{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T22:49:06Z","timestamp":1773182946945,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":81,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T00:00:00Z","timestamp":1729468800000},"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":[[2024,10,21]]},"DOI":"10.1145\/3627673.3679793","type":"proceedings-article","created":{"date-parts":[[2024,10,20]],"date-time":"2024-10-20T19:34:21Z","timestamp":1729452861000},"page":"1909-1919","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["UniMEL: A Unified Framework for Multimodal Entity Linking with Large Language Models"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-5874-6966","authenticated-orcid":false,"given":"Qi","family":"Liu","sequence":"first","affiliation":[{"name":"School of Data Science, University of Science and Technology of China &amp; State Key Laboratory of Cognitive Intelligence, HeFei, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-4172-9777","authenticated-orcid":false,"given":"Yongyi","family":"He","sequence":"additional","affiliation":[{"name":"School of Data Science, University of Science and Technology of China &amp; State Key Laboratory of Cognitive Intelligence, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4246-5386","authenticated-orcid":false,"given":"Tong","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Data Science, University of Science and Technology of China &amp; State Key Laboratory of Cognitive Intelligence, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3507-9607","authenticated-orcid":false,"given":"Defu","family":"Lian","sequence":"additional","affiliation":[{"name":"School of Data Science, University of Science and Technology of China &amp; State Key Laboratory of Cognitive Intelligence, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-6548-1365","authenticated-orcid":false,"given":"Che","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, University of Science and Technology of China &amp; State Key Laboratory of Cognitive Intelligence, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7758-8904","authenticated-orcid":false,"given":"Zhi","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Data Science, University of Science and Technology of China &amp; State Key Laboratory of Cognitive Intelligence, Hefei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4835-4102","authenticated-orcid":false,"given":"Enhong","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Data Science, University of Science and Technology of China &amp; State Key Laboratory of Cognitive Intelligence, Hefei, China"}]}],"member":"320","published-online":{"date-parts":[[2024,10,21]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Josh Achiam et al. 2023. Gpt-4 technical report. arXiv preprint arXiv:2303.08774."},{"key":"e_1_3_2_1_2_1","volume-title":"ECIR 2020, Lisbon, Portugal, April 14--17, 2020, Proceedings, Part I.","volume":"12035","author":"Adjali Omar","year":"2020","unstructured":"Omar Adjali, Romaric Besan\u00e7on, Olivier Ferret, Herv\u00e9 Le Borgne, and Brigitte Grau. 2020. Multimodal entity linking for tweets. In Advances in Information Retrieval - 42nd European Conference on IR Research, ECIR 2020, Lisbon, Portugal, April 14--17, 2020, Proceedings, Part I. Vol. 12035. Springer, 463--478."},{"key":"e_1_3_2_1_3_1","volume-title":"Proceedings of the Twelfth Language Resources and Evaluation Conference","author":"Adjali Omar","year":"2020","unstructured":"Omar Adjali, Romaric Besan\u00e7on, Olivier Ferret, Herv\u00e9 Le Borgne, and Brigitte Grau. 2020. Building a multimodal entity linking dataset from tweets. In Proceedings of the Twelfth Language Resources and Evaluation Conference. Marseille, France, (May 2020), 4285--4292."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0306-4573(02)00021-3"},{"key":"e_1_3_2_1_5_1","unstructured":"Jinze Bai et al. 2023. Qwen technical report. arXiv preprint arXiv:2309.16609."