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Scientific Programming
Volume 2017, Article ID 2938369, 8 pages
https://doi.org/10.1155/2017/2938369
Research Article

Advertisement Click-Through Rate Prediction Based on the Weighted-ELM and Adaboost Algorithm

1School of Automation & Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
2Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education, Beijing 100083, China

Correspondence should be addressed to Sen Zhang; nc.ude.btsu@nesgnahz

Received 13 July 2017; Accepted 4 October 2017; Published 9 November 2017

Academic Editor: Wenbing Zhao

Copyright © 2017 Sen Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Accurate click-through rate (CTR) prediction can not only improve the advertisement company’s reputation and revenue, but also help the advertisers to optimize the advertising performance. There are two main unsolved problems of the CTR prediction: low prediction accuracy due to the imbalanced distribution of the advertising data and the lack of the real-time advertisement bidding implementation. In this paper, we will develop a novel online CTR prediction approach by incorporating the real-time bidding (RTB) advertising by the following strategies: user profile system is constructed from the historical data of the RTB advertising to describe the user features, the historical CTR features, the ID features, and the other numerical features. A novel CTR prediction approach is presented to address the imbalanced learning sample distribution by integrating the Weighted-ELM (WELM) and the Adaboost algorithm. Compared to the commonly used algorithms, the proposed approach can improve the CTR significantly.