{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T08:21:27Z","timestamp":1774945287981,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":52,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,11,12]],"date-time":"2021-11-12T00:00:00Z","timestamp":1636675200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Natural Science Foundation of China","award":["61902219, 61972234, 62072279, 62102234"],"award-info":[{"award-number":["61902219, 61972234, 62072279, 62102234"]}]},{"name":"the Helmholtz Association within the project ``Trustworthy Federated Data Analytics' (TFDA)","award":["ZT-I-OO1 4"],"award-info":[{"award-number":["ZT-I-OO1 4"]}]},{"name":"National Key R&D Program of China","award":["2020YFB1406704"],"award-info":[{"award-number":["2020YFB1406704"]}]},{"name":"Key Scientific and Technological Innovation Program of Shandong Province","award":["2019JZZY010129"],"award-info":[{"award-number":["2019JZZY010129"]}]},{"name":"Shandong University multidisciplinary research and innovation team of young scholars","award":["2020QNQT017"],"award-info":[{"award-number":["2020QNQT017"]}]},{"name":"Tencent WeChat Rhino-Bird Focused Research Program","award":["JR-WXG2021411"],"award-info":[{"award-number":["JR-WXG2021411"]}]},{"name":"Hybrid Intelligence Center"},{"name":"A 10-year program funded by the Dutch Ministry of Education, Culture and Science through the Netherlands Organisation for Scientific Research, https:\/\/hybrid-intelligence-centre.nl"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,11,12]]},"DOI":"10.1145\/3460120.3484770","type":"proceedings-article","created":{"date-parts":[[2021,11,13]],"date-time":"2021-11-13T12:05:34Z","timestamp":1636805134000},"page":"864-879","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":53,"title":["Membership Inference Attacks Against Recommender Systems"],"prefix":"10.1145","author":[{"given":"Minxing","family":"Zhang","sequence":"first","affiliation":[{"name":"Shandong University & CISPA Helmholtz Center for Information Security, Qingdao, China"}]},{"given":"Zhaochun","family":"Ren","sequence":"additional","affiliation":[{"name":"Shandong University, Qingdao, China"}]},{"given":"Zihan","family":"Wang","sequence":"additional","affiliation":[{"name":"Shandong University, Qingdao, China"}]},{"given":"Pengjie","family":"Ren","sequence":"additional","affiliation":[{"name":"Shandong University, Qingdao, China"}]},{"given":"Zhunmin","family":"Chen","sequence":"additional","affiliation":[{"name":"Shandong University, Qingdao, China"}]},{"given":"Pengfei","family":"Hu","sequence":"additional","affiliation":[{"name":"Shandong University, Qingdao, China"}]},{"given":"Yang","family":"Zhang","sequence":"additional","affiliation":[{"name":"CISPA Helmholtz Center for Information Security, Saarbr\u00fccken, Germany"}]}],"member":"320","published-online":{"date-parts":[[2021,11,13]]},"reference":[{"key":"e_1_3_2_2_1_1","first-page":"1943","volume-title":"ACM SIGSAC Conference on Computer and Communications Security (CCS)","author":"Backes Michael","year":"2017","unstructured":"Michael Backes, Mathias Humbert, Jun Pang, and Yang Zhang. walk2friends: Inferring Social Links from Mobility Profiles. In ACM SIGSAC Conference on Computer and Communications Security (CCS), pages 1943--1957. ACM, 2017."},{"key":"e_1_3_2_2_2_1","first-page":"1979","volume-title":"Wayne Xin Zhao. A Neural Collaborative Filtering Model with Interaction-based Neighborhood. In ACM International Conference on Information and Knowledge Management (CIKM)","author":"Bai Ting","year":"2017","unstructured":"Ting Bai, Ji-Rong Wen, Jun Zhang, and Wayne Xin Zhao. A Neural Collaborative Filtering Model with Interaction-based Neighborhood. In ACM International Conference on Information and Knowledge Management (CIKM), pages 1979--1982. ACM, 2017."},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-40994-3_25"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2039320"},{"key":"e_1_3_2_2_5_1","first-page":"267","volume-title":"Dawn Song. The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks. In USENIX Security Symposium (USENIX Security)","author":"Carlini Nicholas","year":"2019","unstructured":"Nicholas Carlini, Chang Liu, \u00dalfar Erlingsson, Jernej Kos, and Dawn Song. The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks. In USENIX Security Symposium (USENIX Security), pages 267--284. USENIX, 2019."