{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T11:22:56Z","timestamp":1785410576672,"version":"3.56.0"},"reference-count":57,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2023,8,10]],"date-time":"2023-08-10T00:00:00Z","timestamp":1691625600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2024,1,31]]},"abstract":"<jats:p>\n            Cross-domain recommender systems could potentially improve the recommendation performance by means of transferring abundant knowledge from the auxiliary domain to the target domain. They could help address some key challenges in recommender systems, such as data sparsity and cold start. However, most existing cross-domain recommendation approaches represent the user preferences based on a single kind of user\u2019s feature or behavior and fail to explore the hidden interaction effects of different kinds of features or behaviors. In this article, we propose the\n            <jats:bold>S<\/jats:bold>\n            equential and\n            <jats:bold>G<\/jats:bold>\n            raphical\n            <jats:bold>Cross<\/jats:bold>\n            -Domain Recommendations with a Multi-View Hierarchical Transfer Gate (SGCross) to transfer user representations from multiple perspectives. The SGCross model constructs a user profile by learning the personal preference from a personal view, the dynamic preference from a temporal view, as well as the collaborative preference from a collaborative view. Specifically, a Multi-view Hierarchical Gate (MHG) is designed to transfer the informative representations of user knowledge on different views from the auxiliary domain separately, aiming to enhance the user representations. Furthermore, a two-stage attentive fusion module is designed to integrate transferred information at two levels: the domain level and the view level. Extensive experiments on the Amazon dataset and the Douban dataset have demonstrated that SGCross effectively improves the accuracy of cross-domain recommendations and outperforms the state-of-the-art baseline models.\n          <\/jats:p>","DOI":"10.1145\/3604615","type":"journal-article","created":{"date-parts":[[2023,6,19]],"date-time":"2023-06-19T15:37:19Z","timestamp":1687189039000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Sequential and Graphical Cross-Domain Recommendations with a Multi-View Hierarchical Transfer Gate"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6789-8693","authenticated-orcid":false,"given":"Huiyuan","family":"Li","sequence":"first","affiliation":[{"name":"Renmin University of China, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8503-2535","authenticated-orcid":false,"given":"Li","family":"Yu","sequence":"additional","affiliation":[{"name":"Renmin University of China, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5418-6969","authenticated-orcid":false,"given":"Xi","family":"Niu","sequence":"additional","affiliation":[{"name":"University of North Carolina at Charlotte, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5371-2197","authenticated-orcid":false,"given":"Youfang","family":"Leng","sequence":"additional","affiliation":[{"name":"Renmin University of China, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8607-6246","authenticated-orcid":false,"given":"Qihan","family":"Du","sequence":"additional","affiliation":[{"name":"Renmin University of China, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,8,10]]},"reference":[{"key":"e_1_3_1_2_2","first-page":"2211","volume-title":"Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR\u201920)","author":"Bi Ye","year":"2020","unstructured":"Ye Bi, Liqiang Song, Mengqiu Yao, Zhenyu Wu, Jianming Wang, and Jing Xiao. 2020. 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