{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T19:09:47Z","timestamp":1770491387537,"version":"3.49.0"},"reference-count":49,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2020,12,14]],"date-time":"2020-12-14T00:00:00Z","timestamp":1607904000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>With the development of E-commerce, online advertising began to thrive and has gradually developed into a new mode of business, of which Click-Through Rates (CTR) prediction is the essential driving technology. Given a user, commodities and scenarios, the CTR model can predict the user\u2019s click probability of an online advertisement. Recently, great progress has been made with the introduction of Deep Neural Networks (DNN) into CTR. In order to further advance the DNN-based CTR prediction models, this paper introduces a new model of FO-FTRL-DCN, based on the prestigious model of Deep&amp;Cross Network (DCN) augmented with the latest optimization technique of Follow The Regularized Leader (FTRL) for DNN. The extensive comparative experiments on the iPinYou datasets show that the proposed model has outperformed other state-of-the-art baselines, with better generalization across different datasets in the benchmark.<\/jats:p>","DOI":"10.3390\/a13120342","type":"journal-article","created":{"date-parts":[[2020,12,14]],"date-time":"2020-12-14T21:25:08Z","timestamp":1607981108000},"page":"342","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["A New Click-Through Rates Prediction Model Based on Deep&amp;Cross Network"],"prefix":"10.3390","volume":"13","author":[{"given":"Guojing","family":"Huang","sequence":"first","affiliation":[{"name":"The Industrial and Commercial Bank of China Limited, Guangzhou Branch, Guangzhou 510100, China"},{"name":"Department of Computer Science, Jinan University, Guangzhou 510632, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qingliang","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Jinan University, Guangzhou 510632, China"},{"name":"Yunqu-Jinan University Joint AI Research Center, Guangzhou 510632, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Congjian","family":"Deng","sequence":"additional","affiliation":[{"name":"Yunqu-Jinan University Joint AI Research Center, Guangzhou 510632, China"},{"name":"Guangzhou Yunqu Information Technology Company, Ltd., Guangzhou 510665, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bayer, E., Srinivasan, S., Riedl, E., and Skiera, B. 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