{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,12]],"date-time":"2025-07-12T22:59:46Z","timestamp":1752361186411,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":42,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T00:00:00Z","timestamp":1665964800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Guoqiang Research Institute, Tsinghua University","award":["2021-GQG1012"],"award-info":[{"award-number":["2021-GQG1012"]}]},{"name":"National Key R&D Program of China","award":["2020AAA0105200"],"award-info":[{"award-number":["2020AAA0105200"]}]},{"name":"Key Research Program of the Chinese Academy of Sciences","award":["NO.ZDBS-SSW-JSC006"],"award-info":[{"award-number":["NO.ZDBS-SSW-JSC006"]}]},{"name":"Vanke Special Fund for Public Health and Health Discipline Development, Tsinghua University","award":["No.20221080053"],"award-info":[{"award-number":["No.20221080053"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,10,17]]},"DOI":"10.1145\/3511808.3557142","type":"proceedings-article","created":{"date-parts":[[2022,10,16]],"date-time":"2022-10-16T01:22:22Z","timestamp":1665883342000},"page":"3505-3513","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["PEMP: Leveraging Physics Properties to Enhance Molecular Property Prediction"],"prefix":"10.1145","author":[{"given":"Yuancheng","family":"Sun","sequence":"first","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Beijing Academy of Artificial Intelligence, Beijing, China"}]},{"given":"Yimeng","family":"Chen","sequence":"additional","affiliation":[{"name":"Academy of Mathematics and Systems Science &amp; University of Chinese Academy of Sciences, Beijing, China"}]},{"given":"Weizhi","family":"Ma","sequence":"additional","affiliation":[{"name":"Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China"}]},{"given":"Wenhao","family":"Huang","sequence":"additional","affiliation":[{"name":"Beijing Academy of Artificial Intelligence, Beijing, China"}]},{"given":"Kang","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Automation, Chinese Academy of Sciences &amp; University of Chinese Academy of Sciences, Beijing, China"}]},{"given":"Zhiming","family":"Ma","sequence":"additional","affiliation":[{"name":"Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China"}]},{"given":"Wei-Ying","family":"Ma","sequence":"additional","affiliation":[{"name":"Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China"}]},{"given":"Yanyan","family":"Lan","sequence":"additional","affiliation":[{"name":"Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2022,10,17]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1021\/js9901007"},{"key":"e_1_3_2_1_2_1","volume-title":"Julio De Paula, and James Keeler","author":"Atkins Peter","year":"2018","unstructured":"Peter Atkins , Julio De Paula, and James Keeler . 2018 . Atkins' physical chemistry. OUP Oxford . Peter Atkins, Julio De Paula, and James Keeler. 2018. Atkins' physical chemistry. OUP Oxford."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMp1500848"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1021\/jm9602928"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.8b00839"},{"volume-title":"Essentials of computational chemistry: theories and models","author":"Cramer Christopher J","key":"e_1_3_2_1_6_1","unstructured":"Christopher J Cramer . 2013. Essentials of computational chemistry: theories and models . John Wiley & Sons . Christopher J Cramer. 2013. Essentials of computational chemistry: theories and models. John Wiley & Sons."},{"key":"e_1_3_2_1_7_1","volume-title":"Multi-task neural networks for QSAR predictions. arXiv preprint arXiv:1406.1231","author":"Dahl George E","year":"2014","unstructured":"George E Dahl , Navdeep Jaitly , and Ruslan Salakhutdinov . 2014. Multi-task neural networks for QSAR predictions. arXiv preprint arXiv:1406.1231 ( 2014 ). George E Dahl, Navdeep Jaitly, and Ruslan Salakhutdinov. 2014. Multi-task neural networks for QSAR predictions. arXiv preprint arXiv:1406.1231 (2014)."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1021\/ci034243x"},{"key":"e_1_3_2_1_9_1","first-page":"1970","article-title":"Se (3)-transformers: 3d roto-translation equivariant attention networks","volume":"33","author":"Fuchs Fabian","year":"2020","unstructured":"Fabian Fuchs , Daniel Worrall , Volker Fischer , and Max Welling . 