{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T00:32:27Z","timestamp":1770337947934,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":63,"publisher":"ACM","license":[{"start":{"date-parts":[[2025,7,20]],"date-time":"2025-07-20T00:00:00Z","timestamp":1752969600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["226202400182"],"award-info":[{"award-number":["226202400182"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62472387, 62072408, 62394341, U244120033, U24A20336 and 62402425"],"award-info":[{"award-number":["62472387, 62072408, 62394341, U244120033, U24A20336 and 62402425"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["LDQ24F020001, LR24F020004 and LD24F020002"],"award-info":[{"award-number":["LDQ24F020001, LR24F020004 and LD24F020002"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,7,20]]},"DOI":"10.1145\/3690624.3709346","type":"proceedings-article","created":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T18:48:32Z","timestamp":1743792512000},"page":"1587-1598","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["<scp>FLMarket:<\/scp>\n            Enabling Privacy-preserved Pre-training Data Pricing for Federated Learning"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2914-912X","authenticated-orcid":false,"given":"Zhenyu","family":"Wen","sequence":"first","affiliation":[{"name":"Zhejiang University of Technology, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-7416-4575","authenticated-orcid":false,"given":"Wanglei","family":"Feng","sequence":"additional","affiliation":[{"name":"Zhejiang University of Technology, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6631-4887","authenticated-orcid":false,"given":"Di","family":"Wu","sequence":"additional","affiliation":[{"name":"University of St. Andrews, St Andrews, Scotland, United Kingdom"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5957-2781","authenticated-orcid":false,"given":"Haozhen","family":"Hu","sequence":"additional","affiliation":[{"name":"Zhejiang University of Technology, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4466-3805","authenticated-orcid":false,"given":"Chang","family":"Xu","sequence":"additional","affiliation":[{"name":"Zhejiang University of Technology, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7058-0360","authenticated-orcid":false,"given":"Bin","family":"Qian","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9956-3732","authenticated-orcid":false,"given":"Zhen","family":"Hong","sequence":"additional","affiliation":[{"name":"Zhejiang University of Technology, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9419-5635","authenticated-orcid":false,"given":"Cong","family":"Wang","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4268-372X","authenticated-orcid":false,"given":"Shouling","family":"Ji","sequence":"additional","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}]}],"member":"320","published-online":{"date-parts":[[2025,7,20]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.econlp-1.7"},{"key":"e_1_3_2_2_2_1","volume-title":"A Theory of Learning from Different Domains. Machine learning 79","author":"Ben-David Shai","year":"2010","unstructured":"Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan. 2010. A Theory of Learning from Different Domains. Machine learning 79 (2010), 151--175."},{"key":"e_1_3_2_2_3_1","volume-title":"Practical Secure Aggregation for Privacy-Preserving Machine Learning. In ACM Conference on Computer and Communications Security (CCS). 1175--1191","author":"Bonawitz Keith","year":"2017","unstructured":"Keith Bonawitz, Vladimir Ivanov, Ben Kreuter, Antonio Marcedone, H. Brendan McMahan, Sarvar Patel, Daniel Ramage, Aaron Segal, and Karn Seth. 2017. Practical Secure Aggregation for Privacy-Preserving Machine Learning. In ACM Conference on Computer and Communications Security (CCS). 1175--1191."},{"key":"e_1_3_2_2_4_1","volume-title":"Protecting Global Properties of Datasets with Distribution Privacy Mechanisms. In International Conference on Artificial Intelligence and Statistics (AISTATS). 