{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T23:35:41Z","timestamp":1780443341555,"version":"3.54.1"},"reference-count":84,"publisher":"Association for Computing Machinery (ACM)","issue":"6","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["71774159"],"award-info":[{"award-number":["71774159"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"crossref","award":["2021T140707"],"award-info":[{"award-number":["2021T140707"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Jiangsu Postdoctoral Science Foundation","award":["2021K565C"],"award-info":[{"award-number":["2021K565C"]}]},{"name":"Science and Technology Foundation of Xuzhou","award":["KC22047"],"award-info":[{"award-number":["KC22047"]}]},{"DOI":"10.13039\/501100000923","name":"Australian Research Council","doi-asserted-by":"crossref","award":["DP230101122"],"award-info":[{"award-number":["DP230101122"]}],"id":[{"id":"10.13039\/501100000923","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Graduate Innovation Program of China University of Mining and Technology","award":["2024WLKXJ183"],"award-info":[{"award-number":["2024WLKXJ183"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"crossref","award":["2024-10949"],"award-info":[{"award-number":["2024-10949"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Postgraduate Research & Practice Innovation Program of Jiangsu Province","award":["KYCX24_2781"],"award-info":[{"award-number":["KYCX24_2781"]}]},{"name":"Research Fund of Guangxi Key Lab of Multi-source Information Mining & Security","award":["MIMS24-13"],"award-info":[{"award-number":["MIMS24-13"]}]},{"name":"Guangxi Key Laboratory of Big Data in Finance and Economics","award":["FEDOP2022A03"],"award-info":[{"award-number":["FEDOP2022A03"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2025,11,30]]},"abstract":"<jats:p>The rapid development of Large Language Models (LLMs) creates new opportunities for recommender systems, especially by exploiting the side information (e.g., descriptions and analyses of items) generated by these models. However, aligning this side information with collaborative information from historical interactions poses significant challenges. The inherent biases within LLMs can skew recommendations, resulting in distorted and potentially unfair user experiences. On the other hand, propensity bias causes side information to be aligned in such a way that it often tends to represent all inputs in a low-dimensional subspace, leading to a phenomenon known as dimensional collapse, which severely restricts the recommender system\u2019s ability to capture user preferences and behaviors. To address these issues, we introduce a novel framework named Counterfactual LLM Recommendation (CLLMR). Specifically, we propose a spectrum-based side information encoder that implicitly embeds structural information from historical interactions into the side information representation, thereby circumventing the risk of dimension collapse. Furthermore, our CLLMR approach explores the causal relationships inherent in LLM-based recommender systems. By leveraging counterfactual inference, we counteract the biases introduced by LLMs. Extensive experiments demonstrate that our CLLMR approach consistently enhances the performance of various recommender models.<\/jats:p>","DOI":"10.1145\/3736404","type":"journal-article","created":{"date-parts":[[2025,5,19]],"date-time":"2025-05-19T06:48:36Z","timestamp":1747637316000},"page":"1-26","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["Mitigating Propensity Bias of Large Language Models for Recommender Systems"],"prefix":"10.1145","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7632-8411","authenticated-orcid":false,"given":"Guixian","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China and Mine Digitization Engineering Research Center of the Ministry of Education, China University of Mining and Technology, Xuzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3148-9817","authenticated-orcid":false,"given":"Guan","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, China and Mine Digitization Engineering Research Center of the Ministry of Education, China University of Mining and Technology, Xuzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0383-1462","authenticated-orcid":false,"given":"Debo","family":"Cheng","sequence":"additional","affiliation":[{"name":"UniSA STEM, University of South Australia, Adelaide, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2843-5738","authenticated-orcid":false,"given":"Lin","family":"Liu","sequence":"additional","affiliation":[{"name":"UniSA STEM, University of South Australia, Adelaide, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9023-1878","authenticated-orcid":false,"given":"Jiuyong","family":"Li","sequence":"additional","affiliation":[{"name":"UniSA STEM, University of South