A Systematic Review of Advancements in Context-Aware Recommendation Systems

A Systematic Review of Advancements in Context-Aware Recommendation Systems | 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 Systematic Review A Systematic Review of Advancements in Context-Aware Recommendation Systems Mohamed El Amine Chafiki, Oumaima Stitini, Soulaimane Kaloun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6305569/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Dec, 2025 Read the published version in Knowledge and Information Systems → Version 1 posted 12 You are reading this latest preprint version Abstract Users can benefit from intelligent data handling strategies by analyzing and accessing the massive amounts of data generated by automated and technological gadgets. Using Recommendation Systems (RS) to sift through the enormous volume of data and extract pertinent information is the most popular method. Nevertheless, early RS lacked the capability to integrate contextual information, limiting their ability to provide personalized suggestions. Context-Aware Recommendation Systems (CARS) address this gap by incorporating contextual factors to align recommendations with dynamic user preferences and conditions. However, effectively utilizing context remains challenging, with limited resources to guide researchers and developers in designing efficient CARS. This study conducts a systematic review to summarize the current state of CARS, identify limitations, and propose future research directions. Using the Rayyan AI tool for study selection, 56 primary studies, including journal articles, conference papers, and book chapters from 2020 to 2024, were analyzed. The findings highlight the diverse paradigms, context types, and methodologies adopted in CARS, along with varying evaluation techniques across multiple application domains. Key insights from this research emphasize the complexity of leveraging context to enhance recommendation quality. Persistent challenges include managing diverse contextual factors, improving scalability, and addressing evaluation inconsistencies. This study also identifies opportunities for innovation, such as dynamic context modeling and multi objective optimization. By providing a comprehensive overview of CARS research, this work contributes to understanding the field's progress and future potential. These findings are valuable for both academic researchers and industry professionals, offering practical guidance for developing effective, context-aware recommendation systems capable of addressing evolving user needs. recommender systems context-aware recommender systems pre-filtering post-filtering contextual modeling personalization contextual information systematic review Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 19 Dec, 2025 Read the published version in Knowledge and Information Systems → Version 1 posted Editorial decision: Revision requested 18 Jul, 2025 Reviews received at journal 30 Jun, 2025 Reviews received at journal 06 Jun, 2025 Reviewers agreed at journal 03 Jun, 2025 Reviewers agreed at journal 17 May, 2025 Reviewers agreed at journal 15 May, 2025 Reviews received at journal 29 Apr, 2025 Reviewers agreed at journal 21 Apr, 2025 Reviewers invited by journal 19 Apr, 2025 Editor assigned by journal 31 Mar, 2025 Submission checks completed at journal 25 Mar, 2025 First submitted to journal 25 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. 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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-6305569","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":478281884,"identity":"d24f482d-d898-426b-a78b-693dc3449f7f","order_by":0,"name":"Mohamed El Amine Chafiki","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYHACNgYGGwkDNhCTp4JoLWkwLWeI18JgAGbythGhXreB+dmDHwkWxnwMzA8fvJ13WJ6/gfnhB8YcO5xazA6wmRv2JEiYsTGwGRvO3XbYcMYBNmMJxm3JeLTwsEnw/pCwYWPgYZPm3XY4geEAgxkD4zZmvFok/ySAtbD/5p1zOEH+APs3oJZ6vFqkecAO42Fj5m04nGBwgAdky2HcWg6zmUnLJEgYszGzGUvOOZZuuPEwT7FE4rbjuLUcb34m+SahznB+e/PDD29qrOXljrdv/PBxWzVOLQzMWBkJuDWMglEwCkbBKCACAAACz0Rwwy5+fwAAAABJRU5ErkJggg==","orcid":"","institution":"Faculty of Science and Technology, Laboratory of Computer and System Engineering (L2IS), Cadi Ayyad University","correspondingAuthor":true,"prefix":"","firstName":"Mohamed","middleName":"El Amine","lastName":"Chafiki","suffix":""},{"id":478281885,"identity":"4e7950cc-6d49-4d51-a9cd-9e86df281ca2","order_by":1,"name":"Oumaima Stitini","email":"","orcid":"","institution":"Faculty of Science and Technology, Laboratory of Computer and System Engineering (L2IS), Cadi Ayyad University","correspondingAuthor":false,"prefix":"","firstName":"Oumaima","middleName":"","lastName":"Stitini","suffix":""},{"id":478281886,"identity":"8d955f49-c111-49f2-b01b-89fee516aeb0","order_by":2,"name":"Soulaimane Kaloun","email":"","orcid":"","institution":"Faculty of Science and Technology, Laboratory of Computer and System Engineering (L2IS), Cadi Ayyad University","correspondingAuthor":false,"prefix":"","firstName":"Soulaimane","middleName":"","lastName":"Kaloun","suffix":""}],"badges":[],"createdAt":"2025-03-25 16:08:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6305569/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6305569/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10115-025-02627-8","type":"published","date":"2025-12-19T15:57:57+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":98813944,"identity":"62869505-1798-4cd1-8967-8c02d08277f0","added_by":"auto","created_at":"2025-12-22 16:08:06","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1051618,"visible":true,"origin":"","legend":"","description":"","filename":"ASystematicReviewofAdvancementsinContextAwareRecommendationSystems.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6305569/v1_covered_54219a1c-6a76-4745-944f-dd47fe97170c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Systematic Review of Advancements in Context-Aware Recommendation Systems","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":"knowledge-and-information-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"kais","sideBox":"Learn more about [Knowledge and Information Systems](http://link.springer.com/journal/10115)","snPcode":"10115","submissionUrl":"https://submission.nature.com/new-submission/10115/3","title":"Knowledge and Information Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"recommender systems, context-aware recommender systems, pre-filtering, post-filtering, contextual modeling, personalization, contextual information, systematic review","lastPublishedDoi":"10.21203/rs.3.rs-6305569/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6305569/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUsers can benefit from intelligent data handling strategies by analyzing and accessing the massive amounts of data generated by automated and technological gadgets. 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