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Статті в журналах з теми "Sponsored Search Auctions"
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.
Повний текст джерелаEdelman, 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.
Повний текст джерелаCeppi, 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.
Повний текст джерелаYao, 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.
Повний текст джерелаRoberts, 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.
Повний текст джерелаJerath, 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.
Повний текст джерелаChe, 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.
Повний текст джерелаEdelman, 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.
Повний текст джерелаBu, 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.
Повний текст джерелаTsung, 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.
Повний текст джерелаДисертації з теми "Sponsored Search Auctions"
Lorenzon, Emmanuel. "Sponsored Search and Sequential Auctions : Three Essays in Auction Theory." Thesis, Bordeaux, 2016. http://www.theses.fr/2016BORD0357/document.
Повний текст джерелаThis thesis is a collection of three essays in theoretical auction analysis. Chapter 1 considersbid delegation in the GSP auction mechanism. In a game involving side-contracts and a compensationpolicy set by an agency, the first-best collusive outcome is achieved. We offer a characterization of the implementablebid profiles for the two-position game with three players. Chapter 2 considers the sequentialsale of an object to two buyers: one knows his private information and the other buyer does not. Buyershave a multi-unit demand and private valuations for each unit are perfectly correlated. An asymmetricequilibrium exists when the uninformed player adopts an aggressive bidding strategy. Conversely, hisinformed opponent behaves more conservatively by using bid shading. The bidding behaviour of theuninformed bidder is driven by the opportunity to learn his private valuation for free. This dynamic is atthe root of the decline in the equilibrium price across both sales. In chapter 3, information is observableduring the first-stage auction in a sequential-move game in which the first-mover bidder is observed byhis opponent. A separating equilibrium exists in which the informed bidder bids aggressively when he isthe first-mover which entails a non-participation strategy from his uninformed competitor. Conversely,the latter adopts a conservative behaviour when he is the first-mover. A pooling equilibrium in which theinformed bidder blurs his valuation can only exist if his uninformed opponent adopts a non-participatingstrategy
Klinger, Lu. "A Mean Field Game Analysis of Sponsored Search Auctions." Thesis, The University of Sydney, 2019. http://hdl.handle.net/2123/20248.
Повний текст джерелаPereira, Vinicius de Novaes Guimarães 1985. "O leilão GSP e preço da anarquia." [s.n.], 2013. http://repositorio.unicamp.br/jspui/handle/REPOSIP/275638.
Повний текст джерелаDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação
Made available in DSpace on 2018-08-23T01:59:45Z (GMT). No. of bitstreams: 1 Pereira_ViniciusdeNovaesGuimaraes_M.pdf: 1343382 bytes, checksum: e44e4ecf8abf29e4b44af22979e1269b (MD5) Previous issue date: 2013
Resumo: Uma das fontes de receita mais lucrativas da internet são os anúncios para sites de busca. O crescimento deste mercado bilionário foi, em média, 20% ao ano nos últimos anos. Como o público alvo e variedade de anunciantes deste mercado são grandes e diversificados, um pequeno aumento da eficiência deste mecanismo representa um grande aumento de receita para os sites. Neste trabalho discutimos a evolução dos mecanismos usados neste mercado, identificando as razões destas mudanças. Avaliamos os mecanismos usados atualmente, modelando-o de formas diferentes e calculando o seu preço da anarquia
Abstract: Sponsored search auction is one of the most profitable sources of revenue on the internet. The growth of this market was, on average, 20% per year over the past years. Since the target audience and advertiser variety are big and diverse, a small increase in efficiency in this mechanism can bring a huge increase in the sites profits. In this work we discuss the evolution of the mechanisms used in this market, identifying the reasons of these changes. We evaluate the currently used mechanism, modeling in different ways and calculating the price of anarchy
Mestrado
Ciência da Computação
Mestre em Ciência da Computação
Hsu, Chi-Ying, and 徐綺營. "Designing Adaptive MIP Strategies in Multi-Round Sponsored Search Auctions." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/71059381873388984435.
Повний текст джерела國立中正大學
資訊工程所
98
Generalized second price is not a dominant strategy in sponsored search auction, so advertisers may reduce the bids to improve their utilities, and that may reduce the search engine’s revenue. Thus, to improve the revenue of search engine, we impose two restrictions on the advertisers. First, the advertiser cannot bid the value which is less than the previous bid. Second, the advertiser is to bid the value which is no less than the previous bid plus the minimum increase price (MIP), if he wants to raise bid. To decide the sizes of the MIPs, we consider two strategies: fix strategy and adaptive strategy. The latter strategy has two policies: common MIP and respective MIP. Fix strategy fixes the size of the MIP throughout the MRSSA, and adaptive strategy adopts additive increase/ multiplicative decrease (AIMD) to modify the sizes of the MIPs. Common MIP is that the sizes of the ads’ MIPs are the same, and respective MIP is that each ad has his own MIP. Our simulation results of those strategies show that, first, search engine will obtain a lower revenue if we adopt the fix strategy and set a larger MIP. Second, the adaptive strategy resolves the low revenue problem of the fix strategy with large MIP, and slightly accelerates the advertisers’ bidding.
