{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T16:01:28Z","timestamp":1783008088099,"version":"3.54.5"},"reference-count":5,"publisher":"SAGE Publications","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,5,4]]},"abstract":"<jats:p>Cross-domain recommendation aims to alleviate the target domain\u2019s data sparsity problem by leveraging source domain knowledge. Existing GCN-based approaches perform graph convolution operations in each domain separately. However, the direct effect of item feature and topological structure information in the source domain are neglected for user preference modeling in the target domain. In this paper, we propose a novel Dual Attentive Graph Convolutional Network for Cross-Domain Recommendation (DAG4CDR). Specifically, we integrate the source and target domain\u2019s interaction data to construct a unified user-item bipartite graph and then perform GCN propagation on the graph to learn user and item embeddings. Over the unified graph, the interaction data from both domains can be leveraged to learn user and item embeddings via information propagation. In the embedding aggregation phase, the messages passed from different items of two domains to users are weighted by a designed dual attention mechanism, which considers the contributions of different items from both node- and domain-level. We conducted extensive experiments to validate the effectiveness of our method on several publicly available datasets, and the results demonstrate the superiority of our model on preference modeling for both common and non-common users.<\/jats:p>","DOI":"10.3233\/jifs-222411","type":"journal-article","created":{"date-parts":[[2023,2,10]],"date-time":"2023-02-10T11:42:05Z","timestamp":1676029325000},"page":"7367-7378","source":"Crossref","is-referenced-by-count":3,"title":["Dual attentive graph convolutional networks for cross-domain recommendation"],"prefix":"10.1177","volume":"44","author":[{"given":"Yu","family":"Zhang","sequence":"first","affiliation":[{"name":"Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fan","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computing, National University of Singapore, Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yupeng","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Software, Shandong University, Jinan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoli","family":"Li","sequence":"additional","affiliation":[{"name":"Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangjun","family":"Dong","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiyong","family":"Cheng","sequence":"additional","affiliation":[{"name":"Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-222411_ref8","first-page":"2354","article-title":"Nais: Neural attentive item similarity model for recommendation","volume":"30","author":"Xiangnan He","year":"2018","journal-title":"TKDE"},{"key":"10.3233\/JIFS-222411_ref9","unstructured":"Andriy Mnih and Russ Salakhutdinov, R. , Probabilistic matrix factorization, NIPS 20 (2007)."},{"issue":"4","key":"10.3233\/JIFS-222411_ref13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3490477","article-title":"Feature-level attentive icf for recommendation","volume":"40","author":"Zhiyong Cheng","year":"2022","journal-title":"ACM Transactions on Information Systems (TOIS)"},{"key":"10.3233\/JIFS-222411_ref20","doi-asserted-by":"crossref","unstructured":"Babak Loni , Yue Shi , Martha Larson and Alan Hanjalic , Cross-domain collaborative filtering with factorization machines, In ECIR, pages 656\u2013661. Springer, 2014.","DOI":"10.1007\/978-3-319-06028-6_72"},{"issue":"8","key":"10.3233\/JIFS-222411_ref23","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1109\/MC.2009.263","article-title":"Matrix factorization techniques for recommender systems","volume":"42","author":"Yehuda Koren","year":"2009","journal-title":"Computer"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-222411","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:44:45Z","timestamp":1777455885000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-222411"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,4]]},"references-count":5,"journal-issue":{"issue":"5"},"URL":"https:\/\/doi.org\/10.3233\/jifs-222411","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,4]]}}}