{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T13:46:45Z","timestamp":1785592005006,"version":"3.56.0"},"reference-count":36,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/OAPA.html"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61772321"],"award-info":[{"award-number":["61772321"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","award":["ZR2016FP07"],"award-info":[{"award-number":["ZR2016FP07"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Innovation Foundation of Science and Technology Development Center of Ministry of Education and New H3C Group","award":["2017A15047"],"award-info":[{"award-number":["2017A15047"]}]},{"name":"CERNET Innovation Project","award":["NGII20170508"],"award-info":[{"award-number":["NGII20170508"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2019]]},"DOI":"10.1109\/access.2018.2885399","type":"journal-article","created":{"date-parts":[[2018,12,7]],"date-time":"2018-12-07T20:21:54Z","timestamp":1544214114000},"page":"12779-12789","source":"Crossref","is-referenced-by-count":16,"title":["Research on CTR Prediction Based on Deep Learning"],"prefix":"10.1109","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7839-9707","authenticated-orcid":false,"given":"Qianqian","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fang'ai","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuning","family":"Xing","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2636-1982","authenticated-orcid":false,"given":"Xiaohui","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianlai","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref33","first-page":"1725","author":"guo","year":"2017","journal-title":"Deepfm A factorization-machine based neural network for CTR prediction"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/1242572.1242643"},{"key":"ref31","author":"effendi","year":"2017","journal-title":"Click through rate prediction for contextual advertisment using linear regression"},{"key":"ref30","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1007\/978-3-319-18038-0_35","article-title":"A learning-rate schedule for stochastic gradient methods to matrix factorization","author":"chin","year":"2015","journal-title":"Proc Knowledge Discovery and Data Mining Conf"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/2661829.2661935"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1155\/2017\/7259762"},{"key":"ref34","article-title":"Deep learning over multi-field categorical data","author":"zhang","year":"2016","journal-title":"Proc Eur Conf Inform Retr"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1145\/2648584.2648589"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/2124295.2124336"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/IADCC.2015.7154880"},{"key":"ref13","author":"baqapuri","year":"2014","journal-title":"Using neural networks for click prediction of sponsored search"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/2806416.2806603"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939704"},{"key":"ref16","first-page":"1149","article-title":"Product-based neural networks for user response prediction","author":"qu","year":"2017","journal-title":"Proc IEEE Int Conf Data Mining"},{"key":"ref17","first-page":"100","article-title":"Algorithm AS 136: A k-means clustering algorithm","volume":"28","author":"hartigan","year":"2010","journal-title":"J Roy Statist Soc Ser C (Appl Statist )"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2008.89"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-58274-0_2"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/GLOCOMW.2016.7849067"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.3724\/SP.J.1016.2011.01805"},{"key":"ref27","first-page":"3","article-title":"Autoencoders, minimum description length and Helmholtz free energy","author":"hinton","year":"1993","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref3","first-page":"13","article-title":"Web-scale Bayesian click-through rate prediction for sponsored search advertising in Microsoft’s bing search engine","author":"graepel","year":"2010","journal-title":"Proc 27th Int Conf Mach Learn"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.4018\/978-1-60960-189-8.ch001"},{"key":"ref29","first-page":"257","article-title":"Adaptive subgradient methods for online learning and stochastic optimization","volume":"12","author":"duchi","year":"2011","journal-title":"J Mach Learn Res"},{"key":"ref5","author":"genzel","year":"2016","journal-title":"A mathematical framework for feature selection from real-world data with non-linear observations"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623634"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/2487575.2488200"},{"key":"ref2","article-title":"Simple and scalable response prediction for display advertising","volume":"5","author":"chapelle","year":"2015","journal-title":"Trans Intell Syst Technol"},{"key":"ref9","first-page":"1222","article-title":"Ensemble of collaborative filtering and feature engineered models for click through rate prediction","author":"jahrer","year":"2012","journal-title":"Proc 18th ACM SIGKDD Int Conf Knowl Discovery Data Min"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/2988450.2988454"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.04.059"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2017.2759728"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/1718487.1718531"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/1718487.1718498"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/2964284.2964325"},{"key":"ref26","first-page":"1","article-title":"Ensemble of collaborative filtering and feature engineered models for click through rate prediction","author":"jahrer","year":"2012","journal-title":"Proc KDD Workshop"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2010.127"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8600701\/08566156.pdf?arnumber=8566156","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,26]],"date-time":"2022-01-26T23:50:09Z","timestamp":1643241009000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8566156\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"references-count":36,"URL":"https:\/\/doi.org\/10.1109\/access.2018.2885399","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019]]}}}