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Inf. Syst."],"published-print":{"date-parts":[[2025,3,31]]},"abstract":"<jats:p>\n            Recently, the dual-target cross-domain recommendation has been an emerging research problem, which aims to improve the performances of both source and target domains by transferring the preferences of overlapping users. Most of the existing work adopted a coarse-grained manner to detach general users\u2019 preferences and associate them with domain-specific information for enhancing user representation learning, which fails to depict the differences in users\u2019 diverse preferences and aggregate relevant preferences with improper propagation. To this end, in this article, we propose a multi-factor user representation pre-training framework, dubbed MF-GSLAE, with a focus on fine-grained preference learning and transferring. Specifically, we first propose a fine-grained factor representation pre-training paradigm. It projects the behavior records of both domains into several subspaces and introduces a compactness regularization to generate multiple fine-grained preference factors. Furthermore, we propose a multi-factor graph structure learning method within linear complexity to efficiently construct preference connections on different scales of users, which could aggregate the intrinsic relationship of user preferences in immediate embedding spaces to capture high-order information. Following the pre-training, we subsequently design a factor selection module with the bootstrapping mechanism to adaptively choose the corresponding domain-related preferences and transfer domain-shared information through partial overlapping factors for addressing the negative transfer problem. Finally, the optimization objectives of both domains are formalized in a multi-task learning framework and derive the learned user representation in an end-to-end training manner. Extensive experimental results on several publicly available datasets have not only demonstrated the effectiveness of the learned user representations with the comparison of state-of-the-art baselines but also indicated the interpretability and robustness. The code of our work is publicly available at\n            <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/USTC-StarTeam\/MF-GSLAE\">https:\/\/github.com\/USTC-StarTeam\/MF-GSLAE<\/jats:ext-link>\n            .\n          <\/jats:p>","DOI":"10.1145\/3690382","type":"journal-article","created":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T10:06:11Z","timestamp":1729764371000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["MF-GSLAE: A Multi-Factor User Representation Pre-Training Framework for Dual-Target Cross-Domain Recommendation"],"prefix":"10.1145","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9921-2078","authenticated-orcid":false,"given":"Hao","family":"Wang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-0853-1089","authenticated-orcid":false,"given":"Mingjia","family":"Yin","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-5833-5999","authenticated-orcid":false,"given":"Luankang","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8103-0321","authenticated-orcid":false,"given":"Sirui","family":"Zhao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4835-4102","authenticated-orcid":false,"given":"Enhong","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China, Hefei, China"}]}],"member":"320","published-online":{"date-parts":[[2025,1,17]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531967"},{"key":"e_1_3_2_3_2","first-page":"2209","volume-title":"Proceedings of the 2022 IEEE 38th International Conference on Data Engineering (ICDE \u201922)","author":"Cao Jiangxia","year":"2022","unstructured":"Jiangxia Cao, Jiawei Sheng, Xin Cong, Tingwen Liu, and Bin Wang. 2022. Cross-domain recommendation to cold-start users via variational information bottleneck. In Proceedings of the 2022 IEEE 38th International Conference on Data Engineering (ICDE \u201922). IEEE, 2209\u20132223."},{"issue":"114","key":"e_1_3_2_4_2","first-page":"1","article-title":"ReduNet: A white-box deep network from the principle of maximizing rate reduction","volume":"23","author":"Chan K.","year":"2022","unstructured":"K. Chan, Yaodong Yu, Chong You, Haozhi Qi, John Wright, and Yi Ma. 2022. ReduNet: A white-box deep network from the principle of maximizing rate reduction. Journal of Machine Learning Research 23, 114 (2022), 1\u2013103.","journal-title":"Journal of Machine Learning Research"},{"issue":"1","key":"e_1_3_2_5_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3522762","article-title":"Toward equivalent transformation of user preferences in cross domain recommendation","volume":"41","author":"Chen Xu","year":"2023","unstructured":"Xu Chen, Ya Zhang, Ivor W. 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