{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T09:16:08Z","timestamp":1780391768806,"version":"3.54.1"},"publisher-location":"New York, NY, USA","reference-count":54,"publisher":"ACM","license":[{"start":{"date-parts":[[2025,5,8]],"date-time":"2025-05-08T00:00:00Z","timestamp":1746662400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"the National Natural Science Foundation of China","award":["U22A2098"],"award-info":[{"award-number":["U22A2098"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,5,8]]},"DOI":"10.1145\/3701716.3715860","type":"proceedings-article","created":{"date-parts":[[2025,5,23]],"date-time":"2025-05-23T16:06:11Z","timestamp":1748016371000},"page":"21-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["A Tutorial of Personalized Federated Recommender Systems: Recent Advances and Future Directions"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5301-7779","authenticated-orcid":false,"given":"Jing","family":"Jiang","sequence":"first","affiliation":[{"name":"Australian Artificial Intelligence Institute, School of Computer Science, University of Technology Sydney, Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0825-872X","authenticated-orcid":false,"given":"Chunxu","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Jilin University, Jilin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3840-4815","authenticated-orcid":false,"given":"Honglei","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8897-8905","authenticated-orcid":false,"given":"Zhiwei","family":"Li","sequence":"additional","affiliation":[{"name":"Australian Artificial Intelligence Institute, University of Technology Sydney, Sydney, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2965-6196","authenticated-orcid":false,"given":"Yidong","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5559-2547","authenticated-orcid":false,"given":"Bo","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Jilin University, Jilin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,5,23]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Xiangmin Zhou, Yan Zhang, and Jie Shao.","author":"Ali Waqar","year":"2024","unstructured":"Waqar Ali, Muhammad Ammad-ud din, Xiangmin Zhou, Yan Zhang, and Jie Shao. 2024. Communication-Efficient Federated Neural Collaborative Filtering with Multi-Armed Bandits. RecSys (2024)."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2020.3014880"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3501812"},{"key":"e_1_3_2_1_4_1","volume-title":"Xiaoming Wei, and Enhua Wu.","author":"Chen Dengsheng","year":"2023","unstructured":"Dengsheng Chen, Jie Hu, Vince Junkai Tan, Xiaoming Wei, and Enhua Wu. 2023. Elastic Aggregation for Federated Optimization. In CVPR. 12187--12197."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"crossref","unstructured":"Tong Chen Hongzhi Yin Yujia Zheng Zi Huang Yang Wang and Meng Wang. 2021. Learning Elastic Embeddings for Customizing On-Device Recommenders. In SIGKDD. 138--147.","DOI":"10.1145\/3447548.3467220"},{"key":"e_1_3_2_1_6_1","volume-title":"FedRL: A Reinforcement Learning Federated Recommender System for Efficient Communication Using Reinforcement Selector and Hypernet Generator. RecSys","author":"Di Yicheng","year":"2024","unstructured":"Yicheng Di, Hongjian Shi, Ruhui Ma, Honghao Gao, Yuan Liu, and Weiyu Wang. 2024. FedRL: A Reinforcement Learning Federated Recommender System for Efficient Communication Using Reinforcement Selector and Hypernet Generator. RecSys (2024)."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"crossref","unstructured":"Xuanang Ding Guohui Li Ling Yuan Lu Zhang and Qian Rong. 2023. Combining Autoencoder with Adaptive Differential Privacy for Federated Collaborative Filtering. In DASFAA. 