Next App Prediction Based on Graph NeuralNetworks and Self-Attention Enhancement

Next App Prediction Based on Graph NeuralNetworks and Self-Attention 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 Article Next App Prediction Based on Graph NeuralNetworks and Self-Attention Enhancement Junxin Chen, Zhiqiong Liu, Jing Liu, Wang Li, Chang Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5986284/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Next mobile app prediction aims to recommend the apps that users will most likely to use next based on their historicalusage behavior. It is critical for optimizing app preloading strategies and personalized recommendations, enhancing the userexperience on mobile devices. However, it faces fundamental challenges such as interactions sparsity, rapid expansion ofthe app ecosystem and long-term interest neglect. To overcome the limitations of existing methods in next-app prediction,particularly in personalized feature extraction and temporal dynamics modeling, we propose a temporal-personalized next-app prediction framework, which employs multi-perspective graph representation learning with self-attention mechanisms toenhance user and app embeddings. It can effectively capture both long-term and short-term evolving user interests in appusage, enhancing dynamic temporal features of users and apps. Moreover, it can integrate global interactions into graphrepresentation learning by multi-perspective feature aggregations. With a context-aware attention fusion mechanism applied,we effectively integrate temporal and personalized features. The comprehensive user and app embeddings are obtained tonext-app prediction, which significantly improve the accuracy of next app prediction. Experimental results on real datasetsdemonstrate that our model outperforms other baselines. Physical sciences/Mathematics and computing/Computer science Physical sciences/Mathematics and computing/Information technology Physical sciences/Mathematics and computing/Scientific data Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 01 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 06 May, 2025 Reviews received at journal 03 May, 2025 Reviews received at journal 29 Apr, 2025 Reviews received at journal 24 Apr, 2025 Reviewers agreed at journal 23 Apr, 2025 Reviewers agreed at journal 19 Apr, 2025 Reviewers agreed at journal 17 Apr, 2025 Reviewers invited by journal 17 Apr, 2025 Submission checks completed at journal 16 Apr, 2025 First submitted to journal 08 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. 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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-5986284","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":444431997,"identity":"8f3598b1-b8a7-4036-84d2-45cf07ec2e60","order_by":0,"name":"Junxin Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIie3RMUvDQBTA8SfCdXma9ZVC+hWeHAiC9LP0CMTlqIJLB8FC4dycA36MQufoQVzOPUMHq9DJQRA6iXjp7MWMgveHwHHJj3fhAGKxvxjtnlNMAKFsNg47kjztz3aEQXQiAA+SS2wWHUhyN6+eT0ypFo9P9/bi6jMVvfn6pYbRJDhkVZ0xuZVausnYFhVLgZWUGrLLEGHSx0TTjVrWyBYFK0NaDDSUahYk51sitmpRNOSLr83w7TeihZ9iJZMnB4bHgrCdUJ37g7k8Jac9uZVHBnP/L5wFSVJkm0Hf+Ku8cfIDt+kw6dn1q56OgqRpn344cMv3vr339vexWCz23/sGZAFRgKlJ4MAAAAAASUVORK5CYII=","orcid":"","institution":"Research Institute of China Telecom Company Ltd.","correspondingAuthor":true,"prefix":"","firstName":"Junxin","middleName":"","lastName":"Chen","suffix":""},{"id":444431998,"identity":"24ce310d-5d4b-4371-b9d7-17ef644e4108","order_by":1,"name":"Zhiqiong Liu","email":"","orcid":"","institution":"Research Institute of China Telecom Company Ltd.","correspondingAuthor":false,"prefix":"","firstName":"Zhiqiong","middleName":"","lastName":"Liu","suffix":""},{"id":444431999,"identity":"369061d3-392d-44f1-83d6-9da581ab2efa","order_by":2,"name":"Jing Liu","email":"","orcid":"","institution":"Research Institute of China Telecom Company Ltd.","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Liu","suffix":""},{"id":444432000,"identity":"df8b8c4a-d44a-444f-87ec-6e40f684b891","order_by":3,"name":"Wang Li","email":"","orcid":"","institution":"Research Institute of China Telecom Company Ltd.","correspondingAuthor":false,"prefix":"","firstName":"Wang","middleName":"","lastName":"Li","suffix":""},{"id":444432001,"identity":"1ecf2413-9d99-45a1-b743-49c2339c18f2","order_by":4,"name":"Chang Wang","email":"","orcid":"","institution":"Research Institute of China Telecom Company Ltd.","correspondingAuthor":false,"prefix":"","firstName":"Chang","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-02-08 08:23:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5986284/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5986284/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-05260-1","type":"published","date":"2025-07-01T15:58:36+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86180501,"identity":"edc28d2b-74ef-4c01-b5de-31afb073ff98","added_by":"auto","created_at":"2025-07-07 16:22:22","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1048570,"visible":true,"origin":"","legend":"","description":"","filename":"NextAppPredictionBasedonGraphNeuralNetworksandSelfAttentionEnhancementrevised0404.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5986284/v1_covered_6ee9dd67-0997-47f7-a377-7585b01b3002.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Next App Prediction Based on Graph NeuralNetworks and Self-Attention 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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5986284/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5986284/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Next mobile app prediction aims to recommend the apps that users will most likely to use next based on their historicalusage behavior. It is critical for optimizing app preloading strategies and personalized recommendations, enhancing the userexperience on mobile devices. However, it faces fundamental challenges such as interactions sparsity, rapid expansion ofthe app ecosystem and long-term interest neglect. To overcome the limitations of existing methods in next-app prediction,particularly in personalized feature extraction and temporal dynamics modeling, we propose a temporal-personalized next-app prediction framework, which employs multi-perspective graph representation learning with self-attention mechanisms toenhance user and app embeddings. It can effectively capture both long-term and short-term evolving user interests in appusage, enhancing dynamic temporal features of users and apps. Moreover, it can integrate global interactions into graphrepresentation learning by multi-perspective feature aggregations. 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