Optimising trade-offs among stakeholders in ad auctions

Bachrach, Yoram; Ceppi, Sofia; Kash, Ian A.; Key, Peter; Kurokawa, David

doi:10.1145/2600057.2602879

Cited by 44 publications

(35 citation statements)

References 22 publications

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“…Recall that v i is not a probability, but it denotes i's value for making a sale. 12 This is a simple statistical model to illustrate how results are affected when one varies the correlation between attention probabilities and values, captured by the ρ parameter (if ρ = 0, all variables are independent; if ρ = 1, p i and v i are perfectly correlated). We also let α ∈ [0, 1] denote the fraction of bidders who are subject to the framing effects discussed above.…”

Section: Ppa Evaluationmentioning

confidence: 99%

A Price-per-attention Auction Scheme Using Mouse Cursor Information

Arapakis

Penta

Joho

et al. 2020

ACM Trans. Inf. Syst.

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Payments in online ad auctions are typically derived from click-through rates, so that advertisers do not pay for ineffective ads. But advertisers often care about more than just clicks. That is, for example, if they aim to raise brand awareness or visibility. There is thus an opportunity to devise a more effective ad pricing paradigm, in which ads are paid only if they are actually noticed. This article contributes a novel auction format based on a pay-per-attention (PPA) scheme. We show that the PPA auction inherits the desirable properties (strategy-proofness and efficiency) as its pay-per-impression and pay-per-click counterparts, and that it also compares favourably in terms of revenues. To make the PPA format feasible, we also contribute a scalable diagnostic technology to predict user attention to ads in sponsored search using raw mouse cursor coordinates only, regardless of the page content and structure. We use the user attention predictions in numerical simulations to evaluate the PPA auction scheme. Our results show that, in relevant economic settings, the PPA revenues would be strictly higher than the existing auction payment schemes.

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Section: Ppa Evaluationmentioning

confidence: 99%

A Price-per-attention Auction Scheme Using Mouse Cursor Information

Arapakis

Penta

Joho

et al. 2020

ACM Trans. Inf. Syst.

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“…The ad creation optimization was less studied by academia. But there was some research work about how to design the combinatorial auction mechanism for rich ads [3,11,22]. Two sampled rich search ads in Baidu are presented in Figure 1, there are text creations, images, download links, phone call button, sitelink extensions in the ads.…”

Section: Related Workmentioning

confidence: 99%

AiAds

Yang

Sun

Zhu

et al. 2019

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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Sponsored search has more than 20 years of history, and it has been proven to be a successful business model for online advertising. Based on the pay-per-click pricing model and the keyword targeting technology, the sponsored system runs online auctions to determine the allocations and prices of search advertisements. In the traditional setting, advertisers should manually create lots of ad creatives and bid on some relevant keywords to target their audience. Due to the huge amount of search traffic and a wide variety of ad creations, the limits of manual optimizations from advertisers become the main bottleneck for improving the efficiency of this market. Moreover, as many emerging advertising forms and supplies are growing, it's crucial for sponsored search platform to pay more attention to the ROI metrics of ads for getting the marketing budgets of advertisers.In this paper, we present the AiAds system developed at Baidu, which use machine learning techniques to build an automated and intelligent advertising system. By designing and implementing the automated bidding strategy, the intelligent targeting and the intelligent creation models, the AiAds system can transform the manual optimizations into multiple automated tasks and optimize these tasks in advanced methods. AiAds is a brand-new architecture of sponsored search system which changes the bidding language and allocation mechanism, breaks the limit of keyword targeting with end-to-end ad retrieval framework and provides global optimization of ad creation. This system can increase the advertiser's campaign performance, the user experience and the revenue of the advertising platform simultaneously and significantly. We present the overall architecture and modeling techniques for each module of the system and share our lessons learned in solving several key challenges. Finally, online A/B test and long-term grouping experiment demonstrate the advancement and effectiveness of this system. CCS CONCEPTS• Information systems → Sponsored search advertising; Computational advertising.

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“…More broadly, it can be thought of as approximating the virtual valuation function with a linear function, which seems to be reasonable for a number of relevant families of distributions [Bachrach et al 2014]. …”

Section: Proposed Ranking Algorithmmentioning

confidence: 99%

“…In subsequent work [Bachrach et al 2014], we have explored optimal tradeoffs from a theoretical perspective. We show that, under some simplifying assumptions, our proposed mechanism makes them optimally.…”

Section: Related Workmentioning

confidence: 99%

Ranking and Tradeoffs in Sponsored Search Auctions

RobertsBen¹,

GunawardenaDinan²,

kashian³

et al. 2016

ACM Trans. Econ. Comput.

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In a sponsored search auction, decisions about how to rank ads impose tradeoffs between objectives, such as revenue and welfare. In this article, we examine how these tradeoffs should be made. We begin by arguing that the most natural solution concept to evaluate these tradeoffs is the lowest symmetric Nash equilibrium (SNE). As part of this argument, we generalise the well-known connection between the lowest SNE and the VCG outcome. We then propose a new ranking algorithm, loosely based on the revenue-optimal auction, that uses a reserve price to order the ads (not just to filter them) and give conditions under which it raises more revenue than simply applying that reserve price. Finally, we conduct extensive simulations examining the tradeoffs enabled by different ranking algorithms and show that our proposed algorithm enables superior operating points by a variety of metrics.

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Optimising trade-offs among stakeholders in ad auctions

Cited by 44 publications

References 22 publications

A Price-per-attention Auction Scheme Using Mouse Cursor Information

A Price-per-attention Auction Scheme Using Mouse Cursor Information

AiAds

Ranking and Tradeoffs in Sponsored Search Auctions

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