Design of an optimal auction for sponsored search auction

Garg, Dinesh; Narahari, Y.; Reddy, Siddharth

doi:10.1109/cec-eee.2007.40

Cited by 5 publications

(5 citation statements)

References 7 publications

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“…Since the first sponsored search auction was initiated in 1997, a series of auction mechanisms have been proposed, such as Overture's Generalized First Price (GFP) auction [ 39 ] and Google's Generalized Second Price (GSP), which has caused sponsored search auctions to become an important source of income for online platforms. Due to the inauthenticity of GSP, Garg et al [ 40 ], based on the previous work, modeled the sponsored search auction on the Internet as a mechanism design problem, and designed a novel optimal auction mechanism (OPT), which can maximize the expected benefits of search engines while achieving Bayesian incentive compatibility and individual rationality. Lahaie et al [ 41 ] have proposed another idea of compressing GSP parameters to increase income.…”

Section: Literature Reviewmentioning

confidence: 99%

Design of Sponsored Search Auction Mechanism for Federated Learning Advertising Platform

Hong

Cui

Yang

2022

Computational Intelligence and Neuroscience

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Federated learning has demonstrated strong capabilities in terms of addressing concerns related to data islands and privacy protection. However, in real application scenarios, participants in federated learning have difficulty matching. For example, two companies distributed in different regions do not know that the other party also needs federated learning in the case of information asymmetry. Therefore, it is difficult to build alliances. To enable suppliers and consumers to find one or more federated learning objects that are relatively satisfactory in a short time, this paper considers the idea of establishing a federated learning advertising platform, where data transactions need to consider privacy protection. A sponsored search auction mechanism design method is introduced to solve the problem of ranking the presentation order of participant advertisements. Due to the potential malicious bidding problem, which occurs when using the classic sponsored search auction mechanism under the federated learning scenario, this paper proposes a novel federated sponsored search auction mechanism based on the Myerson theorem, improving upon the ranking index used in the classic sponsored search auction mechanism. A large number of experimental results on a simulation data set show that our proposed method can fairly select and rank the data providers participating in the bidding. Compared with other benchmark mechanisms, the malicious bidding rate is significantly decreased. In the long run, the proposed mechanism can encourage more data providers to participate in the federated learning platform, thus continuously promoting the establishment of a federated learning ecosystem.

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Section: Literature Reviewmentioning

confidence: 99%

Design of Sponsored Search Auction Mechanism for Federated Learning Advertising Platform

Hong

Cui

Yang

2022

Computational Intelligence and Neuroscience

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“…In the literature of game theory, there have been some attempts along this direction. For example, in [Lahaie and Pennock, 2007] the worst-case revenue in the symmetric Nash equilibria is maximized, and in [Garg and Narahari, 2009;Garg et al, 2007] the Bayesian optimal auction mechanism design is investigated with the value distribution of the bidders as public knowledge. In these works, some ideal assumptions have been employed.…”

Section: Game Theory Vs Machine Learningmentioning

confidence: 99%

“…Given the critical role of the auction mechanism in sponsored search, many people have studied the optimization of its performance in terms of search engine revenue [Lahaie and Pennock, 2007;Garg et al, 2007;Zhu et al, 2009b;Radlinski et al, 2008;Zhu et al, 2009a]. These studies can be categorized into two groups.…”

Section: Introductionmentioning

confidence: 99%

“…These studies can be categorized into two groups. The first group [Lahaie and Pennock, 2007;Garg et al, 2007] addresses the problem from a game-theoretic perspective. Some works in this group optimize the worse-case revenue in symmetric Nash equilibria; some others consider Bayesian optimal mechanism design with the knowledge of bidders' value distribution.…”

Section: Introductionmentioning

confidence: 99%

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A Game-theoretic Machine Learning Approach for Revenue Maximization in Sponsored Search

He¹,

Chen²,

Wang³

et al. 2014

Preprint

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Sponsored search is an important monetization channel for search engines, in which an auction mechanism is used to select the ads shown to users and determine the prices charged from advertisers. There have been several pieces of work in the literature that investigate how to design an auction mechanism in order to optimize the revenue of the search engine. However, due to some unrealistic assumptions used, the practical values of these studies are not very clear. In this paper, we propose a novel game-theoretic machine learning approach, which naturally combines machine learning and game theory, and learns the auction mechanism using a bilevel optimization framework. In particular, we first learn a Markov model from historical data to describe how advertisers change their bids in response to an auction mechanism, and then for any given auction mechanism, we use the learnt model to predict its corresponding future bid sequences. Next we learn the auction mechanism through empirical revenue maximization on the predicted bid sequences. We show that the empirical revenue will converge when the prediction period approaches infinity, and a Genetic Programming algorithm can effectively optimize this empirical revenue. Our experiments indicate that the proposed approach is able to produce a much more effective auction mechanism than several baselines.

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“…In this paper, we do not want to discuss the details of specific mechanisms. Readers please refer to [4][5][6][7][8][9][10] for the discussions of equilibrium in keywords auction mechanisms; and [11][12][13][14][15][16][17] for the design and comparison of new auction mechanisms.…”

Section: Introductionmentioning

confidence: 99%

Daily Budget Optimization Basedon the Newsboy Model for Advertisers in the SSA Markets

Li¹,

Zeng²,

Yang³

2010

Proceedings of the 2010 International Conference on E-Business Intelligence

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This paper provides a method for planing the optimal daily budget in the online sponsored search advertising market, which is now the most important marketing place. The proposed method is mainly based the well-known newsboy model, which has been widely applied in single period inventory management settings. We model this budget optimization question as a decision problem under a situation where both the future price and demand are uncertain. We also indicate that advertisers will invest more money in the SSA market if the potential profit of clicks in this market is relatively larger than the financial cost of money.

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Design of an optimal auction for sponsored search auction

Abstract: In this paper, we first describe a framework to model the sponsored search auction on the web as a mechanism design problem. Using this framework, we design a novel auction which we call the OPT (optimal)

Cited by 5 publications

References 7 publications

Design of Sponsored Search Auction Mechanism for Federated Learning Advertising Platform

Design of Sponsored Search Auction Mechanism for Federated Learning Advertising Platform

A Game-theoretic Machine Learning Approach for Revenue Maximization in Sponsored Search

Daily Budget Optimization Basedon the Newsboy Model for Advertisers in the SSA Markets

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