RBM-Q Learning for Rate Prediction in Recommendation Systems

RBM-Q Learning for Rate Prediction in 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 Research Article RBM-Q Learning for Rate Prediction in Recommendation Systems aref YELGHI This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3726733/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Jul, 2025 Read the published version in Soft Computing → Version 1 posted 5 You are reading this latest preprint version Abstract Through the recommendation of personalized information, recommendation systems are essential in improving the user experience on a variety of online websites. Some popular and conventional techniques have not been able to perform well in data volume and sparse data problems. In order to compete with them, this paper presents and assesses two algorithms: RBM-Q-Learning 1 and RBM-Q-Learning 2. By proposing novel mechanisms for state representation and action selection, these algorithms attempt to overcome the shortcomings of conventional methods, utilizing Restricted Boltzmann Machines (RBMs) as a major component. The performance of recommendation algorithms on two datasets from the MovieLens platform—MovieLens 100K and MovieLens 1M—are compared in-depth through an experimental research presented in this research. The experiments have been performed based on MAE, RMSE, HR, ARHR, Diversity and Novelty measurement indicators and the proposed algorithms have found proper stability. RBM Q-learning Rate Prediction Markov Chain Collaborative Filtering Full Text Cite Share Download PDF Status: Published Journal Publication published 24 Jul, 2025 Read the published version in Soft Computing → Version 1 posted Editorial decision: Major Revision 04 Aug, 2024 Reviewers agreed at journal 18 Feb, 2024 Reviewers invited by journal 17 Feb, 2024 Editor invited by journal 02 Feb, 2024 First submitted to journal 09 Dec, 2023 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. 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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-3726733","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":273484500,"identity":"9a005846-746f-42d7-921c-c6dbc42d5cae","order_by":0,"name":"aref YELGHI","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYNCCAgYefhCdUEC0FgMGHskGkBYDErQwGByAMggC/tkH2CQ+GFjLGJ9fnfjhgQGDPL/YAfxaJM4lsEnOMEjnMbvxdrME0GGGM2cnELDmDAObNI/BYaCWsxtAWhIMbhPQIg/S8geoxXjG2c0/iNJiANLCANRiwN+7jThbDM8wNlv2AP0icYN3m0WCgQRhv8idYT5440eFtT1//9nNN39U2MjzSxPQwsDA2CLBwMAMDDuwSglCysGA+QNYC/8BolSPglEwCkbBCAQAlGk9e40qAZoAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-2380-8718","institution":"Avrasya Universitesi","correspondingAuthor":true,"prefix":"","firstName":"aref","middleName":"","lastName":"YELGHI","suffix":""}],"badges":[],"createdAt":"2023-12-08 17:20:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3726733/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3726733/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00500-025-10744-3","type":"published","date":"2025-07-24T15:57:55+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":88506200,"identity":"2d9b7b01-1626-4cdd-9cbc-be8eb66ae4d1","added_by":"auto","created_at":"2025-08-07 07:32:28","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":432232,"visible":true,"origin":"","legend":"","description":"","filename":"Article.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3726733/v1_covered_e4d7ed27-5ca1-4d44-a33c-05bd39ec2049.pdf"}],"financialInterests":"","formattedTitle":"RBM-Q Learning for Rate Prediction in 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":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"soft-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"soco","sideBox":"Learn more about [Soft Computing](https://www.springer.com/journal/500)","snPcode":"500","submissionUrl":"https://submission.nature.com/new-submission/500/3","title":"Soft Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"RBM, Q-learning, Rate Prediction, Markov Chain, Collaborative Filtering","lastPublishedDoi":"10.21203/rs.3.rs-3726733/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3726733/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThrough the recommendation of personalized information, recommendation systems are essential in improving the user experience on a variety of online websites. 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