Australia, Adelaide, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9981-2970","authenticated-orcid":false,"given":"Shichao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Guangxi Key Lab of Multisource Information Mining &amp; Security, Guangxi Normal University, Guilin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,9,11]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3461702.3462624"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/3604915.3608857"},{"issue":"6","key":"e_1_3_1_4_2","first-page":"1","article-title":"Spatial-temporal clustering enhanced multi-graph convolutional network for traffic flow prediction","volume":"55","author":"Bao Yinxin","year":"2025","unstructured":"Yinxin Bao, Qinqin Shen, Yang Cao, and Quan Shi. 2025. Spatial-temporal clustering enhanced multi-graph convolutional network for traffic flow prediction. Applied Intelligence 55, 6 (2025), 1\u201319.","journal-title":"Applied Intelligence"},{"key":"e_1_3_1_5_2","unstructured":"Federico Barbero Andrea Banino Steven Kapturowski Dharshan Kumaran Jo\u00e3o G. M. Ara\u00fajo Alex Vitvitskyi Razvan Pascanu and Petar Veli\u010dkovi\u0107. 2024. Transformers need glasses! Information over-squashing in language tasks. arXiv:2406.04267. Retrieved from https:\/\/arxiv.org\/abs\/2406.04267"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.5555\/3157382.3157584"},{"key":"e_1_3_1_7_2","series-title":"Proceedings of Machine Learning Research","first-page":"77","volume-title":"Proceedings of the 1st Conference on Fairness, Accountability and Transparency","author":"Buolamwini Joy","year":"2018","unstructured":"Joy Buolamwini and Timnit Gebru. 2018. Gender shades: Intersectional accuracy disparities in commercial gender classification. In Proceedings of the 1st Conference on Fairness, Accountability and Transparency. Sorelle A. Friedler and Christo Wilson (Eds.), Proceedings of Machine Learning Research, Vol. 81, PMLR, 77\u201391."},{"key":"e_1_3_1_8_2","first-page":"1","volume-title":"Proceedings of the 11th International Conference on Learning Representations","author":"Cai Xuheng","year":"2023","unstructured":"Xuheng Cai, Chao Huang, Lianghao Xia, and Xubin Ren. 2023. LightGCL: Simple yet effective graph contrastive learning for recommendation. In Proceedings of the 11th International Conference on Learning Representations, 1\u201315."},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1126\/science.aal4230"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5330"},{"key":"e_1_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1145\/3636423"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1145\/3488560.3498407"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.emnlp-main.669"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1145\/3287560.3287572"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1145\/3450528"},{"issue":"8","key":"e_1_3_1_16_2","first-page":"7665","article-title":"A multi-view multi-task learning framework for multi-variate time series forecasting","volume":"35","author":"Deng Jinliang","year":"2022","unstructured":"Jinliang Deng, Xiusi Chen, Renhe Jiang, Xuan Song, and Ivor W. Tsang. 2022. A multi-view multi-task learning framework for multi-variate time series forecasting. IEEE Transactions on Knowledge and Data Engineering 35, 8 (2022), 7665\u20137680.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1145\/3605894"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.08.112"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3251897"},{"key":"e_1_3_1_20_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2024.3464648","article-title":"Spatial-spectral graph contrastive clustering with hard sample mining for hyperspectral images","author":"Guan Renxiang","year":"2024","unstructured":"Renxiang Guan, Wenxuan Tu, Zihao Li, Hao Yu, Dayu Hu, Yuzeng Chen, Chang Tang, Qiangqiang Yuan, and Xinwang Liu. 2024. Spatial-spectral graph contrastive clustering with hard sample mining for hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing 62\u00a0(2024), 1\u201316.","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.5555\/3692070.3692741"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1145\/2872427.2883037"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401063"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1177\/1536867X1201100407"},{"key":"e_1_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1037\/a0020761"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781139025751"},{"key":"e_1_3_1_27_2","doi-asserted-by":"crossref","unstructured":"Meng Jiang Keqin Bao Jizhi Zhang Wenjie Wang Zhengyi Yang Fuli Feng and Xiangnan He. 2024. Item-side fairness of large language model-based recommendation system. arXiv:2402.15215. Retrieved from https:\/\/arxiv.org\/abs\/2402.15215","DOI":"10.1145\/3589334.3648158"},{"key":"e_1_3_1_28_2","volume-title":"Proceedings of the 10th International Conference on Learning Representations (ICLR \u201922)","author":"Jing Li","year":"2022","unstructured":"Li Jing, Pascal Vincent, Yann LeCun, and Yuandong Tian. 2022. Understanding dimensional collapse in contrastive self-supervised learning. In Proceedings of the 10th International Conference on Learning Representations (ICLR \u201922)."