2020 . Se (3)-transformers: 3d roto-translation equivariant attention networks . Advances in Neural Information Processing Systems , Vol. 33 (2020), 1970 -- 1981 . Fabian Fuchs, Daniel Worrall, Volker Fischer, and Max Welling. 2020. Se (3)-transformers: 3d roto-translation equivariant attention networks. Advances in Neural Information Processing Systems, Vol. 33 (2020), 1970--1981.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_10_1","volume-title":"Shaun McGlinchey, David Michalovich, Bissan Al-Lazikani, et al.","author":"Gaulton Anna","year":"2012","unstructured":"Anna Gaulton , Louisa J Bellis , A Patricia Bento , Jon Chambers , Mark Davies , Anne Hersey , Yvonne Light , Shaun McGlinchey, David Michalovich, Bissan Al-Lazikani, et al. 2012 . ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic acids research, Vol. 40 , D1 (2012), D1100--D1107. Anna Gaulton, Louisa J Bellis, A Patricia Bento, Jon Chambers, Mark Davies, Anne Hersey, Yvonne Light, Shaun McGlinchey, David Michalovich, Bissan Al-Lazikani, et al. 2012. ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic acids research, Vol. 40, D1 (2012), D1100--D1107."},{"key":"e_1_3_2_1_11_1","volume-title":"A data-driven approach to predicting successes and failures of clinical trials. Cell chemical biology","author":"Gayvert Kaitlyn M","year":"2016","unstructured":"Kaitlyn M Gayvert , Neel S Madhukar , and Olivier Elemento . 2016. A data-driven approach to predicting successes and failures of clinical trials. Cell chemical biology , Vol. 23 , 10 ( 2016 ), 1294--1301. Kaitlyn M Gayvert, Neel S Madhukar, and Olivier Elemento. 2016. A data-driven approach to predicting successes and failures of clinical trials. Cell chemical biology, Vol. 23, 10 (2016), 1294--1301."},{"key":"e_1_3_2_1_12_1","volume-title":"International Conference on Machine Learning. PMLR, 1263--1272","author":"Gilmer Justin","year":"2017","unstructured":"Justin Gilmer , Samuel S Schoenholz , Patrick F Riley , Oriol Vinyals , and George E Dahl . 2017 . Neural message passing for quantum chemistry . In International Conference on Machine Learning. PMLR, 1263--1272 . Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl. 2017. Neural message passing for quantum chemistry. In International Conference on Machine Learning. PMLR, 1263--1272."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1615\/CritRevTherDrugCarrierSyst.2013007419"},{"key":"e_1_3_2_1_14_1","volume-title":"Geometrically equivariant graph neural networks: A survey. arXiv preprint arXiv:2202.07230","author":"Han Jiaqi","year":"2022","unstructured":"Jiaqi Han , Yu Rong , Tingyang Xu , and Wenbing Huang . 2022. Geometrically equivariant graph neural networks: A survey. arXiv preprint arXiv:2202.07230 ( 2022 ). Jiaqi Han, Yu Rong, Tingyang Xu, and Wenbing Huang. 2022. Geometrically equivariant graph neural networks: A survey. arXiv preprint arXiv:2202.07230 (2022)."},{"key":"e_1_3_2_1_15_1","volume-title":"Strategies for pre-training graph neural networks. arXiv preprint arXiv:1905.12265","author":"Hu Weihua","year":"2019","unstructured":"Weihua Hu , Bowen Liu , Joseph Gomes , Marinka Zitnik , Percy Liang , Vijay Pande , and Jure Leskovec . 2019. Strategies for pre-training graph neural networks. arXiv preprint arXiv:1905.12265 ( 2019 ). Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec. 2019. Strategies for pre-training graph neural networks. arXiv preprint arXiv:1905.12265 (2019)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10822-016-9938-8"},{"key":"e_1_3_2_1_17_1","volume-title":"Directional message passing for molecular graphs. arXiv preprint arXiv:2003.03123","author":"Klicpera Johannes","year":"2020","unstructured":"Johannes Klicpera , Janek Gro\u00df , and Stephan G\u00fcnnemann . 2020. Directional message passing for molecular graphs. arXiv preprint arXiv:2003.03123 ( 2020 ). Johannes Klicpera, Janek Gro\u00df, and Stephan G\u00fcnnemann. 2020. Directional message passing for molecular graphs. arXiv preprint arXiv:2003.03123 (2020)."