{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T13:46:46Z","timestamp":1785592006807,"version":"3.56.0"},"reference-count":38,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2020YFF0305300"],"award-info":[{"award-number":["2020YFF0305300"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2021]]},"DOI":"10.1109\/access.2021.3116705","type":"journal-article","created":{"date-parts":[[2021,9,29]],"date-time":"2021-09-29T21:37:51Z","timestamp":1632951471000},"page":"140022-140030","source":"Crossref","is-referenced-by-count":12,"title":["GCN-Int: A Click-Through Rate Prediction Model Based on Graph Convolutional Network Interaction"],"prefix":"10.1109","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0684-1364","authenticated-orcid":false,"given":"Yuchen","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4554-3207","authenticated-orcid":false,"given":"Chuanzhen","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3537-6812","authenticated-orcid":false,"given":"Han","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3286-2126","authenticated-orcid":false,"given":"Juanjuan","family":"Cai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357925"},{"key":"ref33","article-title":"Research on the algorithm of click-through rate prediction","author":"zhao","year":"2020"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-014-0231-7"},{"key":"ref31","first-page":"4278","article-title":"Inception-v4, inception-resnet and the impact of residual connections on learning","author":"szegedy","year":"2016","journal-title":"Proc 31st AAAI Conf Artif Intell"},{"key":"ref30","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"srivastava","year":"2014","journal-title":"J Mach Learn Res"},{"key":"ref37","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220023"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1145\/3077136.3080777"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939704"},{"key":"ref34","first-page":"1471","article-title":"Training and testing low-degree polynomial data mappings via linear SVM","volume":"11","author":"chang","year":"2010","journal-title":"J Mach Learn Res"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1155\/2009\/421425"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/2532128"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313497"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/2556195.2556240"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/2959100.2959134"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/2813448.2813511"},{"key":"ref16","first-page":"45","article-title":"Deep learning over multi-field categorical data—A case study on user response prediction","author":"zhang","year":"2016","journal-title":"Proc Adv Inf Retr 38th Eur Conf IR Res (ECIR)"},{"key":"ref17","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"krizhevsky","year":"2012","journal-title":"Commun ACM"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1145\/2661829.2661935"},{"key":"ref28","first-page":"3844","article-title":"Convolutional neural networks on graphs with fast localized spectral filtering","volume":"29","author":"defferrard","year":"2016","journal-title":"Proc 30th Int Conf Neural Inf Process Syst (NIPS)"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-72079-9_10"},{"key":"ref27","first-page":"20286","article-title":"Factorizable graph convolutional networks","volume":"33","author":"yang","year":"2020","journal-title":"Proc Neural Inf Process Syst"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2005.99"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/192844.192905"},{"key":"ref29","first-page":"448","article-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift","volume":"1","author":"ioffe","year":"2015","journal-title":"Proc 32nd Int Conf Mach Learn (ICML)"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-85820-3_3"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.elerap.2019.100879"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1145\/371920.372071"},{"key":"ref2","first-page":"1","article-title":"A hybrid network based CTR prediction model for online advertising","volume":"42","author":"liu","year":"2019","journal-title":"Chin J Comput"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1145\/138859.138867"},{"key":"ref9","first-page":"1","article-title":"A literature review of recommendation systems","author":"bhareti","year":"2020","journal-title":"Proc IEEE Int Conf Innov Technol (INOCON)"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/2988450.2988454"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2016.0151"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1145\/3124749.3124754"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1145\/3077136.3080797"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2017\/435"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/569"},{"key":"ref25","first-page":"1","article-title":"Semi-supervised classification with graph convolutional networks","author":"kipf","year":"2016","journal-title":"Proc 5th Int Conf Learn Represent (ICLR)"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/9312710\/09552883.pdf?arnumber=9552883","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,2,21]],"date-time":"2022-02-21T22:17:32Z","timestamp":1645481852000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9552883\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"references-count":38,"URL":"https:\/\/doi.org\/10.1109\/access.2021.3116705","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021]]}}}