{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T16:42:06Z","timestamp":1778258526714,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":50,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,4,30]],"date-time":"2023-04-30T00:00:00Z","timestamp":1682812800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72101176"],"award-info":[{"award-number":["72101176"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,4,30]]},"DOI":"10.1145\/3543507.3583263","type":"proceedings-article","created":{"date-parts":[[2023,4,26]],"date-time":"2023-04-26T23:30:51Z","timestamp":1682551851000},"page":"887-896","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":96,"title":["Cross-domain recommendation via user interest alignment"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6220-0540","authenticated-orcid":false,"given":"Chuang","family":"Zhao","sequence":"first","affiliation":[{"name":"College of Management and Economics, Tianjin University, China and AI Lab at Lenovo Research, China"}],"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, 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, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6520-9006","authenticated-orcid":false,"given":"Jian","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Cyberspace Security, Hangzhou Dianzi University, China"}],"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, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,4,30]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Cross-Domain Recommendation to Cold-Start Users via Variational Information Bottleneck. arXiv preprint arXiv:2203.16863","author":"Cao Jiangxia","year":"2022","unstructured":"[1] Jiangxia Cao, Jiawei Sheng, Xin Cong, Tingwen Liu, and Bin Wang. 2022. Cross-Domain Recommendation to Cold-Start Users via Variational Information Bottleneck. arXiv preprint arXiv:2203.16863 (2022)."},{"key":"e_1_3_2_2_2_1","first-page":"9912","article-title":"Unsupervised learning of visual features by contrasting cluster assignments","volume":"33","author":"Caron Mathilde","year":"2020","unstructured":"[2] Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin. 2020. Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33 (2020), 9912\u20139924.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_3_1","volume-title":"International conference on machine learning. PMLR, 1597\u20131607","author":"Chen Ting","year":"2020","unstructured":"[3] Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In International conference on machine learning. PMLR, 1597\u20131607."},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3522762"},{"key":"e_1_3_2_2_5_1","unstructured":"[5] Qiang Cui Tao Wei Yafeng Zhang and Qing Zhang. 2020. HeroGRAPH: A Heterogeneous Graph Framework for Multi-Target Cross-Domain Recommendation.. In ORSUM@ RecSys."},{"key":"e_1_3_2_2_6_1","volume-title":"Document embedding with paragraph vectors. arXiv preprint arXiv:1507.07998","author":"Dai M","year":"2015","unstructured":"[6] Andrew\u00a0M Dai, Christopher Olah, and Quoc\u00a0V Le. 2015. Document embedding with paragraph vectors. arXiv preprint arXiv:1507.07998 (2015)."},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-019-09744-1"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3412734"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741667"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"crossref","unstructured":"[10] Chen Gao Xiangning Chen Fuli Feng Kai Zhao Xiangnan He Yong Li and Depeng Jin. 2019. Cross-domain recommendation without sharing user-relevant data. In The world wide web conference. 491\u2013502.","DOI":"10.1145\/3308558.3313538"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00881"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/2843948"},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.123"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3159652.3159675"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401063"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052569"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.3390\/technologies9010002"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3073565"},{"key":"e_1_3_2_2_20_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma P","year":"2014","unstructured":"[20] Diederik\u00a0P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"key":"e_1_3_2_2_21_1","volume-title":"Heterogeneous Graph Embedding for Cross-Domain Recommendation Through Adversarial Learning. In International Conference on Database Systems for Advanced Applications. Springer, 507\u2013522","author":"Li Jin","year":"2020","unstructured":"[21] Jin Li, Zhaohui Peng, Senzhang Wang, Xiaokang Xu, Philip\u00a0S Yu, and Zhenyun Hao. 2020. Heterogeneous Graph Embedding for Cross-Domain Recommendation Through Adversarial Learning. In International Conference on Database Systems for Advanced Applications. Springer, 507\u2013522."},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371793"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3074395"},{"key":"e_1_3_2_2_24_1","volume-title":"com recommendations: Item-to-item collaborative filtering","author":"Linden Greg","year":"2003","unstructured":"[24] Greg Linden, Brent Smith, and Jeremy York. 2003. Amazon. com recommendations: Item-to-item collaborative filtering. IEEE Internet computing 7, 1 (2003), 76\u201380."