},{"key":"e_1_3_2_1_18_1","first-page":"1","article-title":"The simulating study of HOMO, LUMO, thermo physical and QSAR of some anticancer active ionic liquids","volume":"3","author":"Kumer Ajoy","year":"2019","unstructured":"Ajoy Kumer , Md Nuruzzaman Sarker , and PAUL Sunanda . 2019 . The simulating study of HOMO, LUMO, thermo physical and QSAR of some anticancer active ionic liquids . Eurasian Journal of Environmental Research , Vol. 3 , 1 (2019), 1 -- 10 . Ajoy Kumer, Md Nuruzzaman Sarker, and PAUL Sunanda. 2019. The simulating study of HOMO, LUMO, thermo physical and QSAR of some anticancer active ionic liquids. Eurasian Journal of Environmental Research, Vol. 3, 1 (2019), 1--10.","journal-title":"Eurasian Journal of Environmental Research"},{"key":"e_1_3_2_1_19_1","volume-title":"Mehmet Furkan Demirel, and Yingyu Liang","author":"Liu Shengchao","year":"2018","unstructured":"Shengchao Liu , Mehmet Furkan Demirel, and Yingyu Liang . 2018 . N-gram graph: Simple unsupervised representation for graphs, with applications to molecules. arXiv preprint arXiv:1806.09206 (2018). Shengchao Liu, Mehmet Furkan Demirel, and Yingyu Liang. 2018. N-gram graph: Simple unsupervised representation for graphs, with applications to molecules. arXiv preprint arXiv:1806.09206 (2018)."},{"key":"e_1_3_2_1_20_1","volume-title":"Multi-task Learning with Domain Knowledge for Molecular Property Prediction. In NeurIPS 2021 AI for Science Workshop.","author":"Liu Shengchao","year":"2021","unstructured":"Shengchao Liu , Meng Qu , Zuobai Zhang , Huiyu Cai , and Jian Tang . 2021 . Multi-task Learning with Domain Knowledge for Molecular Property Prediction. In NeurIPS 2021 AI for Science Workshop. Shengchao Liu, Meng Qu, Zuobai Zhang, Huiyu Cai, and Jian Tang. 2021. Multi-task Learning with Domain Knowledge for Molecular Property Prediction. In NeurIPS 2021 AI for Science Workshop."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1021\/ci300124c"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/0045-6535(94)90299-2"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10822-014-9747-x"},{"key":"e_1_3_2_1_24_1","volume-title":"How to improve R&D productivity: the pharmaceutical industry's grand challenge. Nature reviews Drug discovery","author":"Paul Steven M","year":"2010","unstructured":"Steven M Paul , Daniel S Mytelka , Christopher T Dunwiddie , Charles C Persinger , Bernard H Munos , Stacy R Lindborg , and Aaron L Schacht . 2010. How to improve R&D productivity: the pharmaceutical industry's grand challenge. Nature reviews Drug discovery , Vol. 9 , 3 ( 2010 ), 203--214. Steven M Paul, Daniel S Mytelka, Christopher T Dunwiddie, Charles C Persinger, Bernard H Munos, Stacy R Lindborg, and Aaron L Schacht. 2010. How to improve R&D productivity: the pharmaceutical industry's grand challenge. Nature reviews Drug discovery, Vol. 9, 3 (2010), 203--214."},{"key":"e_1_3_2_1_25_1","volume-title":"Quantum chemistry structures and properties of 134 kilo molecules. Scientific data","author":"Ramakrishnan Raghunathan","year":"2014","unstructured":"Raghunathan Ramakrishnan , Pavlo O Dral , Matthias Rupp , and O Anatole Von Lilienfeld . 2014. Quantum chemistry structures and properties of 134 kilo molecules. Scientific data , Vol. 1 , 1 ( 2014 ), 1--7. Raghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, and O Anatole Von Lilienfeld. 2014. Quantum chemistry structures and properties of 134 kilo molecules. Scientific data, Vol. 1, 1 (2014), 1--7."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1021\/ci100050t"},{"key":"e_1_3_2_1_27_1","first-page":"12559","article-title":"Self-supervised graph transformer on large-scale molecular data","volume":"33","author":"Rong Yu","year":"2020","unstructured":"Yu Rong , Yatao Bian , Tingyang Xu , Weiyang Xie , Ying Wei , Wenbing Huang , and Junzhou Huang . 2020 . Self-supervised graph transformer on large-scale molecular data . Advances in Neural Information Processing Systems , Vol. 33 (2020), 12559 -- 12571 . Yu Rong, Yatao Bian, Tingyang Xu, Weiyang Xie, Ying Wei, Wenbing Huang, and Junzhou Huang. 2020. Self-supervised graph transformer on large-scale molecular data. Advances in Neural Information Processing Systems, Vol. 33 (2020), 12559--12571.