{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T01:42:08Z","timestamp":1765503728194,"version":"3.48.0"},"publisher-location":"New York, NY, USA","reference-count":68,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,11,10]]},"DOI":"10.1145\/3746252.3761369","type":"proceedings-article","created":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T00:18:04Z","timestamp":1762561084000},"page":"707-717","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["<scp>LangPTune:<\/scp>\n                    Optimizing Language-based User Profiles for Recommendation"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1647-4898","authenticated-orcid":false,"given":"Zhaolin","family":"Gao","sequence":"first","affiliation":[{"name":"Cornell University, Ithaca, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1205-3970","authenticated-orcid":false,"given":"Joyce","family":"Zhou","sequence":"additional","affiliation":[{"name":"Cornell University, Ithaca, USA"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-6431-0851","authenticated-orcid":false,"given":"Yijia","family":"Dai","sequence":"additional","affiliation":[{"name":"Cornell University, Ithaca, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3654-3683","authenticated-orcid":false,"given":"Thorsten","family":"Joachims","sequence":"additional","affiliation":[{"name":"Cornell University, Ithaca, USA"}]}],"member":"320","published-online":{"date-parts":[[2025,11,10]]},"reference":[{"key":"e_1_3_2_2_1_1","unstructured":"Alekh Agarwal Sham M. Kakade Jason D. Lee and Gaurav Mahajan. 2020. On the Theory of Policy Gradient Methods: Optimality Approximation and Distribution Shift. arXiv:1908.00261 [cs.LG] https:\/\/arxiv.org\/abs\/1908.00261"},{"key":"e_1_3_2_2_2_1","unstructured":"Anthropic. 2024. Introducing the next generation of Claude. https:\/\/www.anthropic.com\/news\/claude-3-family"},{"key":"e_1_3_2_2_3_1","unstructured":"Mohammad Gheshlaghi Azar Mark Rowland Bilal Piot Daniel Guo Daniele Calandriello Michal Valko and R\u00e9mi Munos. 2023. A General Theoretical Paradigm to Understand Learning from Human Preferences. arXiv:2310.12036 [cs.AI] https:\/\/arxiv.org\/abs\/2310.12036"},{"key":"e_1_3_2_2_4_1","unstructured":"Yuntao Bai Saurav Kadavath Sandipan Kundu Amanda Askell Jackson Kernion Andy Jones Anna Chen Anna Goldie Azalia Mirhoseini Cameron McKinnon Carol Chen Catherine Olsson Christopher Olah Danny Hernandez Dawn Drain Deep Ganguli Dustin Li Eli Tran-Johnson Ethan Perez Jamie Kerr Jared Mueller Jeffrey Ladish Joshua Landau Kamal Ndousse Kamile Lukosuite Liane Lovitt Michael Sellitto Nelson Elhage Nicholas Schiefer Noemi Mercado Nova DasSarma Robert Lasenby Robin Larson Sam Ringer Scott Johnston Shauna Kravec Sheer El Showk Stanislav Fort Tamera Lanham Timothy Telleen-Lawton Tom Conerly Tom Henighan Tristan Hume Samuel R. Bowman Zac Hatfield-Dodds Ben Mann Dario Amodei Nicholas Joseph Sam McCandlish Tom Brown and Jared Kaplan. 2022. Constitutional AI: Harmlessness from AI Feedback. arXiv:2212.08073 [cs.CL] https:\/\/arxiv.org\/abs\/2212.08073"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331211"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3604915.3608857"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1037\/14805-009"},{"key":"e_1_3_2_2_8_1","unstructured":"Ting Chen Simon Kornblith Mohammad Norouzi and Geoffrey Hinton. 