Bid Optimization by Multivariable Control in Display Advertising

Yang, Xun; Li, Yasong; Wang, Hao; Wu, Di; Tan, Qing; Xu, Jian; Gai, Kun

doi:10.1145/3292500.3330681

Cited by 61 publications

(35 citation statements)

References 37 publications

(57 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Second, it is common for the bidder to have some constraints on the advertising campaign, such as a maximum budget per day, or a maximum cost per click [Conitzer et al, 2018, Heymann, 2019. While in several cases a linear scaling applied to the display valuation may be enough to optimally satisfy a budget constraint, the value of this scaling factor is not known in advance, and several articles propose to update the bidding strategy to better take such constraint into account [Cai et al, 2017, Grislain et al, 2019, Lee et al, 2013, Yang et al, 2019. In the present work, we do not consider such constraints, but those methods might be applied on top of ours.…”

Section: Related Work and Contributionmentioning

confidence: 99%

“…The bidder should set its bid as a function of the valuation ( ) following auction theory [Krishna, 2009]. This bid adjustment in the specific context of RTB has been studied in [Cai et al, 2017, Despotakis et al, 2019, Grislain et al, 2019, Lee et al, 2013, Yang et al, 2019 and is not in the scope on this work. For example, in the simple setting of second price auction, the optimal bid is deterministic, * = ( ).…”

Section: Optimal Policymentioning

confidence: 99%

See 1 more Smart Citation

Causal Models for Real Time Bidding with Repeated User Interactions

Bompaire

Gilotte

Heymann

2021

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &Amp; Data Mining

View full text Add to dashboard Cite

A large portion of online advertising displays are sold through an auction mechanism called Real Time Bidding (RTB). Each auction corresponds to a display opportunity, for which the competing advertisers need to precisely estimate the economical value in order to bid accordingly. This estimate is typically taken as the advertiser's payoff for the target event -such as a purchase on the merchant website attributed to this display -times this event estimated probability. However, this greedy approach is too naive when several displays are shown to the same user. The purpose of the present paper is to discuss how such an estimation should be made when a user has already been shown one or more displays. Intuitively, while a user is more likely to make a purchase if the number of displays increases, the marginal effect of each display is expected to be decreasing. In this work, we first frame this bidding problem with repeated user interactions by using causal models to value each display individually. Then, based on this approach, we introduce a simple rule to improve the value estimate. This change shows both interesting qualitative properties that follow our previous intuition as well as quantitative improvements on a public data set and online in a production environment.

show abstract

Section: Related Work and Contributionmentioning

confidence: 99%

Section: Optimal Policymentioning

confidence: 99%

Causal Models for Real Time Bidding with Repeated User Interactions

Bompaire

Gilotte

Heymann

2021

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &Amp; Data Mining

View full text Add to dashboard Cite

show abstract

“…Bid optimization is one of the most fundamental problems in online advertising [6,18,37,40,41]. Recently, bid shading [11,20,42] attracts much attention since most ad exchanges and SSPs are shifting from second to first price auctions.…”

Section: Related Workmentioning

confidence: 99%

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

Zhou

Pan

et al. 2021

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &Amp; Data Mining

View full text Add to dashboard Cite

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.

show abstract

“…If all the competitors stay unchanged, the optimal bidding formula for (•) can be calculated as [22]:…”

Section: Theoretical Analysismentioning

confidence: 99%

Multi-Agent Cooperative Bidding Games for Multi-Objective Optimization in e-Commercial Sponsored Search

Guan

Cao

et al. 2021

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &Amp; Data Mining

Self Cite

View full text Add to dashboard Cite

Bid optimization for online advertising from single advertiser's perspective has been thoroughly investigated in both academic research and industrial practice. However, existing work typically assume competitors do not change their bids, i.e., the wining price is fixed, leading to poor performance of the derived solution. Although a few studies use multi-agent reinforcement learning to set up a cooperative game, they still suffer the following drawbacks:(1) They fail to avoid collusion solutions where all the advertisers involved in an auction collude to bid an extremely low price on purpose. (2) Previous works cannot well handle the underlying complex bidding environment, leading to poor model convergence. This problem could be amplified when handling multiple objectives of advertisers which are practical demands but not considered by previous work. In this paper, we propose a novel multi-objective cooperative bid optimization formulation called Multi-Agent Cooperative bidding Games (MACG). MACG sets up a carefully designed multi-objective optimization framework where different objectives of advertisers are incorporated. A global objective to maximize the overall profit of all advertisements is added in order to encourage better cooperation and also to protect self-bidding advertisers. To avoid collusion, we also introduce an extra platform revenue constraint. We analyze the optimal functional form of the bidding formula theoretically and design a policy network accordingly to generate auction-level bids. Then we design an efficient multi-agent evolutionary strategy for model optimization. Evolutionary strategy does not need to model the underlying environment explicitly and is more suitable for bid optimization. Offline experiments and online A/B tests conducted on the Taobao platform indicate both single advertiser's objective and global profit have been significantly improved compared to state-of-art methods.

show abstract

Bid Optimization by Multivariable Control in Display Advertising

Cited by 61 publications

References 37 publications

Causal Models for Real Time Bidding with Repeated User Interactions

Causal Models for Real Time Bidding with Repeated User Interactions

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

Multi-Agent Cooperative Bidding Games for Multi-Objective Optimization in e-Commercial Sponsored Search

Contact Info

Product

Resources

About