},{"key":"e_1_3_2_2_6_1","unstructured":"Nicholas Carlini Florian Tram\u00e8 r Eric Wallace Matthew Jagielski Ariel Herbert-Voss Katherine Lee Adam Roberts Tom B. Brown Dawn Song \u00da lfar Erlingsson Alina Oprea and Colin Raffel. Extracting Training Data from Large Language Models. CoRR abs\/2012.07805 2020."},{"key":"e_1_3_2_2_7_1","first-page":"39","volume-title":"Carlini and David Wagner. Towards Evaluating the Robustness of Neural Networks. In IEEE Symposium on Security and Privacy (S&P)","author":"Nicholas","year":"2017","unstructured":"Nicholas Carlini and David Wagner. Towards Evaluating the Robustness of Neural Networks. In IEEE Symposium on Security and Privacy (S&P), pages 39--57. IEEE, 2017."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3372297.3417238"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3343117"},{"key":"e_1_3_2_2_10_1","volume-title":"Label-Only Membership Inference Attacks. CoRR abs\/2007.14321","author":"Choquette Choo Christopher A.","year":"2020","unstructured":"Christopher A. Choquette Choo, Florian Tram\u00e8r, Nicholas Carlini, and Nicolas Papernot. Label-Only Membership Inference Attacks. CoRR abs\/2007.14321, 2020."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/963770.963776"},{"key":"e_1_3_2_2_12_1","volume-title":"Badnets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain. CoRR abs\/1708.06733","author":"Gu Tianyu","year":"2017","unstructured":"Tianyu Gu, Brendan Dolan-Gavitt, and Siddharth Grag. Badnets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain. CoRR abs\/1708.06733, 2017."},{"key":"e_1_3_2_2_13_1","volume-title":"ACM Transactions on Interactive Intelligent Systems","author":"Maxwell Harper F.","year":"2015","unstructured":"F. Maxwell Harper and Joseph A Konstan. The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligent Systems, 2015."},{"key":"e_1_3_2_2_14_1","volume-title":"Emiliano De Cristofaro. LOGAN: Evaluating Privacy Leakage of Generative Models Using Generative Adversarial Networks. Symposium on Privacy Enhancing Technologies Symposium","author":"Hayes Jamie","year":"2019","unstructured":"Jamie Hayes, Luca Melis, George Danezis, and Emiliano De Cristofaro. LOGAN: Evaluating Privacy Leakage of Generative Models Using Generative Adversarial Networks. Symposium on Privacy Enhancing Technologies Symposium, 2019."},{"key":"e_1_3_2_2_15_1","first-page":"507","volume-title":"He and Julian McAuley. Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering. In The Web Conference (WWW)","author":"Ruining","year":"2016","unstructured":"Ruining He and Julian McAuley. Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering. In The Web Conference (WWW), pages 507--517. ACM, 2016."},{"key":"e_1_3_2_2_16_1","first-page":"173","volume-title":"Tat-Seng Chua. Neural Collaborative Filtering. In International Conference on World Wide Web (WWW)","author":"He Xiangnan","year":"2017","unstructured":"Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. Neural Collaborative Filtering. In International Conference on World Wide Web (WWW), pages 173--182. ACM, 2017."},{"key":"e_1_3_2_2_17_1","first-page":"549","volume-title":"Tat-Seng Chua. Fast Matrix Factorization for Online Recommendation with Implicit Feedback. In International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)","author":"He Xiangnan","year":"2016","unstructured":"Xiangnan He, Hanwang Zhang, Min-Yen Kan, and Tat-Seng Chua. Fast Matrix Factorization for Online Recommendation with Implicit Feedback. In International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), pages 549--558. ACM, 2016."},{"key":"e_1_3_2_2_18_1","volume-title":"USENIX Security Symposium (USENIX Security). USENIX","author":"He Xinlei","year":"2021","unstructured":"Xinlei He, Jinyuan Jia, Michael Backes, Neil Zhenqiang Gong, and Yang Zhang. Stealing Links from Graph Neural Networks. In USENIX Security Symposium (USENIX Security). USENIX, 2021."