AHP Celebrity Recommendation Based On Luscher Color Theory: Twitter Use Case

AHP Celebrity Recommendation Based On Luscher Color Theory: Twitter Use Case | 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 AHP Celebrity Recommendation Based On Luscher Color Theory: Twitter Use Case Mir Saman Tajbakhsh, Vahid Solouk This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4474866/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract Social Networks are among the emerging services in the current virtual world which are used as a connecting medium between users over the Internet. Users generally connect to social networks to interact with their family members, coworkers, or even for commercial goals. Hence, the key for the success of every social network provider lies on satisfying user interest and thereby, expanding the network. Accordingly, one way to increase users' interest toward a social network is introducing them to other users with the similar characteristics using recommender systems. The current article introduces a recommender system based on AHP method and semantic similarities as well as color psychology between users in an online social network. To evaluate the proposed system, we used 5705 real Twitter celebrity profiles as alternative for recommendation, together with 100 virtual profiles as ordinary users. The experimental results indicate maximum recommendations error rate as low as 12.5. Friend Recommendation AHP Semantic Similarity Social Network Twitter Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 11 Jun, 2024 Submission checks completed at journal 26 May, 2024 First submitted to journal 24 May, 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. 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-4474866","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":307019075,"identity":"f9b1fce6-a066-4c89-9a6a-2356c4cc81d4","order_by":0,"name":"Mir Saman Tajbakhsh","email":"","orcid":"","institution":"Urmia University","correspondingAuthor":false,"prefix":"","firstName":"Mir","middleName":"Saman","lastName":"Tajbakhsh","suffix":""},{"id":307019076,"identity":"93335fc1-8b2f-49e3-a2e8-54589b440955","order_by":1,"name":"Vahid Solouk","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIie3RMQ6CMBTG8UeawFLTFRa8wjNNuuhhyuQNDCMJCYxcwMOUdGBpnB3L4uSgu4NIiCEORTeH/seX/IYvD8Dn+9csADIyO6hFIgeSlKPBHwiqGXGGKuqtzDXnHVNkVT2A1SrQuZNQjtJoITSBgSDERkJrHCQpaBhnld69CZwB2sJJostIeDmR9RJhAOJFBJKJ4CIh45Y9jzXB9njidGOywknCqO7tLd9umqbt7fWQpmmn9d1F4PPpFCBwAp/P5/N90ROtzkLxJZ7puAAAAABJRU5ErkJggg==","orcid":"","institution":"Urmia University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Vahid","middleName":"","lastName":"Solouk","suffix":""}],"badges":[],"createdAt":"2024-05-25 02:53:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4474866/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4474866/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57898601,"identity":"9e22a832-60ee-4ef3-827f-4967403f265c","added_by":"auto","created_at":"2024-06-07 08:18:19","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":332919,"visible":true,"origin":"","legend":"","description":"","filename":"AFReS.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4474866/v1_covered_10347a5e-d8f7-4741-99e3-d4e840819e24.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"AHP Celebrity Recommendation Based On Luscher Color Theory: Twitter Use Case","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":"international-journal-of-data-science-and-analytics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jdsa","sideBox":"Learn more about [International Journal of Data Science and Analytics](http://link.springer.com/journal/41060)","snPcode":"41060","submissionUrl":"https://submission.nature.com/new-submission/41060/3","title":"International Journal of Data Science and Analytics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Friend Recommendation, AHP, Semantic Similarity, Social Network, Twitter","lastPublishedDoi":"10.21203/rs.3.rs-4474866/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4474866/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Social Networks are among the emerging services in the current virtual world which are used as a connecting medium between users over the Internet. 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