A New Approach for Mobile Advertising Click-Through Rate Estimation Based on Deep Belief Nets
Joint Authors
Chen, Jie-Hao
Zhao, Zi-Qian
Shi, Ji-Yun
Zhao, Chong
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-10-25
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
In recent years, with the rapid development of mobile Internet and its business applications, mobile advertising Click-Through Rate (CTR) estimation has become a hot research direction in the field of computational advertising, which is used to achieve accurate advertisement delivery for the best benefits in the three-side game between media, advertisers, and audiences.
Current research on the estimation of CTR mainly uses the methods and models of machine learning, such as linear model or recommendation algorithms.
However, most of these methods are insufficient to extract the data features and cannot reflect the nonlinear relationship between different features.
In order to solve these problems, we propose a new model based on Deep Belief Nets to predict the CTR of mobile advertising, which combines together the powerful data representation and feature extraction capability of Deep Belief Nets, with the advantage of simplicity of traditional Logistic Regression models.
Based on the training dataset with the information of over 40 million mobile advertisements during a period of 10 days, our experiments show that our new model has better estimation accuracy than the classic Logistic Regression (LR) model by 5.57% and Support Vector Regression (SVR) model by 5.80%.
American Psychological Association (APA)
Chen, Jie-Hao& Zhao, Zi-Qian& Shi, Ji-Yun& Zhao, Chong. 2017. A New Approach for Mobile Advertising Click-Through Rate Estimation Based on Deep Belief Nets. Computational Intelligence and Neuroscience،Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1141065
Modern Language Association (MLA)
Chen, Jie-Hao…[et al.]. A New Approach for Mobile Advertising Click-Through Rate Estimation Based on Deep Belief Nets. Computational Intelligence and Neuroscience No. 2017 (2017), pp.1-8.
https://search.emarefa.net/detail/BIM-1141065
American Medical Association (AMA)
Chen, Jie-Hao& Zhao, Zi-Qian& Shi, Ji-Yun& Zhao, Chong. A New Approach for Mobile Advertising Click-Through Rate Estimation Based on Deep Belief Nets. Computational Intelligence and Neuroscience. 2017. Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1141065
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1141065