Journal articles: 'Sponsored Search Auctions'

1

Qin, Tao, Wei Chen, and Tie-Yan Liu. "Sponsored Search Auctions." ACM Transactions on Intelligent Systems and Technology 5, no. 4 (January 23, 2015): 1–34. http://dx.doi.org/10.1145/2668108.

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Edelman, Benjamin, and Michael Schwarz. "Optimal Auction Design and Equilibrium Selection in Sponsored Search Auctions." American Economic Review 100, no. 2 (May 1, 2010): 597–602. http://dx.doi.org/10.1257/aer.100.2.597.

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Ceppi, Sofia, Nicola Gatti, and Enrico Gerding. "Mechanism Design for Federated Sponsored Search Auctions." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (August 4, 2011): 608–13. http://dx.doi.org/10.1609/aaai.v25i1.7868.

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Recently there is an increase in smaller, domain-specific search engines that scour the deep web finding information that general-purpose engines are unable to discover. These search engines play a crucial role in the new generation of search paradigms where federated search engines (FSEs) integrate search results from heterogeneous sources. In this paper we pose, for the first time, the problem to design a revenue mechanism that ensures profits both to individual search engines and FSEs as a mechanism design problem. To this end, we extend the sponsored search auction models and we discuss possibility and impossibility results on the implementation of an incentive compatible mechanism. Specifically, we develop an execution-contingent VCG (where payments depend on the observed click behavior) that satisfies both individual rationality and weak budget balance in expectation.

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Yao, Song, and Carl F. Mela. "Sponsored Search Auctions: Research Opportunities in Marketing." Foundations and Trends® in Marketing 3, no. 2 (2007): 75–126. http://dx.doi.org/10.1561/1700000013.

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Roberts, Ben, Dinan Gunawardena, Ian A. Kash, and Peter Key. "Ranking and Tradeoffs in Sponsored Search Auctions." ACM Transactions on Economics and Computation 4, no. 3 (June 15, 2016): 1–21. http://dx.doi.org/10.1145/2910576.

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Jerath, Kinshuk, Liye Ma, Young-Hoon Park, and Kannan Srinivasan. "A “Position Paradox” in Sponsored Search Auctions." Marketing Science 30, no. 4 (July 2011): 612–27. http://dx.doi.org/10.1287/mksc.1110.0645.

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Che, Yeon-Koo, Syngjoo Choi, and Jinwoo Kim. "An experimental study of sponsored-search auctions." Games and Economic Behavior 102 (March 2017): 20–43. http://dx.doi.org/10.1016/j.geb.2016.10.008.

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Edelman, Benjamin, and Michael Ostrovsky. "Strategic bidder behavior in sponsored search auctions." Decision Support Systems 43, no. 1 (February 2007): 192–98. http://dx.doi.org/10.1016/j.dss.2006.08.008.

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Bu, Tian-Ming, Xiaotie Deng, and Qi Qi. "Multi-bidding strategy in sponsored search auctions." Journal of Combinatorial Optimization 23, no. 3 (February 9, 2010): 356–72. http://dx.doi.org/10.1007/s10878-010-9297-7.

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Tsung, Chen-Kun, Hann-Jang Ho, and Sing-Ling Lee. "Strategic Bidding Behaviors in Nondecreasing Sponsored Search Auctions." Mathematical Problems in Engineering 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/206386.

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To realize the specific results in the sponsored search auctions, most advertisers submit particular bid prices. The bidding behaviors with specific purposes are called as the strategic bidding. However, some strategic bidding behaviors will result in negative effects, such as the elimination of the equilibrium and the payment increase for some advertisers. The bidding behaviors with negative results are termed as the vindictive bidding. We survey four strategic bidding behaviors which include a rational bidding and three vindictive bidding strategies. In this paper, we study the relationship between the effects resulted by the vindictive bidding and the valuations of the vindictive advertisers. In our experiments, the search engine provider (SEP) is benefited by all vindictive bidding behaviors, and the increment of the SEP's revenue is proportional to the degree of the vindictiveness. Bidding vindictively without sacrificing the own utility improves the advertiser's utility with high probability. Moreover, we observe that the SEP's revenue is improved by the following situations. First, the vindictive advertiser with low valuation in the keywords with high market value results in more SEP's revenue than that in the keywords with low market value. The second case is to raise the bidding competition between advertisers.

