{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T09:22:44Z","timestamp":1780392164965,"version":"3.54.1"},"publisher-location":"New York, NY, USA","reference-count":69,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,10,8]],"date-time":"2024-10-08T00:00:00Z","timestamp":1728345600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,10,8]]},"DOI":"10.1145\/3640457.3688140","type":"proceedings-article","created":{"date-parts":[[2024,10,8]],"date-time":"2024-10-08T11:39:28Z","timestamp":1728387568000},"page":"475-485","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Scalable Cross-Entropy Loss for Sequential Recommendations with Large Item Catalogs"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-7591-3082","authenticated-orcid":false,"given":"Gleb","family":"Mezentsev","sequence":"first","affiliation":[{"name":"Skolkovo Institute of Science and Technology, Russian Federation"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-1238-6533","authenticated-orcid":false,"given":"Danil","family":"Gusak","sequence":"additional","affiliation":[{"name":"Skolkovo Institute of Science and Technology, Russian Federation and HSE University, Russian Federation"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2071-2163","authenticated-orcid":false,"given":"Ivan","family":"Oseledets","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Research Institute, Russian Federation and Skolkovo Institute of Science and Technology, Russian Federation"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3679-5311","authenticated-orcid":false,"given":"Evgeny","family":"Frolov","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Research Institute, Russian Federation; Skolkovo Institute of Science and Technology, Russian Federation and HSE University, Russian Federation"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,10,8]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-019-0598-1"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-88942-5_24"},{"key":"e_1_3_2_1_3_1","volume-title":"Practical and optimal LSH for angular distance. Advances in neural information processing systems 28","author":"Andoni Alexandr","year":"2015","unstructured":"Alexandr Andoni, Piotr Indyk, Thijs Laarhoven, Ilya Razenshteyn, and Ludwig Schmidt. 2015. Practical and optimal LSH for angular distance. Advances in neural information processing systems 28 (2015)."},{"key":"e_1_3_2_1_4_1","volume-title":"Yelp Dataset Challenge: Review Rating Prediction. arXiv preprint arXiv:1605.05362","author":"Asghar Nabiha","year":"2016","unstructured":"Nabiha Asghar. 2016. Yelp Dataset Challenge: Review Rating Prediction. arXiv preprint arXiv:1605.05362 (2016)."},{"key":"e_1_3_2_1_5_1","volume-title":"Clustering is efficient for approximate maximum inner product search. arXiv preprint arXiv:1507.05910","author":"Auvolat Alex","year":"2015","unstructured":"Alex Auvolat, Sarath Chandar, Pascal Vincent, Hugo Larochelle, and Yoshua Bengio. 2015. Clustering is efficient for approximate maximum inner product search. arXiv preprint arXiv:1507.05910 (2015)."},{"key":"e_1_3_2_1_6_1","volume-title":"Tapas: Two-pass approximate adaptive sampling for softmax. arXiv preprint arXiv:1707.03073","author":"Bai Yu","year":"2017","unstructured":"Yu Bai, Sally Goldman, and Li Zhang. 2017. Tapas: Two-pass approximate adaptive sampling for softmax. arXiv preprint arXiv:1707.03073 (2017)."},{"key":"e_1_3_2_1_7_1","volume-title":"International Workshop on Artificial Intelligence and Statistics. PMLR, 17\u201324","author":"Bengio Yoshua","year":"2003","unstructured":"Yoshua Bengio and Jean-S\u00e9bastien Sen\u00e9cal. 2003. Quick training of probabilistic neural nets by importance sampling. In International Workshop on Artificial Intelligence and Statistics. PMLR, 17\u201324."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2007.912312"},{"key":"e_1_3_2_1_9_1","volume-title":"International conference on machine learning. PMLR, 590\u2013599","author":"Blanc Guy","year":"2018","unstructured":"Guy Blanc and Steffen Rendle. 2018. Adaptive sampled softmax with kernel based sampling. In International conference on machine learning. PMLR, 590\u2013599."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","unstructured":"Roc\u00edo Ca\u00f1amares and Pablo Castells. 2020. On Target Item Sampling in Offline Recommender System Evaluation. 259\u2013268. https:\/\/doi.org\/10.1145\/3383313.3412259","DOI":"10.1145\/3383313.3412259"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"crossref","unstructured":"Huiyuan Chen Yusan Lin Menghai Pan Lan Wang Chin-Chia\u00a0Michael Yeh Xiaoting Li Yan Zheng Fei Wang and Hao Yang. 2022. Denoising Self-attentive Sequential Recommendation.","DOI":"10.1145\/3523227.3546788"},{"key":"e_1_3_2_1_12_1","volume-title":"Generating Negative Samples for Sequential Recommendation. arXiv preprint arXiv:2208.03645","author":"Chen Yongjun","year":"2022","unstructured":"Yongjun Chen, Jia Li, Zhiwei Liu, Nitish\u00a0Shirish Keskar, Huan Wang, Julian McAuley, and Caiming Xiong. 