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Knowl. Discov. Data"],"published-print":{"date-parts":[[2025,4,30]]},"abstract":"<jats:p>\n            Data sparsity poses a significant challenge for recommendation systems, prompting the research of Cross-Domain Recommendation (\n            <jats:italic>CDR<\/jats:italic>\n            ). CDR aims to leverage more user-item interaction information from source domains to improve the recommendation performance in the target domain. However, a major challenge in CDR is the identification of transferable features. Traditional CDR methods struggle to distinguish between the various features of users, including domain-invariant features that are effective for feature transfer and domain-specific features that are detrimental to cross-domain information transfer. In this article, we aim to disentangle domain-invariant features and domain-specific features and effectively utilize these different features. This enables effective domain-to-domain information transfer by only transferring domain-invariant features while still considering the role of domain-specific features within their respective domains. Based on the superiority of graph structural feature learning and disentangled represent learning, we propose\n            <jats:inline-formula content-type=\"math\/tex\">\n              <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(\\mathbf{DMGCDR}\\)<\/jats:tex-math>\n            <\/jats:inline-formula>\n            \u2014a model that learns\n            <jats:italic>D<\/jats:italic>\n            isentangled user feature representations and constructs a\n            <jats:italic>M<\/jats:italic>\n            ulti-\n            <jats:italic>G<\/jats:italic>\n            raph network for bidirectional knowledge transfer of shared features for\n            <jats:italic>CDR<\/jats:italic>\n            . Specifically, we designed two regularization terms to disentangle domain-invariant features and domain-specific features. Subsequently, we established a multi-graph convolutional network to enhance domain-specific features within single-domain graphs and transfer domain-invariant features across cross-domain graphs. Our approach also includes designing feature constraints to enhance the combination of features derived from different graphs and to uncover potential correlations among them. Extensive experiments on real-world datasets have demonstrated that our model significantly outperforms state-of-the-art CDR approaches.\n          <\/jats:p>","DOI":"10.1145\/3715151","type":"journal-article","created":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T15:38:07Z","timestamp":1737733087000},"page":"1-28","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Disentangled Multi-Graph Convolution for Cross-Domain Recommendation"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0003-2455-9274","authenticated-orcid":false,"given":"Yibo","family":"Gao","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0001-9452-606X","authenticated-orcid":false,"given":"Zhen","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0003-0363-2903","authenticated-orcid":false,"given":"Xinxin","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0009-0006-4700-5775","authenticated-orcid":false,"given":"Sibo","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0009-0001-7299-5647","authenticated-orcid":false,"given":"Yafan","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,2,22]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2005.99"},{"key":"e_1_3_2_3_2","unstructured":"Rianne van den Berg Thomas N. 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