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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.

Linked References

  1. Z. Meng, Research on the Personalized Advertising Push Services for Internet Users, Donghua University, Shanghai, China, 2014.
  2. A. K. Menon, K. Chitrapura, S. Garg, D. Agarwal, and N. Kota, “Response prediction using collaborative filtering with hierarchies and side-information,” in Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '11), pp. 141–149, San Diego, Calif, USA, August 2011. View at Publisher · View at Google Scholar
  3. M. Richardson, E. Dominowska, and R. Ragno, “Predicting clicks: estimating the click-through rate for new ads,” in Proceedings of the 16th International World Wide Web Conference (WWW '07), pp. 521–530, May 2007. View at Publisher · View at Google Scholar · View at Scopus
  4. O. Chapelle, “Modeling delayed feedback in display advertising,” in Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '14), pp. 1097–1105, New York, NY, USA, August 2014. View at Publisher · View at Google Scholar · View at Scopus
  5. Di. Shao, Research on High Level Feature Representation and Predicting Methods in Online Advertising, Harbin Institute Of Technology, Harbin, China, 2014.
  6. T. Fawcett, “ROC graphs: notes and practical considerations for researchers,” Machine Learning, vol. 31, no. 1, pp. 1–38, 2004. View at Google Scholar
  7. W. Xiao-Shu, Click-Through Rate Prediction Based on Deep Neural Netwook Model, Beijing University Of Posts And Telecommunications, Beijing, China, 2015.
  8. G. B. Huang, Q. Y. Zhu, and C. K. Siew, “Extreme learning machine: theory and applications,” Neurocomputing, vol. 70, no. 1–3, pp. 489–501, 2006. View at Publisher · View at Google Scholar · View at Scopus
  9. W. Zong, G.-B. Huang, and Y. Chen, “Weighted extreme learning machine for imbalance learning,” Neurocomputing, vol. 101, pp. 229–242, 2013. View at Publisher · View at Google Scholar · View at Scopus
  10. X. Zhang, Y. Zhuang, W. Wang, and W. Pedrycz, “Transfer boosting with synthetic instances for class imbalanced object recognition,” IEEE Transactions on Cybernetics, no. 99, pp. 1–14, 2016. View at Publisher · View at Google Scholar · View at Scopus
  11. K. Li, X. Kong, Z. Lu, L. Wenyin, and J. Yin, “Boosting weighted ELM for imbalanced learning,” Neurocomputing, vol. 128, pp. 15–21, 2014. View at Publisher · View at Google Scholar · View at Scopus
  12. G. B. Huang, Q. Y. Zhu, and C. K. Siew, “Extreme learning machine: a new learning scheme of feedforward neural networks,” in Proceedings of the IEEE International Joint Conference on Neural Networks, vol. 2, pp. 985–990, July 2004. View at Publisher · View at Google Scholar · View at Scopus
  13. H.-J. Rong, Y.-S. Ong, A.-H. Tan, and Z. Zhu, “A fast pruned-extreme learning machine for classification problem,” Neurocomputing, vol. 72, no. 1–3, pp. 359–366, 2008. View at Publisher · View at Google Scholar · View at Scopus
  14. Y. Xu, Q. Wang, Z. Wei, and S. Ma, “Traffic sign recognition based on weighted ELM and AdaBoost,” IEEE Electronics Letters, vol. 52, no. 24, pp. 1988–1990, 2016. View at Publisher · View at Google Scholar · View at Scopus