TriDeepRec: A Hybrid Deep Learning Approach to Content and Behaviour-based Recommendation Systems

TriDeepRec: A Hybrid Deep Learning Approach to Content and Behaviour-based 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 TriDeepRec: A Hybrid Deep Learning Approach to Content and Behaviour-based Recommendation Systems Amirhossein Ghadami, Thomas Tran This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4006730/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Oct, 2024 Read the published version in User Modeling and User-Adapted Interaction → Version 1 posted 11 You are reading this latest preprint version Abstract Hybrid recommendation systems are increasingly crucial for businesses aiming to boost revenue and customer engagement. These systems integrate various algorithms, each with unique strengths, to outperform traditional recommendation methods. Our study introduces a novel hybrid recommendation system, TriDeepRec, which effectively combines content-based and behaviour-based data to enhance recommendation accuracy. We first introduce a Convolutional Autoencoder-based Recommendation System (CAERS), designed to process content data and extract complex, meaningful patterns, translating these into predictive ratings. Notably, CAERS tackles the cold-start problem by leveraging content information alone, making it robust in scenarios where historical user interaction data is sparse or unavailable. Next, we incorporate Neural Collaborative Filtering (NCF), a deep learning approach, to analyze past user behaviour and predict ratings. The outputs from CAERS and NCF are then integrated using a Multilayer Perceptron (MLP), a type of neural network, to generate the final recommendations. Our methodology employs three deep learning techniques to create TriDeepRec, a system capable of utilizing both past interactions and content attributes. We evaluate our system using two datasets, initially focusing on CAERS to demonstrate its effectiveness in addressing the cold-start problem. Subsequently, we assess the performance of TriDeepRec as a whole. The results, measured in terms of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), indicate significant improvements over both the individual components and other leading models in the field. This demonstrates that TriDeepRec, by 1 harnessing the strengths of both content and behaviour data, provides a more accurate and reliable recommendation system. Recommendation System Hybrid Recommendation System Deep Learning Convolutional Autoencoder Cold Start Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 23 Oct, 2024 Read the published version in User Modeling and User-Adapted Interaction → Version 1 posted Editorial decision: Revision requested 17 May, 2024 Reviews received at journal 04 May, 2024 Reviews received at journal 29 Apr, 2024 Reviews received at journal 14 Apr, 2024 Reviewers agreed at journal 05 Apr, 2024 Reviewers agreed at journal 04 Apr, 2024 Reviewers agreed at journal 03 Apr, 2024 Reviewers invited by journal 01 Apr, 2024 Submission checks completed at journal 02 Mar, 2024 Editor assigned by journal 02 Mar, 2024 First submitted to journal 02 Mar, 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. 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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-4006730","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":276004467,"identity":"429456d2-b609-419e-bee7-6f5a0abf815b","order_by":0,"name":"Amirhossein Ghadami","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIie3NsQrCMBCA4Yhgl5OuKRV9hZRCQVD6KpZCJwfHDg6Z4iK6OugjCD5CpGCXiGugDoLgpNLJRVDr6hB1E8zPTcd9HEI63U8GxZAbmF+SmNcsikqUf04EbxH+KTHH82V+YltwZZju837SQEayUxIsz6E1YwfwZBRQvkocChFRv5GC2FVWLkjXobySlShGatKQwr0+iTt5klvmU2zkSkI2Q88GkQDBBVmyLKAY1F8cCV5zGkeAxSGYrEf3kEG3pyT1jXDlkbR8cxDyPL5E7bGRLpQE4c7LoqK+LzL52xOdTqf79x4Fn08uddHMywAAAABJRU5ErkJggg==","orcid":"","institution":"University of Ottawa","correspondingAuthor":true,"prefix":"","firstName":"Amirhossein","middleName":"","lastName":"Ghadami","suffix":""},{"id":276004468,"identity":"74edc77d-12c7-45ec-a379-ccfd7c66334b","order_by":1,"name":"Thomas Tran","email":"","orcid":"","institution":"University of Ottawa","correspondingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Tran","suffix":""}],"badges":[],"createdAt":"2024-03-02 14:46:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4006730/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4006730/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11257-024-09418-w","type":"published","date":"2024-10-23T15:57:57+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":67682477,"identity":"4e5bd465-cdcd-4afb-afab-406ba7b01916","added_by":"auto","created_at":"2024-10-28 16:14:25","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1801629,"visible":true,"origin":"","legend":"","description":"","filename":"TriDeepRec.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4006730/v1_covered_db467fa3-e928-4a6c-a537-ae392aee7d39.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"TriDeepRec: A Hybrid Deep Learning Approach to Content and Behaviour-based 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":"user-modeling-and-user-adapted-interaction","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"umui","sideBox":"Learn more about [User Modeling and User-Adapted Interaction](http://link.springer.com/journal/11257)","snPcode":"11257","submissionUrl":"https://submission.nature.com/new-submission/11257/3","title":"User Modeling and User-Adapted Interaction","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Recommendation System, Hybrid Recommendation System, Deep Learning, Convolutional Autoencoder, Cold Start","lastPublishedDoi":"10.21203/rs.3.rs-4006730/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4006730/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Hybrid recommendation systems are increasingly crucial for businesses aiming to boost revenue and customer engagement. 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