661--676.","DOI":"10.1007\/978-3-031-30637-2_44"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"crossref","unstructured":"Yuchen Ding Siqing Zhang Boyu Fan Wei Sun Yong Liao and Peng Yuan Zhou. 2024. FedLoCA: Low-Rank Coordinated Adaptation with Knowledge Decoupling for Federated Recommendations. In RecSys. 690--700.","DOI":"10.1145\/3640457.3688112"},{"key":"e_1_3_2_1_9_1","unstructured":"Avishek Ghosh Jichan Chung Dong Yin and Kannan Ramchandran. 2020. An Efficient Framework for Clustered Federated Learning. In NeurIPS. 19586--19597."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"crossref","unstructured":"Xinrui He Shuo Liu Jacky Keung and Jingrui He. 2024. Co-clustering for Federated Recommender System. In WWW. 3821--3832.","DOI":"10.1145\/3589334.3645626"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"crossref","unstructured":"Shaoxiong Ji Shirui Pan Guodong Long Xue Li Jing Jiang and Zi Huang. 2019. Learning Private Neural Language Modeling with Attentive Aggregation. In IJCNN. 1--8.","DOI":"10.1109\/IJCNN.2019.8852464"},{"key":"e_1_3_2_1_12_1","volume-title":"Zareen Alamgir, and Muhammad Ammad-Ud-Din.","author":"Khan Farwa K","year":"2021","unstructured":"Farwa K Khan, Adrian Flanagan, Kuan Eeik Tan, Zareen Alamgir, and Muhammad Ammad-Ud-Din. 2021. A Payload Optimization Method for Federated Recommender Systems. In RecSys. 432--442."},{"key":"e_1_3_2_1_13_1","volume-title":"Personalized Item Representations in Federated Multimodal Recommendation. arXiv e-prints","author":"Li Zhiwei","year":"2024","unstructured":"Zhiwei Li, Guodong Long, Jing Jiang, and Chengqi Zhang. 2024b. Personalized Item Representations in Federated Multimodal Recommendation. arXiv e-prints (2024), arXiv--2410."},{"key":"e_1_3_2_1_14_1","unstructured":"Zhiwei Li Guodong Long and Tianyi Zhou. 2024a. Federated Recommendation with Additive Personalization. In ICLR. 1--18."},{"key":"e_1_3_2_1_15_1","volume-title":"Personalized Federated Collaborative Filtering: A Variational AutoEncoder Approach. AAAI","author":"Li Zhiwei","year":"2025","unstructured":"Zhiwei Li, Guodong Long, Tianyi Zhou, Jing Jiang, and Chengqi Zhang. 2025. Personalized Federated Collaborative Filtering: A Variational AutoEncoder Approach. AAAI (2025), Just Accepted."},{"key":"e_1_3_2_1_16_1","volume-title":"Fedrec: Lossless federated recommendation with explicit feedback. In AAAI. 4224--4231.","author":"Liang Feng","year":"2021","unstructured":"Feng Liang, Weike Pan, and Zhong Ming. 2021. Fedrec: Lossless federated recommendation with explicit feedback. In AAAI. 4224--4231."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2020.3017205"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"crossref","unstructured":"Zhaohao Lin Weike Pan and Zhong Ming. 2021. FR-FMSS: Federated Recommendation Via Fake Marks and Secret Sharing. In RecSys. 668--673.","DOI":"10.1145\/3460231.3478855"},{"key":"e_1_3_2_1_19_1","volume-title":"Pfa: Privacy-Preserving Federated Adaptation for Effective Model Personalization. In WWW. 923--934.","author":"Liu Bingyan","year":"2021","unstructured":"Bingyan Liu, Yao Guo, and Xiangqun Chen. 2021. Pfa: Privacy-Preserving Federated Adaptation for Effective Model Personalization. In WWW. 923--934."},{"key":"e_1_3_2_1_20_1","first-page":"1","article-title":"Federated Social Recommendation with Graph Neural Network","volume":"13","author":"Liu Zhiwei","year":"2022","unstructured":"Zhiwei Liu, Liangwei Yang, Ziwei Fan, Hao Peng, and Philip S Yu. 2022. Federated Social Recommendation with Graph Neural Network. TIST, Vol. 13, 4 (2022), 1--24.","journal-title":"TIST"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"crossref","unstructured":"Sichun Luo Yuanzhang Xiao and Linqi Song. 2022. Personalized Federated Recommendation Via Joint Representation Learning User Clustering and Model Adaptation. In CIKM. 4289--4293.","DOI":"10.1145\/3511808.3557668"},{"key":"e_1_3_2_1_22_1","unstructured":"Xiaosong Ma Jie Zhang Song Guo and Wenchao Xu. 2022. Layer-Wised Model Aggregation for Personalized Federated Learning. In CVPR. 10092--10101."