{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T10:03:12Z","timestamp":1775815392283,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":35,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T00:00:00Z","timestamp":1697846400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["CNS-1932574,ECCS-1933279,CNS-2028748,OAC-2104007"],"award-info":[{"award-number":["CNS-1932574,ECCS-1933279,CNS-2028748,OAC-2104007"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000015","name":"DOE U.S. Department of Energy","doi-asserted-by":"publisher","award":["DE-EE0009353,DE-NA0004104"],"award-info":[{"award-number":["DE-EE0009353,DE-NA0004104"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,10,21]]},"DOI":"10.1145\/3583780.3615051","type":"proceedings-article","created":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T07:45:26Z","timestamp":1697874326000},"page":"2686-2695","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Selecting Top-k Data Science Models by Example Dataset"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8150-8436","authenticated-orcid":false,"given":"Mengying","family":"Wang","sequence":"first","affiliation":[{"name":"Case Western Reserve University, Cleveland, OH, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0977-1787","authenticated-orcid":false,"given":"Sheng","family":"Guan","sequence":"additional","affiliation":[{"name":"Case Western Reserve University, Cleveland, OH, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5811-4305","authenticated-orcid":false,"given":"Hanchao","family":"Ma","sequence":"additional","affiliation":[{"name":"Case Western Reserve University, Cleveland, OH, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9860-2725","authenticated-orcid":false,"given":"Yiyang","family":"Bian","sequence":"additional","affiliation":[{"name":"Case Western Reserve University, Cleveland, OH, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-1989-0734","authenticated-orcid":false,"given":"Haolai","family":"Che","sequence":"additional","affiliation":[{"name":"Case Western Reserve University, Cleveland, OH, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8105-8073","authenticated-orcid":false,"given":"Abhishek","family":"Daundkar","sequence":"additional","affiliation":[{"name":"Case Western Reserve University, Cleveland, OH, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9708-4049","authenticated-orcid":false,"given":"Alp","family":"Sehirlioglu","sequence":"additional","affiliation":[{"name":"Case Western Reserve University, Cleveland, OH, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3991-5155","authenticated-orcid":false,"given":"Yinghui","family":"Wu","sequence":"additional","affiliation":[{"name":"Case Western Reserve University, Cleveland, OH, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,10,21]]},"reference":[{"key":"e_1_3_2_2_1_1","unstructured":"[n.d.]. Github. https:\/\/github.com\/  [n.d.]. Github. https:\/\/github.com\/"},{"key":"e_1_3_2_2_2_1","unstructured":"[n.d.]. Kaggle: Your Home for Data Science. https:\/\/www.kaggle.com\/  [n.d.]. Kaggle: Your Home for Data Science. https:\/\/www.kaggle.com\/"},{"key":"e_1_3_2_2_3_1","series-title":"SIAM J. Comput. (2012)","volume-title":"Improved approximation algorithms for bipartite correlation clustering","author":"Ailon Nir","unstructured":"Nir Ailon , Noa Avigdor-Elgrabli , Edo Liberty , and Anke Van Zuylen . 2012. Improved approximation algorithms for bipartite correlation clustering . SIAM J. Comput. (2012) . Nir Ailon, Noa Avigdor-Elgrabli, Edo Liberty, and Anke Van Zuylen. 2012. Improved approximation algorithms for bipartite correlation clustering. SIAM J. Comput. (2012)."