An Efficient Deep Distribution Network for Bid Shading in First-Price Auctions
arXiv:2107.06650 · doi:10.1145/3447548.3467167
Abstract
Since 2019, most ad exchanges and sell-side platforms (SSPs), in the online advertising industry, shifted from second to first price auctions. Due to the fundamental difference between these auctions, demand-side platforms (DSPs) have had to update their bidding strategies to avoid bidding unnecessarily high and hence overpaying. Bid shading was proposed to adjust the bid price intended for second-price auctions, in order to balance cost and winning probability in a first-price auction setup. In this study, we introduce a novel deep distribution network for optimal bidding in both open (non-censored) and closed (censored) online first-price auctions. Offline and online A/B testing results show that our algorithm outperforms previous state-of-art algorithms in terms of both surplus and effective cost per action (eCPX) metrics. Furthermore, the algorithm is optimized in run-time and has been deployed into VerizonMedia DSP as production algorithm, serving hundreds of billions of bid requests per day. Online A/B test shows that advertiser's ROI are improved by +2.4%, +2.4%, and +8.6% for impression based (CPM), click based (CPC), and conversion based (CPA) campaigns respectively.
arXiv commentIn Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'21), August 14-18, 2021, Singapore
References in corpus (6)
- Real-Time Bidding by Reinforcement Learning in Display Advertising
- Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising
- Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertising
- Bid Optimization by Multivariable Control in Display Advertising
- Predicting Different Types of Conversions with Multi-Task Learning in Online Advertising
- Bid Shading by Win-Rate Estimation and Surplus Maximization