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)


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.


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


  1. 1.
    Yuan, S., Wang, J., Zhao, X.: Real-time bidding for online advertising: measurement and analysis. In: Proceedings of the Seventh International Workshop on Data Mining for Online Advertising, p. 3. ACM (2013)Google Scholar
  2. 2.
    Broder, A.Z.: Computational advertising. In: Proceedings of the Nineteenth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2008, San Francisco, California, USA (2008)Google Scholar
  3. 3.
    McMahan, H.B., Holt, G., Sculley, D., Young, M., Ebner, D., Grady, J., Nie, L., Phillips, T., Davydov, E., Golovin, D., Chikkerur, S., Liu, D., Wattenberg, M., Mar Hrafnkelsson, A., Boulos, T., Kubica, J.: Ad click prediction: a view from the trenches. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1222–1230. ACM (2013)Google Scholar
  4. 4.
    Graepel, T., Candela, J.Q., Borchert, T., Herbrich, R.: Web-scale bayesian click through rate prediction for sponsored search advertising in microsoft’s bing search engine. In: Proceedings of the 27th International Conference on Machine Learning, pp. 13–20 (2010)Google Scholar
  5. 5.
    Hillard, D., Schroedl, S., Manavoglu, E., Raghavan, H., Leggetter, C.: Improving ad relevance in sponsored search. In: Proceedings of the Third ACM International Conference on Web Search and Data mining, pp. 361–370. ACM (2010)Google Scholar
  6. 6.
    Richardson, M., Dominowska, E., Ragno, R.: Predicting clicks: estimating the click through rate for new ads. In: Proceedings of the 16th International Conference on World Wide Web, pp. 521–530. ACM (2007)Google Scholar
  7. 7.
    Zhu, Z.A., Chen, W., Minka, T., Zhu, C., Chen, Z.: A novel click model and its applications to online advertising. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, pp. 321–330. ACM (2010)Google Scholar
  8. 8.
    Dave, K.S., Varma, V.: Learning the click-through rate for rare/new ads from similar ads. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 897–898. ACM (2010)Google Scholar
  9. 9.
    Cheng, H.: Personalized click prediction in sponsored search. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, pp. 351–360. ACM (2010)Google Scholar
  10. 10.
    Berger, A.L., Pietra, V.J.D., Pietra, S.A.D.: A maximum entropy approach to natural language processing. Comput. Linguist. 22(1), 39–71 (1996)Google Scholar
  11. 11.
    Chapelle, O., Zhang, Y.: A dynamic bayesian network click model for web search ranking. In: Proceedings of the 18th International Conference on World Wide Web, pp. 1–10. ACM (2009)Google Scholar
  12. 12.
    Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)CrossRefGoogle Scholar
  13. 13.
    Chakrabarti, D., Agarwal, D., Josifovski, V.: Contextual advertising by combining relevance with click feedback. In: Proceedings of the 17th International Conference on World Wide Web, pp. 417–426. ACM (2008)Google Scholar
  14. 14.
    Liu, C., Wang, H., Mcclean, S., Liu, J., Wu, S.: Syntactic information retrieval. In: IEEE International Conference on Granular Computing, p. 703. IEEE (2007)Google Scholar
  15. 15.
    Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Dahl, G.E., Yu, D., Deng, L., Acero, A.: Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. IEEE Trans. Audio Speech Lang. Process. 20(1), 30–42 (2012)CrossRefGoogle Scholar
  18. 18.
    Coates, A., Ng, A.Y., Lee, H.: An analysis of single-layer networks in unsupervised feature learning. In: International Conference on Artificial Intelligence and Statistics, pp. 215–223 (2011)Google Scholar
  19. 19.
    Bengio, Y., Schwenk, H., Senecal, J.S., Morin, F., Gauvain, J.L.: Neural probabilistic language models. In: Innovations in Machine Learning, pp. 137–186. Springer (2006)Google Scholar
  20. 20.
    Smolensky, P.: Information processing in dynamical systems: foundations of harmony theory. In: Parallel Distributed Processing: Explorations in the Microstructure of Cognition, pp. 194–281. MIT Press (1986)Google Scholar
  21. 21.
    Fischer, A., Igel, C.: An introduction to restricted Boltzmann machines. In: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, pp. 14–36. Springer (2012)Google Scholar
  22. 22.
    Hinton, G.E.: Training products of experts by minimizing contrastive divergence. Neural Comput. 14(8), 1771–1800 (2002)CrossRefGoogle Scholar
  23. 23.
    iPinYou: Global Bidding Algorithm Competition.

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

Personalised recommendations