Reserve price optimization with header bidding and Ad Exchange

The extremely high turnover of online advertising makes it one of the most important sources of income for many online ad publishers. Advertising through world wide web is mainly performed by Real Time Bidding in which the advertisers and the publishers participate to online auctions for trading the ad slots. Publishers usually set the reserve prices for their ad slots and any winning buyer in the auctions performed by ad exchanges has to pay at least the value of reserve price. Header bidding is a way of real time bidding and it becomes very popular, but how to use it together with advertising Exchanges (AdX) to achieve good revenue for online publishers is not well studied. In this paper, we propose a method that makes use of the historical auction data from header bidding and AdX to learn and optimize the reserve price for AdX. We propose a method based on supervised learning and survival analysis to increase the reserve price. The method assumes no information about current auctions and the bids of header bidding and AdX response are predicted and used to determine the highest possible reserve price. The experiments with real-world auction data show the promising results of our method in increasing the expected revenue of online publishers.

[1]  Shuai Yuan,et al.  A Dynamic Pricing Model for Unifying Programmatic Guarantee and Real-Time Bidding in Display Advertising , 2014, ADKDD'14.

[2]  Feiyue Wang,et al.  A survey on real time bidding advertising , 2014, Proceedings of 2014 IEEE International Conference on Service Operations and Logistics, and Informatics.

[3]  Alp Akcay,et al.  Fuzzy Logic based Pricing combined with Adaptive Search for Reserve Price Optimization in Online Ad Auctions , 2019, 2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[4]  Uzay Kaymak,et al.  A PSO-based Algorithm for Reserve Price Optimization in Online Ad Auctions , 2019, 2019 IEEE Congress on Evolutionary Computation (CEC).

[5]  Jun Wang,et al.  Real-Time Bidding by Reinforcement Learning in Display Advertising , 2017, WSDM.

[6]  Amin S. Sayedi-Roshkhar.  Real-Time Bidding in Online Display Advertising , 2018, Mark. Sci..

[7]  Fei-Yue Wang,et al.  Optimizing the revenue for ad exchanges in header bidding advertising markets , 2017, 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[8]  Aurélien Garivier,et al.  Optimization of a SSP's Header Bidding Strategy using Thompson Sampling , 2018, KDD.

[9]  Gilles Louppe,et al.  Understanding Random Forests: From Theory to Practice , 2014, 1407.7502.

[10]  Yingqian Zhang,et al.  A Reinforcement Learning Method to Select Ad Networks in Waterfall Strategy , 2019, BNAIC/BENELEARN.

[11]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[12]  Jun Wang,et al.  Real-time bidding for online advertising: measurement and analysis , 2013, ADKDD '13.

[13]  Yingqian Zhang,et al.  Reinforcement Learning Method for Ad Networks Ordering in Real-Time Bidding , 2019, ICAART.

[14]  Evangelos P. Markatos,et al.  No More Chasing Waterfalls: A Measurement Study of the Header Bidding Ad-Ecosystem , 2019, Internet Measurement Conference.

[15]  Zhihui Xie,et al.  Optimal Reserve Price for Online Ads Trading Based on Inventory Identification , 2017, ADKDD@KDD.

[16]  J. Friedman.  Stochastic gradient boosting , 2002 .

[17]  E. Kaplan,et al.  Nonparametric Estimation from Incomplete Observations , 1958 .

[18]  Jun Wang,et al.  Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting , 2016, Found. Trends Inf. Retr..

[19]  Uzay Kaymak,et al.  A Decision Support Method to Increase the Revenue of Ad Publishers in Waterfall Strategy , 2019, 2019 IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr).

[20]  Chong Wang,et al.  Reserve Price Failure Rate Prediction with Header Bidding in Display Advertising , 2019, KDD.