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Colini-Baldeschi, Riccardo, Stefano Leonardi, Monika Henzinger, and Martin Starnberger. "On Multiple Keyword Sponsored Search Auctions with Budgets." ACM Transactions on Economics and Computation 4, no. 1 (January 5, 2016): 1–34. http://dx.doi.org/10.1145/2818357.

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Tsung, Chen-Kun, Hann-Jang Ho, and Sing-Ling Lee. "Correcting vindictive bidding behaviors in sponsored search auctions." Journal of Supercomputing 69, no. 3 (August 28, 2013): 1166–82. http://dx.doi.org/10.1007/s11227-013-1002-z.

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Langford, John, Lihong Li, Yevgeniy Vorobeychik, and Jennifer Wortman. "Maintaining Equilibria During Exploration in Sponsored Search Auctions." Algorithmica 58, no. 4 (May 13, 2009): 990–1021. http://dx.doi.org/10.1007/s00453-009-9318-z.

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Hortaçsu, Ali, and David McAdams. "Empirical Work on Auctions of Multiple Objects." Journal of Economic Literature 56, no. 1 (March 1, 2018): 157–84. http://dx.doi.org/10.1257/jel.20160961.

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Abundant data has led to new opportunities for empirical auctions research in recent years, with much of the newest work on auctions of multiple objects, including: (1) auctions of ranked objects (such as sponsored search ads), (2) auctions of identical objects (such as Treasury bonds), and (3) auctions of dissimilar objects (such as FCC spectrum licenses). This paper surveys recent developments in the empirical analysis of such auctions. (JEL D44, H82)

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Börgers, Tilman, Ingemar Cox, Martin Pesendorfer, and Vaclav Petricek. "Equilibrium Bids in Sponsored Search Auctions: Theory and Evidence." American Economic Journal: Microeconomics 5, no. 4 (November 1, 2013): 163–87. http://dx.doi.org/10.1257/mic.5.4.163.

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This paper presents a game theoretic analysis of the generalized second-price auction that the company Overture operated in 2004 to sell sponsored search listings on search engines. We construct a model that embodies few prior assumptions about parameters, and we present results that indicate that this model has under quite general assumptions a multiplicity of Nash equilibria. We then analyze bid data assuming that advertisers choose Nash equilibrium bids. We offer preliminary conclusions about advertisers' true willingness to bid for sponsored search listings. We find that advertisers' true willingness to bid is multi-dimensional and decreasing in listing position. (JEL D44, L86, M31, M37)

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Abhishek, Vibhanshu, and Kartik Hosanagar. "Optimal Bidding in Multi-Item Multislot Sponsored Search Auctions." Operations Research 61, no. 4 (August 2013): 855–73. http://dx.doi.org/10.1287/opre.2013.1187.

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Gatti, N., M. Rocco, P. Serafino, and C. Ventre. "Towards better models of externalities in sponsored search auctions." Theoretical Computer Science 745 (October 2018): 150–62. http://dx.doi.org/10.1016/j.tcs.2018.06.011.

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Xia, Zheng-Dong, Tian-Ming Bu, and Wen-Hui Gong. "Optimal bundles for sponsored search auctions via bracketing scheme." Frontiers of Computer Science 13, no. 2 (April 2019): 333–42. http://dx.doi.org/10.1007/s11704-017-6102-0.

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Vorobeychik, Yevgeniy, and Daniel M. Reeves. "Equilibrium analysis of dynamic bidding in sponsored search auctions." International Journal of Electronic Business 6, no. 2 (2008): 172. http://dx.doi.org/10.1504/ijeb.2008.018071.

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Yang, Yanwu, Rui Qin, Bernard J. Jansen, Jie Zhang, and Daniel Zeng. "Budget Planning for Coupled Campaigns in Sponsored Search Auctions." International Journal of Electronic Commerce 18, no. 3 (April 2014): 39–66. http://dx.doi.org/10.2753/jec1086-4415180302.

