{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T16:18:47Z","timestamp":1781367527718,"version":"3.54.1"},"reference-count":75,"publisher":"Association for Computing Machinery (ACM)","issue":"3","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["U2336202, 62402491, and 62502404"],"award-info":[{"award-number":["U2336202, 62402491, and 62502404"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"crossref","award":["2025M771524"],"award-info":[{"award-number":["2025M771524"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Hong Kong Research Grants Council","award":["R1015-23, C1043-24GF, 11218325"],"award-info":[{"award-number":["R1015-23, C1043-24GF, 11218325"]}]},{"name":"Institute of Digital Medicine of City University of Hong Kong","award":["9229503"],"award-info":[{"award-number":["9229503"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2026,3,31]]},"abstract":"<jats:p>\n                    Cross-Domain Recommendation (CDR) aims to alleviate data sparsity in the target domain by incorporating knowledge from external domains. Existing approaches typically rely on overlapping users between the source and target domains as a bridge for knowledge transfer. However, in practice, user information across domains is often unavailable due to privacy protection, platform isolation, and data sharing restrictions, rendering most methods ineffective. In this article, we propose the D2TCDR, a two-stage generative CDR framework to address this critical limitation. By modeling the domain-level distribution that captures user preferences shared across domains, we extract transferable knowledge and guide its transfer through a generative process, reducing reliance on overlapping users and alleviating data sparsity in the target domain. D2TCDR first proposes a domain disentanglement module to extract the domain-invariant representations, capturing shared preferences across domains by eliminating domain-specific interference. Subsequently, a guided diffusion model is designed to model the domain-level distribution of these domain-invariant representations. By injecting target-domain signals into the guided diffusion model, we further steer the learned distribution toward the target domain, achieving knowledge transfer without relying on overlapping users. Extensive experiments on multiple cross-domain datasets show the superior performance of D2TCDR, validating its recommendation capabilities in complex transfer scenarios. Code is available at:\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/Red-Week\/D2TCDR\">https:\/\/github.com\/Red-Week\/D2TCDR<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1145\/3795793","type":"journal-article","created":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T13:56:35Z","timestamp":1770299795000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["D2TCDR: Disentangled Diffusion-Based Transfer for Cross-Domain Recommendation"],"prefix":"10.1145","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-7454-8953","authenticated-orcid":false,"given":"Hong","family":"Zhou","sequence":"first","affiliation":[{"name":"Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China and School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-6645-0597","authenticated-orcid":false,"given":"Xixun","family":"Lin","sequence":"additional","affiliation":[{"name":"Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China, and School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3534-1094","authenticated-orcid":false,"given":"Yanan","family":"Cao","sequence":"additional","affiliation":[{"name":"Institute of Information Engineering, Chinese Academy of Sciences, School of Cyber Security, Beijing, China and University of Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1755-4267","authenticated-orcid":false,"given":"Shichao","family":"Zhu","sequence":"additional","affiliation":[{"name":"Bytedance, Culver City, California, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3752-3492","authenticated-orcid":false,"given":"Renqi","family":"Jia","sequence":"additional","affiliation":[{"name":"Department of Computer Science, City University of Hong Kong, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2926-4416","authenticated-orcid":false,"given":"Xiangyu","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Data Science, City University of Hong Kong, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4493-6663","authenticated-orcid":false,"given":"Guandong","family":"Xu","sequence":"additional","affiliation":[{"name":"The Education University of Hong Kong, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2529-7643","authenticated-orcid":false,"given":"Li","family":"Guo","sequence":"additional","affiliation":[{"name":"Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,3,9]]},"reference":[{"key":"e_1_3_2_2_2","unstructured":"Juan Miguel Lopez Alcaraz and Nils Strodthoff. 2022. Diffusion-based time series imputation and forecasting with structured state space models. arXiv:2208.09399. Retrieved from https:\/\/arxiv.org\/abs\/2208.09399"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2017.04.028"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671605"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2013.03.012"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557262"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531967"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512192"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1145\/2959100.2959190"},{"key":"e_1_3_2_10_2","volume-title":"Proceedings of the Workshop on Online Recommender Systems and User Modeling at the ACM Conference on Recommender Systems (ORSUM@RecSys)","author":"Cui Qiang","year":"2020","unstructured":"Qiang Cui, Tao Wei, Yafeng Zhang, and Qing Zhang. 