Structure-Activated and Interest-Aware Multimodal Recommendation Method

Structure-Activated and Interest-Aware Multimodal Recommendation Method | 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 Structure-Activated and Interest-Aware Multimodal Recommendation Method HaoYu Wang, HongBin Xia, XiaoFeng Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7717674/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Dec, 2025 Read the published version in Journal of Intelligent Information Systems → Version 1 posted 9 You are reading this latest preprint version Abstract Multimodal recommendation systems improve recommendation performance by leveraging information from different modalities and complex relational networks. However, existing methods still face two main challenges: first, randomly initialized ID embeddings lack semantic and structural priors, making them vulnerable to optimization bias during training, which results in unstable representations and weakens their effectiveness as anchors for multimodal fusion. Second, GCNs typically adopt uniform aggregation for higher-order interactions, which oversmooths node representations and causes semantic degradation. To address these issues, the article proposes a novel multimodal recommendation method based on Structural Activation and Interest-Aware (SAIA). By introducing a perturbation mechanism to enhance the differentiation of ID embeddings, and incorporating gated fusion of modality features with dynamic graph convolution strategies, SAIA effectively solves the issues of homogeneous representations and the identification of node importance. Additionally, we utilize a semantic disentangling mechanism to achieve personalized modality fusion, and employ cross-space contrastive learning to further enhance the semantic compatibility and structural consistency of the fused modality representations. Experimental results show that SAIA outperforms existing methods on four benchmark datasets. Multimodal Recommendation Graph Neural Networks Semantic Disentangling Self-supervised Learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 29 Dec, 2025 Read the published version in Journal of Intelligent Information Systems → Version 1 posted Editorial decision: Revision requested 05 Nov, 2025 Reviews received at journal 05 Nov, 2025 Reviewers agreed at journal 10 Oct, 2025 Reviews received at journal 09 Oct, 2025 Reviewers agreed at journal 08 Oct, 2025 Reviewers invited by journal 05 Oct, 2025 Editor assigned by journal 02 Oct, 2025 Submission checks completed at journal 02 Oct, 2025 First submitted to journal 26 Sep, 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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