},{"key":"e_1_3_2_1_23_1","volume-title":"Fedfast: Going Beyond Average for Faster Training of Federated Recommender Systems. In SIGKDD. 1234--1242.","author":"Muhammad Khalil","year":"2020","unstructured":"Khalil Muhammad, Qinqin Wang, Diarmuid O'Reilly-Morgan, Elias Tragos, Barry Smyth, Neil Hurley, James Geraci, and Aonghus Lawlor. 2020. Fedfast: Going Beyond Average for Faster Training of Federated Recommender Systems. In SIGKDD. 1234--1242."},{"key":"e_1_3_2_1_24_1","volume-title":"Dung D Le, and Kok-Seng Wong.","author":"Nguyen Ngoc-Hieu","year":"2024","unstructured":"Ngoc-Hieu Nguyen, Tuan-Anh Nguyen, Tuan Nguyen, Vu Tien Hoang, Dung D Le, and Kok-Seng Wong. 2024. Towards Efficient Communication and Secure Federated Recommendation System via Low-rank Training. In WWW. 3940--3951."},{"key":"e_1_3_2_1_25_1","volume-title":"Fedgkd: Unleashing the Power of Collaboration in Federated Graph Neural Networks. In NeurIPS Workshop.","author":"Pan Qiying","year":"2023","unstructured":"Qiying Pan, Ruofan Wu, Tengfei Liu, Tianyi Zhang, Yifei Zhu, and Weiqiang Wang. 2023. Fedgkd: Unleashing the Power of Collaboration in Federated Graph Neural Networks. In NeurIPS Workshop."},{"key":"e_1_3_2_1_26_1","first-page":"2975","article-title":"Privacy-Preserving Multi-Granular Federated Neural Architecture Search--A General Framework","volume":"35","author":"Pan Zijie","year":"2021","unstructured":"Zijie Pan, Li Hu, Weixuan Tang, Jin Li, Yi He, and Zheli Liu. 2021. Privacy-Preserving Multi-Granular Federated Neural Architecture Search--A General Framework. TKDE, Vol. 35, 3 (2021), 2975--2986.","journal-title":"TKDE"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"crossref","unstructured":"Qinyong Wang Hongzhi Yin Tong Chen Zi Huang Hao Wang Yanchang Zhao and Nguyen Quoc Viet Hung. 2020b. Next Point-of-Interest Recommendation on Resource-Constrained Mobile Devices. In WWW. 906--916.","DOI":"10.1145\/3366423.3380170"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"crossref","unstructured":"Yuan Wang Huazhu Fu Renuga Kanagavelu Qingsong Wei Yong Liu and Rick Siow Mong Goh. 2024. An Aggregation-Free Federated Learning for Tackling Data Heterogeneity. In CVPR. 26233--26242.","DOI":"10.1109\/CVPR52733.2024.02478"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"crossref","unstructured":"Yansheng Wang Yongxin Tong and Dingyuan Shi. 2020a. Federated Latent Dirichlet Allocation: A Local Differential Privacy Based Framework. In AAAI. 6283--6290.","DOI":"10.1609\/aaai.v34i04.6096"},{"key":"e_1_3_2_1_30_1","volume-title":"ICML Workshop","author":"Wu Chuhan","year":"2021","unstructured":"Chuhan Wu, Fangzhao Wu, Yang Cao, Yongfeng Huang, and Xing Xie. 2021b. Fedgnn: Federated graph neural network for privacy-preserving recommendation. ICML Workshop (2021)."},{"key":"e_1_3_2_1_31_1","first-page":"2032","article-title":"Communication-Efficient Federated Learning Via Knowledge Distillation","volume":"13","author":"Wu Chuhan","year":"2022","unstructured":"Chuhan Wu, Fangzhao Wu, Lingjuan Lyu, Yongfeng Huang, and Xing Xie. 2022. Communication-Efficient Federated Learning Via Knowledge Distillation. NC, Vol. 13, 1 (2022), 2032.","journal-title":"NC"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"crossref","unstructured":"Jinze Wu Qi Liu Zhenya Huang Yuting Ning Hao Wang Enhong Chen Jinfeng Yi and Bowen Zhou. 2021a. Hierarchical Personalized Federated Learning for User Modeling. In WWW. 957--968.","DOI":"10.1145\/3442381.3449926"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"crossref","unstructured":"Xin Xia Hongzhi Yin Junliang Yu Qinyong Wang Guandong Xu and Quoc Viet Hung Nguyen. 