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Milgrom, Paul. "Simplified mechanisms with an application to sponsored-search auctions." Games and Economic Behavior 70, no. 1 (September 2010): 62–70. http://dx.doi.org/10.1016/j.geb.2008.12.003.

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Farina, Gabriele, and Nicola Gatti. "Adopting the Cascade Model in Ad Auctions: Efficiency Bounds and Truthful Algorithmic Mechanisms." Journal of Artificial Intelligence Research 59 (July 17, 2017): 265–310. http://dx.doi.org/10.1613/jair.5438.

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Sponsored Search Auctions (SSAs) are one of the most successful applications of microeconomic mechanisms, with a revenue of about $72 billion in the US alone in 2016. However, the problem of designing the best economic mechanism for sponsored search auctions is far from being solved, and, given the amount at stake, it is no surprise that it has received growing attention over the past few years. The most common auction mechanism for SSAs is the Generalized Second Price (GSP). However, the GSP is known not to be truthful: the agents participating in the auction might have an incentive to report false values, generating economic inefficiency and suboptimal revenues in turn. Superior, efficient truthful mechanisms, such as the Vickrey-Clarke-Groves (VCG) auction, are well known in the literature. However, while the VCG auction is currently adopted for the strictly related scenario of contextual advertising, e.g., by Google and Facebook, companies are reluctant to extend it to SSAs, fearing prohibitive switching costs. Other than truthfulness, two issues are of paramount importance in designing effective SSAs. First, the choice of the user model; not only does an accurate user model better target ads to users, it also is a critical factor in reducing the inefficiency of the mechanism. Often an antagonist to this, the second issue is the running time of the mechanism, given the performance pressure these mechanisms undertake in real-world applications. In our work, we argue in favor of adopting the VCG mechanism based on the cascade model with ad/position externalities (APDC-VCG). Our study includes both the derivation of inefficiency bounds and the design and the experimental evaluation of exact and approximate algorithms.

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Xu, Yan Yan, Pin Jia Zou, and Zheng Fang. "Boost Keywords Conversion of Search Engine." Advanced Materials Research 834-836 (October 2013): 1803–6. http://dx.doi.org/10.4028/www.scientific.net/amr.834-836.1803.

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more and more people are used to purchase online. And companies also want to pay for the keyword on big search engine, such as Baidu, Google. So the strength of the competition plays a significant role in the efficiency of an online advertising campaign. Models for keyword auctions usually assume that, the value of a click is fixed and independent of the other sponsored ads in the auctions result, for the advertiser. In china, the big Online shopping mallTmall. Consumers can search products and service through Tmall Search Engine, a search engine allows consumers search in the Tmall. And we employ a zero-inflated Poisson regression to model the conversion of keywords.

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Zou, Pin Jia, Yang Cheng, Yan Yan Xu, and Zheng Fang. "Boost Keywords Conversion of Search Engine." Advanced Materials Research 915-916 (April 2014): 1332–35. http://dx.doi.org/10.4028/www.scientific.net/amr.915-916.1332.

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more and more people are used to purchase online. And companies also want to pay for the keyword on big search engine, such as Baidu, Google. So the strength of the competition plays a significant role in the efficiency of an online advertising campaign. Models for keyword auctions usually assume that, the value of a click is fixed and independent of the other sponsored ads in the auctions result, for the advertiser. In china, the big Online shopping mallTmall. Consumers can search products and service through Tmall Search Engine, a search engine allows consumers search in the Tmall. And we employ a zero-inflated Poisson regression to model the conversion of keywords.

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25

Asdemir, Kursad. "A dynamic model of bidding patterns in sponsored search auctions." Information Technology and Management 12, no. 1 (December 9, 2010): 1–16. http://dx.doi.org/10.1007/s10799-010-0077-y.

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Yang, Wei, Jun Qiao, Youyi Feng, and Baichun Xiao. "Optimal reserve price in static and dynamic sponsored search auctions." Journal of Systems Science and Systems Engineering 22, no. 4 (December 2013): 440–56. http://dx.doi.org/10.1007/s11518-013-5229-2.

