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Inf. Syst."],"published-print":{"date-parts":[[2024,7,31]]},"abstract":"<jats:p>Tackling the pervasive issue of data sparsity in recommender systems, we present an insightful investigation into the burgeoning area of non-overlapping cross-domain recommendation, a technique that facilitates the transfer of interaction knowledge across domains without necessitating inter-domain user\/item correspondence. Existing approaches have predominantly depended on auxiliary information, such as user reviews and item tags, to establish inter-domain connectivity, but these resources may become inaccessible due to privacy and commercial constraints.<\/jats:p>\n          <jats:p>To address these limitations, our study introduces an in-depth exploration of Target-unknown Cross-domain Recommendation (CDR), which contends with the distinct challenge of lacking target domain information during the training phase in the source domain. We illustrate two critical obstacles inherent to Target-unknown CDR: the lack of an inter-domain bridge due to insufficient user\/item correspondence or side information and the potential pitfalls of source-domain training biases when confronting distribution shifts across domains. To surmount these obstacles, we propose the CMCDR framework, a novel approach that leverages causal mechanisms extracted from meta-user\/item representations. The CMCDR framework employs a vector-quantized encoder\u2013decoder architecture, enabling the disentanglement of user\/item characteristics. We posit that domain-transferable knowledge is more readily discernible from user\/item characteristics, i.e., the meta-representations, rather than raw users and items. Capitalizing on these meta-representations, our CMCDR framework adeptly incorporates an attention-driven predictor that approximates the front-door adjustment method grounded in causal theory. This cutting-edge strategy effectively mitigates source-domain training biases and enhances generalization capabilities against distribution shifts. Extensive experiments demonstrate the empirical effectiveness and the rationality of CMCDR for target-unknown cross-domain recommendation.<\/jats:p>","DOI":"10.1145\/3643807","type":"journal-article","created":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T11:55:22Z","timestamp":1706788522000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Transferring Causal Mechanism over Meta-representations for Target-Unknown Cross-domain Recommendation"],"prefix":"10.1145","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0030-8289","authenticated-orcid":false,"given":"Shengyu","family":"Zhang","sequence":"first","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8538-0342","authenticated-orcid":false,"given":"Qiaowei","family":"Miao","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4260-879X","authenticated-orcid":false,"given":"Ping","family":"Nie","sequence":"additional","affiliation":[{"name":"Peking University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3482-234X","authenticated-orcid":false,"given":"Mengze","family":"Li","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9863-556X","authenticated-orcid":false,"given":"Zhengyu","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5828-9842","authenticated-orcid":false,"given":"Fuli","family":"Feng","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7024-9790","authenticated-orcid":false,"given":"Kun","family":"Kuang","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2139-8807","authenticated-orcid":false,"given":"Fei","family":"Wu","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,3,22]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"726","volume-title":"RecSys 2020: 14th ACM Conference on Recommender Systems, Virtual Event, Brazil, September 22\u201326, 2020","author":"Abdollahpouri Himan","year":"2020","unstructured":"Himan Abdollahpouri, Masoud Mansoury, Robin Burke, and Bamshad Mobasher. 2020. 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