Hybrid Personalized Sequence Recommendation based on LSTM and Filter Enhancement

Hybrid Personalized Sequence Recommendation based on LSTM and Filter Enhancement | 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 Hybrid Personalized Sequence Recommendation based on LSTM and Filter Enhancement Zhengshun Fei, Xiangyu Qin, Haotian Zhou, Gui Chen, Xinjian Xiang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5900335/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Sequence recommendation captures dynamic user intent based on historical interaction sequences and has increasingly become a prominent area of research. Models such as CNN and Transformer have become mainstream approaches for sequence recommendation. However, single-model methods face clear limitations in data utilization and noise filtering. To effectively leverage valuable information and filter out noise, we propose a novel Hybrid Personalized Sequence Recommendation based on LSTM and Filter Enhancement, termed as LFPRec. LFPRec comprises two core components: the FT block and the LSTM block. The Filter Enhancement module applies Fast Fourier Transform (FFT) and Inverse Fast Fourier Transform (IFFT) to perform time-domain and frequency-domain transformations, isolating low-frequency trends reflective of stable user preferences while filtering out high-frequency noise, such as transient or erroneous interactions. Utilizing its gating mechanism, the LSTM module models long-term dependencies in sequences to capture dynamic user intent. The processed data from these modules is subsequently integrated using a learnable, adaptive hybrid approach. Theoretical analysis suggests that frequency-domain filtering enhances the signal-to-noise ratio, enabling more accurate modeling of user behavior patterns critical to recommendation tasks. To evaluate the effectiveness of LFPRec, we conducted experiments on three public datasets. Experimental results demonstrate that LFPRec consistently outperforms six state-of-the-art recommendation models in terms of recommendation accuracy and robustness, highlighting its enhanced capabilities in data utilization and noise reduction. Recommender system Sequential Recommendation Filter Enhancement Mixture models Sequential Learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 01 May, 2025 Reviews received at journal 27 Apr, 2025 Reviewers agreed at journal 25 Apr, 2025 Reviewers agreed at journal 24 Apr, 2025 Reviewers agreed at journal 21 Apr, 2025 Reviewers invited by journal 21 Apr, 2025 Submission checks completed at journal 14 Apr, 2025 First submitted to journal 13 Apr, 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. 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-5900335","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":448643439,"identity":"0796f758-a57e-4931-b5e9-d682f89598da","order_by":0,"name":"Zhengshun Fei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvklEQVRIiWNgGAWjYFACxgcMCRUMzCCmBJFamA0YEs6QrIWxDcIkTot8/2HGBw/n1bEbHGA+eJuHwS6PoBbGGcnMBonb2JgNDrAlW/MwJBcTdpYE/zGJxG08QC08ZtI8DAcSGwhpYeM/zP4jcY4EUAv/N+K0AJ3CxpDYYACyhY04LRISycwSCccSmCUPsxlbzjFIJqwFFGIff9TUJfMdb354402FHWEtMJAMiUwDYtUDgR0JakfBKBgFo2CkAQC3zDFfTV5y/AAAAABJRU5ErkJggg==","orcid":"","institution":"Zhejiang University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Zhengshun","middleName":"","lastName":"Fei","suffix":""},{"id":448643440,"identity":"9420e8a4-8916-47c8-bf9a-05fb9dd41b70","order_by":1,"name":"Xiangyu Qin","email":"","orcid":"","institution":"Zhejiang University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Xiangyu","middleName":"","lastName":"Qin","suffix":""},{"id":448643441,"identity":"a528a59a-9dc4-452e-b833-474654330ee8","order_by":2,"name":"Haotian Zhou","email":"","orcid":"","institution":"Zhejiang University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Haotian","middleName":"","lastName":"Zhou","suffix":""},{"id":448643442,"identity":"d0c67d52-38ec-414b-9553-c71cc1c537c7","order_by":3,"name":"Gui Chen","email":"","orcid":"","institution":"Bingwu (Ningbo) Intelligent Equipment Co., LTD","correspondingAuthor":false,"prefix":"","firstName":"Gui","middleName":"","lastName":"Chen","suffix":""},{"id":448643443,"identity":"c8a39cab-2a8b-4f50-aff9-471325f474b7","order_by":4,"name":"Xinjian Xiang","email":"","orcid":"","institution":"Zhejiang University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Xinjian","middleName":"","lastName":"Xiang","suffix":""}],"badges":[],"createdAt":"2025-01-25 08:23:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5900335/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5900335/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81506978,"identity":"c473e0e7-23e0-4f13-bf46-49b417d0b9b6","added_by":"auto","created_at":"2025-04-28 05:33:11","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1049075,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5900335/v1_covered_348d3840-6c96-4094-9936-bbda9f483f7c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Hybrid Personalized Sequence Recommendation based on LSTM and Filter Enhancement","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":"the-journal-of-supercomputing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [The Journal of Supercomputing](https://www.springer.com/journal/11227)","snPcode":"11227","submissionUrl":"https://submission.nature.com/new-submission/11227/3","title":"The Journal of Supercomputing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Recommender system, Sequential Recommendation, Filter Enhancement, Mixture models, Sequential Learning","lastPublishedDoi":"10.21203/rs.3.rs-5900335/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5900335/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSequence recommendation captures dynamic user intent based on historical interaction sequences and has increasingly become a prominent area of research. Models such as CNN and Transformer have become mainstream approaches for sequence recommendation. However, single-model methods face clear limitations in data utilization and noise filtering. To effectively leverage valuable information and filter out noise, we propose a novel Hybrid Personalized Sequence Recommendation based on LSTM and Filter Enhancement, termed as LFPRec. LFPRec comprises two core components: the FT block and the LSTM block. The Filter Enhancement module applies Fast Fourier Transform (FFT) and Inverse Fast Fourier Transform (IFFT) to perform time-domain and frequency-domain transformations, isolating low-frequency trends reflective of stable user preferences while filtering out high-frequency noise, such as transient or erroneous interactions. Utilizing its gating mechanism, the LSTM module models long-term dependencies in sequences to capture dynamic user intent. 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