{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T15:14:08Z","timestamp":1783437248094,"version":"3.54.6"},"reference-count":50,"publisher":"Association for Computing Machinery (ACM)","issue":"1","funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["U22A20102"],"award-info":[{"award-number":["U22A20102"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2026,1,31]]},"abstract":"<jats:p>\n                    Multimodal recommendation provides richer and more accurate personalized recommendations by jointly modeling user\u2019s historical behaviors and different modality of items, such as text, image, audio, and video in online platforms. Most existing work of multimodal recommendation focuses on leveraging modal features and modal correlation graph structures to learn user preferences. Due to insufficient exploration of user collaborative preferences and the noise during high-order multimodal data connections, valuable information may be lost, leading to deviations in understanding user preferences. Therefore, an\n                    <jats:italic toggle=\"yes\">I<\/jats:italic>\n                    ndependent\n                    <jats:italic toggle=\"yes\">T<\/jats:italic>\n                    opological Preference-Aware and\n                    <jats:italic toggle=\"yes\">Co<\/jats:italic>\n                    operative\n                    <jats:italic toggle=\"yes\">H<\/jats:italic>\n                    ypergraph\n                    <jats:italic toggle=\"yes\">D<\/jats:italic>\n                    iffusion-based\n                    <jats:italic toggle=\"yes\">M<\/jats:italic>\n                    ultimodal\n                    <jats:italic toggle=\"yes\">Rec<\/jats:italic>\n                    ommender Model (ITCoHD-MRec) is necessary for online platforms. This article aims to develop an ITCoHD-MRec that incorporates topological perception as well as generative diffusion models in multimodal hypergraph recommendation to make the model more adaptive and robust in complex environments. Firstly, leveraging a Graph Convolutional Network (GCN), the model independently captures user preference representations for both collaborative relevance and modal relevance from the user-item interaction graph, which contains ID embeddings and modal features. This enables the extraction of deeper associations between users and items. Secondly, leveraging topological pruning techniques, the model learns differentiated features in different modal blocks to prevent node representations from becoming homogenized. This helps further identify user preferred connectivity patterns and removes redundant noisy connections. Finally, by employing the diffusion model, information regarding the higher-order interaction patterns between attributes and items within the hypergraph structure is propagated. This effectively captures the potential global dependencies between attributes and items, thereby providing deeper associations enriched with more substantial semantic information for subsequent recommendation tasks. The model autonomously learns different features and higher-order connectivity of nodes, which enables the model to obtain a wider and more accurate perception of user preferences in complex interaction environments. Experimental comparisons with 15 models on four real datasets\u2014Baby, Sports, Clothing, and Electronics show that the model improves the recall by 0.85%\u20133.57% and the normalized discounted cumulative gain by 2.31%\u20133.43%, which validates the effectiveness of ITCoHD-MRec.\n                  <\/jats:p>","DOI":"10.1145\/3767337","type":"journal-article","created":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T22:22:00Z","timestamp":1757629320000},"page":"1-29","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["ITCoHD-MRec: An Independent Topological Preference-Aware and Cooperative Hypergraph Diffusion-Based Multimodal Recommender Model"],"prefix":"10.1145","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5686-0278","authenticated-orcid":false,"given":"Xiulan","family":"Hao","sequence":"first","affiliation":[{"name":"School of Information Engineering, Huzhou University, Huzhou, China, Zhejiang-French Digital Monitoring Lab for Aquatic Resources and Environment, Huzhou University, Huzhou, China, and Zhejiang Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-8701-8574","authenticated-orcid":false,"given":"Xinwei","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Huzhou University, Huzhou, China   and Zhejiang-French Digital Monitoring Lab for Aquatic Resources and Environment, Huzhou University, Huzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8465-0996","authenticated-orcid":false,"given":"Hua","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5271-9215","authenticated-orcid":false,"given":"Zhonglong","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Zhejiang Normal University, Jinhua, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4500-5836","authenticated-orcid":false,"given":"Yunliang","family":"Jiang","sequence":"additional","affiliation":[{"name":"Zhejiang Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua, China, School of Computer Science and Technology, Zhejiang Normal University, Jinhua, China, and School of Information Engineering, Huzhou University, Huzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5094-5980","authenticated-orcid":false,"given":"Yanchun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Zhejiang Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua, China   and Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,11,14]]},"reference":[{"key":"e_1_3_2_2_2","volume-title":"Proceedings of the 11th International Conference on Learning Representations (ICLR \u201923)","author":"Cai Xuheng","year":"2023","unstructured":"Xuheng Cai, Chao Huang, Lianghao Xia, and Xubin Ren. 2023. 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