| COMPUTER SCIENCE AND TECHNOLOGY |
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CTR prediction for online advertising based on a features conjunction model |
| SHEN Fangyao1, DAI Guojun1, DAI Chenglei2, GUO Hongjie3, ZHANG Hua1 |
1. School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China;
2. Department of Mathematical Science, Zhejiang University, Hangzhou 310058, China;
3. School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China |
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Abstract Click-through rate (CTR) predictions are important for internet companies. The CTR is closely related to the context, user attributes and advertising attributes, with effective CTR predictions essential for improving company revenue. The traditional LR model was optimized to predict the relationship between the user and advertiser characteristics for the CTR which were added to the Sigmoid function to obtain a new features conjunction model. The online optimization algorithm follow-the-regularized-leader (FTRL) was used to improve the efficiency of the parameter, and the mixed regularization was used to prevent over fitting. Tests on a real-world advertising dataset show that this method has better accuracy, efficiency, parameter sensitivity and reliability compared with previous algorithms.
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| Keywords
click-through rate (CTR)
features conjunction
online optimization
mixed regularization
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Issue Date: 15 April 2018
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2018,
Vol. 58
