{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T22:49:09Z","timestamp":1778539749618,"version":"3.51.4"},"reference-count":87,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2023,1,9]],"date-time":"2023-01-09T00:00:00Z","timestamp":1673222400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2019YFB1804304"],"award-info":[{"award-number":["2019YFB1804304"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"name":"111 plan","award":["BP0719010"],"award-info":[{"award-number":["BP0719010"]}]},{"DOI":"10.13039\/501100003399","name":"STCSM","doi-asserted-by":"crossref","award":["18DZ2270700"],"award-info":[{"award-number":["18DZ2270700"]}],"id":[{"id":"10.13039\/501100003399","id-type":"DOI","asserted-by":"crossref"}]},{"name":"State Key Laboratory of UHD Video and Audio Production and Presentation"},{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"crossref","award":["DP180100106 and DP200101328"],"award-info":[{"award-number":["DP180100106 and DP200101328"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"crossref"}]},{"name":"A*STAR Centre for Frontier AI Research"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2023,1,31]]},"abstract":"<jats:p>Cross domain recommendation (CDR) is one popular research topic in recommender systems. This article focuses on a popular scenario for CDR where different domains share the same set of users but no overlapping items. The majority of recent methods have explored the shared-user representation to transfer knowledge across domains. However, the idea of shared-user representation resorts to learning the overlapped features of user preferences and suppresses the domain-specific features. Other works try to capture the domain-specific features by an MLP mapping but require heuristic human knowledge of choosing samples to train the mapping. In this article, we attempt to learn both features of user preferences in a more principled way. We assume that each user\u2019s preferences in one domain can be expressed by the other one, and these preferences can be mutually converted to each other with the so-called equivalent transformation. Based on this assumption, we propose an equivalent transformation learner (ETL), which models the joint distribution of user behaviors across domains. The equivalent transformation in ETL relaxes the idea of shared-user representation and allows the learned preferences in different domains to preserve the domain-specific features as well as the overlapped features. Extensive experiments on three public benchmarks demonstrate the effectiveness of ETL compared with recent state-of-the-art methods. Codes and data are available online:\u00a0https:\/\/github.com\/xuChenSJTU\/ETL-master.<\/jats:p>","DOI":"10.1145\/3522762","type":"journal-article","created":{"date-parts":[[2022,3,9]],"date-time":"2022-03-09T13:37:22Z","timestamp":1646833042000},"page":"1-31","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":31,"title":["Toward Equivalent Transformation of User Preferences in Cross Domain Recommendation"],"prefix":"10.1145","volume":"41","author":[{"given":"Xu","family":"Chen","sequence":"first","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai Shi and University of Technology, Sydney, Australia"}]},{"given":"Ya","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai Shi"}]},{"given":"Ivor W.","family":"Tsang","sequence":"additional","affiliation":[{"name":"University of Technology, Sydney, Australia and CFAR, A*STAR Singapore, Sydney, Australia"}]},{"given":"Yuangang","family":"Pan","sequence":"additional","affiliation":[{"name":"University of Technology, Sydney, Australia and CFAR, A*STAR Singapore, Sydney, Australia"}]},{"given":"Jingchao","family":"Su","sequence":"additional","affiliation":[{"name":"Shanghai Jiao Tong University, Shanghai Shi"}]}],"member":"320","published-online":{"date-parts":[[2023,1,9]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"214","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Arjovsky Martin","year":"2017","unstructured":"Martin Arjovsky, Soumith Chintala, and L\u00e9on Bottou. 2017. Wasserstein generative adversarial networks. In Proceedings of the International Conference on Machine Learning. 214\u2013223."},{"key":"e_1_3_2_3_2","first-page":"355","volume-title":"Proceedings of the International Conference on User Modeling","author":"Berkovsky Shlomo","year":"2007","unstructured":"Shlomo Berkovsky, Tsvi Kuflik, and Francesco Ricci. 