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Appl."],"published-print":{"date-parts":[[2025,10,31]]},"abstract":"<jats:p>\n            The burgeoning presence of multimodal content-sharing platforms propels the development of personalized recommender systems. Previous works usually suffer from data sparsity and cold-start problems and may fail to adequately explore semantic user\u2013product associations from multimodal data. To address these issues, we propose a novel Multi-Modal Hypergraph Contrastive Learning (MMHCL) framework for user recommendation. For a comprehensive information exploration from user\u2013product relations, we construct two hypergraphs, i.e., a user-to-user (u2u) hypergraph and an item-to-item (i2i) hypergraph, to mine shared preferences among users and intricate multimodal semantic resemblance among items, respectively. This process yields denser second-order semantics that are fused with first-order user\u2013item interaction as complementary to alleviate the data sparsity issue. Then, we design a contrastive feature enhancement paradigm by applying synergistic contrastive learning. By maximizing\/minimizing the mutual information between second-order (e.g., shared preference pattern for users) and first-order (information of selected items for users) embeddings of the same\/different users and items, the feature distinguishability can be effectively enhanced. Compared with using sparse primary user\u2013item interaction only, our MMHCL obtains denser second-order hypergraphs and excavates more abundant shared attributes to explore the user\u2013product associations, which to a certain extent alleviates the problems of data sparsity and cold-start. Extensive experiments have comprehensively demonstrated the effectiveness of our method. Our code is publicly available at\n            <jats:ext-link xmlns:xlink=\"https:\/\/2.zoppoz.workers.dev:443\/http\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/2.zoppoz.workers.dev:443\/https\/github.com\/Xu107\/MMHCL\">https:\/\/2.zoppoz.workers.dev:443\/https\/github.com\/Xu107\/MMHCL<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3762665","type":"journal-article","created":{"date-parts":[[2025,8,25]],"date-time":"2025-08-25T15:01:35Z","timestamp":1756134095000},"page":"1-23","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["MMHCL: Multi-Modal Hypergraph Contrastive Learning for Recommendation"],"prefix":"10.1145","volume":"21","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0009-0006-8407-7188","authenticated-orcid":false,"given":"Xu","family":"Guo","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Beijing Normal University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0001-6212-4891","authenticated-orcid":false,"given":"Tong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0009-0003-7843-5215","authenticated-orcid":false,"given":"Fuyun","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0009-0002-2278-5251","authenticated-orcid":false,"given":"Xudong","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0003-4136-7880","authenticated-orcid":false,"given":"Xiaoya","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0009-0005-1270-3920","authenticated-orcid":false,"given":"Xin","family":"Liu","sequence":"additional","affiliation":[{"name":"SeetaCloud (Nanjing) Technology Co., Ltd., Nanjing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-0543-4196","authenticated-orcid":false,"given":"Zhen","family":"Cui","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Beijing Normal University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,10,14]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"15535","article-title":"Learning representations by maximizing mutual information across views","volume":"32","author":"Bachman Philip","year":"2019","unstructured":"Philip Bachman, R. 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