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

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The 8th International Conference on Computer Engineering and Networks (CENet2018) (CENet2018 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 905))

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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.

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References

  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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)

    Article  Google Scholar 

  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. 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. Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)

    Article  MathSciNet  Google Scholar 

  16. Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. 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. 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. Hinton, G.E.: Training products of experts by minimizing contrastive divergence. Neural Comput. 14(8), 1771–1800 (2002)

    Article  Google Scholar 

  23. iPinYou: Global Bidding Algorithm Competition. http://contest.ipinyou.com/index.shtm

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Author information

Authors and Affiliations

  1. Information Center of Shandong Province People’s Congress Standing Committee General Office, Jinan, China

    Qiang Gao

  2. Harbin Institute of Technology, 92 West Da-zhi Street, Harbin, China

    Chengjie Sun

Authors
  1. Qiang Gao
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  2. Chengjie Sun
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Corresponding author

Correspondence to Qiang Gao .

Editor information

Editors and Affiliations

  1. School of Computing, Edinburgh Napier University, Edinburgh, UK

    Qi Liu

  2. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Mustafa Mısır

  3. School of Computer, Fudan University, Shanghai, China

    Xin Wang

  4. College of Information Science and Technology/College of Cyber Security, Jinan University, Guangzhou, China

    Weiping Liu

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Gao, Q., Sun, C. (2020). Learning High Level Features with Deep Neural Network for Click Prediction in Search and Real-Time Bidding Advertising. In: Liu, Q., Mısır, M., Wang, X., Liu, W. (eds) The 8th International Conference on Computer Engineering and Networks (CENet2018). CENet2018 2018. Advances in Intelligent Systems and Computing, vol 905. Springer, Cham. https://doi.org/10.1007/978-3-030-14680-1_27

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