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27

Gatti, Nicola, Alessandro Lazaric, Marco Rocco, and Francesco Trovò. "Truthful learning mechanisms for multi-slot sponsored search auctions with externalities." Artificial Intelligence 227 (October 2015): 93–139. http://dx.doi.org/10.1016/j.artint.2015.05.012.

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Shen, Weiran, Binghui Peng, Hanpeng Liu, Michael Zhang, Ruohan Qian, Yan Hong, Zhi Guo, Zongyao Ding, Pengjun Lu, and Pingzhong Tang. "Reinforcement Mechanism Design: With Applications to Dynamic Pricing in Sponsored Search Auctions." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 02 (April 3, 2020): 2236–43. http://dx.doi.org/10.1609/aaai.v34i02.5600.

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In many social systems in which individuals and organizations interact with each other, there can be no easy laws to govern the rules of the environment, and agents' payoffs are often influenced by other agents' actions. We examine such a social system in the setting of sponsored search auctions and tackle the search engine's dynamic pricing problem by combining the tools from both mechanism design and the AI domain. In this setting, the environment not only changes over time, but also behaves strategically. Over repeated interactions with bidders, the search engine can dynamically change the reserve prices and determine the optimal strategy that maximizes the profit. We first train a buyer behavior model, with a real bidding data set from a major search engine, that predicts bids given information disclosed by the search engine and the bidders' performance data from previous rounds. We then formulate the dynamic pricing problem as an MDP and apply a reinforcement-based algorithm that optimizes reserve prices over time. Experiments demonstrate that our model outperforms static optimization strategies including the ones that are currently in use as well as several other dynamic ones.

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Yong Yuan, Daniel Zeng, Huimin Zhao, and Linjing Li. "Analyzing Positioning Strategies in Sponsored Search Auctions Under CTR-Based Quality Scoring." IEEE Transactions on Systems, Man, and Cybernetics: Systems 45, no. 4 (April 2015): 688–701. http://dx.doi.org/10.1109/tsmc.2014.2366434.

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Zhang, Jie, Yanwu Yang, Xin Li, Rui Qin, and Daniel Zeng. "Dynamic dual adjustment of daily budgets and bids in sponsored search auctions." Decision Support Systems 57 (January 2014): 105–14. http://dx.doi.org/10.1016/j.dss.2013.08.004.

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31

Razzazi, Mohammadreza, and Ali Esmaeeli. "Balanced allocation mechanism: An optimal mechanism for multiple keywords sponsored search auctions." Information Sciences 262 (March 2014): 190–214. http://dx.doi.org/10.1016/j.ins.2013.08.033.

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Chaitanya, Nittala, and Yadati Narahari. "Optimal equilibrium bidding strategies for budget constrained bidders in sponsored search auctions." Operational Research 12, no. 3 (December 23, 2010): 317–43. http://dx.doi.org/10.1007/s12351-010-0097-8.

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Maillé, Patrick, Evangelos Markakis, Maurizio Naldi, George D. Stamoulis, and Bruno Tuffin. "Sponsored search auctions: an overview of research with emphasis on game theoretic aspects." Electronic Commerce Research 12, no. 3 (July 18, 2012): 265–300. http://dx.doi.org/10.1007/s10660-012-9094-8.

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Chu, Leon Yang, Hamid Nazerzadeh, and Heng Zhang. "Position Ranking and Auctions for Online Marketplaces." Management Science 66, no. 8 (August 2020): 3617–34. http://dx.doi.org/10.1287/mnsc.2019.3372.

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Online e-commerce platforms, such as Amazon and Taobao, connect thousands of sellers and consumers every day. In this work, we study how such platforms should rank products displayed to consumers and utilize the top and most salient slots. We present a model that considers consumers’ search costs and the externalities sellers impose on each other. This model allows us to study a multiobjective optimization, whose objective includes consumer and seller surplus as well as the sales revenue, and derive the optimal ranking decision. In addition, we propose a surplus-ordered ranking mechanism for selling some of the top slots. This mechanism is motivated in part by Amazon’s sponsored search program. We show that the Vickrey–Clarke–Groves mechanism would not be applicable to our setting and propose a new mechanism. This mechanism is near optimal, performing significantly better than those that do not incentivize sellers to reveal their private information regarding each consumer purchase, such as their profit. Moreover, we generalize our model to settings in which platforms can provide partial information about the products and facilitate the consumer search and show the robustness of our findings. This paper was accepted by David Simchi-Levi, operations management.