},{"key":"e_1_3_2_1_6_1","first-page":"1877","article-title":"Language models are few-shot learners","volume":"33","author":"Tom Brown","year":"2020","unstructured":"Tom Brown et al. 2020. Language models are few-shot learners. In Advances in Neural Information Processing Systems. Vol. 33, 1877--1901.","journal-title":"Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_1_7_1","volume-title":"9th International Conference on Learning Representations, ICLR 2021","author":"Cao Nicola De","year":"2021","unstructured":"Nicola De Cao, Gautier Izacard, Sebastian Riedel, and Fabio Petroni. 2021. Autoregressive entity retrieval. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3--7, 2021. OpenReview.net."},{"key":"e_1_3_2_1_8_1","unstructured":"Tianyu Cao Natraj Raman Danial Dervovic and Chenhao Tan. 2024. Characterizing multimodal long-form summarization: a case study on financial reports. arXiv preprint arXiv:2404.06162."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2023.3291503"},{"key":"e_1_3_2_1_10_1","unstructured":"Zhuo Chen et al. 2024. Knowledge graphs meet multi-modal learning: a comprehensive survey. arXiv preprint arXiv:2402.05391."},{"key":"e_1_3_2_1_11_1","unstructured":"Jiaxi Cui Zongjia Li Yang Yan Bohua Chen and Li Yuan. 2023. Chatlaw: opensource legal large language model with integrated external knowledge bases. ArXiv abs\/2306.16092."},{"key":"e_1_3_2_1_12_1","volume-title":"Junqi Zhao, Weisheng Wang, Boyang Li, Pascale N Fung, and Steven Hoi.","author":"Dai Wenliang","year":"2024","unstructured":"Wenliang Dai, Junnan Li, Dongxu Li, Anthony Meng Huat Tiong, Junqi Zhao, Weisheng Wang, Boyang Li, Pascale N Fung, and Steven Hoi. 2024. Instructblip: towards general-purpose vision-language models with instruction tuning. Advances in Neural Information Processing Systems, 36."},{"key":"e_1_3_2_1_13_1","volume-title":"Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","volume":"1","author":"Cao Nicola De","year":"2019","unstructured":"Nicola De Cao, Wilker Aziz, and Ivan Titov. 2019. Question answering by reasoning across documents with graph convolutional networks. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Association for Computational Linguistics, Minneapolis, Minnesota, (June 2019), 2306--2317."},{"key":"e_1_3_2_1_14_1","unstructured":"Ailin Deng Zhirui Chen and Bryan Hooi. 2024. Seeing is believing: mitigating hallucination in large vision-language models via clip-guided decoding. arXiv preprint arXiv:2402.15300."},{"key":"e_1_3_2_1_15_1","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."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/K17-1008"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N16-1150"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3474085.3475400"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D17-1277"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3523227.3546767"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/K19-1049"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"crossref","unstructured":"Sepp Hochreiter and J\u00fcrgen Schmidhuber. 1997. Long short-term memory. Neural computation 9 8 1735--1780.","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_2_1_23_1","volume-title":"ECIR 2024, Glasgow, UK, March 24--28, 2024, Proceedings, Part II.","volume":"14609","author":"Hou Yupeng","year":"2024","unstructured":"Yupeng Hou, Junjie Zhang, Zihan Lin, Hongyu Lu, Ruobing Xie, Julian J. McAuley, and Wayne Xin Zhao. 2024. Large language models are zero-shot rankers for recommender systems. In Advances in Information Retrieval - 46th European Conference on Information Retrieval, ECIR 2024, Glasgow, UK, March 24--28, 2024, Proceedings, Part II. Vol. 14609, 364--381."},{"key":"e_1_3_2_1_24_1","volume-title":"The Tenth International Conference on Learning Representations, ICLR 2022","author":"Hu Edward J.","year":"2022","unstructured":"Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2022. Lora: low-rank adaptation of large language models. In The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25--29, 2022."