},{"key":"e_1_3_2_2_4_1","volume-title":"Graph convolutional matrix completion. arXiv preprint arXiv:1706.02263","author":"van den Berg Rianne","year":"2017","unstructured":"Rianne van den Berg , Thomas N Kipf , and Max Welling . 2017. Graph convolutional matrix completion. arXiv preprint arXiv:1706.02263 ( 2017 ). Rianne van den Berg, Thomas N Kipf, and Max Welling. 2017. Graph convolutional matrix completion. arXiv preprint arXiv:1706.02263 (2017)."},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"crossref","unstructured":"Heng-Tze Cheng Levent Koc Jeremiah Harmsen Tal Shaked Tushar Chandra Hrishi Aradhye Glen Anderson Greg Corrado Wei Chai Mustafa Ispir etal 2016. Wide & deep learning for recommender systems. In DLRS.  Heng-Tze Cheng Levent Koc Jeremiah Harmsen Tal Shaked Tushar Chandra Hrishi Aradhye Glen Anderson Greg Corrado Wei Chai Mustafa Ispir et al. 2016. Wide & deep learning for recommender systems. In DLRS.","DOI":"10.1145\/2988450.2988454"},{"key":"e_1_3_2_2_6_1","volume-title":"Automated machine learning: State-of-the-art and open challenges. arXiv preprint arXiv:1906.02287","author":"Elshawi Radwa","year":"2019","unstructured":"Radwa Elshawi , Mohamed Maher , and Sherif Sakr . 2019. Automated machine learning: State-of-the-art and open challenges. arXiv preprint arXiv:1906.02287 ( 2019 ). Radwa Elshawi, Mohamed Maher, and Sherif Sakr. 2019. Automated machine learning: State-of-the-art and open challenges. arXiv preprint arXiv:1906.02287 (2019)."},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"crossref","unstructured":"Wenqi Fan Yao Ma Qing Li Yuan He Eric Zhao Jiliang Tang and Dawei Yin. 2019. Graph neural networks for social recommendation. In WWW.  Wenqi Fan Yao Ma Qing Li Yuan He Eric Zhao Jiliang Tang and Dawei Yin. 2019. Graph neural networks for social recommendation. In WWW.","DOI":"10.1145\/3308558.3313488"},{"key":"e_1_3_2_2_8_1","unstructured":"Chen Gao Yu Zheng Nian Li Yinfeng Li Yingrong Qin Jinghua Piao Yuhan Quan Jianxin Chang Depeng Jin Xiangnan He etal 2022. A Survey of Graph Neural Networks for Recommender Systems: Challenges Methods and Directions. TORS (2022).  Chen Gao Yu Zheng Nian Li Yinfeng Li Yingrong Qin Jinghua Piao Yuhan Quan Jianxin Chang Depeng Jin Xiangnan He et al. 2022. A Survey of Graph Neural Networks for Recommender Systems: Challenges Methods and Directions. TORS (2022)."},{"key":"e_1_3_2_2_9_1","volume-title":"Johnson","author":"Garey Michael R.","year":"1990","unstructured":"Michael R. Garey and David S . Johnson . 1990 . Computers and Intractability; A Guide to the Theory of NP-Completeness . Michael R. Garey and David S. Johnson. 1990. Computers and Intractability; A Guide to the Theory of NP-Completeness."},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiopen.2021.08.002"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401063"},{"key":"e_1_3_2_2_12_1","unstructured":"Hugging Face AI [n.d.]. Hugging Face -- The AI Community Building the Future. https:\/\/huggingface.co\/  Hugging Face AI [n.d.]. Hugging Face -- The AI Community Building the Future. https:\/\/huggingface.co\/"},{"key":"e_1_3_2_2_13_1","unstructured":"Simon Kornblith Mohammad Norouzi Honglak Lee and Geoffrey Hinton. 2019. Similarity of neural network representations revisited. In ICML. 3519--3529.  Simon Kornblith Mohammad Norouzi Honglak Lee and Geoffrey Hinton. 2019. Similarity of neural network representations revisited. In ICML. 3519--3529."},{"key":"e_1_3_2_2_14_1","volume-title":"Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781","author":"Mikolov Tomas","year":"2013","unstructured":"Tomas Mikolov , Kai Chen , Greg Corrado , and Jeffrey Dean . 2013. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 ( 2013 ). Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781 (2013)."