Advertisement

Dynamic Bidding Strategy Based on Probabilistic Feedback in Display Advertising

  • Yuzhu Wu
  • Shumin Pan
  • Qianwen Zhang
  • Jinkui Xie
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10635)

Abstract

Bidding strategy is an issue of fundamental importance to Demand Side Platform (DSP) in real-time bidding (RTB). Bidding strategies employed by the Demand Siders may have significant impacts on their own benefits. In this paper, we design a dynamic bidding strategy based on probabilistic feedback, called PFDBS, which is different from previous work that is mainly focused on fixed strategies or continuous feedback strategies. Our dynamic bidding strategy is more in accordance with environment of Internet advertising to solve the instability problem. If evaluated valid, we will retain the current strategy, otherwise, we present an approach to amend strategy combined with previous feedback. The experiments on real-world RTB dataset demonstrate that our method has the best performance on Key Performance Indicator (KPI) compared to other popular strategies, meanwhile, the consumption trend of overall budget is the most consistent with real market situation.

Keywords

Display advertising Probabilistic feedback Dynamic bidding strategy 

References

  1. 1.
    Wang, J., Yuan, S.: Real-time bidding: A new frontier of computational advertising research. In: 8th ACM International Conference on Web Search and Data Mining, pp. 415–416 (2015)Google Scholar
  2. 2.
    Google.: The arrival of real-time bidding and what it means for media buyers. Google (2011)Google Scholar
  3. 3.
    Yuan, Y., Wang, F.Y., Li, J.J., Qin, R.: A survey on real time bidding advertising. In: IEEE International Conference on Service Operations and Logistics, and Informatics, pp. 418–423 (2014)Google Scholar
  4. 4.
    Perlich, C., Dalessandro, B., Hook, R., Stitelman, O., Raeder, T., Provost, F.: Bid optimizing and inventory scoring in targeted online advertising. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 804–812 (2012)Google Scholar
  5. 5.
    Li, X., Guan, D.: Programmatic buying bidding strategies with win rate and winning price estimation in real time mobile advertising. In: Tseng, V.S., Ho, T.B., Zhou, Z.-H., Chen, A.L.P., Kao, H.-Y. (eds.) PAKDD 2014. LNCS (LNAI), vol. 8443, pp. 447–460. Springer, Cham (2014). doi: 10.1007/978-3-319-06608-0_37 CrossRefGoogle Scholar
  6. 6.
    Zhang, W.N., Yuan, S., Wang, J.: Optimal real-time bidding for display advertising. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1077–1086 (2014)Google Scholar
  7. 7.
    Zhang, W.N., Rong, Y.F., Wang, J., Zhu, T.C., Wang, X.F.: Feedback control of real-time display advertising. In: ACM International Conference on Web Search and Data Mining, pp. 407–416 (2016)Google Scholar
  8. 8.
    Wu, C.H., Yeh, M.Y., Chen, M.S.: Predicting winning price in real time bidding with censored data. In: The ACM SIGKDD International Conference, pp. 1305–1314 (2015)Google Scholar
  9. 9.
    Lin, C.C., Chuang, K.T., Wu, C.H., Chen, M.S.: Combining powers of two predictors in optimizing real-time bidding strategy under constrained budget. In: ACM International on Conference on Information and Knowledge Management, pp. 2143–2148 (2016)Google Scholar
  10. 10.
    Kellerer, H., Pferschy, U., Pisinger, D.: Introduction to NP-Completeness of knapsack problems. Knapsack problems, pp. 483–493. Springer, Heidelberg (2004)Google Scholar
  11. 11.
    Broder, A.: Computational advertising. In: 9th ACM-SIAM Symposium on Discrete Algorithms, pp. 992–992 (2008)Google Scholar
  12. 12.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)MATHGoogle Scholar
  13. 13.
    Zhang, W.N., Yuan, S., Wang, J., Shen, X.H.: Real-time bidding benchmarking with ipinyou dataset. Comput. Sci. (2014)Google Scholar
  14. 14.
    Han, J.W., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kaufmann, San Francisco (2006)MATHGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Yuzhu Wu
    • 1
  • Shumin Pan
    • 1
  • Qianwen Zhang
    • 1
  • Jinkui Xie
    • 1
  1. 1.Department of Computer Science and TechnologyEast China Normal UniversityShanghaiChina

Personalised recommendations