VEGAR: A tourist attraction recommender system based on signed feedback and a signed spatial-sentiment knowledge graph

VEGAR: A tourist attraction recommender system based on signed feedback and a signed spatial-sentiment knowledge graph | 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 VEGAR: A tourist attraction recommender system based on signed feedback and a signed spatial-sentiment knowledge graph Renjun Cao, Yong Gao, Yi Zhang, Changjian Liu, Zhiyang Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9169431/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract Tourism has grown rapidly. Travelers face abundant attractions and heterogeneous online content. This increases the cognitive effort of trip planning and motivates personalized recommendation. Existing recommender systems emphasize objective features but they exploit subjective review signals superficially, overlooking user personality cues and attraction-level signed sentiment. They also tend to treat users’ visits as uniformly positive feedback, even though negative preferences are common and informative in textual reviews. To address these issues, we propose VEGAR, a tourist attraction recommender system that jointly models signed feedback within a signed spatial-sentiment knowledge graph. To exploit subjective review signals, VEGAR introduces a subjective feature extraction module and designs a signed spatial-sentiment knowledge graph construction framework. The resulting signed spatial-sentiment knowledge graph organizes both individual user personality and signed collective inclinations toward attractions in three subgraphs. To model feedback polarity and align it with signed sentiment, VEGAR proposes a prediction module with five propagation procedures over three subgraphs to learn signed user and attraction preference profiles. VEGAR further introduces high-order cross aggregation module and gated fusion to achieve informative user and attraction embeddings. To capture spatial constraints while preserving semantic relevance, VEGAR proposes spatial-sentiment attention that jointly models semantic relevance and distance-based influence during propagation. Experiments on the Suzhou Sina Weibo dataset show that VEGAR achieves average relative improvements of 2.85% in AUC and 2.80% in F1 score over all compared baselines. VEGAR also achieves the best Top-K ranking performance. Knowledge graph Tourist attraction recommendation Signed sentiment feedback Spatial-sentiment attention Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 24 Apr, 2026 Reviews received at journal 22 Apr, 2026 Reviewers agreed at journal 20 Apr, 2026 Reviews received at journal 09 Apr, 2026 Reviewers agreed at journal 24 Mar, 2026 Reviewers invited by journal 24 Mar, 2026 Editor assigned by journal 20 Mar, 2026 Submission checks completed at journal 20 Mar, 2026 First submitted to journal 19 Mar, 2026 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-9169431","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":611421113,"identity":"f19b1fc8-9c39-4da0-85c7-ffe928705ada","order_by":0,"name":"Renjun Cao","email":"","orcid":"","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Renjun","middleName":"","lastName":"Cao","suffix":""},{"id":611421114,"identity":"a6221737-9f04-49f4-8daf-5f40f2439ad7","order_by":1,"name":"Yong Gao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYBADOQYeEMVGghZj0rUkNhCtRd798DGJjztq07fznDFg+FB2mIF/dgN+LYZn0tIkZ545nruzt8eAcca5wwwSdw4Q0NKQYybN23Ysd8N5HgNm3rbDDAYSCQS09L8Ba0k3AGn5S4wWeQmwLTUJBmd7DJgZidFiIPEs2XJm2wHDnT3HCg72nEvnkbhByJb+5IM3PrbVyZvzJG988KPMWo5/BiFbDjCwSDAwAN0D5BwAYh786kG2NDAwf2BgqANrGQWjYBSMglGAFQAA3mFDVRhJNrkAAAAASUVORK5CYII=","orcid":"","institution":"Peking University","correspondingAuthor":true,"prefix":"","firstName":"Yong","middleName":"","lastName":"Gao","suffix":""},{"id":611421115,"identity":"22e25842-4bad-4663-9e1f-deb787d0098c","order_by":2,"name":"Yi Zhang","email":"","orcid":"","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Zhang","suffix":""},{"id":611421116,"identity":"e5b2dc91-2a6e-4ee6-9810-2c9b4af6d88b","order_by":3,"name":"Changjian Liu","email":"","orcid":"","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Changjian","middleName":"","lastName":"Liu","suffix":""},{"id":611421117,"identity":"f0bb8104-59bb-40c9-8d73-1c9af95d44f0","order_by":4,"name":"Zhiyang Wang","email":"","orcid":"","institution":"Peking University","correspondingAuthor":false,"prefix":"","firstName":"Zhiyang","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2026-03-19 12:24:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9169431/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9169431/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105566724,"identity":"2c9e1221-c743-4ebc-9495-40ab7e0b2d21","added_by":"auto","created_at":"2026-03-27 