},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531975"},{"key":"e_1_3_2_2_26_1","volume-title":"Self-supervised learning: Generative or contrastive","author":"Liu Xiao","year":"2021","unstructured":"[26] Xiao Liu, Fanjin Zhang, Zhenyu Hou, Li Mian, Zhaoyu Wang, Jing Zhang, and Jie Tang. 2021. Self-supervised learning: Generative or contrastive. IEEE Transactions on Knowledge and Data Engineering (2021)."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3082948"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"crossref","unstructured":"[28] Tong Man Huawei Shen Xiaolong Jin and Xueqi Cheng. 2017. Cross-domain recommendation: An embedding and mapping approach.. In IJCAI Vol.\u00a017. 2464\u20132470.","DOI":"10.24963\/ijcai.2017\/343"},{"key":"e_1_3_2_2_29_1","volume-title":"Deep learning recommendation model for personalization and recommendation systems. arXiv preprint arXiv:1906.00091","author":"Naumov Maxim","year":"2019","unstructured":"[29] Maxim Naumov, Dheevatsa Mudigere, Hao-Jun\u00a0Michael Shi, Jianyu Huang, Narayanan Sundaraman, Jongsoo Park, Xiaodong Wang, Udit Gupta, Carole-Jean Wu, Alisson\u00a0G Azzolini, 2019. Deep learning recommendation model for personalization and recommendation systems. arXiv preprint arXiv:1906.00091 (2019)."},{"key":"e_1_3_2_2_30_1","volume-title":"Cross-Domain Explicit-Implicit-Mixed Collaborative Filtering Neural Network","author":"Wang Chang-Dong","year":"2021","unstructured":"[30] Chang-Dong Wang, Yan-Hui Chen, Wu-Dong Xi, Ling Huang, and Guangqiang Xie. 2021. Cross-Domain Explicit-Implicit-Mixed Collaborative Filtering Neural Network. IEEE Transactions on Systems, Man, and Cybernetics: Systems (2021)."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482137"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331267"},{"key":"e_1_3_2_2_33_1","volume-title":"A survey on accuracy-oriented neural recommendation: From collaborative filtering to information-rich recommendation","author":"Wu Le","year":"2022","unstructured":"[33] Le Wu, Xiangnan He, Xiang Wang, Kun Zhang, and Meng Wang. 2022. A survey on accuracy-oriented neural recommendation: From collaborative filtering to information-rich recommendation. IEEE Transactions on Knowledge and Data Engineering (2022)."},{"key":"e_1_3_2_2_34_1","volume-title":"Self-supervised learning on graphs: Contrastive, generative, or predictive","author":"Wu Lirong","year":"2021","unstructured":"[34] Lirong Wu, Haitao Lin, Cheng Tan, Zhangyang Gao, and Stan\u00a0Z Li. 2021. Self-supervised learning on graphs: Contrastive, generative, or predictive. IEEE Transactions on Knowledge and Data Engineering (2021)."},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00393"},{"key":"e_1_3_2_2_36_1","volume-title":"A survey on cross-domain recommendation: taxonomies, methods, and future directions. arXiv preprint arXiv:2108.03357","author":"Zang Tianzi","year":"2021","unstructured":"[36] Tianzi Zang, Yanmin Zhu, Haobing Liu, Ruohan Zhang, and Jiadi Yu. 2021. A survey on cross-domain recommendation: taxonomies, methods, and future directions. arXiv preprint arXiv:2108.03357 (2021)."},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330961"},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33015773"},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20888"},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3158369"},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401169"},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"crossref","unstructured":"[42] Chuang Zhao Hongke Zhao Runze Wu Qilin Deng Yu Ding Jianrong Tao and Changjie Fan. 2022. Multi-dimensional Prediction of Guild Health in Online Games: A Stability-Aware Multi-task Learning Approach. (2022).","DOI":"10.1609\/aaai.v36i4.20358"},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-91452-7_10"},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33015941"},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357992"},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"crossref","unstructured":"[46] Feng Zhu Yan Wang Chaochao Chen Guanfeng Liu and Xiaolin Zheng. 2020. A Graphical and Attentional Framework for Dual-Target Cross-Domain Recommendation.. In IJCAI. 3001\u20133008.","DOI":"10.24963\/ijcai.2020\/415"},{"key":"e_1_3_2_2_47_1","volume-title":"Cross-domain recommendation: challenges, progress, and prospects. arXiv preprint arXiv:2103.01696","author":"Zhu Feng","year":"2021","unstructured":"[47] Feng Zhu, Yan Wang, Chaochao Chen, Jun Zhou, Longfei Li, and Guanfeng Liu. 2021. Cross-domain recommendation: challenges, progress, and prospects. arXiv preprint arXiv:2103.01696 (2021)."},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3104873"},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3463010"},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/3488560.3498392"}],"event":{"name":"WWW '23: The ACM Web Conference 2023","location":"Austin TX USA","acronym":"WWW '23","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Proceedings of the ACM Web Conference 2023"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3543507.3583263","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3543507.3583263","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:37:22Z","timestamp":1750178242000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3543507.3583263"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,30]]},"references-count":50,"alternative-id":["10.1145\/3543507.3583263","10.1145\/3543507"],"URL":"https:\/\/doi.org\/10.1145\/3543507.3583263","relation":{},"subject":[],"published":{"date-parts":[[2023,4,30]]},"assertion":[{"value":"2023-04-30","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}