{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T02:38:25Z","timestamp":1776998305617,"version":"3.51.4"},"reference-count":47,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2026,4,19]],"date-time":"2026-04-19T00:00:00Z","timestamp":1776556800000},"content-version":"vor","delay-in-days":18,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Concurrency and Computation"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>In the context of the highly abundant information landscape of modern society, recommender systems are designed to provide personalized information by analyzing user behavior data to uncover latent user preferences. Recent research has demonstrated that recommendation approaches integrating knowledge graphs with attention mechanisms can significantly address prevalent challenges in traditional recommender systems, such as sparse data problems and the cold\u2010start problem. Furthermore, with the widespread presence of multi\u2010modal content in real\u2010world applications, incorporating multi\u2010modal item information into knowledge graphs has garnered increasing attention, as it enables a deeper insight into user preferences. Despite these advances, conventional techniques largely focus on modeling users' single\u2010dimensional preferences, overlooking the complex and dynamic nature of user interests. Additionally, the heterogeneity of multi\u2010modal features is often insufficiently addressed. To tackle these issues, this study proposes a Multi\u2010modal Knowledge Graph Hybrid Attention Network (MTKGHAT) for recommendation, aiming to improve both accuracy and robustness. The model first constructs a multi\u2010modal knowledge graph by converting initial data into knowledge triples. Based on this foundation, the CLIP model is employed to capture the textual and visual representations of items and map them into a unified embedding space, thereby reducing discrepancies across modalities. Concerning user modeling, a hybrid attention fusion framework is designed, which integrates time\u2010sensitive attention to capture users' short\u2010term dynamic preferences and multi\u2010head graph attention to learn their multi\u2010dimensional structural preferences. These two types of preferences are adaptively integrated via a parameterized weighted fusion mechanism that balances their contributions. Experimental findings indicate that the model outperforms existing baseline methods on multiple evaluation metrics, demonstrating robust recommendation effectiveness and accuracy.<\/jats:p>","DOI":"10.1002\/cpe.70674","type":"journal-article","created":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T04:52:12Z","timestamp":1776660732000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi\u2010Modal Knowledge Graph Hybrid Attention Network for Recommendation"],"prefix":"10.1002","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-3344-4471","authenticated-orcid":false,"given":"Mingzhu","family":"Huang","sequence":"first","affiliation":[{"name":"Key Laboratory of Computing Power Network and Information Security, Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences)  Jinan China"},{"name":"Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science  Jinan China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhen","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Computing Power Network and Information Security, Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences)  Jinan China"},{"name":"Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science  Jinan China"},{"name":"Shu Ju (Shandong) Intelligent Technology Co. Ltd.  Jinan China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heng","family":"Qian","sequence":"additional","affiliation":[{"name":"Key Laboratory of Computing Power Network and Information Security, Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences)  Jinan China"},{"name":"Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science  Jinan China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenbo","family":"Li","sequence":"additional","affiliation":[{"name":"Inspur Yunzhou Industrial Internet Co. Ltd.  Jinan China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiuyue","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Computing Power Network and Information Security, Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences)  Jinan China"},{"name":"Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science  Jinan China"},{"name":"Shandong Standard Institute of Emerging Technologies and Innovations Co. Ltd.  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