},{"key":"e_1_3_2_2_19_1","first-page":"241","volume-title":"John Riedl. Explaining Collaborative Filtering Recommendations. In ACM Conference on Computer Supported Cooperative Work (CSCW)","author":"Herlocker Jonathan L.","year":"2000","unstructured":"Jonathan L. Herlocker, Joseph A. Konstan, and John Riedl. Explaining Collaborative Filtering Recommendations. In ACM Conference on Computer Supported Cooperative Work (CSCW), pages 241--250. ACM, 2000."},{"key":"e_1_3_2_2_20_1","first-page":"1345","volume-title":"Nicolas Papernot. High Accuracy and High Fidelity Extraction of Neural Networks. In USENIX Security Symposium (USENIX Security)","author":"Jagielski Matthew","year":"2020","unstructured":"Matthew Jagielski, Nicholas Carlini, David Berthelot, Alex Kurakin, and Nicolas Papernot. High Accuracy and High Fidelity Extraction of Neural Networks. In USENIX Security Symposium (USENIX Security), pages 1345--1362. USENIX, 2020."},{"key":"e_1_3_2_2_21_1","first-page":"19","volume-title":"Bo Li. Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning. In IEEE Symposium on Security and Privacy (S&P)","author":"Jagielski Matthew","year":"2018","unstructured":"Matthew Jagielski, Alina Oprea, Battista Biggio, Chang Liu, Cristina Nita-Rotaru, and Bo Li. Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning. In IEEE Symposium on Security and Privacy (S&P), pages 19--35. IEEE, 2018."},{"key":"e_1_3_2_2_22_1","first-page":"259","volume-title":"ACM SIGSAC Conference on Computer and Communications Security (CCS)","author":"Jia Jinyuan","year":"2019","unstructured":"Jinyuan Jia, Ahmed Salem, Michael Backes, Yang Zhang, and Neil Zhenqiang Gong. MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples. In ACM SIGSAC Conference on Computer and Communications Security (CCS), pages 259--274. ACM, 2019."},{"key":"e_1_3_2_2_23_1","first-page":"247","volume-title":"Karypis. Evaluation of Item-Based Top-N Recommendation Algorithms. In ACM International Conference on Information and Knowledge Management (CIKM)","author":"George","year":"2001","unstructured":"George Karypis. Evaluation of Item-Based Top-N Recommendation Algorithms. In ACM International Conference on Information and Knowledge Management (CIKM), pages 247--254. ACM, 2001."},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/1401890.1401944"},{"key":"e_1_3_2_2_25_1","first-page":"447","volume-title":"Koren. Collaborative Filtering with Temporal Dynamics. In ACM Conference on Knowledge Discovery and Data Mining (KDD)","author":"Yehuda","year":"2009","unstructured":"Yehuda Koren. Collaborative Filtering with Temporal Dynamics. In ACM Conference on Knowledge Discovery and Data Mining (KDD), pages 447--456. ACM, 2009."},{"key":"e_1_3_2_2_26_1","first-page":"1605","volume-title":"Leino and Matt Fredrikson. Stolen Memories: Leveraging Model Memorization for Calibrated White-Box Membership Inference. In USENIX Security Symposium (USENIX Security)","author":"Klas","year":"2020","unstructured":"Klas Leino and Matt Fredrikson. Stolen Memories: Leveraging Model Memorization for Calibrated White-Box Membership Inference. In USENIX Security Symposium (USENIX Security), pages 1605--1622. USENIX, 2020."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3359789.3359801"},{"key":"e_1_3_2_2_28_1","volume-title":"Li and Yang Zhang. Membership Leakage in Label-Only Exposures. In ACM SIGSAC Conference on Computer and Communications Security (CCS). ACM","author":"Zheng","year":"2021","unstructured":"Zheng Li and Yang Zhang. Membership Leakage in Label-Only Exposures. In ACM SIGSAC Conference on Computer and Communications Security (CCS). ACM, 2021."},{"key":"e_1_3_2_2_29_1","first-page":"497","volume-title":"Vitaly Shmatikov. Exploiting Unintended Feature Leakage in Collaborative Learning. In IEEE Symposium on Security and Privacy (S&P)","author":"Melis Luca","year":"2019","unstructured":"Luca Melis, Congzheng Song, Emiliano De Cristofaro, and Vitaly Shmatikov. Exploiting Unintended Feature Leakage in Collaborative Learning. In IEEE Symposium on Security and Privacy (S&P), pages 497--512. IEEE, 2019."},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3243734.3243855"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2019.00065"},{"key":"e_1_3_2_2_32_1","volume-title":"Nicholas Carlini. Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning. In IEEE Symposium on Security and Privacy (S&P). IEEE","author":"Nasr Milad","year":"2021","unstructured":"Milad Nasr, Shuang Song, Abhradeep Thakurta, Nicolas Papernot, and Nicholas Carlini. Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning. In IEEE Symposium on Security and Privacy (S&P). IEEE, 2021."},{"key":"e_1_3_2_2_33_1","first-page":"399","volume-title":"Michael Wellman. SoK: Towards the Science of Security and Privacy in Machine Learning. In IEEE European Symposium on Security and Privacy (Euro S&P)","author":"Papernot Nicolas","year":"2018","unstructured":"Nicolas Papernot, Patrick McDaniel, Arunesh Sinha, and Michael Wellman. SoK: Towards the Science of Security and Privacy in Machine Learning. In IEEE European Symposium on Security and Privacy (Euro S&P), pages 399--414. IEEE, 2018."},{"key":"e_1_3_2_2_34_1","volume-title":"Erlingsson. Scalable Private Learning with PATE. In International Conference on Learning Representations (ICLR)","author":"Papernot Nicolas","year":"2018","unstructured":"Nicolas Papernot, Shuang Song, Ilya Mironov, Ananth Raghunathan, Kunal Talwar, and \u00da lfar Erlingsson. Scalable Private Learning with PATE. In International Conference on Learning Representations (ICLR), 2018."},{"key":"e_1_3_2_2_35_1","first-page":"325","volume-title":"The Adaptive Web, Methods and Strategies of Web Personalization","author":"Michael","year":"2007","unstructured":"Michael J. Pazzani and Daniel Billsus. Content-Based Recommendation Systems. In The Adaptive Web, Methods and Strategies of Web Personalization, pages 325--341. Springer, 2007."},{"key":"e_1_3_2_2_36_1","first-page":"791","volume-title":"Polat and Wenliang Du. SVD-based Collaborative Filtering with Privacy. In ACM Symposium on Applied Computing (SAC)","author":"Huseyin","year":"2005","unstructured":"Huseyin Polat and Wenliang Du. SVD-based Collaborative Filtering with Privacy. In ACM Symposium on Applied Computing (SAC), pages 791--795. ACM, 2005."},{"key":"e_1_3_2_2_37_1","first-page":"5558","volume-title":"International Conference on Machine Learning (ICML)","author":"Sablayrolles Alexandre","year":"2019","unstructured":"Alexandre Sablayrolles, Matthijs Douze, Cordelia Schmid, Yann Ollivier, and Herv\u00e9 J\u00e9gou. White-box vs Black-box: Bayes Optimal Strategies for Membership Inference. In International Conference on Machine Learning (ICML), pages 5558--5567. PMLR, 2019."},{"key":"e_1_3_2_2_38_1","first-page":"1257","volume-title":"Salakhutdinov and Andriy Mnih. Probabilistic Matrix Factorization. In Annual Conference on Neural Information Processing Systems (NIPS)","author":"Ruslan","year":"2007","unstructured":"Ruslan Salakhutdinov and Andriy Mnih. Probabilistic Matrix Factorization. In Annual Conference on Neural Information Processing Systems (NIPS), pages 1257--1264. NIPS, 2007."},{"key":"e_1_3_2_2_39_1","volume-title":"Michael Backes. ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models. In Network and Distributed System Security Symposium (NDSS). Internet Society","author":"Salem Ahmed","year":"2019","unstructured":"Ahmed Salem, Yang Zhang, Mathias Humbert, Pascal Berrang, Mario Fritz, and Michael Backes. ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models. In Network and Distributed System Security Symposium (NDSS). Internet Society, 2019."},{"key":"e_1_3_2_2_40_1","first-page":"285","volume-title":"John Riedl. Item-Based Collaborative Filtering Recommendation Algorithms. In International Conference on World Wide Web (WWW)","author":"Sarwar Badrul Munir","year":"2001","unstructured":"Badrul Munir Sarwar, George Karypis, Joseph A. Konstan, and John Riedl. Item-Based Collaborative Filtering Recommendation Algorithms. In International Conference on World Wide Web (WWW), pages 285--295. ACM, 2001."},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-72079-9_9"},{"key":"e_1_3_2_2_42_1","volume-title":"Shejwalkar and Amir Houmansadr. Membership Privacy for Machine Learning Models Through Knowledge Transfer. In AAAI Conference on Artificial Intelligence (AAAI). AAAI","author":"Virat","year":"2021","unstructured":"Virat Shejwalkar and Amir Houmansadr. Membership Privacy for Machine Learning Models Through Knowledge Transfer. In AAAI Conference on Artificial Intelligence (AAAI). AAAI, 2021."