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_28_1","volume-title":"Cosolvency and cosolvent polarity. Pharmaceutical research","author":"Rubino Joseph T","year":"1987","unstructured":"Joseph T Rubino and Samuel H Yalkowsky . 1987. Cosolvency and cosolvent polarity. Pharmaceutical research , Vol. 4 , 3 ( 1987 ), 220--230. Joseph T Rubino and Samuel H Yalkowsky. 1987. Cosolvency and cosolvent polarity. Pharmaceutical research, Vol. 4, 3 (1987), 220--230."},{"key":"e_1_3_2_1_29_1","volume-title":"International conference on machine learning. PMLR, 9323--9332","author":"Satorras Victor Garcia","year":"2021","unstructured":"Victor Garcia Satorras , Emiel Hoogeboom , and Max Welling . 2021 . E (n) equivariant graph neural networks . In International conference on machine learning. PMLR, 9323--9332 . Victor Garcia Satorras, Emiel Hoogeboom, and Max Welling. 2021. E (n) equivariant graph neural networks. In International conference on machine learning. PMLR, 9323--9332."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41573-019-0050-3"},{"key":"e_1_3_2_1_31_1","volume-title":"International Conference on Machine Learning. PMLR, 9377--9388","author":"Sch\u00fctt Kristof","year":"2021","unstructured":"Kristof Sch\u00fctt , Oliver Unke , and Michael Gastegger . 2021 . Equivariant message passing for the prediction of tensorial properties and molecular spectra . In International Conference on Machine Learning. PMLR, 9377--9388 . Kristof Sch\u00fctt, Oliver Unke, and Michael Gastegger. 2021. Equivariant message passing for the prediction of tensorial properties and molecular spectra. In International Conference on Machine Learning. PMLR, 9377--9388."},{"key":"e_1_3_2_1_32_1","volume-title":"Schnet: A continuous-filter convolutional neural network for modeling quantum interactions. arXiv preprint arXiv:1706.08566","author":"Sch\u00fctt Kristof T","year":"2017","unstructured":"Kristof T Sch\u00fctt , Pieter-Jan Kindermans , Huziel E Sauceda , 2017 . Schnet: A continuous-filter convolutional neural network for modeling quantum interactions. arXiv preprint arXiv:1706.08566 (2017). Kristof T Sch\u00fctt, Pieter-Jan Kindermans, Huziel E Sauceda, et al. 2017. Schnet: A continuous-filter convolutional neural network for modeling quantum interactions. arXiv preprint arXiv:1706.08566 (2017)."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"crossref","unstructured":"Ying Song Shuangjia Zheng Zhangming Niu Zhang-Hua Fu Yutong Lu and Yuedong Yang. 2020. Communicative Representation Learning on Attributed Molecular Graphs. In IJCAI. 2831--2838.  Ying Song Shuangjia Zheng Zhangming Niu Zhang-Hua Fu Yutong Lu and Yuedong Yang. 2020. Communicative Representation Learning on Attributed Molecular Graphs. In IJCAI. 2831--2838.","DOI":"10.24963\/ijcai.2020\/392"},{"key":"e_1_3_2_1_34_1","unstructured":"Raphael JL Townshend Martin V\u00f6gele Patricia Suriana Alexander Derry Alexander Powers Yianni Laloudakis Sidhika Balachandar Bowen Jing Brandon Anderson Stephan Eismann etal 2020. Atom3d: Tasks on molecules in three dimensions. arXiv preprint arXiv:2012.04035 (2020).  Raphael JL Townshend Martin V\u00f6gele Patricia Suriana Alexander Derry Alexander Powers Yianni Laloudakis Sidhika Balachandar Bowen Jing Brandon Anderson Stephan Eismann et al. 2020. Atom3d: Tasks on molecules in three dimensions. arXiv preprint arXiv:2012.04035 (2020)."},{"key":"e_1_3_2_1_35_1","first-page":"17441","article-title":"Property-aware relation networks for few-shot molecular property prediction","volume":"34","author":"Wang Yaqing","year":"2021","unstructured":"Yaqing Wang , Abulikemu Abuduweili , Quanming Yao , and Dejing Dou . 2021 . Property-aware relation networks for few-shot molecular property prediction . Advances in Neural Information Processing Systems , Vol. 34 (2021), 17441 -- 17454 . Yaqing Wang, Abulikemu Abuduweili, Quanming Yao, and Dejing Dou. 2021. Property-aware relation networks for few-shot molecular property prediction. Advances in Neural Information Processing Systems, Vol. 34 (2021), 17441--17454.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.8b00785"},{"key":"e_1_3_2_1_37_1","volume-title":"MoleculeNet: a benchmark for molecular machine learning. Chemical science","author":"Wu Zhenqin","year":"2018","unstructured":"Zhenqin Wu , Bharath Ramsundar , Evan N Feinberg , Joseph Gomes , Caleb Geniesse , Aneesh S Pappu , Karl Leswing , and Vijay Pande . 2018. MoleculeNet: a benchmark for molecular machine learning. Chemical science , Vol. 9 , 2 ( 2018 ), 513--530. Zhenqin Wu, Bharath Ramsundar, Evan N Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S Pappu, Karl Leswing, and Vijay Pande. 2018. MoleculeNet: a benchmark for molecular machine learning. Chemical science, Vol. 9, 2 (2018), 513--530."},{"key":"e_1_3_2_1_38_1","first-page":"8749","article-title":"Pushing the boundaries of molecular representation for drug discovery with the graph attention mechanism","volume":"63","author":"Xiong Zhaoping","year":"2019","unstructured":"Zhaoping Xiong , Dingyan Wang , Xiaohong Liu , Feisheng Zhong , Xiaozhe Wan , Xutong Li , Zhaojun Li , Xiaomin Luo , Kaixian Chen , Hualiang Jiang , 2019 . Pushing the boundaries of molecular representation for drug discovery with the graph attention mechanism . Journal of Chemical Information and Modeling , Vol. 63 , 16 (2019), 8749 -- 8760 . Zhaoping Xiong, Dingyan Wang, Xiaohong Liu, Feisheng Zhong, Xiaozhe Wan, Xutong Li, Zhaojun Li, Xiaomin Luo, Kaixian Chen, Hualiang Jiang, et al. 2019. Pushing the boundaries of molecular representation for drug discovery with the graph attention mechanism. Journal of Chemical Information and Modeling, Vol. 63, 16 (2019), 8749--8760.","journal-title":"Journal of Chemical Information and Modeling"},{"key":"e_1_3_2_1_39_1","volume-title":"NeurIPS","volume":"34","author":"Yang Shuwen","year":"2021","unstructured":"Shuwen Yang , Ziyao Li , Guojie Song , and Lingsheng Cai . 2021 . Deep Molecular Representation Learning via Fusing Physical and Chemical Information . NeurIPS , Vol. 34 (2021). Shuwen Yang, Ziyao Li, Guojie Song, and Lingsheng Cai. 2021. Deep Molecular Representation Learning via Fusing Physical and Chemical Information. NeurIPS, Vol. 34 (2021)."},{"key":"e_1_3_2_1_40_1","volume-title":"An integrated transfer learning and multitask learning approach for pharmacokinetic parameter prediction. Molecular pharmaceutics","author":"Ye Zhuyifan","year":"2018","unstructured":"Zhuyifan Ye , Yilong Yang , Xiaoshan Li , Dongsheng Cao , and Defang Ouyang . 2018. An integrated transfer learning and multitask learning approach for pharmacokinetic parameter prediction. Molecular pharmaceutics , Vol. 16 , 2 ( 2018 ), 533--541. Zhuyifan Ye, Yilong Yang, Xiaoshan Li, Dongsheng Cao, and Defang Ouyang. 2018. An integrated transfer learning and multitask learning approach for pharmacokinetic parameter prediction. Molecular pharmaceutics, Vol. 16, 2 (2018), 533--541."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btab195"},{"key":"e_1_3_2_1_42_1","volume-title":"ADMET properties: overview and current topics. Drug design: principles and applications","author":"Zhong Haizhen A","year":"2017","unstructured":"Haizhen A Zhong . 2017. ADMET properties: overview and current topics. Drug design: principles and applications ( 2017 ), 113--133. Haizhen A Zhong. 2017. ADMET properties: overview and current topics. Drug design: principles and applications (2017), 113--133."}],"event":{"name":"CIKM '22: The 31st ACM International Conference on Information and Knowledge Management","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"],"location":"Atlanta GA USA","acronym":"CIKM '22"},"container-title":["Proceedings of the 31st ACM International Conference on Information &amp; Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3511808.3557142","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3511808.3557142","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:30:57Z","timestamp":1750188657000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3511808.3557142"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,17]]},"references-count":42,"alternative-id":["10.1145\/3511808.3557142","10.1145\/3511808"],"URL":"https:\/\/doi.org\/10.1145\/3511808.3557142","relation":{},"subject":[],"published":{"date-parts":[[2022,10,17]]},"assertion":[{"value":"2022-10-17","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}