Estimating Ads’ Click through Rate with Recurrent Neural Network
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 rela...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2016-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | http://dx.doi.org/10.1051/itmconf/20160704001 |
| Summary: | 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. |
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| ISSN: | 2271-2097 |