{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,23]],"date-time":"2025-08-23T00:01:16Z","timestamp":1755907276498,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":41,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,3,4]],"date-time":"2024-03-04T00:00:00Z","timestamp":1709510400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100006374","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U21B2026"],"award-info":[{"award-number":["U21B2026"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,3,4]]},"DOI":"10.1145\/3616855.3635808","type":"proceedings-article","created":{"date-parts":[[2024,3,4]],"date-time":"2024-03-04T18:18:12Z","timestamp":1709576292000},"page":"797-805","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["AutoPooling: Automated Pooling Search for Multi-valued Features in Recommendations"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1248-1804","authenticated-orcid":false,"given":"He","family":"Wei","sequence":"first","affiliation":[{"name":"Machine learning platform department, Tencent TEG, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8709-3128","authenticated-orcid":false,"given":"Yuekui","family":"Yang","sequence":"additional","affiliation":[{"name":"Machine learning platform department, Tencent TEG & Department of Computer Science and Technology, Tsinghua University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8762-8268","authenticated-orcid":false,"given":"Shaoping","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Tsinghua University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7314-7618","authenticated-orcid":false,"given":"Haiyang","family":"Wu","sequence":"additional","affiliation":[{"name":"Machine learning platform department, Tencent TEG, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6563-6734","authenticated-orcid":false,"given":"Yangyang","family":"Tang","sequence":"additional","affiliation":[{"name":"Machine learning platform department, Tencent TEG, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7488-8143","authenticated-orcid":false,"given":"Meixi","family":"Liu","sequence":"additional","affiliation":[{"name":"Machine learning platform department, Tencent TEG, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-0698-8361","authenticated-orcid":false,"given":"Yang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Machine learning platform department, Tencent TEG, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2024,3,4]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2016.7498326"},{"key":"e_1_3_2_2_2_1","volume-title":"Deep learning based recommender system: A survey and new perspectives. ACM Computing Surveys (CSUR), 52(1):1--38","author":"Zhang Shuai","year":"2019","unstructured":"Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay. Deep learning based recommender system: A survey and new perspectives. ACM Computing Surveys (CSUR), 52(1):1--38, 2019."},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220023"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3450124"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553380"},{"key":"e_1_3_2_2_6_1","first-page":"1149","volume-title":"2016 IEEE 16th International Conference on Data Mining (ICDM)","author":"Weinan Zhang Yong Yu Kan Ren","year":"2016","unstructured":"Kan Ren Weinan Zhang Yong Yu Ying Wen Yanru Qu, Han Cai and Jun Wang. Product-based neural networks for user response prediction. In 2016 IEEE 16th International Conference on Data Mining (ICDM), page 1149--1154, 2016."},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/2988450.2988454"},{"key":"e_1_3_2_2_8_1","volume-title":"Deepfm: a factorization-machine based neural network for ctr prediction. arXiv preprint arXiv:1703.04247","author":"Guo Huifeng","year":"2017","unstructured":"Huifeng Guo, Ruiming Tang, Yunming Ye, Zhenguo Li, and Xiuqiang He. Deepfm: a factorization-machine based neural network for ctr prediction. arXiv preprint arXiv:1703.04247, 2017."