},{"key":"e_1_3_2_1_25_1","unstructured":"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_1_26_1","unstructured":"Albert Q Jiang et al. 2023. Mistral 7b. arXiv preprint arXiv:2310.06825."},{"key":"e_1_3_2_1_27_1","volume-title":"Proceedings of naacL-HLT.","volume":"1","author":"Ming-Wei Chang Jacob Devlin","year":"2019","unstructured":"Jacob Devlin Ming-Wei Chang Kenton and Lee Kristina Toutanova. 2019. Bert: pre-training of deep bidirectional transformers for language understanding. In Proceedings of naacL-HLT. Vol. 1, 4171--4186."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.703"},{"key":"e_1_3_2_1_29_1","volume-title":"Proceedings of the 34th International Conference on Neural Information Processing Systems","author":"Patrick","unstructured":"Patrick Lewis et al. 2020. Retrieval-augmented generation for knowledgeintensive nlp tasks. In Proceedings of the 34th International Conference on Neural Information Processing Systems. Vancouver, BC, Canada, 16 pages."},{"key":"e_1_3_2_1_30_1","volume-title":"Proceedings of the 40th International Conference on Machine Learning","author":"Li Junnan","year":"2023","unstructured":"Junnan Li, Dongxu Li, Silvio Savarese, and Steven Hoi. 2023. Blip-2: bootstrapping language-image pre-training with frozen image encoders and large language models. In Proceedings of the 40th International Conference on Machine Learning. Honolulu, Hawaii, USA, 13 pages."},{"key":"e_1_3_2_1_31_1","unstructured":"Zehan Li Xin Zhang Yanzhao Zhang Dingkun Long Pengjun Xie and Meishan Zhang. 2023. Towards general text embeddings with multi-stage contrastive learning. arXiv preprint arXiv:2308.03281."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380187"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380151"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623638"},{"key":"e_1_3_2_1_35_1","volume-title":"The Twelfth International Conference on Learning Representations.","author":"Liu Fuxiao","year":"2023","unstructured":"Fuxiao Liu, Kevin Lin, Linjie Li, JianfengWang, Yaser Yacoob, and LijuanWang. 2023. Mitigating hallucination in large multi-modal models via robust instruction tuning. In The Twelfth International Conference on Learning Representations."},{"key":"e_1_3_2_1_36_1","unstructured":"Haotian Liu Chunyuan Li Yuheng Li and Yong Jae Lee. 2023. Improved baselines with visual instruction tuning. (2023)."},{"key":"e_1_3_2_1_37_1","volume-title":"Llava-next: improved reasoning, ocr, and world knowledge. (Jan","author":"Liu Haotian","year":"2024","unstructured":"Haotian Liu, Chunyuan Li, Yuheng Li, Bo Li, Yuanhan Zhang, Sheng Shen, and Yong Jae Lee. 2024. Llava-next: improved reasoning, ocr, and world knowledge. (Jan. 2024). https:\/\/llava-vl.github.io\/blog\/2024-01--30-llava-next\/."},{"key":"e_1_3_2_1_38_1","volume-title":"Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023","author":"Liu Haotian","year":"2023","unstructured":"Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. 2023. Visual instruction tuning. In Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023."},{"key":"e_1_3_2_1_39_1","volume-title":"A. Oh","author":"Liu Haotian","unstructured":"Haotian Liu, Chunyuan Li, Qingyang Wu, and Yong Jae Lee. 2023. Visual instruction tuning. In A. Oh, T. Neumann, A. Globerson, K. Saenko, M. Hardt, and S. Levine, (Eds.) Vol. 36. Curran Associates, Inc., 34892--34916."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.565"},{"key":"e_1_3_2_1_41_1","volume-title":"7th International Conference on Learning Representations, ICLR 2019","author":"Loshchilov Ilya","year":"2019","unstructured":"Ilya Loshchilov and Frank Hutter. 2019. Decoupled weight decay regularization. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6--9, 2019. OpenReview.net."