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	An improved advertising CTR prediction approach based on the fuzzy deep neural network
	Jiang, Zilong; Gao, Shu; Li, Mingjiang*
刊名	PLOS ONE
	2018
卷号	13期号:5页码:e0190831
关键词	Advertising,Neural networks,Machine learning algorithms,Data processing,Algorithms,Machine learning,Artificial neural networks,Internet
ISSN号	1932-6203
DOI	10.1371/journal.pone.0190831
URL标识	查看原文
WOS记录号	WOS:000431452100001
内容类型	期刊论文
URI标识	http://www.corc.org.cn/handle/1471x/3403607
专题	武汉理工大学
作者单位	1.[Gao, Shu 2.Jiang, Zilong] Wuhan Univ Technol, Sch Comp Sci & Technol, Wuhan, Hubei, Peoples R China.
推荐引用方式 GB/T 7714	Jiang, Zilong,Gao, Shu,Li, Mingjiang*. An improved advertising CTR prediction approach based on the fuzzy deep neural network[J]. PLOS ONE,2018,13(5):e0190831.
APA	Jiang, Zilong,Gao, Shu,&Li, Mingjiang*.(2018).An improved advertising CTR prediction approach based on the fuzzy deep neural network.PLOS ONE,13(5),e0190831.
MLA	Jiang, Zilong,et al."An improved advertising CTR prediction approach based on the fuzzy deep neural network".PLOS ONE 13.5(2018):e0190831.

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