrl":"https://submission.nature.com/new-submission/10844/3","title":"Journal of Intelligent Information Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"601f08f3-6a27-4916-b637-ea45b7a1ecb8","owner":[],"postedDate":"April 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-05-26T16:03:35+00:00","versionOfRecord":{"articleIdentity":"rs-6252855","link":"https://doi.org/10.1007/s10844-025-00950-8","journal":{"identity":"journal-of-intelligent-information-systems","isVorOnly":false,"title":"Journal of Intelligent Information Systems"},"publishedOn":"2025-05-24 15:57:12","publishedOnDateReadable":"May 24th, 2025"},"versionCreatedAt":"2025-04-02 10:47:19","video":"","vorDoi":"10.1007/s10844-025-00950-8","vorDoiUrl":"https://doi.org/10.1007/s10844-025-00950-8","workflowStages":[]},"version":"v1","identity":"rs-6252855","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6252855","identity":"rs-6252855","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}