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Inf. Syst."],"published-print":{"date-parts":[[2025,7,31]]},"abstract":"<jats:p>\n            In the evolving landscape of recommender systems, the challenge of effectively conducting privacy-preserving Cross-domain Recommendation, especially under strict non-overlapping constraints, has emerged as a key focus. Despite extensive research has made significant progress, several limitations still exist: (1) Previous semantic-based methods fail to deeply exploit rich textual information, since they quantize the text into codes, losing its original rich semantics. (2) The current solution solely relies on the text-modality, while the synergistic effects with the ID-modality are ignored. (3) Existing studies do not consider the impact of irrelevant semantic features, leading to inaccurate semantic representation. To address these challenges, we introduce federated semantic learning and devise FFMSR as our solution. For Limitation 1, we locally learn items\u2019 semantic encodings from their original texts by a multi-layer semantic encoder and then cluster them on the server to facilitate the transfer of semantic knowledge between domains. To tackle Limitation 2, we integrate both ID and Text modalities on the clients, and utilize them to learn different aspects of items. To handle Limitation 3, a Fast Fourier Transform-based filter and a gating mechanism are developed to alleviate the impact of irrelevant semantic information in the local model. We conduct extensive experiments on two real-world datasets, and the results demonstrate the superiority of our FFMSR method over other SOTA methods. Our source codes are publicly available at\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/Sapphire-star\/FFMSR\">https:\/\/github.com\/Sapphire-star\/FFMSR<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3728359","type":"journal-article","created":{"date-parts":[[2025,4,8]],"date-time":"2025-04-08T22:26:27Z","timestamp":1744151187000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Federated Semantic Learning for Privacy-preserving Cross-domain Recommendation"],"prefix":"10.1145","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-5878-5715","authenticated-orcid":false,"given":"Ziang","family":"Lu","sequence":"first","affiliation":[{"name":"Shandong Normal University, Jinan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9408-7594","authenticated-orcid":false,"given":"Lei","family":"Guo","sequence":"additional","affiliation":[{"name":"Shandong Normal University, Jinan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4913-5734","authenticated-orcid":false,"given":"Xu","family":"Yu","sequence":"additional","affiliation":[{"name":"China University of Petroleum (East China), Qingdao, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1109-5028","authenticated-orcid":false,"given":"Zhiyong","family":"Cheng","sequence":"additional","affiliation":[{"name":"Hefei University of Technology, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5883-9218","authenticated-orcid":false,"given":"Xiaohui","family":"Han","sequence":"additional","affiliation":[{"name":"Qilu University of Technology (Shandong Academy of Sciences), Jinan, China and Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Jinan, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2993-7142","authenticated-orcid":false,"given":"Lei","family":"Zhu","sequence":"additional","affiliation":[{"name":"Tongji University, Shanghai, China"}]}],"member":"320","published-online":{"date-parts":[[2025,7,9]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"356","volume-title":"International Conference on Knowledge Science, Engineering and Management","author":"Ai Zhengyang","year":"2022","unstructured":"Zhengyang Ai, Guangjun Wu, Binbin Li, Yong Wang, and Chuantong Chen. 2022. 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