Multi-Tiered Cascading Negative Sampling For Graph Based Recommender System

Multi-Tiered Cascading Negative Sampling For Graph Based Recommender System | 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 Multi-Tiered Cascading Negative Sampling For Graph Based Recommender System Renhao Zhang, Guopeng He, Tieqiao Chen, Bingliang Hu, Xinyin Jia, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4849501/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 Effective negative sampling strategies can accelerate model convergence, suppress excessive randomness in negative sample generation, and enhance the predictive performance of the recommender systems modeled by implicit feedback. However, existing negative sampling methods face two potential issues: limited utilization of user-item collaborative information and treating user-negative sample interactions as driven by a singular motive, thereby ignoring the user’s multi-tiered, gradually progressive implicit preferences, which leads to low-quality negative sampling. To design negative sampling methods suited to different geometric spaces based on their data characteristics, this paper introduces a novel multi-tiered cascading negative sampling method (Multi-Tiered Cascading Negative Sampling, MTCNS) for Euclidean space graph-based collaborative filtering recommender systems. Specifically, this method processes through two cascading levels, producing high-quality overall negative sample embeddings and negative sample feature representations that embody multi-layered progressive relevance semantics. Furthermore, outside the negative sampling method, a multi-task learning framework constructs a contrastive learning auxiliary task to enhance the main task’s performance. Experiments conducted on three real datasets demonstrate that this method improves metrics such as NDCG@20 and Recall@20 by an average of over 1.5%. Recommender systems Collaborative filtering Graph neural networks and Negative sampling 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. 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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-4849501","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":346581751,"identity":"a5ff23a3-42ad-4282-955a-5cb52881fe45","order_by":0,"name":"Renhao 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