{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T13:04:45Z","timestamp":1767704685250,"version":"3.37.3"},"reference-count":47,"publisher":"Oxford University Press (OUP)","issue":"8","license":[{"start":{"date-parts":[[2024,4,6]],"date-time":"2024-04-06T00:00:00Z","timestamp":1712361600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"name":"Science and Technology Research Key Project of Chongqing Municipal Education Commission","award":["KJZD-K202301107"],"award-info":[{"award-number":["KJZD-K202301107"]}]},{"name":"Graduate Innovation Fund of Chongqing University of Technology","award":["gzlcx20223224"],"award-info":[{"award-number":["gzlcx20223224"]}]},{"name":"2021 National Education Examination Research Project","award":["GJK2021028"],"award-info":[{"award-number":["GJK2021028"]}]},{"name":"Regional Innovation Cooperation Project of Science and Technology Department of Sichuan Province","award":["2023YFQ0011"],"award-info":[{"award-number":["2023YFQ0011"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,8,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Most existing cross-domain recommendation (CDR) systems apply the embedding and mapping idea to tackle the cold-start user problem and, to this end, they learn a common bridge function to transfer the user preferences from the source domain into the target domain. However, sharing a bridge function for all users inevitably leads to biased recommendations. This paper proposes a novel method, named CDR to cold-start users via categorized preference transfer (CDRCPT), to overcome the shortcomings of existing approaches. First, the embeddings of users and items in both the source and target domain are learned through pretraining and we utilize preference encoder to obtain the preference embeddings of users in the source domain. Second, mini-batch clustering is applied in the source domain to group users according to their preferences; here, each cluster identifies a specific class of users, and each cluster is represented by its center. Finally, the general representation is fed into a meta network to learn a bridge function for each available class of users. Experiments on two real data sets show that our CDRCPT method is effective in improving the accuracy and robustness of recommendations.<\/jats:p>","DOI":"10.1093\/comjnl\/bxae029","type":"journal-article","created":{"date-parts":[[2024,4,7]],"date-time":"2024-04-07T14:02:15Z","timestamp":1712498535000},"page":"2610-2621","source":"Crossref","is-referenced-by-count":2,"title":["Cross-Domain Recommendation To Cold-Start Users Via Categorized Preference Transfer"],"prefix":"10.1093","volume":"67","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8619-0356","authenticated-orcid":false,"given":"Xiaoyang","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Computer Science , School of Computer Science and Engineering, , Chongqing City, Honghuang Road, No. 69, Chongqing 400054 , China"},{"name":"Chongqing University of Technology , School of Computer Science and Engineering, , Chongqing City, Honghuang Road, No. 69, Chongqing 400054 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1855-5712","authenticated-orcid":false,"given":"Xiaoyang","family":"Fu","sequence":"additional","affiliation":[{"name":"Department of Computer Science , School of Computer Science and Engineering, , Chongqing City, Honghuang Road, No. 69, Chongqing 400054 , China"},{"name":"Chongqing University of Technology , School of Computer Science and Engineering, , Chongqing City, Honghuang Road, No. 69, Chongqing 400054 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7421-216X","authenticated-orcid":false,"given":"Pasquale","family":"De Meo","sequence":"additional","affiliation":[{"name":"Department of Ancient and Modern Civilizations, University of Messina , V.le G. Palatucci, Messina 98166 , Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1528-7203","authenticated-orcid":false,"given":"Giacomo","family":"Fiumara","sequence":"additional","affiliation":[{"name":"MIFT Department, University of Messina , V.le F. Stagno D\u2019Alcontres, 31, Messina 98166 , Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2024,4,6]]},"reference":[{"key":"2024081612060892000_ref1","first-page":"173","volume-title":"Proc. 26th Int. Conf. World Wide Web","author":"He","year":"2017"},{"key":"2024081612060892000_ref2","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1109\/TNSE.2023.3295911","article-title":"Influence nodes identifying method via community-based backward generating network framework","volume":"11","author":"Liu","year":"2024","journal-title":"IEEE Trans. Netw. Sci. 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