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35

Chen, Jianqing, De Liu, and Andrew B. Whinston. "Auctioning Keywords in Online Search." Journal of Marketing 73, no. 4 (July 2009): 125–41. http://dx.doi.org/10.1509/jmkg.73.4.125.

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Keyword advertising, or “sponsored links” that appear alongside online search results or other online content, has grown into a multibillion-dollar market. Providers of keyword advertising, such as Google and Yahoo, profit by auctioning keywords to advertisers. An issue of increasing importance for advertising providers is the “share structure” problem—that is, of the total available resources for each keyword (in terms of exposure), how large a share should be set aside for the highest bidder, for the second-highest bidder, and so on. The authors study this problem under a general specification and characterize the optimal share structures that maximize advertising providers’ revenues. They also derive results on how the optimal share structure should change with advertisers’ price elasticity of demand for exposure, their valuation distribution, total resources, and minimum bids. The authors draw implications for keyword auctions and other applications.

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36

Li, Xiaohui, Hongbin Dong, Yang Zhou, and Jun He. "A Matching Approach to Factor Scores Based on Online Sponsored Search Auction." International Journal of Software Innovation 6, no. 1 (January 2018): 11–30. http://dx.doi.org/10.4018/ijsi.2018010102.

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How does a search engine company decide which advertisements to display for each query to maximize its revenue? This turns out to be a generalization of the online bipartite matching problem. In this paper, search engines decide the strategy to allocate resources with an advertiser credibility factor under budget constraints. Based on the optimal algorithm, which is the notion of a trade-off revealing LP, this paper remains the competitive ratio as 1-1/e with an advertiser credibility factor. During the ranking in the keywords auctions, the authors calculate factor scores. The new arrival keywords should match to the advertisement which has the maximum factor scores. The amount of spent money, budget constraints, CTR (Click Through Rate) and a credibility factor are added to the trade-off function using in the authors' algorithms. In the long term, users tend to use the search engine with high credibility, which will bring greater revenues. The search engine will attract advertisers to bid on keywords and improve their credibility actively.

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37

Garg, D., and Y. Narahari. "An Optimal Mechanism for Sponsored Search Auctions on the Web and Comparison With Other Mechanisms." IEEE Transactions on Automation Science and Engineering 6, no. 4 (October 2009): 641–57. http://dx.doi.org/10.1109/tase.2009.2021975.

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38

Decarolis, Francesco, and Gabriele Rovigatti. "From Mad Men to Maths Men: Concentration and Buyer Power in Online Advertising." American Economic Review 111, no. 10 (October 1, 2021): 3299–327. http://dx.doi.org/10.1257/aer.20190811.

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This paper analyzes the impact of intermediary concentration on the allocation of revenue in online platforms. We study sponsored search documenting how advertisers increasingly bid through a handful of specialized intermediaries. This enhances automated bidding and data pooling, but lessens competition whenever the intermediary represents competing advertisers. Using data on nearly 40 million Google keyword auctions, we first apply machine learning algorithms to cluster keywords into thematic groups serving as relevant markets. Using an instrumental variable strategy, we estimate a decline in the platform’s revenue of approximately 11 percent due to the average rise in concentration associated with intermediary merger and acquisition activity. (JEL C45, D44, G34, L13, L81, M37)

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IMADA, Ryusuke, and Katsuhide FUJITA. "Sponsored Search Auction Considering Combinational Bids with Externalities." IEICE Transactions on Information and Systems E100.D, no. 12 (2017): 2906–14. http://dx.doi.org/10.1587/transinf.2016agp0009.

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Jiang, Hong, Tianxu Cui, and Kaiwen Yang. "Design of Sponsored Search Auction Mechanism for Federated Learning Advertising Platform." Computational Intelligence and Neuroscience 2022 (April 7, 2022): 1–15. http://dx.doi.org/10.1155/2022/5787491.