2022. Generating Negative Samples for Sequential Recommendation. arXiv preprint arXiv:2208.03645 (2022)."},{"key":"e_1_3_2_1_13_1","unstructured":"Eunjoon Cho Seth\u00a0A. Myers and Jure Leskovec. 2011. Friendship and mobility: user movement in location-based social networks. In Knowledge Discovery and Data Mining."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3460231.3475943"},{"key":"e_1_3_2_1_15_1","volume-title":"Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805","author":"Devlin Jacob","year":"2018","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)."},{"key":"e_1_3_2_1_16_1","first-page":"1094","article-title":"Simplify and robustify negative sampling for implicit collaborative filtering","volume":"33","author":"Ding Jingtao","year":"2020","unstructured":"Jingtao Ding, Yuhan Quan, Quanming Yao, Yong Li, and Depeng Jin. 2020. Simplify and robustify negative sampling for implicit collaborative filtering. Advances in Neural Information Processing Systems 33 (2020), 1094\u20131105.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.98"},{"key":"e_1_3_2_1_18_1","unstructured":"Hanwen Du Hui Shi Pengpeng Zhao Deqing Wang Victor\u00a0S. Sheng Yanchi Liu Guanfeng Liu and Lei Zhao. 2022. Contrastive Learning with Bidirectional Transformers for Sequential Recommendation. arxiv:2208.03895\u00a0[cs.IR]"},{"key":"e_1_3_2_1_19_1","unstructured":"Xinyu Du Huanhuan Yuan Pengpeng Zhao Fuzhen Zhuang Guanfeng Liu and Yanchi Liu. 2023. Frequency Enhanced Hybrid Attention Network for Sequential Recommendation."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","unstructured":"Xinyan Fan Zheng Liu Jianxun Lian Wayne Zhao Xing Xie and Ji-Rong Wen. 2021. Lighter and Better: Low-Rank Decomposed Self-Attention Networks for Next-Item Recommendation. 1733\u20131737. https:\/\/doi.org\/10.1145\/3404835.3462978","DOI":"10.1145\/3404835.3462978"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"crossref","unstructured":"Evgeny Frolov and Ivan Oseledets. 2022. Tensor-based Sequential Learning via Hankel Matrix Representation for Next Item Recommendations. arxiv:2212.05720\u00a0[cs.LG]","DOI":"10.1109\/ACCESS.2023.3234863"},{"key":"e_1_3_2_1_22_1","unstructured":"Emil\u00a0Julius Gumbel. 1954. Statistical theory of extreme values and some practical applications: a series of lectures. Vol.\u00a033. US Government Printing Office."},{"key":"e_1_3_2_1_23_1","unstructured":"Ruiqi Guo Sanjiv Kumar Krzysztof Choromanski and David Simcha. 2016. Quantization based fast inner product search. In Artificial intelligence and statistics. PMLR 482\u2013490."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3627673.3679986"},{"key":"e_1_3_2_1_25_1","unstructured":"Nathan Halko Per-Gunnar Martinsson and Joel\u00a0A. Tropp. 2010. Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions. arxiv:0909.4061\u00a0[math.NA]"},{"key":"e_1_3_2_1_26_1","volume-title":"The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis) 5, 4","author":"Harper F\u00a0Maxwell","year":"2015","unstructured":"F\u00a0Maxwell Harper and Joseph\u00a0A Konstan. 2015. The movielens datasets: History and context. Acm transactions on interactive intelligent systems (tiis) 5, 4 (2015), 1\u201319."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/2959100.2959152"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3271761"},{"key":"e_1_3_2_1_29_1","volume-title":"Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939","author":"Hidasi Bal\u00e1zs","year":"2015","unstructured":"Bal\u00e1zs Hidasi, Alexandros Karatzoglou, Linas Baltrunas, and Domonkos Tikk. 2015. Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939 (2015)."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/3569930"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2018.00035"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3604915.3610644"},{"key":"e_1_3_2_1_33_1","volume-title":"On Sampled Metrics for Item Recommendation. In KDD","author":"Krichene Walid","year":"2020","unstructured":"Walid Krichene and Steffen Rendle. 2020. On Sampled Metrics for Item Recommendation. In KDD 2020. https:\/\/dl.acm.org\/doi\/10.1145\/3394486.3403226"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3050571"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","unstructured":"Jiacheng Li Yujie Wang and Julian McAuley. 2020. Time Interval Aware Self-Attention for Sequential Recommendation. 