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

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  • Zilong Jiang
  • Shu Gao
  • Mingjiang Li

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.

Suggested Citation

  • Zilong Jiang & Shu Gao & Mingjiang Li, 2018. "An improved advertising CTR prediction approach based on the fuzzy deep neural network," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-24, May.
    • Handle: RePEc:plo:pone00:0190831
    DOI: 10.1371/journal.pone.0190831
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    References listed on IDEAS

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    1. Taher Osman & Prasanna Divigalpitiya & Takafumi Arima, 2016. "Driving factors of urban sprawl in Giza governorate of the Greater Cairo Metropolitan Region using a logistic regression model," International Journal of Urban Sciences, Taylor & Francis Journals, vol. 20(2), pages 206-225, July.
    2. Sami Najafi-Asadolahi & Kristin Fridgeirsdottir, 2014. "Cost-per-Click Pricing for Display Advertising," Manufacturing & Service Operations Management, INFORMS, vol. 16(4), pages 482-497, October.
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