},{"key":"e_1_3_2_2_15_1","volume-title":"Leep: A new measure to evaluate transferability of learned representations. In ICML.","author":"Nguyen Cuong","year":"2020","unstructured":"Cuong Nguyen , Tal Hassner , Matthias Seeger , and Cedric Archambeau . 2020 . Leep: A new measure to evaluate transferability of learned representations. In ICML. Cuong Nguyen, Tal Hassner, Matthias Seeger, and Cedric Archambeau. 2020. Leep: A new measure to evaluate transferability of learned representations. In ICML."},{"key":"e_1_3_2_2_16_1","first-page":"1","article-title":"Mapping python programs to vectors using recursive neural encodings","volume":"13","author":"Paassen Benjamin","year":"2021","unstructured":"Benjamin Paassen , Jessica McBroom , Bryn Jeffries , Irena Koprinska , Kalina Yacef , 2021 . Mapping python programs to vectors using recursive neural encodings . JEDM 13 , 3 (2021), 1 -- 35 . Benjamin Paassen, Jessica McBroom, Bryn Jeffries, Irena Koprinska, Kalina Yacef, et al. 2021. Mapping python programs to vectors using recursive neural encodings. JEDM 13, 3 (2021), 1--35.","journal-title":"JEDM"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.108101"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2976199"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"crossref","unstructured":"David H Stern Ralf Herbrich and Thore Graepel. 2009. Matchbox: large scale online bayesian recommendations. In WWW.  David H Stern Ralf Herbrich and Thore Graepel. 2009. Matchbox: large scale online bayesian recommendations. In WWW.","DOI":"10.1145\/1526709.1526725"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42254-022-00441-7"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"crossref","unstructured":"Anh T Tran Cuong V Nguyen and Tal Hassner. 2019. Transferability and hardness of supervised classification tasks. In ICCV.  Anh T Tran Cuong V Nguyen and Tal Hassner. 2019. Transferability and hardness of supervised classification tasks. In ICCV.","DOI":"10.1109\/ICCV.2019.00148"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"crossref","unstructured":"Hongwei Wang Miao Zhao Xing Xie Wenjie Li and Minyi Guo. 2019. Knowledge graph convolutional networks for recommender systems. In WWW.  Hongwei Wang Miao Zhao Xing Xie Wenjie Li and Minyi Guo. 2019. Knowledge graph convolutional networks for recommender systems. In WWW.","DOI":"10.1145\/3308558.3313417"},{"key":"e_1_3_2_2_24_1","unstructured":"Mengying Wang Sheng Guan Hanchao Ma Yiyang Bian Haolai Che Abhishek Daundkar Alpi Sehirlioglu and Yinghui Wu. 2023. ModsNet(Full Version). https:\/\/crux-project.github.io\/assets\/docs\/ModsNet_Full.pdf  Mengying Wang Sheng Guan Hanchao Ma Yiyang Bian Haolai Che Abhishek Daundkar Alpi Sehirlioglu and Yinghui Wu. 2023. ModsNet(Full Version). https:\/\/crux-project.github.io\/assets\/docs\/ModsNet_Full.pdf"},{"key":"e_1_3_2_2_25_1","volume-title":"CRUX: Crowdsourced Materials Science Resource and Workflow Exploration. In CIKM.","author":"Wang Mengying","year":"2022","unstructured":"Mengying Wang , Hanchao Ma , Abhishek Daundkar , Sheng Guan , Yiyang Bian , Alpi Sehirlioglu , and Yinghui Wu . 2022 . CRUX: Crowdsourced Materials Science Resource and Workflow Exploration. In CIKM. Mengying Wang, Hanchao Ma, Abhishek Daundkar, Sheng Guan, Yiyang Bian, Alpi Sehirlioglu, and Yinghui Wu. 2022. CRUX: Crowdsourced Materials Science Resource and Workflow Exploration. In CIKM."},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330989"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"crossref","unstructured":"Xiang Wang Xiangnan He Meng Wang Fuli Feng and Tat-Seng Chua. 2019. Neural graph collaborative filtering. In SIGIR.  