2020. HeroGRAPH: A heterogeneous graph framework for multi-target cross-domain recommendation. In Proceedings of the Workshop on Online Recommender Systems and User Modeling at the ACM Conference on Recommender Systems (ORSUM@RecSys)."},{"key":"e_1_3_2_11_2","first-page":"8780","article-title":"Diffusion models beat GANs on image synthesis","volume":"34","author":"Dhariwal Prafulla","year":"2021","unstructured":"Prafulla Dhariwal and Alexander Nichol. 2021. Diffusion models beat GANs on image synthesis. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 34, 8780\u20138794.","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM58522.2023.00017"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1145\/3626772.3657758"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741667"},{"key":"e_1_3_2_15_2","article-title":"Generative adversarial nets","volume":"27","author":"Goodfellow Ian J.","year":"2014","unstructured":"Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 27.","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.5555\/2188385.2188410"},{"key":"e_1_3_2_17_2","doi-asserted-by":"crossref","unstructured":"Lei Guo Li Tang Tong Chen Lei Zhu Quoc Viet Hung Nguyen and Hongzhi Yin. 2021. DA-GCN: A domain-aware attentive graph convolution network for shared-account cross-domain sequential recommendation. arXiv:2105.03300. Retrieved from https:\/\/arxiv.org\/abs\/2105.03300","DOI":"10.24963\/ijcai.2021\/342"},{"key":"e_1_3_2_18_2","unstructured":"Bal\u00e1zs Hidasi Alexandros Karatzoglou Linas Baltrunas and Domonkos Tikk. 2015. Session-based recommendations with recurrent neural networks. arXiv:1511.06939. Retrieved from https:\/\/arxiv.org\/abs\/1511.06939"},{"key":"e_1_3_2_19_2","first-page":"6840","article-title":"Denoising diffusion probabilistic models","volume":"33","author":"Ho Jonathan","year":"2020","unstructured":"Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020. Denoising diffusion probabilistic models. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 33, 6840\u20136851.","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_20_2","unstructured":"Jonathan Ho and Tim Salimans. 2022. Classifier-free diffusion guidance. arXiv:2207.12598. Retrieved from https:\/\/arxiv.org\/abs\/2207.12598"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3271684"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1145\/1864708.1864736"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.1145\/3726302.3730200"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3614945"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v30i1.10001"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2018.00035"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","unstructured":"Diederik P. Kingma and Max Welling. 2013. Auto-encoding variational bayes. arXiv:1312.6114. Retrieved from 10.48550\/arXiv.1312.6114","DOI":"10.48550\/arXiv.1312.6114"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401078"},{"key":"e_1_3_2_29_2","unstructured":"Jeongeun Lee Seongku Kang Won-Yong Shin Jeongwhan Choi Noseong Park and Dongha Lee. 2024. Graph signal processing for cross-domain recommendation. arXiv:2407.12374. Retrieved from https:\/\/arxiv.org\/abs\/2407.12374"},{"issue":"1","key":"e_1_3_2_30_2","first-page":"321","article-title":"Dual metric learning for effective and efficient cross-domain recommendations","volume":"35","author":"Li Pan","year":"2021","unstructured":"Pan Li and Alexander Tuzhilin. 2021. Dual metric learning for effective and efficient cross-domain recommendations. IEEE Transactions on Knowledge and Data Engineering 35, 1 (2021), 321\u2013334.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11704-023-2703-y"},{"key":"e_1_3_2_32_2","unstructured":"Xiaodong Li Hengzhu Tang Jiawei Sheng Xinghua Zhang Li Gao Suqi Cheng Dawei Yin and Tingwen Liu. 2025. Exploring preference-guided diffusion model for cross-domain recommendation. arXiv:2501.11671. Retrieved from https:\/\/arxiv.org\/abs\/2501.11671"},{"key":"e_1_3_2_33_2","first-page":"4328","article-title":"Diffusion-LM improves controllable text generation","volume":"35","author":"Li Xiang","year":"2022","unstructured":"Xiang Li, John Thickstun, Ishaan Gulrajani, Percy S. Liang, and Tatsunori B. Hashimoto. 2022. Diffusion-LM improves controllable text generation. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 35, 4328\u20134343.","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1145\/3626772.3657780"},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1145\/3631116"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1109\/MIC.2003.1167344"},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1145\/3641860"},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380036"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3412012"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3615134"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512166"},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.1145\/1458082.1458205"},{"key":"e_1_3_2_43_2","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331200"},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/343"},{"key":"e_1_3_2_45_2","doi-asserted-by":"publisher","DOI":"10.1145\/3626772.3657710"},{"key":"e_1_3_2_46_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_3_2_47_2","unstructured":"Nikolay Savinov Junyoung Chung Mikolaj Binkowski Erich Elsen and Aaron van den Oord. 2021. Step-unrolled denoising autoencoders for text generation. arXiv:2112.06749. Retrieved from https:\/\/arxiv.org\/abs\/2112.06749"},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.1023\/A:1009804230409"},{"key":"e_1_3_2_49_2","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1401969"},{"key":"e_1_3_2_50_2","first-page":"2256","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Sohl-Dickstein Jascha","year":"2015","unstructured":"Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. 