CPGMA: Spatiotemporal Heterogeneous Graph Convolution with Multi-scale Attention for Sequential POI Recommendation​

CPGMA: Spatiotemporal Heterogeneous Graph Convolution with Multi-scale Attention for Sequential 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 CPGMA: Spatiotemporal Heterogeneous Graph Convolution with Multi-scale Attention for Sequential POI Recommendation​ Chi Ronghua This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6767569/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 With the widespread adoption of mobile social networks, sequential Point-of-Interest (POI) recommendation has become a key technology for enhancing user experience and generating commercial value. This paper proposes the CPGMA model, which integrates Graph Convolutional Networks (GCN) with Multi-Scale Linear Attention, to effectively capture complex spatiotemporal correlations in user check-in behaviors by constructing a spatiotemporal heterogeneous graph. The model innovatively combines GCN with a multi-scale attention mechanism to enable joint modeling of users' long-term and short-term preferences. Experimental results demonstrate that CPGMA significantly outperforms existing state-of-the-art methods on multiple public datasets, especially in sequential POI recommendation scenarios, where it shows clear advantages. POI Graph Convolutional Network (GCN) Multi-Scale Linear Attention Spatiotemporal Heterogeneous Graph 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. 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This paper proposes the CPGMA model, which integrates Graph Convolutional Networks (GCN) with Multi-Scale Linear Attention, to effectively capture complex spatiotemporal correlations in user check-in behaviors by constructing a spatiotemporal heterogeneous graph. The model innovatively combines GCN with a multi-scale attention mechanism to enable joint modeling of users' long-term and short-term preferences. 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