},{"key":"e_1_3_2_2_43_1","first-page":"3","volume-title":"Vitaly Shmatikov. Membership Inference Attacks Against Machine Learning Models. In IEEE Symposium on Security and Privacy (S&P)","author":"Shokri Reza","year":"2017","unstructured":"Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov. Membership Inference Attacks Against Machine Learning Models. In IEEE Symposium on Security and Privacy (S&P), pages 3--18. IEEE, 2017."},{"key":"e_1_3_2_2_44_1","first-page":"247","volume-title":"Jean-Pierre Hubaux. Quantifying Location Privacy. In IEEE Symposium on Security and Privacy (S&P)","author":"Shokri Reza","year":"2011","unstructured":"Reza Shokri, Georgios Theodorakopoulos, Jean-Yves Le Boudec, and Jean-Pierre Hubaux. Quantifying Location Privacy. In IEEE Symposium on Security and Privacy (S&P), pages 247--262. IEEE, 2011."},{"key":"e_1_3_2_2_45_1","first-page":"196","volume-title":"Song and Vitaly Shmatikov. Auditing Data Provenance in Text-Generation Models. In ACM Conference on Knowledge Discovery and Data Mining (KDD)","author":"Congzheng","year":"2019","unstructured":"Congzheng Song and Vitaly Shmatikov. Auditing Data Provenance in Text-Generation Models. In ACM Conference on Knowledge Discovery and Data Mining (KDD), pages 196--206. ACM, 2019."},{"key":"e_1_3_2_2_46_1","first-page":"185","volume-title":"Meng Wang. Attentive Recurrent Social Recommendation. In International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)","author":"Sun Peijie","year":"2018","unstructured":"Peijie Sun, Le Wu, and Meng Wang. Attentive Recurrent Social Recommendation. In International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), pages 185--194. ACM, 2018."},{"key":"e_1_3_2_2_47_1","first-page":"837","volume-title":"Meng Wang. Dual Learning for Explainable Recommendation: Towards Unifying User Preference Prediction and Review Generation. In The Web Conference (WWW)","author":"Sun Peijie","year":"2020","unstructured":"Peijie Sun, Le Wu, Kun Zhang, Yanjie Fu, Richang Hong, and Meng Wang. Dual Learning for Explainable Recommendation: Towards Unifying User Preference Prediction and Review Generation. In The Web Conference (WWW), pages 837--847. ACM, 2020."},{"key":"e_1_3_2_2_48_1","volume-title":"Patrick McDaniel. Ensemble Adversarial Training: Attacks and Defenses. In International Conference on Learning Representations (ICLR)","author":"Tram\u00e8r Florian","year":"2017","unstructured":"Florian Tram\u00e8r, Alexey Kurakin, Nicolas Papernot, Ian Goodfellow, Dan Boneh, and Patrick McDaniel. Ensemble Adversarial Training: Attacks and Defenses. In International Conference on Learning Representations (ICLR), 2017."},{"key":"e_1_3_2_2_49_1","first-page":"601","volume-title":"USENIX Security Symposium (USENIX Security)","author":"Tram\u00e8r Florian","year":"2016","unstructured":"Florian Tram\u00e8r, Fan Zhang, Ari Juels, Michael K. Reiter, and Thomas Ristenpart. Stealing Machine Learning Models via Prediction APIs. In USENIX Security Symposium (USENIX Security), pages 601--618. USENIX, 2016."},{"key":"e_1_3_2_2_50_1","volume-title":"Journal of Machine Learning Research","author":"van der Maaten Laurens","year":"2008","unstructured":"Laurens van der Maaten and Geoffrey Hinton. Visualizing Data using t-SNE. Journal of Machine Learning Research, 2008."},{"key":"e_1_3_2_2_51_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113452"},{"key":"e_1_3_2_2_52_1","doi-asserted-by":"publisher","DOI":"10.1109\/CSF.2018.00027"}],"event":{"name":"CCS '21: 2021 ACM SIGSAC Conference on Computer and Communications Security","location":"Virtual Event Republic of Korea","acronym":"CCS '21","sponsor":["SIGSAC ACM Special Interest Group on Security, Audit, and Control"]},"container-title":["Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3460120.3484770","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3460120.3484770","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,18]],"date-time":"2025-11-18T20:51:15Z","timestamp":1763499075000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3460120.3484770"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,12]]},"references-count":52,"alternative-id":["10.1145\/3460120.3484770","10.1145\/3460120"],"URL":"https:\/\/doi.org\/10.1145\/3460120.3484770","relation":{},"subject":[],"published":{"date-parts":[[2021,11,12]]},"assertion":[{"value":"2021-11-13","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}