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

Zilong Jiang, Shu Gao and Mingjiang Li

PLOS ONE, 2018, vol. 13, issue 5, 1-24

Abstract: 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.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0190831

DOI: 10.1371/journal.pone.0190831

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