},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/2959100.2959190"},{"key":"e_1_3_2_2_10_1","first-page":"84","article-title":"Imagenet classification with deep convolutional","volume":"60","author":"Krizhevsky Alex","year":"2017","unstructured":"Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional. Neural Netw, 60:84--90, 2017.","journal-title":"Neural Netw"},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403314"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219823"},{"key":"e_1_3_2_2_13_1","first-page":"448","volume-title":"In International Conference on Machine Learning","author":"Ioffe Sergey","year":"2015","unstructured":"Sergey Ioffe and Christian Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In In International Conference on Machine Learning, page 448--456, 2015."},{"volume-title":"Statistical theory of extreme values and some practical applications: a series of lectures","author":"Gumbel Emil Julius","key":"e_1_3_2_2_14_1","unstructured":"Emil Julius Gumbel. Statistical theory of extreme values and some practical applications: a series of lectures. Vol. 33. US Government Printing Office, 1048."},{"key":"e_1_3_2_2_15_1","volume-title":"Categorical reparameterization with gumbel-softmax. arXiv preprint arXiv:1611.01144","author":"Jang Eric","year":"2016","unstructured":"Eric Jang, Shixiang Gu, and Ben Poole. Categorical reparameterization with gumbel-softmax. arXiv preprint arXiv:1611.01144, 2016."},{"key":"e_1_3_2_2_16_1","volume-title":"Neural architecture search with reinforcement learning. arXiv preprint arXiv:1611.01578","author":"Zoph Barret","year":"2016","unstructured":"Barret Zoph and Quoc V Le. Neural architecture search with reinforcement learning. arXiv preprint arXiv:1611.01578, 2016."},{"key":"e_1_3_2_2_17_1","volume-title":"Darts: Differentiable architecture search. arXiv preprint arXiv:1806.09055","author":"Liu Hanxiao","year":"2018","unstructured":"Hanxiao Liu, Karen Simonyan, and Yiming Yang. Darts: Differentiable architecture search. arXiv preprint arXiv:1806.09055, 2018."},{"key":"e_1_3_2_2_18_1","first-page":"4095","volume-title":"International conference on machine learning","author":"Pham Hieu","year":"2018","unstructured":"Hieu Pham, Melody Guan, Barret Zoph, Quoc Le, and Jeff Dean. Efficient neural architecture search via parameters sharing. In International conference on machine learning, pages 4095--4104. PMLR, 2018."},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401082"},{"key":"e_1_3_2_2_20_1","first-page":"1437","volume-title":"International Conference on Machine Learning","author":"Falkner Stefan","year":"2018","unstructured":"Stefan Falkner, Aaron Klein, and Frank Hutter. Bohb: Robust and efficient hyperparameter optimization at scale. In International Conference on Machine Learning, pages 1437--1446. PMLR, 2018."},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411912"},{"key":"e_1_3_2_2_22_1","unstructured":"Wenqi Fan Hui Liu Jiliang Tang Chong Wang Ming Chen Xudong Zheng Xiaobing Liu Xiangyu Zhaok Haochen Liu and Xiwang Yang. Autoemb: Automated embedding dimensionality search in streaming recommendations."},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3077136.3080777"},{"key":"e_1_3_2_2_24_1","volume-title":"Handwritten digit recognition with a back-propagation network. Advances in neural information processing systems, 2","author":"LeCun Yann","year":"1989","unstructured":"Yann LeCun, Bernhard Boser, John Denker, Donnie Henderson, Richard Howard, Wayne Hubbard, and Lawrence Jackel. Handwritten digit recognition with a back-propagation network. Advances in neural information processing systems, 2, 1989."},{"key":"e_1_3_2_2_25_1","volume-title":"Sparse feature learning for deep belief networks. Advances in neural information processing systems, 20","author":"Ranzato Marc'Aurelio","year":"2007","unstructured":"Marc'Aurelio Ranzato, Y-Lan Boureau, Yann Cun, et al. Sparse feature learning for deep belief networks. Advances in neural information processing systems, 20, 2007."},{"key":"e_1_3_2_2_26_1","volume-title":"Stochastic pooling for regularization of deep convolutional neural networks. arXiv preprint arXiv:1301.3557","author":"Zeiler Matthew D","year":"2013","unstructured":"Matthew D Zeiler and Rob Fergus. Stochastic pooling for regularization of deep convolutional neural networks. arXiv preprint arXiv:1301.3557, 2013."