},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.5555\/3157096.3157129"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2022.acl-long.556"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599439"},{"key":"e_1_3_2_1_45_1","unstructured":"Tomas Mikolov Kai Chen Gregory S. Corrado and Jeffrey Dean. 2013. Efficient estimation of word representat."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-1186"},{"key":"e_1_3_2_1_47_1","unstructured":"2023. Openai. gpt-4v(ision) system card."},{"key":"e_1_3_2_1_48_1","unstructured":"Long Ouyang et al. 2022. Training language models to follow instructions with human feedback. Advances in neural information processing systems 35 27730--27744."},{"key":"e_1_3_2_1_49_1","unstructured":"Xiao Pu Mingqi Gao and Xiaojun Wan. 2023. Summarization is (almost) dead. arXiv preprint arXiv:2309.09558."},{"key":"e_1_3_2_1_50_1","unstructured":"Yichen Qian et al. 2024. Unidm: a unified framework for data manipulation with large language models. arXiv preprint arXiv:2405.06510."},{"key":"e_1_3_2_1_51_1","unstructured":"Alec Radford Karthik Narasimhan Tim Salimans Ilya Sutskever et al. [n. d.] Improving language understanding by generative pre-training."},{"key":"e_1_3_2_1_52_1","unstructured":"Alec Radford Jeffrey Wu Rewon Child David Luan Dario Amodei Ilya Sutskever et al. 2019. Language models are unsupervised multitask learners. OpenAI blog 1 8 9."},{"key":"e_1_3_2_1_53_1","volume-title":"Proceedings of the 38th International Conference on Machine Learning.","volume":"139","author":"Alec","unstructured":"Alec Radford et al. 2021. Learning transferable visual models from natural language supervision. In Proceedings of the 38th International Conference on Machine Learning. Vol. 139. PMLR, 8748--8763."},{"key":"e_1_3_2_1_54_1","volume-title":"Xiong Caiming, Zhou Yingbo, and Yavuz Semih.","author":"Rui Meng","year":"2024","unstructured":"Meng Rui, Liu Ye, Rayhan Joty Shafiq, Xiong Caiming, Zhou Yingbo, and Yavuz Semih. 2024. Sfr-embedding-mistral:enhance text retrieval with transfer learning. Salesforce AI Research Blog. (2024). https:\/\/blog.salesforceairesearch .com\/sfr-embedded-mistral\/."},{"key":"e_1_3_2_1_55_1","unstructured":"Senbao Shi Zhenran Xu Baotian Hu and Min Zhang. 2023. Generative multimodal entity linking. arXiv preprint arXiv:2306.12725."},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"crossref","unstructured":"Harman Singh Nitish Gupta Shikhar Bharadwaj Dinesh Tewari and Partha Talukdar. 2024. Indicgenbench: a multilingual benchmark to evaluate generation capabilities of llms on indic languages. arXiv preprint arXiv:2404.16816.","DOI":"10.18653\/v1\/2024.acl-long.595"},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i17.29867"},{"key":"e_1_3_2_1_58_1","unstructured":"Hugo Touvron et al. 2023. Llama 2: open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288."},{"key":"e_1_3_2_1_59_1","unstructured":"Hugo Touvron et al. 2023. Llama: open and efficient foundation language models. arXiv preprint arXiv:2302.13971."},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.bdr.2020.100159"},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3531867"},{"key":"e_1_3_2_1_62_1","volume-title":"International Conference on Machine Learning, 23318--23340","author":"Peng","unstructured":"Peng Wang et al. 2022. Ofa: unifying architectures, tasks, and modalities through a simple sequence-to-sequence learning framework. In International Conference on Machine Learning, 23318--23340."},{"key":"e_1_3_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.findings-acl.497"},{"key":"e_1_3_2_1_64_1","doi-asserted-by":"crossref","unstructured":"XuwuWang Junfeng Tian Min Gui Zhixu Li RuiWang Ming Yan Lihan Chen and Yanghua Xiao. 2022. Wikidiverse: a multimodal entity linking dataset with diversified contextual topics and entity types. arXiv preprint arXiv:2204.06347.","DOI":"10.18653\/v1\/2022.acl-long.328"},{"key":"e_1_3_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.emnlp-main.519"},{"key":"e_1_3_2_1_66_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i8.28769"},{"key":"e_1_3_2_1_67_1","unstructured":"LikangWu et al. 2024. A survey on large language models for recommendation. World Wide Web."