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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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Bu, Tian-Ming, Li Liang, and Qi Qi. "On Robustness of Forward-looking in Sponsored Search Auction." Algorithmica 58, no. 4 (February 25, 2009): 970–89. http://dx.doi.org/10.1007/s00453-009-9280-9.

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Tsung, Chen-Kun, Hann-Jang Ho, and Sing-Ling Lee. "Bid Increment Adjusting Strategies in English Auction-based Sponsored Search." Applied Mathematics & Information Sciences 8, no. 3 (May 1, 2014): 1377–87. http://dx.doi.org/10.12785/amis/080355.

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Vragov, Roumen, Richard Di Shang, Vernon Smith, and David Porter. "Let’s play the search game: Strategic and behavioral properties of sponsored search auction mechanisms." Electronic Commerce Research and Applications 33 (January 2019): 100809. http://dx.doi.org/10.1016/j.elerap.2018.10.001.

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Nie, Cheng, Zhiqiang (Eric) Zheng, and Sumit Sarkar. "A Strategic Group Analysis of Competitor Behavior in Search Advertising." Journal of the Association for Information Systems 22, no. 6 (2021): 1659–85. http://dx.doi.org/10.17705/1jais.00710.

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Firms compete intensely in sponsored search. Their bidding strategies hinge on understanding who competes with whom, how they compete, and how consumers react to competing advertisements. In this context, we investigate how firm competition impacts consumers’ click-through behaviors in search advertising from a strategic group perspective. Using search results from Google and consumers’ clickstream data, we found strong negative externality for competitors within the same strategic group relative to competitors across strategic groups: firms reap fewer click-throughs when an advertisement of another firm from the same strategic group is also displayed in search results, relative to when other displayed advertisers are not from the same group. This indicates that when competitors from the same strategic group are likely to appear in the results of a sponsored search auction, the focal firm would be better off avoiding head-to-head competition in the auction. However, we did not find empirical evidence of such firm behaviors, suggesting myopia or the inability of firms to avoid such competition. We also show that when multiple firms from the same strategic group appear in search results, the closer the focal firm is located to such competing firms, the more click-throughs the firm accrues. This suggests that firms should stay close to their within-group competitors when they compete in the same search auction. Further, our empirical results indicate that firms are indeed doing so. Using another set of data from Google AdWords reports, we show that our findings are also robust to multi-keyword bidding scenarios. These findings represent the first attempt to understand the impact of strategic groups in search advertising and provide interesting implications for advertisers and search engines.

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Intelligence and Neuroscience, Computational. "Retracted: Design of Sponsored Search Auction Mechanism for Federated Learning Advertising Platform." Computational Intelligence and Neuroscience 2023 (August 2, 2023): 1. http://dx.doi.org/10.1155/2023/9892141.

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46

Vragov, Roumen, and Richard Di Shang. "A 2020 perspective on “Let’s play the search game: Strategic and behavioral properties of online sponsored search auction mechanisms”." Electronic Commerce Research and Applications 41 (May 2020): 100959. http://dx.doi.org/10.1016/j.elerap.2020.100959.

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47

Edelman, Benjamin G., and Michael Schwarz. "Optimal Auction Design and Equilibrium Selection in Sponsored Search Auctions." SSRN Electronic Journal, 2010. http://dx.doi.org/10.2139/ssrn.1535319.

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48

Jerath, Kinshuk, Liye Ma, Young-Hoon Park, and Kannan Srinivasan. "A 'Position Paradox' in Sponsored Search Auctions." SSRN Electronic Journal, 2010. http://dx.doi.org/10.2139/ssrn.1464545.

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Che, Yeon-Koo, Syngjoo Choi, and Jinwoo Kim. "An Experimental Study of Sponsored-Search Auctions." SSRN Electronic Journal, 2011. http://dx.doi.org/10.2139/ssrn.1818522.

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Arnold, Michael A., Eric Darmon, and Thierry PPnard. "To Sponsor or Not to Sponsor: Sponsored Search Auctions with Organic Links." SSRN Electronic Journal, 2013. http://dx.doi.org/10.2139/ssrn.2445921.

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Journal articles on the topic 'Sponsored Search Auctions'

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