},{"key":"e_1_3_1_29_2","first-page":"2207","volume-title":"Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics","author":"Khemakhem Ilyes","year":"2020","unstructured":"Ilyes Khemakhem, Diederik P. Kingma, Ricardo P. Monti, and Aapo Hyv\u00e4rinen. 2020. Variational autoencoders and nonlinear ICA: A unifying framework. In Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics. PMLR, 2207\u20132217."},{"key":"e_1_3_1_30_2","unstructured":"Diederik P. Kingma and Max Welling. 2013. Auto-encoding variational Bayes. arXiv:1312.6114. Retrieved from https:\/\/arxiv.org\/abs\/1312.6114"},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.1145\/3630106.3658975"},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i12.29271"},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1145\/3643890"},{"key":"e_1_3_1_34_2","unstructured":"Xiaoyu Liu Paiheng Xu Junda Wu Jiaxin Yuan Yifan Yang Yuhang Zhou Fuxiao Liu Tianrui Guan Haoliang Wang Tong Yu et al. 2024. Large language models and causal inference in collaboration: A comprehensive survey. arXiv: 2403.09606. Retrieved from https:\/\/arxiv.org\/abs\/2403.09606"},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671455"},{"key":"e_1_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.1145\/3501815"},{"key":"e_1_3_1_37_2","doi-asserted-by":"publisher","DOI":"10.1145\/3664647.3681339"},{"key":"e_1_3_1_38_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.acl-long.443"},{"key":"e_1_3_1_39_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.nuse-1.5"},{"key":"e_1_3_1_40_2","unstructured":"Jing Ma. 2024. Causal inference with large language model: A survey. arXiv:2409.09822. Retrieved from https:\/\/arxiv.org\/abs\/2409.09822"},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.1145\/3487331"},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511803161"},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-023-10465-9"},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3349397"},{"key":"e_1_3_1_45_2","first-page":"3464","volume-title":"Proceedings of the ACM on Web Conference 2024","author":"Ren Xubin","unstructured":"Xubin Ren, Wei Wei, Lianghao Xia, Lixin Su, Suqi Cheng, Junfeng Wang, Dawei Yin, and Chao Huang. 2024. Representation learning with large language models for recommendation. In Proceedings of the ACM on Web Conference 2024, 3464\u20133475."},{"key":"e_1_3_1_46_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12874-021-01426-3"},{"key":"e_1_3_1_47_2","first-page":"153","volume-title":"Proceedings of the International Conference on Service-Oriented Computing","author":"Rong Dunlei","year":"2024","unstructured":"Dunlei Rong, Lina Yao, Yinting Zheng, Shuang Yu, Xiaofei Xu, Mingyi Liu, and Zhongjie Wang. 2024. LLM enhanced representation for cold start service recommendation. In Proceedings of the International Conference on Service-Oriented Computing. Springer, 153\u2013167."},{"key":"e_1_3_1_48_2","unstructured":"Sarath Sivaprasad Pramod Kaushik Sahar Abdelnabi and Mario Fritz. 2024. Exploring value biases: How LLMs deviate towards the ideal. arXiv:2402.11005. Retrieved from https:\/\/arxiv.org\/abs\/2402.11005"},{"key":"e_1_3_1_49_2","unstructured":"Hongjin Su Weijia Shi Jungo Kasai Yizhong Wang Yushi Hu Mari Ostendorf Wen-tau Yih Noah A. Smith Luke Zettlemoyer and Tao Yu. 2023. One embedder any task: Instruction-finetuned text embeddings. In Findings of the Association for Computational Linguistics (ACL \u201923) 1102\u20131121."},{"key":"e_1_3_1_50_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i4.20335"},{"key":"e_1_3_1_51_2","unstructured":"Yan Tao Olga Viberg Ryan S. Baker and Rene F. Kizilcec. 2023. Auditing and mitigating cultural bias in LLMs. arXiv:2311.14096. Retrieved from https:\/\/arxiv.org\/abs\/2311.14096"},{"key":"e_1_3_1_52_2","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539253"},{"key":"e_1_3_1_53_2","article-title":"Modeling dynamic missingness of implicit feedback for recommendation","volume":"31","author":"Wang Menghan","year":"2018","unstructured":"Menghan Wang, Mingming Gong, Xiaolin Zheng, and Kun Zhang. 2018. Modeling dynamic missingness of implicit feedback for recommendation. In Advances in Neural Information Processing Systems, Vol. 31.