2007. Cross-domain mediation in collaborative filtering. In Proceedings of the International Conference on User Modeling. Springer, 355\u2013359."},{"key":"e_1_3_2_4_2","first-page":"1661","volume-title":"Proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Bi Ye","year":"2020","unstructured":"Ye Bi, Liqiang Song, Mengqiu Yao, Zhenyu Wu, Jianming Wang, and Jing Xiao. 2020. DCDIR: A deep cross-domain recommendation system for cold start users in insurance domain. In Proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval. 1661\u20131664."},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4899-7637-6_27"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/3017429"},{"key":"e_1_3_2_7_2","first-page":"573","volume-title":"Proceedings of the European Conference on Computer Vision","author":"Chang Hsin-Yu","year":"2020","unstructured":"Hsin-Yu Chang, Zhixiang Wang, and Yung-Yu Chuang. 2020. Domain-Specific mappings for generative adversarial style transfer. In Proceedings of the European Conference on Computer Vision. Springer, 573\u2013589."},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3032189"},{"key":"e_1_3_2_9_2","unstructured":"Xu Chen Kenan Cui Ya Zhang and Yanfeng Wang. 2019. Cascading: Association Augmented Sequential Recommendation. Retrieved from https:\/\/arXiv:1910.07792."},{"key":"e_1_3_2_10_2","first-page":"2172","volume-title":"Advances Neural Information Processing Systems","author":"Chen Xi","year":"2016","unstructured":"Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel. 2016. Infogan: Interpretable representation learning by information maximizing generative adversarial nets. In Advances Neural Information Processing Systems. MIT Press, 2172\u20132180."},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1145\/3408298"},{"key":"e_1_3_2_12_2","first-page":"1112","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Chen Ziliang","year":"2019","unstructured":"Ziliang Chen, Zhanfu Yang, Xiaoxi Wang, Xiaodan Liang, Xiaopeng Yan, Guanbin Li, and Liang Lin. 2019. Multivariate-Information adversarial ensemble for scalable joint distribution matching. In Proceedings of the International Conference on Machine Learning. 1112\u20131121."},{"key":"e_1_3_2_13_2","first-page":"496","volume-title":"Proceedings of the International Conference on Data Mining Workshops","author":"Cremonesi Paolo","year":"2011","unstructured":"Paolo Cremonesi, Antonio Tripodi, and Roberto Turrin. 2011. Cross-domain recommender systems. In Proceedings of the International Conference on Data Mining Workshops. IEEE, 496\u2013503."},{"key":"e_1_3_2_14_2","article-title":"Variational collaborative learning for user probabilistic representation","author":"Cui Kenan","year":"2018","unstructured":"Kenan Cui, Xu Chen, Jiangchao Yao, and Ya Zhang. 2018. Variational collaborative learning for user probabilistic representation. Retrieved from https:\/\/arXiv:1809.08400.","journal-title":"Retrieved from https:\/\/arXiv:1809.08400"},{"key":"e_1_3_2_15_2","volume-title":"Proceedings of the International Conference on World Wide Web","author":"Krishnan Matthew D. Hoffman Tony Jebara Dawen Liang, Rahul G.","year":"2018","unstructured":"Matthew D. Hoffman Tony Jebara Dawen Liang, Rahul G. Krishnan. 2018. Variational autoencoders for collaborative filtering. In Proceedings of the International Conference on World Wide Web."},{"key":"e_1_3_2_16_2","first-page":"1348","volume-title":"Proceedings of the International Conference on Knowledge Discovery and Data Mining","author":"Du Changying","year":"2018","unstructured":"Changying Du, Changde Du, Xingyu Xie, Chen Zhang, and Hao Wang. 2018. Multi-view adversarially learned inference for cross-domain joint distribution matching. In Proceedings of the International Conference on Knowledge Discovery and Data Mining. ACM, 1348\u20131357."},{"key":"e_1_3_2_17_2","first-page":"915","volume-title":"Proceedings of the Pacific Rim International Conference on Artificial Intelligence","author":"Du Yingpeng","year":"2018","unstructured":"Yingpeng Du, Hongzhi Liu, Yuanhang Qu, and Zhonghai Wu. 2018. Online personalized next-item recommendation via long short term preference learning. In Proceedings of the Pacific Rim International Conference on Artificial Intelligence. Springer, 915\u2013927."