DPCRec-Dual Preferences Learning with Capsule Network for Next POI Recommendation

DPCRec-Dual Preferences Learning with Capsule Network for Next POI Recommendation | 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 DPCRec-Dual Preferences Learning with Capsule Network for Next POI Recommendation Yitong Song This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5841922/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 The goal of the next Point-of-Interest (POI) recommendation task is to predict a list of POIs that a user might visit next, based on their check-in history, including the locations and times of their check-ins. This list includes the probability of visiting each POI. Clearly, the performance of this task is closely related to how well the user's interests could be extracted. Previous research has made significant progress in this area, but there are still some challenges: 1) Current methods cannot fully extract user interests from different perspectives. 2) They struggle to effectively combine multiple or diverse user interests for prediction. In this paper, a Dual Interest Capsule Recommendation (DPCRec) is proposed to address these two issues. For the first problem, user interests are divided into long-term and short-term interests and introduce Advanced Long-Term Interest Feature Extractor(ALE) and Advanced Short-Term Interest Feature Extractor(ASE) to extract these interests separately. Among them, ALE uses a multi-head attention mechanism with deep attention and residual connections to capture long-term interests, while ASE employs a GRU module enhanced with attention and multi-gating techniques to capture short-term interests. For the second problem, a novel capsule network called Capsule Deep Pointer (CapsDP) is proposed by us, which effectively combines long-term and short-term interests and maps the hierarchical relationship between user interests and the POIs to be predicted. Extensive experiments on three datasets show that our model outperforms ten baseline models, achieving state-of-the-art results. Next POI recommendation Multi-Perspective Interest Extraction Long- and Short-Term Interests Attention mechanism Capsule Network 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-5841922","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":403602861,"identity":"a7940dfd-4636-4a6a-b7c5-789b989daebc","order_by":0,"name":"Yitong Song","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDUlEQVRIiWNgGAWjYHAD5gNAwobBAMTmIUYDDwNbApBKI0kLD0jxYcJa5Nt7D79gbLPLs+c/YPzxx5/z9uYSCYwP3rYxyJvj0GJw5lyaBWNbcjGPREKCgQTP7cSdMxKYDee2MRjubMChRSLHzIBxG3NijwTDAaCe2wkGNxLYpHnbGBIMDuBw2AywlvrEHv6DDUA95+yBWth/49PCcCPH+AHjtsOJPQzJjEA9Bxg3AG1hxqfF4MwZMwbGf8cTe26kMQP1JCduOPOwWXLOOQnDDbgc1t5j/IHhTHVie//5z8AQs7M3OJ588MObMht5nA5jYGCT/oMqwNgAJCRwqgcC5g/4ZEfBKBgFo2AUMAAAm/RbcGK+FIYAAAAASUVORK5CYII=","orcid":"","institution":"Shanghai University","correspondingAuthor":true,"prefix":"","firstName":"Yitong","middleName":"","lastName":"Song","suffix":""}],"badges":[],"createdAt":"2025-01-16 12:38:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5841922/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5841922/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78641122,"identity":"54d6a704-48f9-49ce-96a9-8a4b13beeaf9","added_by":"auto","created_at":"2025-03-17 06:32:01","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2469170,"visible":true,"origin":"","legend":"","description":"","filename":"DPCRecDualPreferencesLearningwithCapsuleNetworkforNextPOIRecommendation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5841922/v1_covered_66246aba-b838-4338-a0d6-fa6044eeb617.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"DPCRec-Dual Preferences Learning with Capsule Network for Next POI Recommendation","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":"Next POI recommendation, Multi-Perspective Interest Extraction, Long- and Short-Term Interests, Attention mechanism, Capsule Network","lastPublishedDoi":"10.21203/rs.3.rs-5841922/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5841922/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe goal of the next Point-of-Interest (POI) recommendation task is to predict a list of POIs that a user might visit next, based on their check-in history, including the locations and times of their check-ins. This list includes the probability of visiting each POI. Clearly, the performance of this task is closely related to how well the user's interests could be extracted. Previous research has made significant progress in this area, but there are still some challenges: 1) Current methods cannot fully extract user interests from different perspectives. 2) They struggle to effectively combine multiple or diverse user interests for prediction. In this paper, a Dual Interest Capsule Recommendation (DPCRec) is proposed to address these two issues. For the first problem, user interests are divided into long-term and short-term interests and introduce Advanced Long-Term Interest Feature Extractor(ALE) and Advanced Short-Term Interest Feature Extractor(ASE) to extract these interests separately. 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