Multi-Objective Contextual Bandits in Recommendation Systems for Smart Tourism

Multi-Objective Contextual Bandits in Recommendation Systems for Smart Tourism | 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 Multi-Objective Contextual Bandits in Recommendation Systems for Smart Tourism Sara Qassimi, Said Rakrak This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4431236/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In the context of smart tourism, the utilization of recommender systems is becoming increasingly critical for enhancing the personalization and quality of travel experiences. Tourists often encounter complex decision-making due to information overload, context-aware recommender systems have emerged as a promising solution, leveraging contextual data such as time, weather, and location. However, these systems face the challenge of the complexity of handling dynamic context. Thus, the static nature of these systems can result in a degradation of performance, as they fail to capture the dynamic nature of user behavior and context. Addressing these issues, this paper presents a novel multi-objective contextual multi-armed bandit-based recommender system. This proposal integrates the strengths of contextual bandit algorithms with multi-objective optimization, offering personalized recommendations and learning from user feedback. The multi-objective optimization includes the dual necessities of relevance and fairness in recommendations, ensuring the promotion of a balanced tourism ecosystem. Extensive experiments were carried out on public datasets to evaluate the performance of our proposed approach. Its effectiveness was compared with baseline methods to establish its performance, demonstrating the significance of multi-objective optimization in enhancing personalized recommendations in smart tourism. To evaluate the performance of our proposed algorithm, we conducted experiments using two datasets, a designed dataset that simulates real-world scenarios and TripAdvisor dataset. The study provides a case scenario of implementing this proposed approach in the smart tourism context of Marrakesh, demonstrating its potential to revolutionize the tourist experience in smart cities. Multi-Objective Optimization Contextual Bandits MAB Recommender System Smart Tourism Smart Cities Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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-4431236","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":303603089,"identity":"e6aa0474-bacb-4e08-a8ce-aab842ec331e","order_by":0,"name":"Sara Qassimi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYFADZgaGAx9I1nJwBukW8RCjSr797MMPDGV29vLt3ImHbdvs5BnYDz9gulGDW4vBmXRjCYZzyYkbDvNuOJzblmzYwJNmwJxzDI8WhjQGCcY25gQDZrCWA4wNDDkMzDlseBzW/4z5B2Nbvb18M1CLZdsB+wb+N0At//B45kYaG9CWw4wNIIcxth1IbJAA2pLbhsdhN56xWSScOw72y8Gec8nJbRLPDA7n9uFzWBrzjQ9l1fby/Wc3f/hRZmfbz5/88HHONzwOA4EEZM+C2AcIaIAqGwWjYBSMglGACwAAUnhM2Mr9Si8AAAAASUVORK5CYII=","orcid":"","institution":"FST, Cadi Ayyad University","correspondingAuthor":true,"prefix":"","firstName":"Sara","middleName":"","lastName":"Qassimi","suffix":""},{"id":303603090,"identity":"74a08eb3-aee5-4e27-a692-02a1f43afa75","order_by":1,"name":"Said Rakrak","email":"","orcid":"","institution":"FST, Cadi Ayyad University","correspondingAuthor":false,"prefix":"","firstName":"Said","middleName":"","lastName":"Rakrak","suffix":""}],"badges":[],"createdAt":"2024-05-16 13:02:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4431236/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4431236/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60231048,"identity":"a7dc7b22-61f5-4e72-8777-916a12ff4122","added_by":"auto","created_at":"2024-07-13 19:35:13","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":737463,"visible":true,"origin":"","legend":"","description":"","filename":"MultiObjectiveContextualRSforSmartTourism.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4431236/v1_covered_9242ebca-5701-483e-83f5-df6027fa897f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multi-Objective Contextual Bandits in Recommendation Systems for Smart Tourism","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Multi-Objective Optimization, Contextual Bandits, MAB, Recommender System, Smart Tourism, Smart Cities","lastPublishedDoi":"10.21203/rs.3.rs-4431236/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4431236/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn the context of smart tourism, the utilization of recommender systems is becoming increasingly critical for enhancing the personalization and quality of travel experiences. Tourists often encounter complex decision-making due to information overload, context-aware recommender systems have emerged as a promising solution, leveraging contextual data such as time, weather, and location. However, these systems face the challenge of the complexity of handling dynamic context. Thus, the static nature of these systems can result in a degradation of performance, as they fail to capture the dynamic nature of user behavior and context. Addressing these issues, this paper presents a novel multi-objective contextual multi-armed bandit-based recommender system. This proposal integrates the strengths of contextual bandit algorithms with multi-objective optimization, offering personalized recommendations and learning from user feedback. The multi-objective optimization includes the dual necessities of relevance and fairness in recommendations, ensuring the promotion of a balanced tourism ecosystem. 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