Optimizing reserve prices for publishers in online ad auctions

In this paper we consider an online publisher that sells advertisement space and propose a method for learning optimal reserve prices in second-price auctions. We study a limited information setting where the values of the bids are not revealed and no historical information about the values of the bids is available. Our proposed method is based on the principle of Thompson sampling combined with a particle filter to approximate and sample from the posterior distribution. Our method is suitable for non-stationary environments, and we show that, when the distribution of the winning bid suffers from estimation uncertainty, taking the gap between the winning bid and second highest bid into account leads to better decisions for the reserve prices. Experiments using real-life ad auction data show that the proposed method outperforms popular bandit algorithms.

[1]  Jun Wang,et al.  An empirical study of reserve price optimisation in real-time bidding , 2014, KDD.

[2]  Wen-Chih Peng,et al.  A gamma-based regression for winning price estimation in real-time bidding advertising , 2017, 2017 IEEE International Conference on Big Data (Big Data).

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

[4]  Ming-Syan Chen,et al.  Predicting Winning Price in Real Time Bidding with Censored Data , 2015, KDD.

[5]  Peter Auer,et al.  Finite-time Analysis of the Multiarmed Bandit Problem , 2002, Machine Learning.

[6]  Benjamin Van Roy,et al.  A Tutorial on Thompson Sampling , 2017, Found. Trends Mach. Learn..

[7]  Marcello Restelli,et al.  A Combinatorial-Bandit Algorithm for the Online Joint Bid/Budget Optimization of Pay-per-Click Advertising Campaigns , 2018, AAAI.

[8]  Jean-Michel Renders,et al.  Real-Time Optimization of Web Publisher RTB Revenues , 2017, KDD.

[9]  Claudio Gentile,et al.  Ieee Transactions on Information Theory 1 Regret Minimization for Reserve Prices in Second-price Auctions , 2022 .

[10]  Lihong Li,et al.  An Empirical Evaluation of Thompson Sampling , 2011, NIPS.

[11]  Ming-Syan Chen,et al.  Deep Censored Learning of the Winning Price in the Real Time Bidding , 2018, KDD.

[12]  Peter Auer,et al.  The Nonstochastic Multiarmed Bandit Problem , 2002, SIAM J. Comput..

[13]  David M. Blei,et al.  Objective Variables for Probabilistic Revenue Maximization in Second-Price Auctions with Reserve , 2015, WWW.

[14]  Sina Jafarpour,et al.  Real-Time Bid Prediction using Thompson Sampling-Based Expert Selection , 2015, KDD.

[15]  Mehryar Mohri,et al.  Learning Algorithms for Second-Price Auctions with Reserve , 2016, J. Mach. Learn. Res..

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

[17]  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).

[18]  Eric Moulines,et al.  Inference in Hidden Markov Models (Springer Series in Statistics) , 2005 .

[19]  Jun Wang,et al.  Real-Time Bidding Benchmarking with iPinYou Dataset , 2014, ArXiv.

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

[21]  Arnaud Doucet,et al.  A survey of convergence results on particle filtering methods for practitioners , 2002, IEEE Trans. Signal Process..

[22]  Daniel Austin,et al.  Reserve Price Optimization at Scale , 2016, 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA).