| Issue |
ITM Web Conf.
Volume 7, 2016
3rd Annual International Conference on Information Technology and Applications (ITA 2016)
|
|
|---|---|---|
| Article Number | 04001 | |
| Number of page(s) | 6 | |
| Section | Session 4: Information System and its Applications | |
| DOI | https://doi.org/10.1051/itmconf/20160704001 | |
| Published online | 21 November 2016 | |
Estimating Ads’ Click through Rate with Recurrent Neural Network
School of Information, Zhejiang Sci-Tech University, Hangzhou, Zhejiang Province, China
a Corresponding author: chen_lisa@zstu.edu.cn
With the development of the Internet, online advertising spreads across every corner of the world, the ads' click through rate (CTR) estimation is an important method to improve the online advertising revenue. Compared with the linear model, the nonlinear models can study much more complex relationships between a large number of nonlinear characteristics, so as to improve the accuracy of the estimation of the ads’ CTR. The recurrent neural network (RNN) based on Long-Short Term Memory (LSTM) is an improved model of the feedback neural network with ring structure. The model overcomes the problem of the gradient of the general RNN. Experiments show that the RNN based on LSTM exceeds the linear models, and it can effectively improve the estimation effect of the ads’ click through rate.
© Owned by the authors, published by EDP Sciences, 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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