12:57:06","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1589987,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9169431/v1_covered_f85ffa26-8f3e-4261-b8a7-78b7307a0fc7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"VEGAR: A tourist attraction recommender system based on signed feedback and a signed spatial-sentiment knowledge graph","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-king-saud-university-computer-and-information-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Journal of King Saud University Computer and Information Sciences](https://link.springer.com/journal/44443)","snPcode":"44443","submissionUrl":"https://submission.springernature.com/new-submission/44443/3","title":"Journal of King Saud University Computer and Information Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Knowledge graph, Tourist attraction recommendation, Signed sentiment feedback, Spatial-sentiment attention","lastPublishedDoi":"10.21203/rs.3.rs-9169431/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9169431/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTourism has grown rapidly. Travelers face abundant attractions and heterogeneous online content. This increases the cognitive effort of trip planning and motivates personalized recommendation. Existing recommender systems emphasize objective features but they exploit subjective review signals superficially, overlooking user personality cues and attraction-level signed sentiment. They also tend to treat users\u0026rsquo; visits as uniformly positive feedback, even though negative preferences are common and informative in textual reviews. To address these issues, we propose VEGAR, a tourist attraction recommender system that jointly models signed feedback within a signed spatial-sentiment knowledge graph. To exploit subjective review signals, VEGAR introduces a subjective feature extraction module and designs a signed spatial-sentiment knowledge graph construction framework. The resulting signed spatial-sentiment knowledge graph organizes both individual user personality and signed collective inclinations toward attractions in three subgraphs. To model feedback polarity and align it with signed sentiment, VEGAR proposes a prediction module with five propagation procedures over three subgraphs to learn signed user and attraction preference profiles. VEGAR further introduces high-order cross aggregation module and gated fusion to achieve informative user and attraction embeddings. To capture spatial constraints while preserving semantic relevance, VEGAR proposes spatial-sentiment attention that jointly models semantic relevance and distance-based influence during propagation. Experiments on the Suzhou Sina Weibo dataset show that VEGAR achieves average relative improvements of 2.85% in AUC and 2.80% in F1 score over all compared baselines. VEGAR also achieves the best Top-K ranking performance.\u003c/p\u003e","manuscriptTitle":"VEGAR: A tourist attraction recommender system based on signed feedback and a signed spatial-sentiment knowledge graph","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-26 09:50:35","doi":"10.21203/rs.3.rs-9169431/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-24T05:38:51+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-22T05:23:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"50249630547833410272180582876635468459","date":"2026-04-20T10:51:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-09T14:48:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"106656563400618239641414788391514539139","date":"2026-03-24T13:43:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-24T12:07:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-20T11:03:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-20T11:02:45+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of King Saud University Computer and Information Sciences","date":"2026-03-19T12:09:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-king-saud-university-computer-and-information-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Journal of King Saud University Computer and Information Sciences](https://link.springer.com/journal/44443)","snPcode":"44443","submissionUrl":"https://submission.springernature.com/new-submission/44443/3","title":"Journal of King Saud University Computer and Information Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e174033a-1324-4ec5-8641-046a0ebae3b9","owner":[],"postedDate":"March 26th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-04-24T05:55:37+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-26 09:50:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9169431","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9169431","identity":"rs-9169431","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}