{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:03:14Z","timestamp":1777705394550,"version":"3.51.4"},"reference-count":6,"publisher":"SAGE Publications","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2021,12,16]]},"abstract":"<jats:p>Click-through rate (CTR) prediction, which aims to predict the probability of a user clicking on an ad, is a critical task in online advertising systems. The problem is very challenging since(1) an effective prediction relies on high-order combinatorial features, and(2)the relationship to auxiliary ads that may impact the CTR. In this paper, we propose Deep Context Interaction Network on Attention Mechanism(DCIN-Attention) to process feature interaction and context at the same time. The context includes other ads in the current search page, historically clicked and unclicked ads of the user. Specifically, we use the attention mechanism to learn the interactions between the target ad and each type of auxiliary ad. The residual network is used to model the feature interactions in the low-dimensional space, and with the multi-head self-attention neural network, high-order feature interactions can be modeled. Experimental results on Avito dataset show that DCIN outperform several existing methods for CTR prediction.<\/jats:p>","DOI":"10.3233\/jifs-210830","type":"journal-article","created":{"date-parts":[[2021,9,3]],"date-time":"2021-09-03T11:31:01Z","timestamp":1630668661000},"page":"6899-6914","source":"Crossref","is-referenced-by-count":1,"title":["Deep context interaction network based on attention mechanism for click-through rate prediction"],"prefix":"10.1177","volume":"41","author":[{"given":"Ling","family":"Yuan","sequence":"first","affiliation":[{"name":"School of Computer Science, Huazhong University of Science and Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhuwen","family":"Pan","sequence":"additional","affiliation":[{"name":"School of Computer Science, Huazhong University of Science and Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ping","family":"Sun","sequence":"additional","affiliation":[{"name":"Huanggang Normal University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yinzhen","family":"Wei","sequence":"additional","affiliation":[{"name":"Huanggang Normal University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haiping","family":"Yu","sequence":"additional","affiliation":[{"name":"Huanggang Normal University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-210830_ref4","first-page":"1145","article-title":"The use of the area under the ROC curve in the evaluation of machine learning algorithms","volume":"1997","author":"Bradley","journal-title":"Pattern Recognition"},{"issue":"02","key":"10.3233\/JIFS-210830_ref7","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1108\/K-07-2018-0371","article-title":"Incentive contract design for internet referral services: cost per click vs cost per sale","volume":"49","author":"Zhou","year":"2019","journal-title":"Kybernetes"},{"issue":"05","key":"10.3233\/JIFS-210830_ref12","first-page":"565","article-title":"A survey on feature learning and technologies of online advertising click-through rate estimation","volume":"46","author":"Liu","year":"2019","journal-title":"Journal of Zhejiang University(Science Edition)"},{"key":"10.3233\/JIFS-210830_ref17","unstructured":"Goodfellow I. , Bengio Y. and Courville A. , Deep learning, MIT Press (2016), 321\u2013362."},{"key":"10.3233\/JIFS-210830_ref18","unstructured":"iResearch, Annual Monitoring Report of China\u2019s Online Advertising Market, iResearch Consulting Series Research Report 2019(06) (2019), 456\u2013537."},{"issue":"11","key":"10.3233\/JIFS-210830_ref22","first-page":"164","article-title":"The Original Climate, Operating Mechanism, and Evolution of Computational Advertising","volume":"2019","author":"Lu","journal-title":"Shandong Social Sciences"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-210830","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:43:43Z","timestamp":1777455823000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-210830"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,16]]},"references-count":6,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.3233\/jifs-210830","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,16]]}}}