An Efficient Deep Distribution Network for Bid Shading in First-Price Auctions

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. CCS CONCEPTS• Applied computing → Online auctions; • Information systems → Display advertising; • Computing methodologies → Machine learning algorithms.

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“…Some efforts have been devoted to designing bidding mechanisms to enhance the effectiveness and fairness of advertising auctions from the platform perspective. For example, Zhou et al in [19] introduced a novel deep distribution network for optimal bidding in both open and closed online first-price auctions. Zhang et al in [20] proposed a succinct and effective bid shading algorithm without parametric assumptions for the win distribution.…”