{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T23:17:38Z","timestamp":1771024658188,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":48,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T00:00:00Z","timestamp":1715558400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key Research and Development Program of China","award":["Grant No. 2021YFF0901003"],"award-info":[{"award-number":["Grant No. 2021YFF0901003"]}]},{"name":"the Anhui Provincial Natural Science Foundation","award":["No. 2308085MG226"],"award-info":[{"award-number":["No. 2308085MG226"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,5,13]]},"DOI":"10.1145\/3589334.3645369","type":"proceedings-article","created":{"date-parts":[[2024,5,8]],"date-time":"2024-05-08T07:08:13Z","timestamp":1715152093000},"page":"3241-3252","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Enhancing Fairness in Meta-learned User Modeling via Adaptive Sampling"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-8689-0763","authenticated-orcid":false,"given":"Zheng","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, University of Science and Technology of China &amp; State Key Laboratory of Cognitive Intelligence, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6956-5550","authenticated-orcid":false,"given":"Qi","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, University of Science and Technology of China &amp; State Key Laboratory of Cognitive Intelligence, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-3346-317X","authenticated-orcid":false,"given":"Zirui","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, University of Science and Technology of China &amp; State Key Laboratory of Cognitive Intelligence, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-7018-1101","authenticated-orcid":false,"given":"Yi","family":"Zhan","sequence":"additional","affiliation":[{"name":"School of Data Science, University of Science and Technology of China &amp; State Key Laboratory of Cognitive Intelligence, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1661-0420","authenticated-orcid":false,"given":"Zhenya","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, University of Science and Technology of China &amp; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0894-7023","authenticated-orcid":false,"given":"Weibo","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, University of Science and Technology of China &amp; State Key Laboratory of Cognitive Intelligence, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6922-856X","authenticated-orcid":false,"given":"Qingyang","family":"Mao","sequence":"additional","affiliation":[{"name":"School of Data Science, University of Science and Technology of China &amp; State Key Laboratory of Cognitive Intelligence, Hefei, China"}]}],"member":"320","published-online":{"date-parts":[[2024,5,13]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371832"},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2019.8852100"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i4.25629"},{"key":"e_1_3_2_2_4_1","volume-title":"Convex optimization","author":"Boyd Stephen P","unstructured":"Stephen P Boyd and Lieven Vandenberghe. 2004. Convex optimization. Cambridge university press."},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583355"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/2988450.2988454"},{"key":"e_1_3_2_2_7_1","volume-title":"Robust fair clustering: A novel fairness attack and defense framework. arXiv preprint arXiv:2210.01953","author":"Chhabra Anshuman","year":"2022","unstructured":"Anshuman Chhabra, Peizhao Li, Prasant Mohapatra, and Hongfu Liu. 2022. Robust fair clustering: A novel fairness attack and defense framework. arXiv preprint arXiv:2210.01953 (2022)."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583307"},{"key":"e_1_3_2_2_9_1","volume-title":"Fairness in graph mining: A survey","author":"Dong Yushun","year":"2023","unstructured":"Yushun Dong, Jing Ma, Song Wang, Chen Chen, and Jundong Li. 2023. Fairness in graph mining: A survey. IEEE Transactions on Knowledge and Data Engineering (2023)."},{"key":"e_1_3_2_2_10_1","volume-title":"International conference on machine learning. PMLR, 1126--1135","author":"Finn Chelsea","year":"2017","unstructured":"Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017. Model-agnostic metalearning for fast adaptation of deep networks. In International conference on machine learning. PMLR, 1126--1135."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3568022"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462932"},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3591774"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/CCAA.2017.8229786"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052569"},{"key":"e_1_3_2_2_16_1","first-page":"2","article-title":"Admissions testing & institutional admissions processes: The search for transparency and fairness","volume":"84","author":"Hossler Don","year":"2009","unstructured":"Don Hossler and David Kalsbeek. 2009. Admissions testing & institutional admissions processes: The search for transparency and fairness. College and University 84, 4 (2009), 2.","journal-title":"College and University"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449904"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330859"},{"key":"e_1_3_2_2_19_1","volume-title":"International Conference on Machine Learning. PMLR, 12917--12930","author":"Li Peizhao","year":"2022","unstructured":"Peizhao Li and Hongfu Liu. 2022. Achieving fairness at no utility cost via data reweighing with influence. In International Conference on Machine Learning. PMLR, 12917--12930."},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449866"},{"key":"e_1_3_2_2_21_1","volume-title":"Fairness in recommendation: A survey. arXiv preprint arXiv:2205.13619","author":"Li Yunqi","year":"2022","unstructured":"Yunqi Li, Hanxiong Chen, Shuyuan Xu, Yingqiang Ge, Juntao Tan, Shuchang Liu, and Yongfeng Zhang. 2022. Fairness in recommendation: A survey. arXiv preprint arXiv:2205.13619 (2022)."