7472--7491","author":"Chen Michelle","year":"2023","unstructured":"Michelle Chen and Olga Ohrimenko. 2023. Protecting Global Properties of Datasets with Distribution Privacy Mechanisms. In International Conference on Artificial Intelligence and Statistics (AISTATS). 7472--7491."},{"key":"e_1_3_2_2_5_1","unstructured":"Luke N Darlow Elliot J Crowley Antreas Antoniou and Amos J Storkey. 2018. Cinic-10 is not Imagenet or Cifar-10. (2018). arXiv:1810.03505"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-021-01506-3"},{"key":"e_1_3_2_2_7_1","volume-title":"Fair: Quality-Aware Federated Learning with Precise User Incentive and Model Aggregation. In IEEE International Conference on Computer Communications (INFOCOM). 1--10","author":"Deng Yongheng","year":"2021","unstructured":"Yongheng Deng, Feng Lyu, Ju Ren, Yi-Chao Chen, Peng Yang, Yuezhi Zhou, and Yaoxue Zhang. 2021. Fair: Quality-Aware Federated Learning with Precise User Incentive and Model Aggregation. In IEEE International Conference on Computer Communications (INFOCOM). 1--10."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2022.3195207"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2021.3134647"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/963770.963776"},{"key":"e_1_3_2_2_11_1","volume-title":"FIFL: A Fair Incentive Mechanism for Federated Learning. In International Conference on Parallel Processing (ICPP). 1--10","author":"Gao Liang","year":"2021","unstructured":"Liang Gao, Li Li, Yingwen Chen, Wenli Zheng, ChengZhong Xu, and Ming Xu. 2021. FIFL: A Fair Incentive Mechanism for Federated Learning. In International Conference on Parallel Processing (ICPP). 1--10."},{"key":"e_1_3_2_2_12_1","volume-title":"Distribution Inference Risks: Identifying and Mitigating Sources of Leakage. In IEEE Conference on Secure and Trustworthy Machine Learning (SaTML). 136--149","author":"Hartmann Valentin","year":"2023","unstructured":"Valentin Hartmann, L\u00e9o Meynent, Maxime Peyrard, Dimitrios Dimitriadis, Shruti Tople, and Robert West. 2023. Distribution Inference Risks: Identifying and Mitigating Sources of Leakage. In IEEE Conference on Secure and Trustworthy Machine Learning (SaTML). 136--149."},{"key":"e_1_3_2_2_13_1","volume-title":"Deep Residual Learning for Image Recognition. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 770--778","author":"He Kaiming","year":"2016","unstructured":"Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep Residual Learning for Image Recognition. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 770--778."},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1976.1055638"},{"key":"e_1_3_2_2_15_1","unstructured":"Tzu-Ming Harry Hsu Hang Qi et al. 2019. Measuring the Effects of Non-identical Data Distribution for Federated Visual Classification. (2019). arXiv:1909.06335"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2022.3148113"},{"key":"e_1_3_2_2_17_1","unstructured":"Jared Kaplan Sam McCandlish Tom Henighan Tom B Brown Benjamin Chess Rewon Child Scott Gray Alec Radford Jeffrey Wu and Dario Amodei. 2020. Scaling Laws for Neural Language Models. (2020). arXiv:2001.08361"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/T-AFFC.2011.15"},{"key":"e_1_3_2_2_19_1","unstructured":"A. Krizhevsky and G. Hinton. 2009. Learning Multiple Layers of Features from Tiny Images. Handbook of Systemic Autoimmune Diseases 1 4 (2009)."},{"key":"e_1_3_2_2_20_1","volume-title":"USENIX Symposium on Operating Systems Design and Implementation (OSDI). 19--35","author":"Lai Fan","year":"2021","unstructured":"Fan Lai, Xiangfeng Zhu, Harsha V Madhyastha, and Mosharaf Chowdhury. 2021. Oort: Efficient Federated Learning via Guided Participant Selection. In USENIX Symposium on Operating Systems Design and Implementation (OSDI). 19--35."},{"key":"e_1_3_2_2_21_1","volume-title":"Sample-Level Data Selection for Federated Learning. In IEEE International Conference on Computer Communications (INFOCOM). 