},{"key":"e_1_3_2_18_2","article-title":"Adversarially learned inference","author":"Dumoulin Vincent","year":"2016","unstructured":"Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Olivier Mastropietro, Alex Lamb, Martin Arjovsky, and Aaron Courville. 2016. Adversarially learned inference. Retrieved from https:\/\/arXiv:1606.00704.","journal-title":"Retrieved from https:\/\/arXiv:1606.00704"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741667"},{"key":"e_1_3_2_20_2","first-page":"25","volume-title":"Proceedings of the International Workshop on Information Heterogeneity and Fusion in Recommender Systems","author":"Fern\u00e1ndez-Tob\u00edas Ignacio","year":"2011","unstructured":"Ignacio Fern\u00e1ndez-Tob\u00edas, Iv\u00e1n Cantador, Marius Kaminskas, and Francesco Ricci. 2011. A generic semantic-based framework for cross-domain recommendation. In Proceedings of the International Workshop on Information Heterogeneity and Fusion in Recommender Systems. 25\u201332."},{"key":"e_1_3_2_21_2","first-page":"1","volume-title":"Proceedings of the Spanish Conference on Information Retrieval","author":"Fern\u00e1ndez-Tob\u00edas Ignacio","year":"2012","unstructured":"Ignacio Fern\u00e1ndez-Tob\u00edas, Iv\u00e1n Cantador, Marius Kaminskas, and Francesco Ricci. 2012. Cross-domain recommender systems: A survey of the state of the art. In Proceedings of the Spanish Conference on Information Retrieval. sn, 1\u201312."},{"key":"e_1_3_2_22_2","first-page":"94","volume-title":"Proceedings of the Association for the Advancement of Artificial Intelligence","volume":"33","author":"Fu Wenjing","year":"2019","unstructured":"Wenjing Fu, Zhaohui Peng, Senzhang Wang, Yang Xu, and Jin Li. 2019. Deeply fusing reviews and contents for cold start users in cross-domain recommendation systems. In Proceedings of the Association for the Advancement of Artificial Intelligence, Vol. 33. 94\u2013101."},{"key":"e_1_3_2_23_2","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Ganin Yaroslav","unstructured":"Yaroslav Ganin and Victor Lempitsky. [n.d.]. Unsupervised domain adaptation by backpropagation. In Proceedings of the International Conference on Machine Learning."},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.5555\/2946645.2946704"},{"key":"e_1_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-40991-2_11"},{"key":"e_1_3_2_26_2","first-page":"249","volume-title":"Proceedings of the International Conference on Artificial Intelligence and Statistics","author":"Glorot Xavier","year":"2010","unstructured":"Xavier Glorot and Yoshua Bengio. 2010. Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the International Conference on Artificial Intelligence and Statistics. 249\u2013256."},{"key":"e_1_3_2_27_2","first-page":"2672","volume-title":"Advances in Neural Information Processing Systems","author":"Goodfellow Ian","year":"2014","unstructured":"Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In Advances in Neural Information Processing Systems. MIT Press, 2672\u20132680."},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.5555\/2188385.2188410"},{"key":"e_1_3_2_29_2","volume-title":"A survey on using side information in recommendation systems","author":"Gunasekar Suriya","year":"2012","unstructured":"Suriya Gunasekar. 2012. A survey on using side information in recommendation systems. Ph. D. Dissertation."},{"key":"e_1_3_2_30_2","article-title":"Canonical correlation analysis (CCA) based multi-view learning: An overview","author":"Guo Chenfeng","year":"2019","unstructured":"Chenfeng Guo and Dongrui Wu. 2019. Canonical correlation analysis (CCA) based multi-view learning: An overview. Retrieved from https:\/\/arXiv:1907.01693.","journal-title":"Retrieved from https:\/\/arXiv:1907.01693"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1016\/0024-3795(79)90044-2"},{"key":"e_1_3_2_32_2","first-page":"173","volume-title":"Proceedings of the International Conference on World Wide Web","author":"He Xiangnan","year":"2017","unstructured":"Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In Proceedings of the International Conference on World Wide Web. 173\u2013182."},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.5555\/2567709.2502622"},{"key":"e_1_3_2_34_2","article-title":"Domain-adversarial network alignment","author":"Hong Huiting","year":"2020","unstructured":"Huiting Hong, Xin Li, Yuangang Pan, and Ivor Tsang. 