Accurate POI Recommendation for Random Groups With Improved Graph Neural Networks and Multi-negotiation Model

Accurate POI Recommendation for Random Groups With Improved Graph Neural Networks and Multi-negotiation Model | 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 Accurate POI Recommendation for Random Groups With Improved Graph Neural Networks and Multi-negotiation Model Xiaoyu Song, Zhizhong Liu, Lingqiang Meng, Dianhui Chu, Jian Yu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5297395/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Mar, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract In recent years, the growing prevalence of group activities has increased interest in Point of Interest (POI) recommendations for groups. While progress has been made in recommending POIs for fixed groups, research on personality-aware recommendations for random groups is relatively scarce. Moreover, existing works always recommend a POI list for the group, and let the group to make further choice to determine the optimal POI, which results in poor experience. To solve the above problems, this work proposes a model for Accurate POI Recommendation for Random Group with improved Graph Neural Networks and Multi-negotiation Model (termed as APRRGM). Specifically, APRRGM first produces the fitted feature of the random group based on members' personalities and members' POI interaction data. Then, APRRGM learns POIs' features from the bipartite graph of user and POI with an improved Graph Neural Networks (GNN) while considering members' personalities. Next, APRRGM recommends a POI sequence based on the fitted feature of the random group and the features of POIs. Finally, based on the recommended POI list and members' personalities, APRRGM determines the optimal POI for the random group with an improved multi-negotiation model. Extensive experiments has been conducted on three public benchmark datasets (Yelp, Gowalla and Foursquare), and experimental results have proved that APRRGM has better performance than that of other baseline models. Physical sciences/Mathematics and computing Physical sciences/Mathematics and computing/Computational science Physical sciences/Mathematics and computing/Computer science Physical sciences/Mathematics and computing/Information technology Physical sciences/Mathematics and computing/Scientific data Biological sciences/Computational biology and bioinformatics/Computational models Biological sciences/Computational biology and bioinformatics/Computational neuroscience Biological sciences/Computational biology and bioinformatics/Data processing Biological sciences/Computational biology and bioinformatics/Machine learning Point-of-interest Recommendation Random Group Graph Neural Network Multi-agent system Multi-negotiation Model User’s personality. Full Text Additional Declarations No competing interests reported. Supplementary Files 1112SREP.rar Cite Share Download PDF Status: Published Journal Publication published 04 Mar, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 10 Dec, 2024 Reviews received at journal 03 Dec, 2024 Reviews received at journal 01 Dec, 2024 Reviewers agreed at journal 18 Nov, 2024 Reviewers agreed at journal 17 Nov, 2024 Reviewers invited by journal 17 Nov, 2024 Editor assigned by journal 17 Nov, 2024 Editor invited by journal 14 Nov, 2024 Submission checks completed at journal 13 Nov, 2024 First submitted to journal 20 Oct, 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-5297395","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":379355731,"identity":"7adfec89-6618-4486-badf-a924d8acc733","order_by":0,"name":"Xiaoyu Song","email":"","orcid":"","institution":"Yantai University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyu","middleName":"","lastName":"Song","suffix":""},{"id":379355732,"identity":"04f4b436-a3bf-4be4-8739-1f3e7ef0d9d0","order_by":1,"name":"Zhizhong Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIie3OMQrCMBSA4RcfxCXaNS56hYDgorRXaSno4iC4CC6VQl10F+wl3ByVrD2AoIMgODkILrqZWDrWdhTMT0hIyEcCYDL9YpwEqNc6AjmnR25JQhFQpLcLCUBK1OClSGsdho/R5GRHVdaZ1p4SrOpQwGubT0i8D9qr5OpHyDpH5kpoLG6CLJN8gtwL/FokfZoRcRgKJFE+oYrIjIw1cYoI494sVMTWBD+v8ALCuRciS6RLkY4bcX/AeHId7ZdfSGs1uDzYRDqWJTf3W6/btOb+5vz6QrK8QE0Vpn6qd7tiAODoiTzLXDWZTKa/6w0eBUdPsyJeEwAAAABJRU5ErkJggg==","orcid":"","institution":"Yantai University","correspondingAuthor":true,"prefix":"","firstName":"Zhizhong","middleName":"","lastName":"Liu","suffix":""},{"id":379355734,"identity":"3eac12b4-09cc-4e7d-bfd8-61546f521968","order_by":2,"name":"Lingqiang Meng","email":"","orcid":"","institution":"Yantai University","correspondingAuthor":false,"prefix":"","firstName":"Lingqiang","middleName":"","lastName":"Meng","suffix":""},{"id":379355736,"identity":"cad880c8-8d94-47d2-81b7-51079a26a9b8","order_by":3,"name":"Dianhui Chu","email":"","orcid":"","institution":"Harbin Institute of Technology","correspondingAuthor":false,"prefix":"","firstName":"Dianhui","middleName":"","lastName":"Chu","suffix":""},{"id":379355738,"identity":"7617585f-349b-43d4-a89b-db71db9a36a6","order_by":4,"name":"Jian Yu","email":"","orcid":"","institution":"Auckland University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Yu","suffix":""},{"id":379355740,"identity":"84e7c5f5-f58f-46a6-941c-8c81694e85ae","order_by":5,"name":"Quan Z. 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