AMIRLe: attribute-enhanced multi-interaction representation learning fore-commerce heterogeneous information networks

AMIRLe: attribute-enhanced multi-interaction representation learning fore-commerce heterogeneous information networks | 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 AMIRLe: attribute-enhanced multi-interaction representation learning fore-commerce heterogeneous information networks Lianhong Ding, Mengxiao Li, Yi Wang, Peng Shi, Fangfeng Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5393781/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Feb, 2026 Read the published version in International Journal of Machine Learning and Cybernetics → Version 1 posted 11 You are reading this latest preprint version Abstract Heterogeneous graph representation learning has attracted considerable interest for its effectiveness in capturing the structural information of nodes within complex and diverse networks. However, existing heterogeneous graph representation learning models primarily focus on structural embeddings, overlooking the importance of node attributes and temporal information in understanding complex interaction networks, particularly in e-commerce heterogeneous networks. To address this issue, this paper proposes an Attribute-enhanced Multi-Interaction Representation Learning method in e-commerce heterogeneous information networks, namely AMIRLe. Specifically, AMIRLe first processes high-dimensional data and identifies hidden patterns via tensor decomposition techniques to construct multi-interaction heterogeneous networks. Then, a novel time-aware attribute embedding method is proposed, which employs an attention mechanism to enhance key node aggregation after integrating interaction time information as auxiliary attributes with node attributes. Finally, a graph neural network (GNN) method is designed to integrate meta-path-based structure embeddings with attribute embeddings to produce the final embeddings. Link prediction experiments on three real-world e-commerce datasets demonstrate that performance of AMIRLe superior to state-of-the-art methods. Representation learning Graph neural networks e-commerce heterogeneous graph networks link prediction Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 28 Feb, 2026 Read the published version in International Journal of Machine Learning and Cybernetics → Version 1 posted Editorial decision: Revision requested 16 Jul, 2025 Reviews received at journal 02 Jul, 2025 Reviewers agreed at journal 13 Jun, 2025 Reviewers agreed at journal 08 Jun, 2025 Reviewers agreed at journal 29 May, 2025 Reviews received at journal 11 Feb, 2025 Reviewers agreed at journal 08 Feb, 2025 Reviewers invited by journal 16 Nov, 2024 Editor assigned by journal 07 Nov, 2024 Submission checks completed at journal 07 Nov, 2024 First submitted to journal 05 Nov, 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-5393781","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":378916764,"identity":"d29a1701-f36d-4826-94a0-21d449279997","order_by":0,"name":"Lianhong Ding","email":"","orcid":"","institution":"Information School, Beijing Wuzi University","correspondingAuthor":false,"prefix":"","firstName":"Lianhong","middleName":"","lastName":"Ding","suffix":""},{"id":378916765,"identity":"18f9e735-dd34-47cd-9d59-47d1f701b335","order_by":1,"name":"Mengxiao Li","email":"","orcid":"","institution":"Information School, Beijing Wuzi 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