322\u2013330. https:\/\/doi.org\/10.1145\/3336191.3371786","DOI":"10.1145\/3336191.3371786"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380187"},{"key":"e_1_3_2_1_37_1","unstructured":"Chang Liu Xiaoguang Li Guohao Cai Zhenhua Dong Hong Zhu and Lifeng Shang. 2021. Non-invasive Self-attention for Side Information Fusion in Sequential Recommendation. arxiv:2103.03578\u00a0[cs.IR]"},{"key":"e_1_3_2_1_38_1","volume-title":"Contrastive Self-supervised Sequential Recommendation with Robust Augmentation. (08","author":"Liu Zhiwei","year":"2021","unstructured":"Zhiwei Liu, Yongjun Chen, Jia Li, Philip Yu, Julian McAuley, and Caiming Xiong. 2021. Contrastive Self-supervised Sequential Recommendation with Robust Augmentation. (08 2021)."},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3604915.3608791"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2012.110"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"crossref","unstructured":"Julian McAuley Christopher Targett Qinfeng Shi and Anton van\u00a0den Hengel. 2015. Image-based Recommendations on Styles and Substitutes. arxiv:1506.04757\u00a0[cs.CV]","DOI":"10.1145\/2766462.2767755"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"crossref","unstructured":"Zaiqiao Meng Richard McCreadie Craig Macdonald and Iadh Ounis. 2020. Exploring Data Splitting Strategies for the Evaluation of Recommendation Models. arxiv:2007.13237\u00a0[cs.IR]","DOI":"10.1145\/3383313.3418479"},{"key":"e_1_3_2_1_43_1","volume-title":"International Conference on Machine Learning. PMLR, 2587\u20132596","author":"Mussmann Stephen","year":"2016","unstructured":"Stephen Mussmann and Stefano Ermon. 2016. Learning and inference via maximum inner product search. In International Conference on Machine Learning. PMLR, 2587\u20132596."},{"key":"e_1_3_2_1_44_1","volume-title":"Fast amortized inference and learning in log-linear models with randomly perturbed nearest neighbor search. arXiv preprint arXiv:1707.03372","author":"Mussmann Stephen","year":"2017","unstructured":"Stephen Mussmann, Daniel Levy, and Stefano Ermon. 2017. Fast amortized inference and learning in log-linear models with randomly perturbed nearest neighbor search. arXiv preprint arXiv:1707.03372 (2017)."},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","unstructured":"Jianmo Ni Jiacheng Li and Julian McAuley. 2019. Justifying Recommendations using Distantly-Labeled Reviews and Fine-Grained Aspects. 188\u2013197. https:\/\/doi.org\/10.18653\/v1\/D19-1018","DOI":"10.18653\/v1\/D19-1018"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3160466"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/3523227.3546753"},{"key":"e_1_3_2_1_48_1","volume-title":"Petrov and Craig Macdonald","author":"V.","year":"2023","unstructured":"Aleksandr\u00a0V. Petrov and Craig Macdonald. 2023. Generative Sequential Recommendation with GPTRec. arxiv:2306.11114\u00a0[cs.IR]"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3604915.3608783"},{"key":"e_1_3_2_1_50_1","doi-asserted-by":"publisher","unstructured":"Ruihong Qiu Zi Huang Hongzhi Yin and Zijian Wang. 2022. Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation. 813\u2013823. https:\/\/doi.org\/10.1145\/3488560.3498433","DOI":"10.1145\/3488560.3498433"},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"crossref","unstructured":"Ruihong Qiu Zi Huang Hongzhi Yin and Zijian Wang. 2022. Contrastive Learning for Representation Degeneration Problem in Sequential Recommendation. 813\u2013823.","DOI":"10.1145\/3488560.3498433"},{"key":"e_1_3_2_1_52_1","unstructured":"Alec Radford Karthik Narasimhan Tim Salimans Ilya Sutskever 2018. Improving language understanding by generative pre-training. (2018)."},{"key":"e_1_3_2_1_53_1","unstructured":"Alec Radford Jeff Wu Rewon Child David Luan Dario Amodei and Ilya Sutskever. 2019. Language Models are Unsupervised Multitask Learners. https:\/\/api.semanticscholar.org\/CorpusID:160025533"},{"key":"e_1_3_2_1_54_1","volume-title":"Ananda\u00a0Theertha Suresh, and Sanjiv Kumar.","author":"Rawat Ankit\u00a0Singh","year":"2019","unstructured":"Ankit\u00a0Singh Rawat, Jiecao Chen, Felix Xinnan\u00a0X Yu, Ananda\u00a0Theertha Suresh, and Sanjiv Kumar. 2019. Sampled softmax with random fourier features. Advances in Neural Information Processing Systems 32 (2019)."},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1145\/2556195.2556248"},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1145\/1772690.1772773"},{"key":"e_1_3_2_1_57_1","volume-title":"A new unbiased and efficient class of lsh-based samplers and estimators for partition function computation in log-linear models. arXiv preprint arXiv:1703.05160","author":"Spring Ryan","year":"2017","unstructured":"Ryan Spring and Anshumali Shrivastava. 