2020. A Simple Framework for Contrastive Learning of Visual Representations. arXiv:2002.05709 [cs.LG] https:\/\/arxiv.org\/abs\/2002.05709"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"crossref","unstructured":"Cheng-Han Chiang and Hung yi Lee. 2023. Can Large Language Models Be an Alternative to Human Evaluations? arXiv:2305.01937 [cs.CL] https:\/\/arxiv.org\/abs\/2305.01937","DOI":"10.18653\/v1\/2023.acl-long.870"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.5555\/3294996.3295184"},{"key":"e_1_3_2_2_11_1","unstructured":"Karl Cobbe Vineet Kosaraju Mohammad Bavarian Mark Chen Heewoo Jun Lukasz Kaiser Matthias Plappert Jerry Tworek Jacob Hilton Reiichiro Nakano Christopher Hesse and John Schulman. 2021. Training Verifiers to Solve Math Word Problems. arXiv:2110.14168 [cs.LG] https:\/\/arxiv.org\/abs\/2110.14168"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/2959100.2959190"},{"key":"e_1_3_2_2_13_1","unstructured":"DeepSeek-AI. 2025. DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning."},{"key":"e_1_3_2_2_14_1","volume-title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv:1810.04805 [cs.CL] https:\/\/arxiv.org\/abs\/1810.04805","author":"Devlin Jacob","year":"2019","unstructured":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv:1810.04805 [cs.CL] https:\/\/arxiv.org\/abs\/1810.04805"},{"key":"e_1_3_2_2_15_1","unstructured":"Jingtao Ding Yuhan Quan Quanming Yao Yong Li and Depeng Jin. 2020. Simplify and Robustify Negative Sampling for Implicit Collaborative Filtering. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741667"},{"key":"e_1_3_2_2_17_1","volume-title":"KTO: Model Alignment as Prospect Theoretic Optimization. arXiv:2402.01306 [cs.LG] https:\/\/arxiv.org\/abs\/2402.01306","author":"Ethayarajh Kawin","year":"2024","unstructured":"Kawin Ethayarajh, Winnie Xu, Niklas Muennighoff, Dan Jurafsky, and Douwe Kiela. 2024. KTO: Model Alignment as Prospect Theoretic Optimization. arXiv:2402.01306 [cs.LG] https:\/\/arxiv.org\/abs\/2402.01306"},{"key":"e_1_3_2_2_18_1","volume-title":"REBEL: Reinforcement Learning via Regressing Relative Rewards. arXiv:2404.16767 [cs.LG] https:\/\/arxiv.org\/abs\/2404.16767","author":"Gao Zhaolin","year":"2024","unstructured":"Zhaolin Gao, Jonathan D. Chang, Wenhao Zhan, Owen Oertell, Gokul Swamy, Kiant\u00e9 Brantley, Thorsten Joachims, J. Andrew Bagnell, Jason D. Lee, and Wen Sun. 2024. REBEL: Reinforcement Learning via Regressing Relative Rewards. arXiv:2404.16767 [cs.LG] https:\/\/arxiv.org\/abs\/2404.16767"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512106"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","unstructured":"Shijie Geng Shuchang Liu Zuohui Fu Yingqiang Ge and Yongfeng Zhang. 2023. Recommendation as Language Processing (RLP): A Unified Pretrain Personalized Prompt & Predict Paradigm (P5). https:\/\/doi.org\/10.48550\/arXiv.2203.13366 arXiv:2203.13366 [cs].","DOI":"10.48550\/arXiv.2203.13366"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1214\/009053604000000553"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3604915.3610639"},{"key":"e_1_3_2_2_23_1","unstructured":"Xiangnan He Kuan Deng Xiang Wang Yan Li Yongdong Zhang and Meng Wang. 2020. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. arXiv:2002.02126 [cs.IR] https:\/\/arxiv.org\/abs\/2002.02126"},{"key":"e_1_3_2_2_24_1","unstructured":"Dan Hendrycks Collin Burns Saurav Kadavath Akul Arora Steven Basart Eric Tang Dawn Song and Jacob Steinhardt. 2021. Measuring Mathematical Problem Solving With the MATH Dataset. arXiv:2103.03874 [cs.LG] https:\/\/arxiv.org\/abs\/2103.03874"},{"key":"e_1_3_2_2_25_1","volume-title":"Bridging Language and Items for Retrieval and Recommendation. arXiv preprint arXiv:2403.03952","author":"Hou Yupeng","year":"2024","unstructured":"Yupeng Hou, Jiacheng Li, Zhankui He, An Yan, Xiusi Chen, and Julian McAuley. 2024. Bridging Language and Items for Retrieval and Recommendation. arXiv preprint arXiv:2403.03952 (2024)."