2020. Domain-adversarial network alignment. IEEE Trans. Knowl. Data Eng. (2020). https:\/\/www.computer.org\/csdl\/journal\/tk\/5555\/01\/09195006\/1n2iZbAWaas.","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"e_1_3_2_35_2","first-page":"667","volume-title":"Proceedings of the International Conference on Information and Knowledge Management","author":"Hu Guangneng","year":"2018","unstructured":"Guangneng Hu, Yu Zhang, and Qiang Yang. 2018. Conet: Collaborative cross networks for cross-domain recommendation. In Proceedings of the International Conference on Information and Knowledge Management. ACM, 667\u2013676."},{"key":"e_1_3_2_36_2","first-page":"1","article-title":"MTNet: A neural approach for cross-domain recommendation with unstructured text","author":"Hu Guangneng","year":"2018","unstructured":"Guangneng Hu, Yu Zhang, and Qiang Yang. 2018. MTNet: A neural approach for cross-domain recommendation with unstructured text. Proceedings of the International Conference on Knowledge Discovery and Data Mining (KDD\u201918). 1\u201310.","journal-title":"Proceedings of the International Conference on Knowledge Discovery and Data Mining (KDD\u201918)"},{"key":"e_1_3_2_37_2","first-page":"595","volume-title":"Proceedings of the International Conference on World Wide Web","author":"Hu Liang","year":"2013","unstructured":"Liang Hu, Jian Cao, Guandong Xu, Longbing Cao, Zhiping Gu, and Can Zhu. 2013. Personalized recommendation via cross-domain triadic factorization. In Proceedings of the International Conference on World Wide Web. ACM, 595\u2013606."},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2008.22"},{"key":"e_1_3_2_39_2","first-page":"2333","volume-title":"Proceedings of the International Conference on Information and Knowledge Management","author":"Huang Po-Sen","year":"2013","unstructured":"Po-Sen Huang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Acero, and Larry Heck. 2013. Learning deep structured semantic models for web search using clickthrough data. In Proceedings of the International Conference on Information and Knowledge Management. ACM, 2333\u20132338."},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01219-9_11"},{"key":"e_1_3_2_41_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.632"},{"key":"e_1_3_2_42_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-15719-7_3"},{"key":"e_1_3_2_43_2","volume-title":"Advances in Neural Information Processing Systems","author":"Kazemi Hadi","unstructured":"Hadi Kazemi, Sobhan Soleymani, Fariborz Taherkhani, Seyed Iranmanesh, and Nasser Nasrabadi. [n.d.]. In Advances in Neural Information Processing Systems, S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett (Eds.)."},{"issue":"3","key":"e_1_3_2_44_2","first-page":"36","article-title":"Cross domain recommender systems: A systematic literature review","volume":"50","author":"Khan Muhammad Murad","year":"2017","unstructured":"Muhammad Murad Khan, Roliana Ibrahim, and Imran Ghani. 2017. Cross domain recommender systems: A systematic literature review. ACM Comput. Surveys 50, 3 (2017), 36.","journal-title":"ACM Comput. Surveys"},{"key":"e_1_3_2_45_2","volume-title":"Proceedings of the International Conference on Learning Representation","author":"Kingma Diederik P.","year":"2015","unstructured":"Diederik P. Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In Proceedings of the International Conference on Learning Representation."},{"key":"e_1_3_2_46_2","article-title":"Auto-encoding variational bayes","author":"Kingma Diederik P.","year":"2013","unstructured":"Diederik P. Kingma and Max Welling. 2013. Auto-encoding variational bayes. Retrieved from https:\/\/arXiv:1312.6114.","journal-title":"Retrieved from https:\/\/arXiv:1312.6114"},{"key":"e_1_3_2_47_2","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2009.263"},{"key":"e_1_3_2_48_2","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553454"},{"key":"e_1_3_2_49_2","volume-title":"Advances in Neural Information Processing Systems","author":"Li Chun-Liang","unstructured":"Chun-Liang Li, Wei-Cheng Chang, Yu Cheng, Yiming Yang, and Barnabas Poczos. [n.d.]. In Advances in Neural Information Processing Systems. MIT Press."},{"key":"e_1_3_2_50_2","article-title":"DDTCDR: Deep dual transfer cross domain recommendation","author":"Li Pan","year":"2020","unstructured":"Pan Li and Alexander Tuzhilin. 