},{"key":"e_1_3_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1145\/3581783.3612575"},{"key":"e_1_3_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1417"},{"key":"e_1_3_2_1_70_1","volume-title":"Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence. Robin J. Evans and Ilya Shpitser, (Eds.)","volume":"216","author":"Yang Chengmei","year":"2023","unstructured":"Chengmei Yang, Bowei He, Yimeng Wu, Chao Xing, Lianghua He, and Chen Ma. 2023. MMEL: a joint learning framework for multi-mention entity linking. In Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence. Robin J. Evans and Ilya Shpitser, (Eds.) Vol. 216. PMLR, (31 Jul--04 Aug 2023), 2411--2421."},{"key":"e_1_3_2_1_71_1","doi-asserted-by":"crossref","unstructured":"Tianyu Yu et al. 2023. Rlhf-v: towards trustworthy mllms via behavior alignment from fine-grained correctional human feedback. arXiv preprint arXiv:2312.00849.","DOI":"10.1109\/CVPR52733.2024.01310"},{"key":"e_1_3_2_1_72_1","volume-title":"Findings of the Association for Computational Linguistics: EMNLP 2023","author":"Hongbo","year":"2023","unstructured":"Hongbo Zhang et al. 2023. HuatuoGPT, towards taming language model to be a doctor. In Findings of the Association for Computational Linguistics: EMNLP 2023. Singapore, (Dec. 2023), 10859--10885."},{"key":"e_1_3_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-73197-7_35"},{"key":"e_1_3_2_1_74_1","doi-asserted-by":"publisher","DOI":"10.1162\/tacl_a_00632"},{"key":"e_1_3_2_1_75_1","unstructured":"Haiquan Zhao Xuwu Wang Shisong Chen Zhixu Li Xin Zheng and Yanghua Xiao. 2024. Ovel: large language model as memory manager for online video entity linking. arXiv preprint arXiv:2403.01411."},{"key":"e_1_3_2_1_76_1","unstructured":"Zhiyuan Zhao Bin Wang Linke Ouyang Xiaoyi Dong Jiaqi Wang and Conghui He. 2023. Beyond hallucinations: enhancing lvlms through hallucinationaware direct preference optimization. arXiv preprint arXiv:2311.16839."},{"key":"e_1_3_2_1_77_1","volume-title":"International conference on machine learning. PMLR, 12697--12706","author":"Zhao Zihao","year":"2021","unstructured":"Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh. 2021. Calibrate before use: improving few-shot performance of language models. In International conference on machine learning. PMLR, 12697--12706."},{"key":"e_1_3_2_1_78_1","unstructured":"Ziwei Zhao Fake Lin Xi Zhu Zhi Zheng Tong Xu Shitian Shen Xueying Li Zikai Yin and Enhong Chen. 2024. Dynllm: when large language models meet dynamic graph recommendation. arXiv preprint arXiv:2405.07580."},{"key":"e_1_3_2_1_79_1","doi-asserted-by":"publisher","DOI":"10.1145\/3539618.3591775"},{"key":"e_1_3_2_1_80_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645358"},{"key":"e_1_3_2_1_81_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-16-6471-7_27"}],"event":{"name":"CIKM '24: The 33rd ACM International Conference on Information and Knowledge Management","location":"Boise ID USA","acronym":"CIKM '24","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 33rd ACM International Conference on Information and Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3627673.3679793","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3627673.3679793","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:58:28Z","timestamp":1750294708000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3627673.3679793"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,21]]},"references-count":81,"alternative-id":["10.1145\/3627673.3679793","10.1145\/3627673"],"URL":"https:\/\/doi.org\/10.1145\/3627673.3679793","relation":{},"subject":[],"published":{"date-parts":[[2024,10,21]]},"assertion":[{"value":"2024-10-21","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}