},{"key":"e_1_3_2_2_26_1","unstructured":"Edward J. Hu Yelong Shen Phillip Wallis Zeyuan Allen-Zhu Yuanzhi Li Shean Wang Lu Wang and Weizhu Chen. 2021. LoRA: Low-Rank Adaptation of Large Language Models. arXiv:2106.09685 [cs.CL] https:\/\/arxiv.org\/abs\/2106.09685"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2008.22"},{"key":"e_1_3_2_2_28_1","volume-title":"Antonin Raffin, Anssi Kanervisto, and Weixun Wang.","author":"Huang Shengyi","year":"2022","unstructured":"Shengyi Huang, Rousslan Fernand Julien Dossa, Antonin Raffin, Anssi Kanervisto, and Weixun Wang. 2022. The 37 Implementation Details of Proximal Policy Optimization. In ICLR Blog Track. https:\/\/iclr-blog-track.github.io\/2022\/03\/25\/ppo-implementation-details\/ https:\/\/iclr-blog-track.github.io\/2022\/03\/25\/ppo-implementation-details\/."},{"key":"e_1_3_2_2_29_1","unstructured":"Jianchao Ji Zelong Li Shuyuan Xu Wenyue Hua Yingqiang Ge Juntao Tan and Yongfeng Zhang. 2023. GenRec: Large Language Model for Generative Recommendation. arXiv:2307.00457 [cs.IR] https:\/\/arxiv.org\/abs\/2307.00457"},{"key":"e_1_3_2_2_30_1","volume-title":"Chi, and Derek Zhiyuan Cheng","author":"Kang Wang-Cheng","year":"2023","unstructured":"Wang-Cheng Kang, Jianmo Ni, Nikhil Mehta, Maheswaran Sathiamoorthy, Lichan Hong, Ed Chi, and Derek Zhiyuan Cheng. 2023. Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction. arXiv:2305.06474 [cs.IR] https:\/\/arxiv.org\/abs\/2305.06474"},{"key":"e_1_3_2_2_31_1","volume-title":"Kipf and Max Welling","author":"Thomas","year":"2017","unstructured":"Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. arXiv:1609.02907 [cs.LG] https:\/\/arxiv.org\/abs\/1609.02907"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2009.263"},{"key":"e_1_3_2_2_33_1","volume-title":"Roweis (Eds.)","volume":"20","author":"Langford John","year":"2007","unstructured":"John Langford and Tong Zhang. 2007. The Epoch-Greedy Algorithm for Multi-armed Bandits with Side Information. In Advances in Neural Information Processing Systems, J. Platt, D. Koller, Y. Singer, and S. Roweis (Eds.), Vol. 20. Curran Associates, Inc. https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2007\/file\/4b04a686b0ad13dce35fa99fa4161c65-Paper.pdf"},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2410.12123"},{"key":"e_1_3_2_2_35_1","volume-title":"Advances in Neural Information Processing Systems","author":"Lee Daniel","year":"2000","unstructured":"Daniel Lee and H. Sebastian Seung. 2000. Algorithms for Non-negative Matrix Factorization. In Advances in Neural Information Processing Systems, T. Leen, T. Dietterich, and V. Tresp (Eds.), Vol. 13. MIT Press. https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2000\/file\/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf"},{"key":"e_1_3_2_2_36_1","unstructured":"Sean Lee Aamir Shakir Darius Koenig and Julius Lipp. 2024. Open Source Strikes Bread - New Fluffy Embeddings Model. https:\/\/www.mixedbread.ai\/blog\/mxbai-embed-large-v1"},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2306.02841"},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"crossref","unstructured":"Jiayi Liao Sihang Li Zhengyi Yang Jiancan Wu Yancheng Yuan Xiang Wang and Xiangnan He. 2024. LLaRA: Large Language-Recommendation Assistant. arXiv:2312.02445 [cs.IR] https:\/\/arxiv.org\/abs\/2312.02445","DOI":"10.1145\/3626772.3657690"},{"key":"e_1_3_2_2_39_1","unstructured":"Junling Liu Chao Liu Peilin Zhou Renjie Lv Kang Zhou and Yan Zhang. 