2020. DDTCDR: Deep dual transfer cross domain recommendation. Proceedings of the International Conference on Web Search and Data Mining.","journal-title":"Proceedings of the International Conference on Web Search and Data Mining"},{"key":"e_1_3_2_51_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11390-020-0314-8"},{"key":"e_1_3_2_52_2","first-page":"817","volume-title":"Proceedings of the International Conference on World Wide Web Companion","author":"Lian Jianxun","year":"2017","unstructured":"Jianxun Lian, Fuzheng Zhang, Xing Xie, and Guangzhong Sun. 2017. CCCFNet: A content-boosted collaborative filtering neural network for cross domain recommender systems. In Proceedings of the International Conference on World Wide Web Companion. 817\u2013818."},{"key":"e_1_3_2_53_2","first-page":"515","volume-title":"Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Lin Tzu-Heng","year":"2019","unstructured":"Tzu-Heng Lin, Chen Gao, and Yong Li. 2019. Cross: Cross-platform recommendation for social e-commerce. In Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 515\u2013524."},{"issue":"1","key":"e_1_3_2_54_2","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1109\/MIC.2003.1167344","article-title":"Amazon. com recommendations: Item-to-item collaborative filtering","author":"Linden Greg","year":"2003","unstructured":"Greg Linden, Brent Smith, and Jeremy York. 2003. Amazon. com recommendations: Item-to-item collaborative filtering. IEEE Internet Comput.1 (2003), 76\u201380.","journal-title":"IEEE Internet Comput."},{"key":"e_1_3_2_55_2","volume-title":"Lectures on the Coupling Method","author":"Lindvall Torgny","year":"2002","unstructured":"Torgny Lindvall. 2002. Lectures on the Coupling Method. Courier Corporation."},{"key":"e_1_3_2_56_2","first-page":"700","volume-title":"Advances Neural Information Processing Systems","author":"Liu Ming-Yu","year":"2017","unstructured":"Ming-Yu Liu, Thomas Breuel, and Jan Kautz. 2017. Unsupervised image-to-image translation networks. In Advances Neural Information Processing Systems. MIT Press, 700\u2013708."},{"key":"e_1_3_2_57_2","article-title":"Implicit autoencoders","author":"Makhzani Alireza","year":"2018","unstructured":"Alireza Makhzani. 2018. Implicit autoencoders. Retrieved from https:\/\/arXiv:1805.09804.","journal-title":"Retrieved from https:\/\/arXiv:1805.09804"},{"key":"e_1_3_2_58_2","article-title":"Adversarial autoencoders","author":"Makhzani Alireza","year":"2015","unstructured":"Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, Ian Goodfellow, and Brendan Frey. 2015. Adversarial autoencoders. Retrieved from https:\/\/arXiv:1511.05644.","journal-title":"Retrieved from https:\/\/arXiv:1511.05644"},{"key":"e_1_3_2_59_2","first-page":"2464","volume-title":"Proceedings of the International Joint Conferences on Artificial Intelligence","author":"Man Tong","year":"2017","unstructured":"Tong Man, Huawei Shen, Xiaolong Jin, and Xueqi Cheng. 2017. Cross-Domain recommendation: An embedding and mapping approach. In Proceedings of the International Joint Conferences on Artificial Intelligence. 2464\u20132470."},{"key":"e_1_3_2_60_2","first-page":"2794","volume-title":"Proceedings of the International Conference on Computer Vision","author":"Mao Xudong","year":"2017","unstructured":"Xudong Mao, Qing Li, Haoran Xie, Raymond Y. K. Lau, Zhen Wang, and Stephen Paul Smolley. 2017. Least squares generative adversarial networks. In Proceedings of the International Conference on Computer Vision. 2794\u20132802."},{"key":"e_1_3_2_61_2","doi-asserted-by":"publisher","DOI":"10.1145\/1961209.1961213"},{"key":"e_1_3_2_62_2","first-page":"2391","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Mescheder Lars","year":"2017","unstructured":"Lars Mescheder, Sebastian Nowozin, and Andreas Geiger. 2017. Adversarial variational bayes: Unifying variational autoencoders and generative adversarial networks. In Proceedings of the International Conference on Machine Learning. JMLR.org, 2391\u20132400."},{"key":"e_1_3_2_63_2","doi-asserted-by":"publisher","DOI":"10.1145\/2507157.2507233"},{"issue":"4","key":"e_1_3_2_64_2","first-page":"33","article-title":"Improving top-n recommendation for cold-start users via cross-domain information","volume":"9","author":"Mirbakhsh Nima","year":"2015","unstructured":"Nima Mirbakhsh and Charles X. Ling. 2015. Improving top-n recommendation for cold-start users via cross-domain information. ACM Trans. Knowl. Discov. Data 9, 4 (2015), 33.","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"e_1_3_2_65_2","first-page":"1257","volume-title":"Advances in Neural Information Processing Systems","author":"Mnih Andriy","year":"2008","unstructured":"Andriy Mnih and Ruslan R. Salakhutdinov. 