An improved advertising CTR prediction approach based on the fuzzy deep neural network.

An improved advertising CTR prediction approach based on the fuzzy deep neural network.

Combining a deep neural network with fuzzy theory, this paper proposes an advertising click-through rate (CTR) prediction approach based on a fuzzy deep neural network (FDNN). In this approach, fuzzy Gaussian-Bernoulli restricted Boltzmann machine (FGBRBM) is first applied to input raw data from adv...

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Bibliographic Details
Main Authors: Zilong Jiang, Shu Gao, Mingjiang Li
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2018-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC5935396?pdf=render
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spelling doaj-bd18d2c6b4aa42d5bed89b72ab363f982020-11-25T00:02:10ZengPublic Library of Science (PLoS)PLoS ONE1932-62032018-01-01135e019083110.1371/journal.pone.0190831An improved advertising CTR prediction approach based on the fuzzy deep neural network.Zilong JiangShu GaoMingjiang LiCombining a deep neural network with fuzzy theory, this paper proposes an advertising click-through rate (CTR) prediction approach based on a fuzzy deep neural network (FDNN). In this approach, fuzzy Gaussian-Bernoulli restricted Boltzmann machine (FGBRBM) is first applied to input raw data from advertising datasets. Next, fuzzy restricted Boltzmann machine (FRBM) is used to construct the fuzzy deep belief network (FDBN) with the unsupervised method layer by layer. Finally, fuzzy logistic regression (FLR) is utilized for modeling the CTR. The experimental results show that the proposed FDNN model outperforms several baseline models in terms of both data representation capability and robustness in advertising click log datasets with noise.http://europepmc.org/articles/PMC5935396?pdf=render
collection DOAJ
language English
format Article
sources DOAJ
author Zilong Jiang
Shu Gao
Mingjiang Li
spellingShingle Zilong Jiang
Shu Gao
Mingjiang Li
An improved advertising CTR prediction approach based on the fuzzy deep neural network.
PLoS ONE
author_facet Zilong Jiang
Shu Gao
Mingjiang Li
author_sort Zilong Jiang
title An improved advertising CTR prediction approach based on the fuzzy deep neural network.
title_short An improved advertising CTR prediction approach based on the fuzzy deep neural network.
title_full An improved advertising CTR prediction approach based on the fuzzy deep neural network.
title_fullStr An improved advertising CTR prediction approach based on the fuzzy deep neural network.
title_full_unstemmed An improved advertising CTR prediction approach based on the fuzzy deep neural network.
title_sort improved advertising ctr prediction approach based on the fuzzy deep neural network.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2018-01-01
description Combining a deep neural network with fuzzy theory, this paper proposes an advertising click-through rate (CTR) prediction approach based on a fuzzy deep neural network (FDNN). In this approach, fuzzy Gaussian-Bernoulli restricted Boltzmann machine (FGBRBM) is first applied to input raw data from advertising datasets. Next, fuzzy restricted Boltzmann machine (FRBM) is used to construct the fuzzy deep belief network (FDBN) with the unsupervised method layer by layer. Finally, fuzzy logistic regression (FLR) is utilized for modeling the CTR. The experimental results show that the proposed FDNN model outperforms several baseline models in terms of both data representation capability and robustness in advertising click log datasets with noise.
url http://europepmc.org/articles/PMC5935396?pdf=render
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AT shugao animprovedadvertisingctrpredictionapproachbasedonthefuzzydeepneuralnetwork
AT mingjiangli animprovedadvertisingctrpredictionapproachbasedonthefuzzydeepneuralnetwork
AT zilongjiang improvedadvertisingctrpredictionapproachbasedonthefuzzydeepneuralnetwork
AT shugao improvedadvertisingctrpredictionapproachbasedonthefuzzydeepneuralnetwork
AT mingjiangli improvedadvertisingctrpredictionapproachbasedonthefuzzydeepneuralnetwork
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