},{"key":"e_1_3_2_2_22_1","unstructured":"Dawen Liang Laurent Charlin and David M Blei. 2016. Causal inference for recommendation. In Causation: Foundation to Application Workshop at UAI. AUAI."},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449908"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"crossref","unstructured":"Qi Liu. 2021. Towards a New Generation of Cognitive Diagnosis. In IJCAI. 4961--4964.","DOI":"10.24963\/ijcai.2021\/703"},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3614837"},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11704-021-0053-1"},{"key":"e_1_3_2_2_27_1","volume-title":"International Conference on Learning Representations.","author":"Roh Yuji","year":"2021","unstructured":"Yuji Roh, Kangwook Lee, Steven Euijong Whang, and Changho Suh. 2021. Fairbatch: Batch selection for model fairness. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3351095.3372839"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583548"},{"key":"e_1_3_2_2_30_1","volume-title":"A meta-learning perspective on cold-start recommendations for items. Advances in neural information processing systems 30","author":"Vartak Manasi","year":"2017","unstructured":"Manasi Vartak, Arvind Thiagarajan, Conrado Miranda, Jeshua Bratman, and Hugo Larochelle. 2017. A meta-learning perspective on cold-start recommendations for items. Advances in neural information processing systems 30 (2017)."},{"key":"e_1_3_2_2_31_1","volume-title":"Dropoutnet: Addressing cold start in recommender systems. Advances in neural information processing systems 30","author":"Volkovs Maksims","year":"2017","unstructured":"Maksims Volkovs, Guangwei Yu, and Tomi Poutanen. 2017. Dropoutnet: Addressing cold start in recommender systems. Advances in neural information processing systems 30 (2017)."},{"key":"e_1_3_2_2_32_1","volume-title":"Deep Meta-learning in Recommendation Systems: A Survey. arXiv preprint arXiv:2206.04415","author":"Wang Chunyang","year":"2022","unstructured":"Chunyang Wang, Yanmin Zhu, Haobing Liu, Tianzi Zang, Jiadi Yu, and Feilong Tang. 2022. Deep Meta-learning in Recommendation Systems: A Survey. arXiv preprint arXiv:2206.04415 (2022)."},{"key":"e_1_3_2_2_33_1","volume-title":"Proceedings of the AAAI conference on artificial intelligence","volume":"34","author":"Liu Qi","year":"2020","unstructured":"FeiWang, Qi Liu, Enhong Chen, Zhenya Huang, Yuying Chen, Yu Yin, Zai Huang, and Shijin Wang. 2020. Neural cognitive diagnosis for intelligent education systems. In Proceedings of the AAAI conference on artificial intelligence, Vol. 34. 6153--6161."},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539269"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM50108.2020.00075"},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3450015"},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583344"},{"key":"e_1_3_2_2_38_1","volume-title":"Beyond parity: Fairness objectives for collaborative filtering. Advances in neural information processing systems 30","author":"Yao Sirui","year":"2017","unstructured":"Sirui Yao and Bert Huang. 2017. Beyond parity: Fairness objectives for collaborative filtering. Advances in neural information processing systems 30 (2017)."},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17287"},{"key":"e_1_3_2_2_40_1","volume-title":"FairLISA: Fair User Modeling with Limited Sensitive Attributes Information. In Thirty-seventh Conference on Neural Information Processing Systems.","author":"Zhang Zheng","year":"2023","unstructured":"Zheng Zhang, Qi Liu, Hao Jiang, Fei Wang, Yan Zhuang, Le Wu, Weibo Gao, and Enhong Chen. 2023. FairLISA: Fair User Modeling with Limited Sensitive Attributes Information. In Thirty-seventh Conference on Neural Information Processing Systems."},{"key":"e_1_3_2_2_41_1","volume-title":"Understanding and Improving Fairness in Cognitive Diagnosis. SCIENCE CHINA Information Sciences","author":"Zhang Zheng","year":"2023","unstructured":"Zheng Zhang, Le Wu, Qi Liu, Jiayu Liu, Zhenya Huang, Yu Yin, Yan Zhuang, Weibo Gao, and Enhong Chen. 2023. Understanding and Improving Fairness in Cognitive Diagnosis. SCIENCE CHINA Information Sciences (2023)."},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467389"},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM50108.2020.00091"},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICBK50248.2020.00047"},{"key":"e_1_3_2_2_45_1","volume-title":"Contrastive Collaborative Filtering for Cold-Start Item Recommendation. arXiv preprint arXiv:2302.02151","author":"Zhou Zhihui","year":"2023","unstructured":"Zhihui Zhou, Lilin Zhang, and Ning Yang. 2023. Contrastive Collaborative Filtering for Cold-Start Item Recommendation. arXiv preprint arXiv:2302.02151 (2023)."},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462948"},{"key":"e_1_3_2_2_47_1","unstructured":"Yan Zhuang Qi Liu Yuting Ning Weizhe Huang Rui Lv Zhenya Huang Guanhao Zhao Zheng Zhang Qingyang Mao Shijin Wang et al. 2023. Efficiently Measuring the Cognitive Ability of LLMs: An Adaptive Testing Perspective. arXiv preprint arXiv:2306.10512 (2023)."},{"key":"e_1_3_2_2_48_1","volume-title":"Predictive statistical models for user modeling. User Modeling and User-Adapted Interaction","author":"Zukerman Ingrid","year":"2001","unstructured":"Ingrid Zukerman and David W Albrecht. 2001. Predictive statistical models for user modeling. User Modeling and User-Adapted Interaction (2001)."}],"event":{"name":"WWW '24: The ACM Web Conference 2024","location":"Singapore Singapore","acronym":"WWW '24","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Proceedings of the ACM Web Conference 2024"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3589334.3645369","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3589334.3645369","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T00:24:48Z","timestamp":1755822288000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3589334.3645369"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,13]]},"references-count":48,"alternative-id":["10.1145\/3589334.3645369","10.1145\/3589334"],"URL":"https:\/\/doi.org\/10.1145\/3589334.3645369","relation":{},"subject":[],"published":{"date-parts":[[2024,5,13]]},"assertion":[{"value":"2024-05-13","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}