1--10","author":"Li Anran","year":"2021","unstructured":"Anran Li, Lan Zhang, Juntao Tan, Yaxuan Qin, Junhao Wang, and Xiang-Yang Li. 2021. Sample-Level Data Selection for Federated Learning. In IEEE International Conference on Computer Communications (INFOCOM). 1--10."},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE53745.2022.00077"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3576915.3623134"},{"key":"e_1_3_2_2_24_1","volume-title":"Federated Optimization in Heterogeneous Networks. Conference on Machine Learning and Systems, 429--450","author":"Li Tian","year":"2020","unstructured":"Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith. 2020. Federated Optimization in Heterogeneous Networks. Conference on Machine Learning and Systems, 429--450."},{"key":"e_1_3_2_2_25_1","first-page":"1269","article-title":"Performance-Based Pricing for Federated Learning via Auction","volume":"17","author":"Li Zitao","year":"2024","unstructured":"Zitao Li, Bolin Ding, Liuyi Yao, Yaliang Li, Xiaokui Xiao, and Jingren Zhou. 2024. Performance-Based Pricing for Federated Learning via Auction. The VLDB Journal 17, 6 (2024), 1269--1282.","journal-title":"The VLDB Journal"},{"key":"e_1_3_2_2_26_1","unstructured":"Jierui Lin Min Du and Jian Liu. 2019. Free-Riders in Federated Learning: Attacks and Defenses. (2019). arXiv:1911.12560"},{"key":"e_1_3_2_2_27_1","volume-title":"Ensemble Distillation for Robust Model Fusion in Federated Learning. Annual Conference on Neural Information Processing Systems (NeurIPS), 2351--2363","author":"Lin Tao","year":"2020","unstructured":"Tao Lin, Lingjing Kong, Sebastian U Stich, and Martin Jaggi. 2020. Ensemble Distillation for Robust Model Fusion in Federated Learning. Annual Conference on Neural Information Processing Systems (NeurIPS), 2351--2363."},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2021.3050339"},{"key":"e_1_3_2_2_29_1","volume-title":"Fedcoin: A Peer-to-Peer Payment system for federated learning. In Federated Learning","author":"Liu Yuan","year":"2020","unstructured":"Yuan Liu, Zhengpeng Ai, Shuai Sun, Shuangfeng Zhang, Zelei Liu, and Han Yu. 2020. Fedcoin: A Peer-to-Peer Payment system for federated learning. In Federated Learning. Springer, 125--138."},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/2588555.2593679"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2024.3364372"},{"key":"e_1_3_2_2_32_1","first-page":"699","article-title":"Auctions and Bidding","volume":"25","author":"McAfee R Preston","year":"1987","unstructured":"R Preston McAfee and John McMillan. 1987. Auctions and Bidding. Journal of Economic Literature 25, 2 (1987), 699--738.","journal-title":"Journal of Economic Literature"},{"key":"e_1_3_2_2_33_1","volume-title":"Game of Gradients: Mitigating Irrelevant Clients in Federated Learning. In AAAI Conference on Artificial Intelligence (AAAI). 9046--9054","author":"Nagalapatti Lokesh","year":"2021","unstructured":"Lokesh Nagalapatti and Ramasuri Narayanam. 2021. Game of Gradients: Mitigating Irrelevant Clients in Federated Learning. In AAAI Conference on Artificial Intelligence (AAAI). 9046--9054."},{"key":"e_1_3_2_2_34_1","volume-title":"Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge. In IEEE International Conference on Communications (ICC). 1--7.","author":"Nishio Takayuki","year":"2019","unstructured":"Takayuki Nishio and Ryo Yonetani. 2019. Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge. In IEEE International Conference on Communications (ICC). 1--7."},{"key":"e_1_3_2_2_35_1","volume-title":"Communication-Efficient Device Scheduling for Federated Learning Using Stochastic Optimization. In IEEE International Conference on Computer Communications (INFOCOM). 1449--1458","author":"Perazzone Jake","year":"2022","unstructured":"Jake Perazzone, Shiqiang Wang, Mingyue Ji, and Kevin S Chan. 2022. Communication-Efficient Device Scheduling for Federated Learning Using Stochastic Optimization. In IEEE International Conference on Computer Communications (INFOCOM). 