2015. Deep unsupervised learning using nonequilibrium thermodynamics. In Proceedings of the International Conference on Machine Learning. PMLR, 2256\u20132265."},{"key":"e_1_3_2_51_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11704-024-3803-z"},{"key":"e_1_3_2_52_2","article-title":"Generative modeling by estimating gradients of the data distribution","volume":"32","author":"Song Yang","year":"2019","unstructured":"Yang Song and Stefano Ermon. 2019. Generative modeling by estimating gradients of the data distribution. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 32.","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_53_2","doi-asserted-by":"crossref","unstructured":"Yang Song Jascha Sohl-Dickstein Diederik P. Kingma Abhishek Kumar Stefano Ermon and Ben Poole. 2020. Score-based generative modeling through stochastic differential equations. arXiv:2011.13456. Retrieved from https:\/\/arxiv.org\/abs\/2011.13456","DOI":"10.1155\/2011\/613695"},{"key":"e_1_3_2_54_2","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357895"},{"key":"e_1_3_2_55_2","doi-asserted-by":"publisher","DOI":"10.1145\/2339530.2339730"},{"key":"e_1_3_2_56_2","first-page":"24804","article-title":"CSDI: Conditional score-based diffusion models for probabilistic time series imputation","volume":"34","author":"Tashiro Yusuke","year":"2021","unstructured":"Yusuke Tashiro, Jiaming Song, Yang Song, and Stefano Ermon. 2021. CSDI: Conditional score-based diffusion models for probabilistic time series imputation. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 34, 24804\u201324816.","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3591663"},{"key":"e_1_3_2_58_2","doi-asserted-by":"publisher","DOI":"10.1145\/3535101"},{"key":"e_1_3_2_59_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE53745.2022.00099"},{"key":"e_1_3_2_60_2","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482429"},{"key":"e_1_3_2_61_2","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645351"},{"key":"e_1_3_2_62_2","unstructured":"Yuner Xuan. 2024. Diffusion cross-domain recommendation. arXiv:2402.02182. Retrieved from https:\/\/arxiv.org\/abs\/2402.02182"},{"key":"e_1_3_2_63_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11704-024-3939-x"},{"key":"e_1_3_2_64_2","doi-asserted-by":"publisher","DOI":"10.1145\/3626235"},{"key":"e_1_3_2_65_2","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583361"},{"key":"e_1_3_2_66_2","first-page":"24247","article-title":"Generate what you prefer: Reshaping sequential recommendation via guided diffusion","volume":"36","author":"Yang Zhengyi","year":"2023","unstructured":"Zhengyi Yang, Jiancan Wu, Zhicai Wang, Xiang Wang, Yancheng Yuan, and Xiangnan He. 2023. Generate what you prefer: Reshaping sequential recommendation via guided diffusion. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 36, 24247\u201324261.","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_67_2","unstructured":"Peiyu Yu Sirui Xie Xiaojian Ma Baoxiong Jia Bo Pang Ruiqi Gao Yixin Zhu Song-Chun Zhu and Ying Nian Wu. 2022. Latent diffusion energy-based model for interpretable text modeling. arXiv:2206.05895. Retrieved from https:\/\/arxiv.org\/abs\/2206.05895"},{"key":"e_1_3_2_68_2","doi-asserted-by":"publisher","DOI":"10.1145\/3548455"},{"key":"e_1_3_2_69_2","doi-asserted-by":"publisher","DOI":"10.1145\/3158369"},{"key":"e_1_3_2_70_2","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3358166"},{"key":"e_1_3_2_71_2","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583263"},{"key":"e_1_3_2_72_2","doi-asserted-by":"publisher","DOI":"10.1145\/3626772.3657825"},{"key":"e_1_3_2_73_2","doi-asserted-by":"publisher","DOI":"10.1145\/3298689.3346997"},{"key":"e_1_3_2_74_2","doi-asserted-by":"crossref","unstructured":"Feng Zhu Yan Wang Chaochao Chen Jun Zhou Longfei Li and Guanfeng Liu. 2021. Cross-domain recommendation: Challenges progress and prospects. arXiv:2103.01696. Retrieved from https:\/\/arxiv.org\/abs\/2103.01696","DOI":"10.24963\/ijcai.2021\/639"},{"key":"e_1_3_2_75_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3463010"},{"key":"e_1_3_2_76_2","doi-asserted-by":"publisher","DOI":"10.1145\/3488560.3498392"}],"container-title":["ACM Transactions on Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3795793","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T13:21:33Z","timestamp":1773494493000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3795793"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,9]]},"references-count":75,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,3,31]]}},"alternative-id":["10.1145\/3795793"],"URL":"https:\/\/doi.org\/10.1145\/3795793","relation":{},"ISSN":["1046-8188","1558-2868"],"issn-type":[{"value":"1046-8188","type":"print"},{"value":"1558-2868","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,9]]},"assertion":[{"value":"2025-06-19","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-01-23","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-03-09","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}