2017. A new unbiased and efficient class of lsh-based samplers and estimators for partition function computation in log-linear models. arXiv preprint arXiv:1703.05160 (2017)."},{"key":"e_1_3_2_1_58_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357895"},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1145\/3159652.3159656"},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","unstructured":"Changxin Tian Zihan Lin Shuqing Bian Jinpeng Wang and Wayne Zhao. 2022. Temporal Contrastive Pre-Training for Sequential Recommendation. 1925\u20131934. https:\/\/doi.org\/10.1145\/3511808.3557468","DOI":"10.1145\/3511808.3557468"},{"key":"e_1_3_2_1_61_1","volume-title":"Attention is all you need. Advances in neural information processing systems 30","author":"Vaswani Ashish","year":"2017","unstructured":"Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan\u00a0N Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. Advances in neural information processing systems 30 (2017)."},{"key":"e_1_3_2_1_62_1","volume-title":"Deep networks with large output spaces. arXiv preprint arXiv:1412.7479","author":"Vijayanarasimhan Sudheendra","year":"2014","unstructured":"Sudheendra Vijayanarasimhan, Jonathon Shlens, Rajat Monga, and Jay Yagnik. 2014. Deep networks with large output spaces. arXiv preprint arXiv:1412.7479 (2014)."},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3463032"},{"key":"e_1_3_2_1_64_1","doi-asserted-by":"publisher","DOI":"10.1145\/3604915.3610236"},{"key":"e_1_3_2_1_65_1","doi-asserted-by":"crossref","unstructured":"Qitian Wu Chenxiao Yang Shuodian Yu Xiaofeng Gao and Guihai Chen. 2021. Seq2Bubbles: Region-Based Embedding Learning for User Behaviors in Sequential Recommenders. 2160\u20132169.","DOI":"10.1145\/3459637.3482296"},{"key":"e_1_3_2_1_66_1","volume-title":"Contrastive Learning for Sequential Recommendation. 2022 IEEE 38th International Conference on Data Engineering (ICDE)","author":"Xie Xu","year":"2022","unstructured":"Xu Xie, Fei Sun, Zhaoyang Liu, Shiwen Wu, Jinyang Gao, Jiandong Zhang, Bolin Ding, and Bin Cui. 2022. Contrastive Learning for Sequential Recommendation. 2022 IEEE 38th International Conference on Data Engineering (ICDE) (2022), 1259\u20131273. https:\/\/api.semanticscholar.org\/CorpusID:251299631"},{"key":"e_1_3_2_1_67_1","volume-title":"Promptcast: A new prompt-based learning paradigm for time series forecasting","author":"Xue Hao","year":"2023","unstructured":"Hao Xue and Flora\u00a0D Salim. 2023. Promptcast: A new prompt-based learning paradigm for time series forecasting. IEEE Transactions on Knowledge and Data Engineering (2023)."},{"key":"e_1_3_2_1_68_1","volume-title":"International Conference on Machine Learning. PMLR, 5640\u20135649","author":"En-Hsu Yen Ian","year":"2018","unstructured":"Ian En-Hsu Yen, Satyen Kale, Felix Yu, Daniel Holtmann-Rice, Sanjiv Kumar, and Pradeep Ravikumar. 2018. Loss decomposition for fast learning in large output spaces. In International Conference on Machine Learning. PMLR, 5640\u20135649."},{"key":"e_1_3_2_1_69_1","volume-title":"Feature-level Deeper Self-Attention Network for Sequential Recommendation. In International Joint Conference on Artificial Intelligence.","author":"Zhang Tingting","year":"2019","unstructured":"Tingting Zhang, Pengpeng Zhao, Yanchi Liu, Victor\u00a0S. Sheng, Jiajie Xu, Deqing Wang, Guanfeng Liu, and Xiaofang Zhou. 2019. Feature-level Deeper Self-Attention Network for Sequential Recommendation. In International Joint Conference on Artificial Intelligence."}],"event":{"name":"RecSys '24: 18th ACM Conference on Recommender Systems","location":"Bari Italy","acronym":"RecSys '24","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval","SIGAI ACM Special Interest Group on Artificial Intelligence","SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data","SIGCHI ACM Special Interest Group on Computer-Human Interaction"]},"container-title":["18th ACM Conference on Recommender Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3640457.3688140","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3640457.3688140","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T19:48:43Z","timestamp":1767815323000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3640457.3688140"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,8]]},"references-count":69,"alternative-id":["10.1145\/3640457.3688140","10.1145\/3640457"],"URL":"https:\/\/doi.org\/10.1145\/3640457.3688140","relation":{},"subject":[],"published":{"date-parts":[[2024,10,8]]},"assertion":[{"value":"2024-10-08","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}