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Learning High Level Features with Deep Neural Network for Click Prediction in Search and Real-Time Bidding Advertising

  • Qiang GaoEmail author
  • Chengjie Sun
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 905)

Abstract

Here you can write the abstract for your paper. Sponsored search advertising and real-time bidding (RTB) advertising have been growing rapidly in recently years. For both of them, one of the key technologies is to estimate the click-through rate (CTR) accurately. Most of current methods utilize shallow features, such as user attributes, statistical data. As in sponsored search advertising and RTB advertising, all parties are connected because of the interests from users, hence the user features may contain richer latent factors or abstract information on higher levels which are helpful to improve the accuracy of click prediction. Based on this assumption, the object of this paper is to use high level features learned from basic features, specially user features, to improve the performance of CTR. A deep neural network framework is proposed to learn the high level features in this work. The proposed framework consists of two different deep neural network model in order to process different types of user features respectively. Experimental results on sponsored search advertising dataset and RTB advertising dataset show that the learned high level features can improve the accuracy of click prediction.

Keywords

Click-through rate prediction Deep neural network Real-time bidding advertising 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Information Center of Shandong Province People’s Congress Standing Committee General OfficeJinanChina
  2. 2.Harbin Institute of TechnologyHarbinChina

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