2022. On-device Next-Item Recommendation with Self-Supervised Knowledge Distillation. In SIGIR. 546--555.","DOI":"10.1145\/3477495.3531775"},{"key":"e_1_3_2_1_34_1","first-page":"1","article-title":"Efficient On-Device Session-based Recommendation","volume":"41","author":"Xia Xin","year":"2023","unstructured":"Xin Xia, Junliang Yu, Qinyong Wang, Chaoqun Yang, Nguyen Quoc Viet Hung, and Hongzhi Yin. 2023. Efficient On-Device Session-based Recommendation. TOIS, Vol. 41, 4 (2023), 1--24.","journal-title":"TOIS"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"crossref","unstructured":"Bo Yan Yang Cao Haoyu Wang Wenchuan Yang Junping Du and Chuan Shi. 2024a. Federated Heterogeneous Graph Neural Network for Privacy-Preserving Recommendation. In WWW. 3919--3929.","DOI":"10.1145\/3589334.3645693"},{"key":"e_1_3_2_1_36_1","unstructured":"Kunda Yan Sen Cui Abudukelimu Wuerkaixi Jingfeng Zhang Bo Han Gang Niu Masashi Sugiyama and Changshui Zhang. 2024b. Balancing Similarity and Complementarity for Federated Learning. In ICML. 1--20."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3652853","article-title":"Discrete Federated Multi-behavior Recommendation for Privacy-Preserving Heterogeneous One-Class Collaborative Filtering","volume":"42","author":"Yang Enyue","year":"2024","unstructured":"Enyue Yang, Weike Pan, Qiang Yang, and Zhong Ming. 2024. Discrete Federated Multi-behavior Recommendation for Privacy-Preserving Heterogeneous One-Class Collaborative Filtering. TOIS, Vol. 42, 5 (2024), 1--50.","journal-title":"TOIS"},{"key":"e_1_3_2_1_38_1","unstructured":"Rui Ye Zhenyang Ni Fangzhao Wu Siheng Chen and Yanfeng Wang. 2023. Personalized Federated Learning with Inferred Collaboration Graphs. In ICML. 39801--39817."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"crossref","unstructured":"Jingwei Yi Fangzhao Wu Bin Zhu Jing Yao Zhulin Tao Guangzhong Sun and Xing Xie. 2023. UA-FedRec: Untargeted Attack on Federated News Recommendation. In SIGKDD. 5428--5438.","DOI":"10.1145\/3580305.3599923"},{"key":"e_1_3_2_1_40_1","volume-title":"Sanshi Lei Yu, and Zaixi Zhang","author":"Yu Yang","year":"2023","unstructured":"Yang Yu, Qi Liu, Likang Wu, Runlong Yu, Sanshi Lei Yu, and Zaixi Zhang. 2023. Untargeted Attack Against Federated Recommendation Systems Via Poisonous Item Embeddings and the Defense. In AAAI. 4854--4863."},{"key":"e_1_3_2_1_41_1","volume-title":"Hetefedrec: Federated Recommender Systems with Model Heterogeneity. In ICDE. 1324--1337.","author":"Yuan Wei","year":"2024","unstructured":"Wei Yuan, Liang Qu, Lizhen Cui, Yongxin Tong, Xiaofang Zhou, and Hongzhi Yin. 2024. Hetefedrec: Federated Recommender Systems with Model Heterogeneity. In ICDE. 1324--1337."},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3630005"},{"key":"e_1_3_2_1_43_1","unstructured":"Chunxu Zhang Guodong Long Hongkuan Guo Xiao Fang Yang Song Zhaojie Liu Guorui Zhou Zijian Zhang Yang Liu and Bo Yang. 2024c. Federated Adaptation for Foundation Model-based Recommendations. In IJCAI. 5453--5461."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"crossref","unstructured":"Chunxu Zhang Guodong Long Tianyi Zhou Peng Yan Zijian Zhang Chengqi Zhang and Bo Yang. 2023a. Dual Personalization on Federated Recommendation. In IJCAI. 4558--4566.","DOI":"10.24963\/ijcai.2023\/507"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"crossref","unstructured":"Chunxu Zhang Guodong Long Tianyi Zhou Zijian Zhang Peng Yan and Bo Yang. 2024 d. GPFedRec: Graph-Guided Personalization for Federated Recommendation. In SIGKDD. 