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-11740-9_34"},{"key":"e_1_3_2_2_28_1","first-page":"4970","volume-title":"Proceedings of the IEEE conference on computer vision and pattern recognition","author":"Zhai Shuangfei","year":"2017","unstructured":"Shuangfei Zhai, Hui Wu, Abhishek Kumar, Yu Cheng, Yongxi Lu, Zhongfei Zhang, and Rogerio Feris. S3pool: Pooling with stochastic spatial sampling. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4970--4978, 2017."},{"key":"e_1_3_2_2_29_1","first-page":"2180","volume-title":"Brian and Juan Pablo Bello. Adaptive pooling operators for weakly labeled sound event detection","author":"McFee Justin Salamon","year":"2018","unstructured":"Justin Salamon McFee, Brian and Juan Pablo Bello. Adaptive pooling operators for weakly labeled sound event detection. In IEEE\/ACM Transactions on Audio,Speech, and Language Processing 26.11, pages 2180--2193, 2018."},{"key":"e_1_3_2_2_30_1","volume-title":"Theodore Lim and Nick Weston. Smash:one-shot model architecture search through hypernetworks. arXiv preprint arXiv:1708.05344","author":"Ritchie Andrew Brock James M","year":"2017","unstructured":"James M Ritchie Andrew Brock, Theodore Lim and Nick Weston. Smash:one-shot model architecture search through hypernetworks. arXiv preprint arXiv:1708.05344, 2017."},{"key":"e_1_3_2_2_31_1","volume-title":"Hehui Zheng and Liang Lin. Snas: stochastic neural architecture search. arXiv preprint arXiv:1812.09926","author":"Sirui Xie Chunxiao Liu","year":"2018","unstructured":"Chunxiao Liu Sirui Xie, Hehui Zheng and Liang Lin. Snas: stochastic neural architecture search. arXiv preprint arXiv:1812.09926, 2018."},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403288"},{"key":"e_1_3_2_2_33_1","volume-title":"Differentiable neural input search for recommender systems. arXiv preprint arXiv:2006.04466","author":"Cheng Weiyu","year":"2020","unstructured":"Weiyu Cheng, Yanyan Shen, and Linpeng Huang. Differentiable neural input search for recommender systems. arXiv preprint arXiv:2006.04466, 2020."},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366424.3383416"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583444"},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00656"},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00850"},{"key":"e_1_3_2_2_38_1","volume-title":"Autoloss: Learning discrete schedules for alternate optimization. arXiv preprint arXiv:1810.02442","author":"Xu Haowen","year":"2018","unstructured":"Haowen Xu, Hao Zhang, Zhiting Hu, Xiaodan Liang, Ruslan Salakhutdinov, and Eric Xing. Autoloss: Learning discrete schedules for alternate optimization. arXiv preprint arXiv:1810.02442, 2018."},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467208"},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01234-2_48"},{"key":"e_1_3_2_2_41_1","volume-title":"Automated self-supervised learning for graphs. arXiv preprint arXiv:2106.05470","author":"Jin Wei","year":"2021","unstructured":"Wei Jin, Xiaorui Liu, Xiangyu Zhao, Yao Ma, Neil Shah, and Jiliang Tang. Automated self-supervised learning for graphs. arXiv preprint arXiv:2106.05470, 2021."}],"event":{"name":"WSDM '24: The 17th ACM International Conference on Web Search and Data Mining","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data","SIGIR ACM Special Interest Group on Information Retrieval"],"location":"Merida Mexico","acronym":"WSDM '24"},"container-title":["Proceedings of the 17th ACM International Conference on Web Search and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3616855.3635808","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3616855.3635808","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T00:49:21Z","timestamp":1755823761000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3616855.3635808"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,4]]},"references-count":41,"alternative-id":["10.1145\/3616855.3635808","10.1145\/3616855"],"URL":"https:\/\/doi.org\/10.1145\/3616855.3635808","relation":{},"subject":[],"published":{"date-parts":[[2024,3,4]]},"assertion":[{"value":"2024-03-04","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}