2008. Probabilistic matrix factorization. In Advances in Neural Information Processing Systems. MIT Press, 1257\u20131264."},{"key":"e_1_3_2_66_2","first-page":"271","volume-title":"Advances Neural Information Processing Systems","author":"Nowozin Sebastian","year":"2016","unstructured":"Sebastian Nowozin, Botond Cseke, and Ryota Tomioka. 2016. f-gan: Training generative neural samplers using variational divergence minimization. In Advances Neural Information Processing Systems. MIT Press, 271\u2013279."},{"key":"e_1_3_2_67_2","volume-title":"Proceedings of the AAAI Workshop on Recommender Systems","volume":"83","author":"Oard Douglas W.","year":"1998","unstructured":"Douglas W. Oard, Jinmook Kim, et\u00a0al. 1998. Implicit feedback for recommender systems. In Proceedings of the AAAI Workshop on Recommender Systems, Vol. 83. WoUongong."},{"key":"e_1_3_2_68_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"e_1_3_2_69_2","article-title":"Variational inference with normalizing flows","author":"Rezende Danilo Jimenez","year":"2015","unstructured":"Danilo Jimenez Rezende and Shakir Mohamed. 2015. Variational inference with normalizing flows. Retrieved from https:\/\/arXiv:1505.05770.","journal-title":"Retrieved from https:\/\/arXiv:1505.05770"},{"key":"e_1_3_2_70_2","first-page":"111","volume-title":"Proceedings of the International Conference on World Wide Web","author":"Sedhain Suvash","year":"2015","unstructured":"Suvash Sedhain, Aditya Krishna Menon, Scott Sanner, and Lexing Xie. 2015. Autorec: Autoencoders meet collaborative filtering. In Proceedings of the International Conference on World Wide Web. ACM, 111\u2013112."},{"key":"e_1_3_2_71_2","doi-asserted-by":"publisher","DOI":"10.1162\/neco_a_01309"},{"key":"e_1_3_2_72_2","first-page":"650","volume-title":"Proceedings of the International Conference on Knowledge Discovery and Data Mining","author":"Singh Ajit P.","year":"2008","unstructured":"Ajit P. Singh and Geoffrey J. Gordon. 2008. Relational learning via collective matrix factorization. In Proceedings of the International Conference on Knowledge Discovery and Data Mining. ACM, 650\u2013658."},{"key":"e_1_3_2_73_2","first-page":"3308","volume-title":"Advances in Neural Information Processing Systems","author":"Srivastava Akash","year":"2017","unstructured":"Akash Srivastava, Lazar Valkov, Chris Russell, Michael U. Gutmann, and Charles Sutton. 2017. Veegan: Reducing mode collapse in gans using implicit variational learning. In Advances in Neural Information Processing Systems. MIT Press, 3308\u20133318."},{"key":"e_1_3_2_74_2","first-page":"1285","volume-title":"Proceedings of the International Conference on Knowledge Discovery and Data Mining","author":"Tang Jie","year":"2012","unstructured":"Jie Tang, Sen Wu, Jimeng Sun, and Hang Su. 2012. Cross-domain collaboration recommendation. In Proceedings of the International Conference on Knowledge Discovery and Data Mining. ACM, 1285\u20131293."},{"key":"e_1_3_2_75_2","article-title":"VAE with a VampPrior","author":"Tomczak Jakub M.","year":"2017","unstructured":"Jakub M. Tomczak and Max Welling. 2017. VAE with a VampPrior. Retrieved from https:\/\/arXiv:1705.07120.","journal-title":"Retrieved from https:\/\/arXiv:1705.07120"},{"key":"e_1_3_2_76_2","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1145\/2835776.2835837","volume-title":"Proceedings of the International Conference on Web Search and Data Mining","author":"Wu Yao","year":"2016","unstructured":"Yao Wu, Christopher DuBois, Alice X. Zheng, and Martin Ester. 2016. Collaborative denoising auto-encoders for top-n recommender systems. In Proceedings of the International Conference on Web Search and Data Mining. ACM, 153\u2013162."},{"key":"e_1_3_2_77_2","volume-title":"Proceedings of the International Conference on Knowledge Discovery and Data Mining","author":"Yao Jiangchao","year":"2021","unstructured":"Jiangchao Yao, Feng Wang, KunYang Jia, Bo Han, Jingren Zhou, and Hongxia Yang. 2021. Device-Cloud collaborative learning for recommendation. In Proceedings of the International Conference on Knowledge Discovery and Data Mining. ACM."