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Knowl. Discov. Data"],"published-print":{"date-parts":[[2024,4,30]]},"abstract":"<jats:p>\n                    Graph-based cross-domain recommendations (CDRs) are useful for suggesting appropriate items because of their promising ability to extract features from user\u2013item interactions and transfer knowledge across domains. Thus, the model can effectively alleviate cold start and data sparsity issues. Although the graph-based CDRs can capture valuable information, they still have some limitations. First, embeddings are highly vulnerable to noisy interactions, because the message aggregation in the graph convolutional network can further enlarge the impact. Second, because of the property of graph-structured data, the influence of high-degree nodes on representation learning is more than that of the long-tail items, and this can cause a poor recommendation performance. In this study, we devised a novel\n                    <jats:bold>A<\/jats:bold>\n                    daptive\n                    <jats:bold>A<\/jats:bold>\n                    dversarial\n                    <jats:bold>C<\/jats:bold>\n                    ontrastive\n                    <jats:bold>L<\/jats:bold>\n                    earning framework for graph-based\n                    <jats:bold>C<\/jats:bold>\n                    ross-\n                    <jats:bold>D<\/jats:bold>\n                    omain\n                    <jats:bold>R<\/jats:bold>\n                    ecommendation (\n                    <jats:bold>ACLCDR<\/jats:bold>\n                    ). The ACLCDR introduces reinforcement learning to generate adaptive augmented samples for contrastive learning tasks. Then, we leveraged a multitask training strategy to jointly optimize the model with auxiliary tasks. Finally, we verified the effectiveness of the ACLCDR through nine real-world cross-domain tasks adopted from Amazon and Douban. We observed that ACLCDR exceeded the best state-of-the-art baseline by 25%, 42.5%, 16.3%, and 23.8% in terms of\n                    <jats:italic>HR@<\/jats:italic>\n                    10 and\n                    <jats:italic>NDCG@10<\/jats:italic>\n                    for the Music &amp; Movie task from Amazon.\n                  <\/jats:p>","DOI":"10.1145\/3630259","type":"journal-article","created":{"date-parts":[[2023,11,2]],"date-time":"2023-11-02T18:23:25Z","timestamp":1698949405000},"page":"1-34","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":17,"title":["Adaptive Adversarial Contrastive Learning for Cross-Domain Recommendation"],"prefix":"10.1145","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-5421-5079","authenticated-orcid":false,"given":"Chi-Wei","family":"Hsu","sequence":"first","affiliation":[{"name":"Institute of Information Management, National Yang Ming Chiao Tung University, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3900-2639","authenticated-orcid":false,"given":"Chiao-Ting","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Computer Science, National Yang Ming Chiao Tung University, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4073-0652","authenticated-orcid":false,"given":"Szu-Hao","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Information Management and Finance, National Yang Ming Chiao Tung University, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,12,9]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE53745.2022.00211"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512192"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3358116"},{"key":"e_1_3_1_5_2","first-page":"1597","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Chen Ting","year":"2020","unstructured":"Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. 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