2023. Is ChatGPT a Good Recommender? A Preliminary Study. arXiv:2304.10149 [cs.IR] https:\/\/arxiv.org\/abs\/2304.10149"},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3556702.3556844"},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"crossref","unstructured":"Hanjia Lyu Song Jiang Hanqing Zeng Yinglong Xia Qifan Wang Si Zhang Ren Chen Christopher Leung Jiajie Tang and Jiebo Luo. 2024. LLM-Rec: Personalized Recommendation via Prompting Large Language Models. arXiv:2307.15780 [cs.CL] https:\/\/arxiv.org\/abs\/2307.15780","DOI":"10.18653\/v1\/2024.findings-naacl.39"},{"key":"e_1_3_2_2_42_1","unstructured":"Meta. 2024. Introducing Meta Llama 3: The most capable openly available LLM to date. https:\/\/ai.meta.com\/blog\/meta-llama-3\/"},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3591677"},{"key":"e_1_3_2_2_44_1","unstructured":"OpenAI. 2023. Gpt-4 technical report."},{"key":"e_1_3_2_2_45_1","volume-title":"TEARS: Textual Representations, for Scrutable Recommendations. arXiv preprint arXiv:2410.19302 (Oct.","author":"Penaloza Emiliano","year":"2024","unstructured":"Emiliano Penaloza, Olivier Gouvert, Haolun Wu, and Laurent Charlin. 2024. TEARS: Textual Representations, for Scrutable Recommendations. arXiv preprint arXiv:2410.19302 (Oct. 2024)."},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531873"},{"key":"e_1_3_2_2_47_1","unstructured":"Rafael Rafailov Archit Sharma Eric Mitchell Stefano Ermon Christopher D. Manning and Chelsea Finn. 2023. Direct Preference Optimization: Your Language Model is Secretly a Reward Model. arXiv:2305.18290 [cs.LG]"},{"key":"e_1_3_2_2_48_1","unstructured":"Jerome Ramos Hossen A. Rahmani Xi Wang Xiao Fu and Aldo Lipani. 2024. Transparent and Scrutable Recommendations Using Natural Language User Profiles. arXiv:2402.05810 [cs.IR] https:\/\/arxiv.org\/abs\/2402.05810"},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645458"},{"key":"e_1_3_2_2_50_1","volume-title":"BPR: Bayesian Personalized Ranking from Implicit Feedback. arXiv:1205.2618 [cs.IR] https:\/\/arxiv.org\/abs\/1205.2618","author":"Rendle Steffen","year":"2012","unstructured":"Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2012. BPR: Bayesian Personalized Ranking from Implicit Feedback. arXiv:1205.2618 [cs.IR] https:\/\/arxiv.org\/abs\/1205.2618"},{"key":"e_1_3_2_2_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/3604915.3608845"},{"key":"e_1_3_2_2_52_1","unstructured":"John Schulman Filip Wolski Prafulla Dhariwal Alec Radford and Oleg Klimov. 2017. Proximal Policy Optimization Algorithms. arXiv:1707.06347 [cs.LG] https:\/\/arxiv.org\/abs\/1707.06347"},{"key":"e_1_3_2_2_53_1","doi-asserted-by":"crossref","unstructured":"Fei Sun Jun Liu Jian Wu Changhua Pei Xiao Lin Wenwu Ou and Peng Jiang. 2019. BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer. arXiv:1904.06690 [cs.IR] https:\/\/arxiv.org\/abs\/1904.06690","DOI":"10.1145\/3357384.3357895"},{"key":"e_1_3_2_2_54_1","unstructured":"Gemma Team. 2024a. Gemma 2: Improving Open Language Models at a Practical Size. arXiv:2408.00118 [cs.CL] https:\/\/arxiv.org\/abs\/2408.00118"},{"key":"e_1_3_2_2_55_1","unstructured":"Llama Team. 2024b. The Llama 3 Herd of Models. arXiv:2407.21783 [cs.AI] https:\/\/arxiv.org\/abs\/2407.21783"},{"key":"e_1_3_2_2_56_1","unstructured":"Aaron van den Oord Yazhe Li and Oriol Vinyals. 2019. Representation Learning with Contrastive Predictive Coding. arXiv:1807.03748 [cs.LG] https:\/\/arxiv.org\/abs\/1807.03748"},{"key":"e_1_3_2_2_57_1","first-page":"2579","article-title":"Visualizing Data using t-SNE","volume":"9","author":"van der Maaten Laurens","year":"2008","unstructured":"Laurens van der Maaten and Geoffrey E. Hinton. 