},{"key":"e_1_3_2_78_2","article-title":"Semi-implicit variational inference","author":"Yin Mingzhang","year":"2018","unstructured":"Mingzhang Yin and Mingyuan Zhou. 2018. Semi-implicit variational inference. In Proceedings of the International Conference on Machine Learning.","journal-title":"Proceedings of the International Conference on Machine Learning"},{"key":"e_1_3_2_79_2","unstructured":"Tao Qin Liwei Wang Nenghai Yu Tie-Yan Liu Wei-Ying Ma Yingce Xia and Di He. 2016. Dual learning for machine translation. In Proceedings of the 30th International Conference on Neural Information Processing Systems (NIPS\u201916) . Curran Associates Inc. Red Hook NY USA 820\u2013828."},{"key":"e_1_3_2_80_2","first-page":"1065","volume-title":"Proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval","author":"Yuan Feng","year":"2019","unstructured":"Feng Yuan, Lina Yao, and Boualem Benatallah. 2019. Adversarial collaborative neural network for robust recommendation. In Proceedings of the ACM SIGIR Conference on Research and Development in Information Retrieval. 1065\u20131068."},{"key":"e_1_3_2_81_2","article-title":"DARec: Deep domain adaptation for cross-domain recommendation via transferring rating patterns","author":"Yuan Feng","year":"2019","unstructured":"Feng Yuan, Lina Yao, and Boualem Benatallah. 2019. DARec: Deep domain adaptation for cross-domain recommendation via transferring rating patterns. In Proceedings of the International Joint Conference on Artificial Intelligence (2019).","journal-title":"Proceedings of the International Joint Conference on Artificial Intelligence"},{"key":"e_1_3_2_82_2","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371818"},{"key":"e_1_3_2_83_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2889774"},{"key":"e_1_3_2_84_2","first-page":"2165","volume-title":"Proceedings of the International Conference on Information and Knowledge Management","author":"Zhao Cheng","year":"2019","unstructured":"Cheng Zhao, Chenliang Li, and Cong Fu. 2019. Cross-Domain recommendation via preference propagation GraphNet. In Proceedings of the International Conference on Information and Knowledge Management. 2165\u20132168."},{"key":"e_1_3_2_85_2","volume-title":"Proceedings of the Association for the Advancement of Artificial Intelligence","author":"Zhao Shengjia","year":"2019","unstructured":"Shengjia Zhao, Jiaming Song, and Stefano Ermon. 2019. Infovae: Information maximizing variational autoencoders. In Proceedings of the Association for the Advancement of Artificial Intelligence."},{"key":"e_1_3_2_86_2","first-page":"3711","volume-title":"Proceedings of the International Joint Conference on Artificial Intelligence","author":"Zhu Feng","year":"2018","unstructured":"Feng Zhu, Yan Wang, Chaochao Chen, Guanfeng Liu, Mehmet Orgun, and Jia Wu. 2018. A deep framework for cross-domain and cross-system recommendations. In Proceedings of the International Joint Conference on Artificial Intelligence. 3711\u20133717."},{"key":"e_1_3_2_87_2","first-page":"2223","volume-title":"Proceedings of the International Conference on Computer Vision","author":"Zhu Jun-Yan","year":"2017","unstructured":"Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A. Efros. 2017. Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the International Conference on Computer Vision. 2223\u20132232."},{"key":"e_1_3_2_88_2","volume-title":"Advances in Neural Information Processing Systems","author":"Zhu Jun-Yan","year":"2017","unstructured":"Jun-Yan Zhu, Richard Zhang, Deepak Pathak, Trevor Darrell, Alexei A. Efros, Oliver Wang, and Eli Shechtman. 2017. Toward multimodal image-to-image translation. In Advances in Neural Information Processing Systems. MIT Press."}],"container-title":["ACM Transactions on Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3522762","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3522762","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:30:16Z","timestamp":1750188616000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3522762"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,9]]},"references-count":87,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,1,31]]}},"alternative-id":["10.1145\/3522762"],"URL":"https:\/\/doi.org\/10.1145\/3522762","relation":{},"ISSN":["1046-8188","1558-2868"],"issn-type":[{"value":"1046-8188","type":"print"},{"value":"1558-2868","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,9]]},"assertion":[{"value":"2020-09-14","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-02-27","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-01-09","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}