4131--4142.","DOI":"10.1145\/3637528.3671702"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"crossref","unstructured":"Chunxu Zhang Guodong Long Tianyi Zhou Zijian Zhang Peng Yan and Bo Yang. 2024 e. When Federated Recommendation Meets Cold-Start Problem: Separating Item Attributes and User Interactions. In WWW. 3632--3642.","DOI":"10.1145\/3589334.3645525"},{"key":"e_1_3_2_1_47_1","volume-title":"Beyond Similarity: Personalized Federated Recommendation with Composite Aggregation. arXiv e-prints","author":"Zhang Honglei","year":"2024","unstructured":"Honglei Zhang, Haoxuan Li, Jundong Chen, Sen Cui, Kunda Yan, Abudukelimu Wuerkaixi, Xin Zhou, Zhiqi Shen, and Yidong Li. 2024a. Beyond Similarity: Personalized Federated Recommendation with Composite Aggregation. arXiv e-prints (2024), arXiv--2406."},{"key":"e_1_3_2_1_48_1","volume-title":"TransFR: Transferable Federated Recommendation with Pre-trained Language Models. arXiv e-prints","author":"Zhang Honglei","year":"2024","unstructured":"Honglei Zhang, He Liu, Haoxuan Li, and Yidong Li. 2024b. TransFR: Transferable Federated Recommendation with Pre-trained Language Models. arXiv e-prints (2024), arXiv--2402."},{"key":"e_1_3_2_1_49_1","first-page":"1","article-title":"LightFR","volume":"41","author":"Zhang Honglei","year":"2023","unstructured":"Honglei Zhang, Fangyuan Luo, Jun Wu, Xiangnan He, and Yidong Li. 2023b. LightFR: Lightweight Federated Recommendation with Privacy-preserving Matrix Factorization. TOIS, Vol. 41, 4 (2023), 1--28.","journal-title":"Lightweight Federated Recommendation with Privacy-preserving Matrix Factorization. TOIS"},{"key":"e_1_3_2_1_50_1","volume-title":"2024 g. PrivFR: Privacy-Enhanced Federated Recommendation With Shared Hash Embedding. TNNLS","author":"Zhang Honglei","year":"2024","unstructured":"Honglei Zhang, Xin Zhou, Zhiqi Shen, and Yidong Li. 2024 g. PrivFR: Privacy-Enhanced Federated Recommendation With Shared Hash Embedding. TNNLS (2024), 1--15."},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"crossref","unstructured":"JianFei Zhang and YuChen Jiang. 2021. A Vertical Federation Recommendation Method Based on Clustering and Latent Factor Model. In EIECS. 362--366.","DOI":"10.1109\/EIECS53707.2021.9587935"},{"key":"e_1_3_2_1_52_1","first-page":"987","article-title":"f","volume":"36","author":"Zhang Shijie","year":"2024","unstructured":"Shijie Zhang, Wei Yuan, and Hongzhi Yin. 2024 f. Comprehensive Privacy Analysis on Federated Recommender System Against Attribute Inference Attacks. TKDE, Vol. 36, 3 (2024), 987--999.","journal-title":"TKDE"},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"crossref","unstructured":"Zheng Zhang and Wei Song. 2023. A New Reconstruction Attack: User Latent Vector Leakage in Federated Recommendation. In DASFAA. 97--112.","DOI":"10.1007\/978-3-031-30672-3_7"},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3361562"}],"event":{"name":"WWW '25: The ACM Web Conference 2025","location":"Sydney NSW Australia","acronym":"WWW '25","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Companion Proceedings of the ACM on Web Conference 2025"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3701716.3715860","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3701716.3715860","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T17:39:49Z","timestamp":1759858789000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3701716.3715860"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,8]]},"references-count":54,"alternative-id":["10.1145\/3701716.3715860","10.1145\/3701716"],"URL":"https:\/\/doi.org\/10.1145\/3701716.3715860","relation":{},"subject":[],"published":{"date-parts":[[2025,5,8]]},"assertion":[{"value":"2025-05-23","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}