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Embedding and mapping, as a classic cross-domain research genre, aims to identify a common mapping function to perform representation transformation between two domains via exploiting the supervision signals of overlapping users. Nevertheless, coarse-grained preference representations, non-personalized mapping functions, and excessive reliance on overlapping users limit their performance, especially in scenarios where overlapping users are sparse. To address the aforementioned challenges, we propose a novel\n            <jats:italic toggle=\"yes\">\n              <jats:bold>C<\/jats:bold>\n            <\/jats:italic>\n            ross-domain transfer of\n            <jats:bold>V<\/jats:bold>\n            alence\n            <jats:bold>P<\/jats:bold>\n            references via a\n            <jats:bold>M<\/jats:bold>\n            eta-optimization approach, namely\n            <jats:italic toggle=\"yes\">\n              <jats:bold>CVPM<\/jats:bold>\n            <\/jats:italic>\n            .\n            <jats:italic toggle=\"yes\">CVPM<\/jats:italic>\n            formalizes cross-domain interest transfer as a hybrid architecture of parametric meta-learning and self-supervised learning, which not only enables a more nuanced transfer of user preferences but also enhances signal quality by incorporating insights from non-overlapping users. Specifically, drawing on in-depth knowledge into user preferences and valence preference theory, we believe that there exists a significant difference between users\u2019 positive preferences and negative behaviors, and thus employ differentiated encoders to learn their distributions. In particular, we further utilize the pre-trained model and item popularity to sample pseudo-interaction items to ensure the integrity of both distributions. To guarantee the personalized preference transfer, we treat each user\u2019s mapping as two parts, the common transformation and the personalized bias, where the network generating the personalized bias is produced by a meta-learner. Furthermore, beyond the supervised loss for overlapping users, we design contrastive tasks for non-overlapping users from both group and individual levels to avoid model skew and enhance the semantic richness of representations. We construct six cross-domain tasks and one cross-system task from 10 data sets assessing model performance under both cold-start and warm-start scenarios. Exhaustive data analysis and extensive experimental results demonstrate the effectiveness and advancement of our proposed framework.\n          <\/jats:p>","DOI":"10.1145\/3749284","type":"journal-article","created":{"date-parts":[[2025,7,17]],"date-time":"2025-07-17T11:21:18Z","timestamp":1752751278000},"page":"1-29","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Cross-domain Transfer of Valence Preferences via a Meta-optimization Approach"],"prefix":"10.1145","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6220-0540","authenticated-orcid":false,"given":"Chuang","family":"Zhao","sequence":"first","affiliation":[{"name":"Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology","place":["Hong Kong, Hong Kong"]},{"name":"College of Management and Economics, Tianjin University","place":["Hong Kong, Hong Kong"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3099-4803","authenticated-orcid":false,"given":"Hongke","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Management and Economics, Tianjin University","place":["Tianjin, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1870-7472","authenticated-orcid":false,"given":"Ming","family":"He","sequence":"additional","affiliation":[{"name":"AI Lab at Lenovo Research","place":["Beijing, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1105-8083","authenticated-orcid":false,"given":"Xiaomeng","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology","place":["Hong Kong, Hong Kong"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2290-1785","authenticated-orcid":false,"given":"Jianping","family":"Fan","sequence":"additional","affiliation":[{"name":"AI Lab at Lenovo Research","place":["Beijing, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,8,22]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/S0306-4573(02)00021-3"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1214\/10-AOS799"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE53745.2022.00211"},{"key":"e_1_3_2_5_2","first-page":"9912","article-title":"Unsupervised learning of visual features by contrasting cluster assignments","volume":"33","author":"Caron Mathilde","year":"2020","unstructured":"Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin. 2020. 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