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Inf. Syst."],"published-print":{"date-parts":[[2026,1,31]]},"abstract":"<jats:p>\n                    User modeling serves as a crucial foundation for researchers to capture useful potential characteristics, playing a pivotal role in various applications such as recommender systems. One common challenge in user modeling is the cold-start problem, where interactions are notably limited for new users. To tackle this issue, the paradigm of meta-learning has been introduced to user modeling, yielding promising results. Similar to a guidebook for a new traveler, meta-learning significantly influences decision-making for new users in critical scenarios, such as career recommendations. Consequently, the issue of fairness in meta-learning has become paramount. Several methods have been proposed to mitigate unfairness in meta-learning and have shown promising results. However, a fundamental question remains unexplored: What is the critical factor leading to unfairness in meta-learned user modeling? Through theoretical analysis that integrates the meta-learning paradigm with group fairness metrics, we identify group proportion imbalance as a critical factor. Subsequently, another question arises: How can we mitigate the influence of this factor to enhance fairness while ensuring accuracy? To this end, we introduce a novel\n                    <jats:underline>F<\/jats:underline>\n                    airness-aware\n                    <jats:underline>A<\/jats:underline>\n                    daptive\n                    <jats:underline>S<\/jats:underline>\n                    ampling framework for me\n                    <jats:underline>T<\/jats:underline>\n                    a-learning, abbreviated as FAST. Its core concept involves adaptively adjusting the sampling distribution for different user groups during the interleaved training process of meta-learning. Moreover, we provide theoretical guarantees demonstrating the convergence of FAST, showcasing its potential to effectively eliminate unfairness. Furthermore, to ensure model accuracy, we enhance FAST with FAST+ by introducing a hybrid sampling strategy at an individual level. This strategy prioritizes fairness and thoroughly explores important users during the sampling process, allowing for a better accuracy-fairness tradeoff. Finally, we conduct extensive experiments on real-world datasets, which demonstrate the effectiveness of both FAST and FAST+ frameworks.\n                  <\/jats:p>","DOI":"10.1145\/3769296","type":"journal-article","created":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T15:05:56Z","timestamp":1759244756000},"page":"1-39","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A Hybrid Adaptive Sampling Strategy for Fair and Accurate Meta-learned User Modeling"],"prefix":"10.1145","volume":"44","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, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6956-5550","authenticated-orcid":false,"given":"Qi","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China and Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-3346-317X","authenticated-orcid":false,"given":"Zirui","family":"Hu","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-7018-1101","authenticated-orcid":false,"given":"Yi","family":"Zhan","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1661-0420","authenticated-orcid":false,"given":"Zhenya","family":"Huang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China and Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0894-7023","authenticated-orcid":false,"given":"Weibo","family":"Gao","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6922-856X","authenticated-orcid":false,"given":"Qingyang","family":"Mao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4835-4102","authenticated-orcid":false,"given":"Enhong","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China, Hefei, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,12,15]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1108\/EUM0000000006984"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371832"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2019.8852100"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i4.25629"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1145\/3209978.3210063"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511804441"},{"key":"e_1_3_2_8_2","unstructured":"Xuheng Cai Chao Huang Lianghao Xia and Xubin Ren. 2023. 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