2008. Visualizing Data using t-SNE. Journal of Machine Learning Research, Vol. 9 (2008), 2579-2605. https:\/\/api.semanticscholar.org\/CorpusID:5855042","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_2_58_1","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331267"},{"key":"e_1_3_2_2_59_1","doi-asserted-by":"publisher","unstructured":"Yunjia Xi Weiwen Liu Jianghao Lin Xiaoling Cai Hong Zhu Jieming Zhu Bo Chen Ruiming Tang Weinan Zhang Rui Zhang and Yong Yu. 2023. Towards Open-World Recommendation with Knowledge Augmentation from Large Language Models. https:\/\/doi.org\/10.48550\/arXiv.2306.10933 arXiv:2306.10933 [cs].","DOI":"10.48550\/arXiv.2306.10933"},{"key":"e_1_3_2_2_60_1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2401.04997"},{"key":"e_1_3_2_2_61_1","volume-title":"PALR: Personalization Aware LLMs for Recommendation. arXiv:2305.07622 [cs.IR] https:\/\/arxiv.org\/abs\/2305.07622","author":"Yang Fan","year":"2023","unstructured":"Fan Yang, Zheng Chen, Ziyan Jiang, Eunah Cho, Xiaojiang Huang, and Yanbin Lu. 2023. PALR: Personalization Aware LLMs for Recommendation. arXiv:2305.07622 [cs.IR] https:\/\/arxiv.org\/abs\/2305.07622"},{"key":"e_1_3_2_2_62_1","unstructured":"Shenghao Yang Weizhi Ma Peijie Sun Qingyao Ai Yiqun Liu Mingchen Cai and Min Zhang. 2024b. Sequential Recommendation with Latent Relations based on Large Language Model. arXiv:2403.18348 [cs.IR] https:\/\/arxiv.org\/abs\/2403.18348"},{"key":"e_1_3_2_2_63_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645357"},{"key":"e_1_3_2_2_64_1","unstructured":"Yang Zhang Fuli Feng Jizhi Zhang Keqin Bao Qifan Wang and Xiangnan He. 2023. CoLLM: Integrating Collaborative Embeddings into Large Language Models for Recommendation. arXiv:2310.19488 [cs.IR] https:\/\/arxiv.org\/abs\/2310.19488"},{"key":"e_1_3_2_2_65_1","unstructured":"Joyce Zhou Yijia Dai and Thorsten Joachims. 2024. Language-Based User Profiles for Recommendation. arXiv:2402.15623 [cs.CL] https:\/\/arxiv.org\/abs\/2402.15623"},{"key":"e_1_3_2_2_66_1","unstructured":"Banghua Zhu Evan Frick Tianhao Wu Hanlin Zhu and Jiantao Jiao. 2023. Starling-7B: Improving LLM Helpfulness & Harmlessness with RLAIF."},{"key":"e_1_3_2_2_67_1","first-page":"1433","volume-title":"Aaai","volume":"8","author":"Ziebart Brian D","year":"2008","unstructured":"Brian D Ziebart, Andrew L Maas, J Andrew Bagnell, Anind K Dey, et al., 2008. Maximum entropy inverse reinforcement learning.. In Aaai, Vol. 8. Chicago, IL, USA, 1433-1438."},{"key":"e_1_3_2_2_68_1","unstructured":"Daniel M. Ziegler Nisan Stiennon Jeffrey Wu Tom B. Brown Alec Radford Dario Amodei Paul Christiano and Geoffrey Irving. 2020. Fine-Tuning Language Models from Human Preferences. arXiv:1909.08593 [cs.CL] https:\/\/arxiv.org\/abs\/1909.08593"}],"event":{"name":"CIKM '25: The 34th ACM International Conference on Information and Knowledge Management","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval","SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"],"location":"Seoul Republic of Korea","acronym":"CIKM '25"},"container-title":["Proceedings of the 34th ACM International Conference on Information and Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3746252.3761369","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T01:40:17Z","timestamp":1765503617000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3746252.3761369"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,10]]},"references-count":68,"alternative-id":["10.1145\/3746252.3761369","10.1145\/3746252"],"URL":"https:\/\/doi.org\/10.1145\/3746252.3761369","relation":{},"subject":[],"published":{"date-parts":[[2025,11,10]]},"assertion":[{"value":"2025-11-10","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}