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

• URL: http://arxiv.org/abs/2107.06650v2
• Date: Thu, 15 Jul 2021 17:24:42 GMT
• Title: An Efficient Deep Distribution Network for Bid Shading in First-Price Auctions
• Authors: Tian Zhou, Hao He, Shengjun Pan, Niklas Karlsson, Bharatbhushan Shetty, Brendan Kitts, Djordje Gligorijevic, San Gultekin, Tingyu Mao, Junwei Pan, Jianlong Zhang and Aaron Flores
• Abstract summary: We introduce a novel deep distribution network for optimal bidding in both open (non-censored) and closed (censored) online first-price auctions. Our algorithm outperforms previous state-of-art algorithms in terms of both surplus and effective cost per action (eCPX) metrics. 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.
• Score: 10.180752196357805
• License: http://creativecommons.org/publicdomain/zero/1.0/
• 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.

Related papers

• Multi-Platform Budget Management in Ad Markets with Non-IC Auctions [6.037383467521294]
  In online advertising markets, budget-constrained advertisers acquire ad placements through repeated bidding in auctions on various platforms. We present a strategy for bidding optimally in a set of auctions that may or may not be incentive-compatible under the presence of budget constraints. Our strategy maximizes the expected total utility across auctions while satisfying the advertiser's budget constraints in expectation.
  arXiv  Detail & Related papers  (2023-06-12T18:21:10Z)
• Autobidders with Budget and ROI Constraints: Efficiency, Regret, and Pacing Dynamics [53.62091043347035]
  We study a game between autobidding algorithms that compete in an online advertising platform. We propose a gradient-based learning algorithm that is guaranteed to satisfy all constraints and achieves vanishing individual regret.
  arXiv  Detail & Related papers  (2023-01-30T21:59:30Z)
• Dynamic Pricing with Volume Discounts in Online Settings [102.00782184214326]
  This paper focuses on pricing in e-commerce when objective function is profit and only transaction data are available. Our work aims to find a pricing strategy that allows defining optimal prices at different volume thresholds to serve different classes of users. We design a two-phase online learning algorithm, namely-B- capable of exploiting the data in an online fashion.
  arXiv  Detail & Related papers  (2022-11-17T16:01:06Z)
• A Reinforcement Learning Approach in Multi-Phase Second-Price Auction Design [158.0041488194202]
  We study reserve price optimization in multi-phase second price auctions. From the seller's perspective, we need to efficiently explore the environment in the presence of potentially nontruthful bidders. Third, the seller's per-step revenue is unknown, nonlinear, and cannot even be directly observed from the environment.
  arXiv  Detail & Related papers  (2022-10-19T03:49:05Z)
• No-regret Learning in Repeated First-Price Auctions with Budget Constraints [5.834615090865286]
  We propose an RL-based bidding algorithm against the optimal non-anticipating strategy under stationary competition. Our algorithm obtains $widetilde O(sqrt T)$-regret if the bids are all revealed at the end of each round.
  arXiv  Detail & Related papers  (2022-05-29T04:32:05Z)
• A Real-World Implementation of Unbiased Lift-based Bidding System [8.35120274877627]
  A typical Demand-Side Platform (DSP)bids based on the predicted probability of click and conversion right after an ad impression. Recent studies find such a strategy is suboptimal and propose a better bidding strategy named lift-based bidding. Lift-based bidding simply bids the price according to the lift effect of the ad impression and achieves target metrics such as sales.
  arXiv  Detail & Related papers  (2022-02-23T01:15:54Z)
• A novel auction system for selecting advertisements in Real-Time bidding [68.8204255655161]
  Real-Time Bidding is a new Internet advertising system that has become very popular in recent years. We propose an alternative betting system with a new approach that not only considers the economic aspect but also other relevant factors for the functioning of the advertising system.
  arXiv  Detail & Related papers  (2020-10-22T18:36:41Z)
• Learning to Bid Optimally and Efficiently in Adversarial First-price Auctions [40.30925727499806]
  We develop the first minimax optimal online bidding algorithm that achieves an $widetildeO(sqrtT)$ regret. We test our algorithm on three real-world first-price auction datasets obtained from Verizon Media.
  arXiv  Detail & Related papers  (2020-07-09T05:40:39Z)
• Reserve Price Optimization for First Price Auctions [14.18752189817994]
  We propose a gradient-based algorithm to adaptively update and optimize reserve prices based on estimates of bidders' responsiveness to experimental shocks in reserves. We show that revenue in a first-price auction can be usefully decomposed into a emphdemand component and a emphbidding component, and introduce techniques to reduce the variance of each component.
  arXiv  Detail & Related papers  (2020-06-11T15:35:19Z)
• Online Joint Bid/Daily Budget Optimization of Internet Advertising Campaigns [115.96295568115251]
  We study the problem of automating the online joint bid/daily budget optimization of pay-per-click advertising campaigns over multiple channels. For every campaign, we capture the dependency of the number of clicks on the bid and daily budget by Gaussian Processes. We design four algorithms and show that they suffer from a regret that is upper bounded with high probability as O(sqrtT) We present the results of the adoption of our algorithms in a real-world application with a daily average spent of 1,000 Euros for more than one year.
  arXiv  Detail & Related papers  (2020-03-03T11:07:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.