Xiang Wang Xiangnan He Meng Wang Fuli Feng and Tat-Seng Chua. 2019. Neural graph collaborative filtering. In SIGIR.","DOI":"10.1145\/3331184.3331267"},{"key":"e_1_3_2_2_28_1","unstructured":"Yining Wang Liwei Wang Yuanzhi Li Di He and Tie-Yan Liu. 2013. A theoretical analysis of NDCG type ranking measures. In COLT.  Yining Wang Liwei Wang Yuanzhi Li Di He and Tie-Yan Liu. 2013. A theoretical analysis of NDCG type ranking measures. In COLT."},{"key":"e_1_3_2_2_29_1","first-page":"4425","article-title":"A survey on accuracy-oriented neural recommendation: From collaborative filtering to information-rich recommendation","volume":"35","author":"Wu Le","year":"2022","unstructured":"Le Wu , Xiangnan He , Xiang Wang , Kun Zhang , and Meng Wang . 2022 . A survey on accuracy-oriented neural recommendation: From collaborative filtering to information-rich recommendation . TKDE 35 , 5 (2022), 4425 -- 4445 . Le Wu, Xiangnan He, Xiang Wang, Kun Zhang, and Meng Wang. 2022. A survey on accuracy-oriented neural recommendation: From collaborative filtering to information-rich recommendation. TKDE 35, 5 (2022), 4425--4445.","journal-title":"TKDE"},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"crossref","unstructured":"Le Wu Peijie Sun Yanjie Fu Richang Hong Xiting Wang and Meng Wang. 2019. A neural influence diffusion model for social recommendation. In SIGIR.  Le Wu Peijie Sun Yanjie Fu Richang Hong Xiting Wang and Meng Wang. 2019. A neural influence diffusion model for social recommendation. In SIGIR.","DOI":"10.1145\/3331184.3331214"},{"key":"e_1_3_2_2_31_1","unstructured":"Qitian Wu Hengrui Zhang Xiaofeng Gao Junchi Yan and Hongyuan Zha. 2021. Towards open-world recommendation: An inductive model-based collaborative filtering approach. In ICML.  Qitian Wu Hengrui Zhang Xiaofeng Gao Junchi Yan and Hongyuan Zha. 2021. Towards open-world recommendation: An inductive model-based collaborative filtering approach. In ICML."},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3494523"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3477495.3532000"},{"key":"e_1_3_2_2_34_1","unstructured":"xray dataset [n.d.]. tolgadincer\/labeled-chest-xray-images. https:\/\/www.kaggle.com\/datasets\/tolgadincer\/labeled-chest-xray-images  xray dataset [n.d.]. tolgadincer\/labeled-chest-xray-images. https:\/\/www.kaggle.com\/datasets\/tolgadincer\/labeled-chest-xray-images"},{"key":"e_1_3_2_2_35_1","volume-title":"Logme: Practical assessment of pre-trained models for transfer learning. In ICML.","author":"You Kaichao","year":"2021","unstructured":"Kaichao You , Yong Liu , Jianmin Wang , and Mingsheng Long . 2021 . Logme: Practical assessment of pre-trained models for transfer learning. In ICML. Kaichao You, Yong Liu, Jianmin Wang, and Mingsheng Long. 2021. Logme: Practical assessment of pre-trained models for transfer learning. In ICML."}],"event":{"name":"CIKM '23: The 32nd ACM International Conference on Information and Knowledge Management","location":"Birmingham United Kingdom","acronym":"CIKM '23","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 32nd ACM International Conference on Information and Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3583780.3615051","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3583780.3615051","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3583780.3615051","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:36:56Z","timestamp":1750178216000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3583780.3615051"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,21]]},"references-count":35,"alternative-id":["10.1145\/3583780.3615051","10.1145\/3583780"],"URL":"https:\/\/doi.org\/10.1145\/3583780.3615051","relation":{},"subject":[],"published":{"date-parts":[[2023,10,21]]},"assertion":[{"value":"2023-10-21","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}