1449--1458."},{"key":"e_1_3_2_2_36_1","first-page":"1","article-title":"Orchestrating the Development Lifecycle of Machine Learning-based IoT Applications:A Taxonomy and","volume":"53","author":"Qian Bin","year":"2020","unstructured":"Bin Qian, Jie Su, Zhenyu Wen, Devki Nandan Jha, Yinhao Li, Yu Guan, Deepak Puthal, Philip James, Renyu Yang, Albert Y Zomaya, et al. 2020. Orchestrating the Development Lifecycle of Machine Learning-based IoT Applications:A Taxonomy and Survey. Comput. Surveys 53, 4 (2020), 1--47.","journal-title":"Survey. Comput. Surveys"},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSC.2023.3332102"},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"crossref","unstructured":"Monica Ribero and Haris Vikalo. 2020. Communication-Efficient Federated Learning via Optimal Client Sampling. (2020). arXiv:2007.15197","DOI":"10.52591\/lxai2020071310"},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2022.3217271"},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/2481528.2481532"},{"key":"e_1_3_2_2_41_1","volume-title":"Budget Feasible Mechanisms. In IEEE Annual Symposium on Foundations of Computer Science (FOCS). 765--774","author":"Singer Yaron","year":"2010","unstructured":"Yaron Singer. 2010. Budget Feasible Mechanisms. In IEEE Annual Symposium on Foundations of Computer Science (FOCS). 765--774."},{"key":"e_1_3_2_2_42_1","volume-title":"Profit Allocation for Federated Learning. In IEEE International Conference on Big Data (Big Data). 2577--2586","author":"Tianshu","unstructured":"Tianshu Song et al. 2019. Profit Allocation for Federated Learning. In IEEE International Conference on Big Data (Big Data). 2577--2586."},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12525-015-0191-0"},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM48880.2022.9796833"},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2024.3351864"},{"key":"e_1_3_2_2_46_1","volume-title":"An Incentive Mechanism for Cross-Silo Federated Learning: A Public Goods Perspective. In IEEE International Conference on Computer Communications (INFOCOM). 1--10","author":"Tang Ming","year":"2021","unstructured":"Ming Tang and Vincent WS Wong. 2021. An Incentive Mechanism for Cross-Silo Federated Learning: A Public Goods Perspective. In IEEE International Conference on Computer Communications (INFOCOM). 1--10."},{"key":"e_1_3_2_2_47_1","volume-title":"Closed-Loop Supply Chain Decision Considering Information Reliability and Security: Should the Supply Chain Adopt Federated Learning Decision Support Systems? Annals of Operations Research","author":"Wan Xiaole","year":"2023","unstructured":"Xiaole Wan, Dongqian Yang, Tongtong Wang, and Muhammet Deveci. 2023. Closed-Loop Supply Chain Decision Considering Information Reliability and Security: Should the Supply Chain Adopt Federated Learning Decision Support Systems? Annals of Operations Research (2023), 1--37."},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPDS.2020.3023905"},{"key":"e_1_3_2_2_49_1","volume-title":"Measure Contribution of Participants in Federated Learning. In IEEE International Conference on Big Data (Big Data). 2597--2604","author":"Dang Charlie Xiaoqian","year":"2019","unstructured":"GuanWang, Charlie Xiaoqian Dang, and Ziye Zhou. 2019. Measure Contribution of Participants in Federated Learning. In IEEE International Conference on Big Data (Big Data). 2597--2604."},{"key":"e_1_3_2_2_50_1","unstructured":"Hongyi Wang Mikhail Yurochkin Yuekai Sun Dimitris Papailiopoulos and Yasaman Khazaeni. 2020. Federated Learning with Matched Averaging. (2020). arXiv:2002.06440"},{"key":"e_1_3_2_2_51_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2023.3288936"},{"key":"e_1_3_2_2_52_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM48880.2022.9796841"},{"key":"e_1_3_2_2_53_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-021-00700-6"},{"key":"e_1_3_2_2_54_1","volume-title":"Annual Conference on Neural Information Processing Systems (NeurIPS), 16104--16117","author":"Xu Xinyi","year":"2021","unstructured":"Xinyi Xu, Lingjuan Lyu, Xingjun Ma, Chenglin Miao, Chuan Sheng Foo, and Bryan Kian Hsiang Low. 2021. Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning. Annual Conference on Neural Information Processing Systems (NeurIPS), 16104--16117."},{"key":"e_1_3_2_2_55_1","volume-title":"Theory and Application of Trapdoor Functions. In IEEE Annual Symposium on Foundations of Computer Science (SFCS). 80--91","author":"Yao Andrew C","year":"1982","unstructured":"Andrew C Yao. 1982. Theory and Application of Trapdoor Functions. In IEEE Annual Symposium on Foundations of Computer Science (SFCS). 80--91."},{"key":"e_1_3_2_2_56_1","doi-asserted-by":"publisher","DOI":"10.1109\/MNET.112.2100706"},{"key":"e_1_3_2_2_57_1","volume-title":"Fmore: An Incentive Scheme of Multi-Dimensional Auction for Federated Learning in Mec. In International Conference on Distributed Computing Systems (ICDCS). 278--288","author":"Zeng Rongfei","year":"2020","unstructured":"Rongfei Zeng, Shixun Zhang, Jiaqi Wang, and Xiaowen Chu. 2020. Fmore: An Incentive Scheme of Multi-Dimensional Auction for Federated Learning in Mec. In International Conference on Distributed Computing Systems (ICDCS). 278--288."},{"key":"e_1_3_2_2_58_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.2967772"},{"key":"e_1_3_2_2_59_1","first-page":"1035","article-title":"A Survey of Incentive Mechanism design for Federated Learning","volume":"10","author":"Zhan Yufeng","year":"2021","unstructured":"Yufeng Zhan, Jie Zhang, Zicong Hong, LeijieWu, Peng Li, and Song Guo. 2021. A Survey of Incentive Mechanism design for Federated Learning. IEEE Transactions on Emerging Topics in Computing 10, 2 (2021), 1035--1044.","journal-title":"IEEE Transactions on Emerging Topics in Computing"},{"key":"e_1_3_2_2_60_1","volume-title":"Incentive Mechanism for Horizontal Federated Learning Based on Reputation and Reverse Auction. In International Conference of World Wide Web (WWW). 947--956","author":"Zhang Jingwen","year":"2021","unstructured":"Jingwen Zhang, Yuezhou Wu, and Rong Pan. 2021. Incentive Mechanism for Horizontal Federated Learning Based on Reputation and Reverse Auction. In International Conference of World Wide Web (WWW). 947--956."},{"key":"e_1_3_2_2_61_1","volume-title":"USENIX Security Symposium (USENIX Security). 2687--2704","author":"Zhang Wanrong","year":"2021","unstructured":"Wanrong Zhang, Shruti Tople, and Olga Ohrimenko. 2021. Leakage of Dataset Properties in {Multi-Party} Machine Learning. In USENIX Security Symposium (USENIX Security). 2687--2704."},{"key":"e_1_3_2_2_62_1","volume-title":"FL-Market: Trading Private Models in Federated Learning. In IEEE International Conference on Big Data (Big Data). 1525--1534","author":"Zheng Shuyuan","year":"2022","unstructured":"Shuyuan Zheng, Yang Cao, Masatoshi Yoshikawa, Huizhong Li, and Qiang Yan. 2022. FL-Market: Trading Private Models in Federated Learning. In IEEE International Conference on Big Data (Big Data). 1525--1534."},{"key":"e_1_3_2_2_63_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2016.2632721"}],"event":{"name":"KDD '25: The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Toronto ON Canada","acronym":"KDD '25","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3690624.3709346","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3690624.3709346","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,16]],"date-time":"2025-08-16T15:45:14Z","timestamp":1755359114000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3690624.3709346"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,20]]},"references-count":63,"alternative-id":["10.1145\/3690624.3709346","10.1145\/3690624"],"URL":"https:\/\/doi.org\/10.1145\/3690624.3709346","relation":{},"subject":[],"published":{"date-parts":[[2025,7,20]]},"assertion":[{"value":"2025-07-20","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}