Revenue optimization in the generalized second-price auction

Thompson, David R. M.; Leyton‐Brown, Kevin

doi:10.1145/2492002.2482612

Cited by 26 publications

(17 citation statements)

References 22 publications

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“…Mechanism design for online advertising has been extensively studied in the literature. Many discussions have been centered around search advertising auctions, such as the generalized first-price auction (Edelman et al, 2007), the generalized second-price auction (Edelman et al, 2007;Lahaie and McAfee, 2011;Lahaie and Pennock, 2007;Varian, 2007), the Vickrey-Clarke-Groves (VCG) auction (Parkes, 2007;Varian, 2009;Varian and Harris, 2014) and the optimal auction (Feldman et al, 2010;Ostrovsky and Schwarz, 2011;Thompson and Leyton-Brown, 2013) which extends Myerson's optimal auction for a single indivisible good (Myerson, 1981). Most of these studies on advertising auctions examine the properties of an auction model with respect to incentive compatibility, expected revenue, individual rationality, and computational complexity.…”

Section: Related Workmentioning

confidence: 99%

Combining guaranteed and spot markets in display advertising: Selling guaranteed page views with stochastic demand

Chen

Huang

et al. 2020

European Journal of Operational Research

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While page views are often sold instantly through real-time auctions when users visit websites, they can also be sold in advance via guaranteed contracts. In this paper, we present a dynamic programming model to study how an online publisher should optimally allocate and price page views between guaranteed and spot markets. The problem is challenging because the allocation and pricing of guaranteed contracts affect advertisers' purchase between the two markets, and the terminal value of the model is endogenously determined by the updated dual force of supply and demand in auctions. We take the advertisers' purchasing behaviour into consideration, i.e., risk aversion and stochastic demand arrivals, and present a scalable and efficient algorithm for the optimal solution. The model is also empirically validated with a commercial dataset. The experimental results show that selling page views via both guaranteed contracts and auctions can increase the publisher's expected total revenue, and the optimal pricing and allocation strategies are robust to different market and advertiser types.

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Section: Related Workmentioning

confidence: 99%

Combining guaranteed and spot markets in display advertising: Selling guaranteed page views with stochastic demand

Chen

Huang

et al. 2020

European Journal of Operational Research

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“…auction using the optimal reserve price. Thompson and Leyton-Brown (2013) also studied several different techniques for increasing the revenue, including via several different reserve prices, squashing, combinations of a reserve price and squashing, ect. Rather than using the reserve price simply as a minimum bid, Roberts et al (2016) presented the idea of incorporating the reserve price into ranking score and showed that this mechanism may increase the revenue compared to the squashing mechanism.…”

Section: Related Workmentioning

confidence: 99%

Optimally Integrating Ad Auction into E-Commerce Platforms

Li¹,

Qi²,

Wang³

2020

Preprint

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Advertising becomes one of the most popular ways of monetizing an online transaction platform. Usually, sponsored advertisements are posted on the most attractive positions to enhance the number of clicks. However, multiple e-commerce platforms are aware that this action may hurt the search experience of users, even though it can bring more incomes. To balance the advertising revenue and the user experience loss caused by advertisements, most e-commerce platforms choose fixing some areas for advertisements and adopting some simple restrictions on the number of ads, such as a fixed number K of ads on the top positions or one advertisement for every N organic searched results. Different from these common rules of treating the allocation of ads separately (from the arrangements of the organic searched items), in this work we build up an integrated system with mixed arrangements of advertisements and organic items. We focus on the design of truthful mechanisms to properly list the advertisements and organic items and optimally trade off the instant revenue and the user experience. Furthermore, for different settings and practical requirements, we extend our optimal truthful allocation mechanisms to cater for these realistic conditions. Finally, with the aid of the real data, our experimental results verify the improvement of our mechanism compared to the classic Myerson optimal advertising mechanism.

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“…Researchers have also examined and improved auction mechanisms, including better CTR estimates [6,16], usage of reserve prices in ad auction [30,34], and optimization in multiplicative bidding [3]. Using a sample from a week's worth of data across all keywords on Bing, other work [4] showed how to optimize linear combinations of the stakeholder utilities, showing that these can be tackled through a GSP auction with a per-click reserve price.…”

Section: Related Workmentioning

confidence: 99%

Measurement and analysis of OSN ad auctions

Liu

Kliman-Silver

Bell

et al. 2014

Proceedings of the Second ACM Conference on Online Social Networks

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Advertising is ubiquitous on the Web; numerous ad networks serve billions of ads daily via keyword or search term auctions. Recently, online social networks (OSNs) such as Facebook have created site-specific ad services that differ from traditional ad networks by letting advertisers bid on users rather than keywords. With Facebook's annual ad revenue exceeding $4 billion, OSN-based ad services are emerging to be a significant fraction of the online ad market. In contrast to other online ad markets (e.g., Google's ad market), there has been little academic study of OSN ad services, and OSNs have released very little data about their advertising markets; as a result, researchers currently lack the tools to measure and understand these markets.In this paper, our goal is to bring visibility to OSN ad markets, focusing on Facebook. We demonstrate that the (undocumented) feature that suggests bids to advertisers is most likely calculated via sampling recent winning bids. We then show how this feature can be used to explore the relative value of different user demographics and the overall stability of the advertising market. Through the exploration of suggested bid data for different demographics, we find dramatic differences in prices paid across different user interests and locations. Finally, we show that the ad market shows long-term variability, suggesting that OSN ad services have yet to mature.

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Revenue optimization in the generalized second-price auction

Cited by 26 publications

References 22 publications

Combining guaranteed and spot markets in display advertising: Selling guaranteed page views with stochastic demand

Combining guaranteed and spot markets in display advertising: Selling guaranteed page views with stochastic demand

Optimally Integrating Ad Auction into E-Commerce Platforms

Measurement and analysis of OSN ad auctions

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