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_54_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2023.103964"},{"key":"e_1_3_1_55_2","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331267"},{"key":"e_1_3_1_56_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.emnlp-main.621"},{"key":"e_1_3_1_57_2","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401141"},{"key":"e_1_3_1_58_2","doi-asserted-by":"publisher","DOI":"10.1145\/3616855.3635853"},{"key":"e_1_3_1_59_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM54844.2022.00063"},{"key":"e_1_3_1_60_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2022.3231352"},{"key":"e_1_3_1_61_2","unstructured":"Anpeng Wu Kun Kuang Minqin Zhu Yingrong Wang Yujia Zheng Kairong Han Baohong Li Guangyi Chen Fei Wu and Kun Zhang .2024. Causality for large language models. arXiv:2410.15319. Retrieved from https:\/\/arxiv.org\/abs\/2410.15319"},{"key":"e_1_3_1_62_2","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531810"},{"key":"e_1_3_1_63_2","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462862"},{"key":"e_1_3_1_64_2","doi-asserted-by":"publisher","DOI":"10.1145\/3535101"},{"key":"e_1_3_1_65_2","doi-asserted-by":"publisher","DOI":"10.1145\/3586993"},{"key":"e_1_3_1_66_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.111872"},{"key":"e_1_3_1_67_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26266"},{"issue":"2","key":"e_1_3_1_68_2","first-page":"913","article-title":"XSimGCL: Towards extremely simple graph contrastive learning for recommendation","volume":"36","author":"Yu Junliang","year":"2023","unstructured":"Junliang Yu, Xin Xia, Tong Chen, Lizhen Cui, Nguyen Quoc Viet Hung, and Hongzhi Yin. 2023. XSimGCL: Towards extremely simple graph contrastive learning for recommendation. IEEE Transactions on Knowledge and Data Engineering 36, 2 (2023), 913\u2013926.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_1_69_2","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531937"},{"key":"e_1_3_1_70_2","first-page":"55734","article-title":"Large language model as attributed training data generator: A tale of diversity and bias","volume":"36","author":"Yu Yue","year":"2023","unstructured":"Yue Yu, Yuchen Zhuang, Jieyu Zhang, Yu Meng, Alexander J. Ratner, Ranjay Krishna, Jiaming Shen, and Chao Zhang. 2023. Large language model as attributed training data generator: A tale of diversity and bias. In Advances in Neural Information Processing Systems, Vol. 36, 55734\u201355784.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_71_2","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3591932"},{"key":"e_1_3_1_72_2","doi-asserted-by":"publisher","DOI":"10.1145\/3626772.3657844"},{"key":"e_1_3_1_73_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2023.103570"},{"key":"e_1_3_1_74_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11280-023-01178-8"},{"key":"e_1_3_1_75_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2024.106781"},{"key":"e_1_3_1_76_2","doi-asserted-by":"publisher","DOI":"10.1145\/3639408"},{"key":"e_1_3_1_77_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3049250"},{"issue":"03","key":"e_1_3_1_78_2","first-page":"2711","article-title":"KNN classification with one-step computation","volume":"35","author":"Zhang Shichao","year":"2023","unstructured":"Shichao Zhang and Jiaye Li. 2023. KNN classification with one-step computation. IEEE Transactions on Knowledge & Data Engineering 35, 03 (2023), 2711\u20132723.","journal-title":"IEEE Transactions on Knowledge & Data Engineering"},{"key":"e_1_3_1_79_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2022.06.082"},{"key":"e_1_3_1_80_2","doi-asserted-by":"publisher","DOI":"10.1145\/2990508"},{"key":"e_1_3_1_81_2","doi-asserted-by":"publisher","DOI":"10.1145\/3158369"},{"key":"e_1_3_1_82_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2024.acl-long.497"},{"key":"e_1_3_1_83_2","doi-asserted-by":"publisher","DOI":"10.1145\/3527662"},{"key":"e_1_3_1_84_2","first-page":"67533","volume-title":"Proceedings of the 37th International Conference on Neural Information Processing Systems","author":"Zhang Yifei","year":"2023","unstructured":"Yifei Zhang, Hao Zhu, Yankai Chen, Zixing Song, Piotr Koniusz, and Irwin King. 2023. Mitigating the popularity bias of graph collaborative filtering: a dimensional collapse perspective. In Proceedings of the 37th International Conference on Neural Information Processing Systems, 67533\u201367550."},{"key":"e_1_3_1_85_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N18-2003"}],"container-title":["ACM Transactions on Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3736404","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T20:56:54Z","timestamp":1757624214000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3736404"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,11]]},"references-count":84,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2025,11,30]]}},"alternative-id":["10.1145\/3736404"],"URL":"https:\/\/doi.org\/10.1145\/3736404","relation":{},"ISSN":["1046-8188","1558-2868"],"issn-type":[{"value":"1046-8188","type":"print"},{"value":"1558-2868","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,11]]},"assertion":[{"value":"2024-09-30","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-04-05","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-09-11","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}