Kannan, Ramakrishnan. "A Nash Bargaining Based Bid Optimizer for Sponsored Search Auctions." Thesis, 2008. https://etd.iisc.ac.in/handle/2005/4609.
Повний текст джерелаTsung, Chen-Kun, and 欉振坤. "Improving the Robustness of the Generalized Second Price in Sponsored Search Auctions." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/33sbmv.
Повний текст джерела國立中正大學
資訊工程研究所
102
In the current online advertising industry, the sponsored search auction (SSA) with the generalized second price (GSP) strategy is the most popular advertising application. The SSA combines Internet search with advertising service to recommend advertisements interested by the Internet users. To enhance the robustness of the SSA, we take into account the following issues: 1) the computation efficiency in determining winners as the advertisers have various valuations, 2) the outcome satisfaction between the SSA participants, and 3) the outcome fairness for advertisers with different bidding strategies. First, we apply the rank-by-bid principle to the English auction-based SSA to improve the computation time in determining winners. The proposed mechanism not only maintains the easy implementation of the English auction, but also provides the lower bound of revenue for the search engine provider (SEP). Next, we study the balance outcome that is accepted by the SEP and advertisers simultaneously. We investigate six factors to formulate the outcome satisfaction for the SEP and advertisers. Then, five similarity/distance measurement approaches are applied to estimate the way of realizing balance outcomes. In the end of this study, we measure the negative effects resulted from the vindictive bidding, and provide an auction mechanism to detect and correct vindictive bidding behaviors. From our theoretical analysis and simulation experiment, the proposed mechanisms provide efficient process for determining winners, balance outcomes accepted by the SEP and advertisers simultaneously, and fair and stable outcomes. This study is important in the implementation consideration since the proposed mechanisms take advantage of the properties applied in the real world environment.
Yao, Song. "Online Auction Markets." Diss., 2009. http://hdl.handle.net/10161/1073.
Повний текст джерелаCentral to the explosive growth of the Internet has been the desire
of dispersed buyers and sellers to interact readily and in a manner
hitherto impossible. Underpinning these interactions, auction
pricing mechanisms have enabled Internet transactions in novel ways.
Despite this massive growth and new medium, empirical work in
marketing and economics on auction use in Internet contexts remains
relatively nascent. Accordingly, this dissertation investigates the
role of online auctions; it is composed of three essays.
The first essay, ``Online Auction Demand,'' investigates seller and
buyer interactions via online auction websites, such as eBay. Such
auction sites are among the earliest prominent transaction sites on
the Internet (eBay started in 1995, the same year Internet Explorer
was released) and helped pave the way for e-commerce. Hence, online
auction demand is the first topic considered in my dissertation. The
second essay, ``A Dynamic Model of Sponsored Search Advertising,''
investigates sponsored search advertising auctions, a novel approach
that allocates premium advertising space to advertisers at popular
websites, such as search engines. Because sponsored search
advertising targets buyers in active purchase states, such
advertising venues have grown very rapidly in recent years and have
become a highly topical research domain. These two essays form the
foundation of the empirical research in this dissertation. The third
essay, ``Sponsored Search Auctions: Research Opportunities in
Marketing,'' outlines areas of future inquiry that I intend to
pursue in my research.
Of note, the problems underpinning the two empirical essays exhibits
a common form, that of a two-sided network wherein two parties
interact on a common platform (Rochet and Tirole, 2006). Although
theoretical research on two-sided markets is abundant, this
dissertation focuses on their use in e-commerce and adopts an
empirical orientation. I assume an empirical orientation because I
seek to guide firm behavior with concrete policy recommendations and
offer new insights into the actual behavior of the agents who
interact in these contexts. Although the two empirical essays share
this common feature, they also exhibit notable differences,
including the nature of the auction mechanism itself, the
interactions between the agents, and the dynamic frame of the
problem, thus making the problems distinct. The following abstracts
for these two essays as well as the chapter that describes my future
research serve to summarize these contributions, commonalities and
differences.
Online Auction Demand
With $40B in annual gross merchandise volume, electronic auctions
comprise a substantial and growing sector of the retail economy. For
example, eBay alone generated a gross merchandise volume of $14.4B
during the fourth quarter of 2006. Concurrent with this growth has
been an attendant increase in empirical research on Internet
auctions. However, this literature focuses primarily on the bidder;
I extend this research to consider both seller and bidder behavior
in an integrated system within a two-sided network of the two
parties. This extension of the existing literature enables an
exploration of the implications of the auction house's marketing on
its revenues as well as the nature of bidder and seller interactions
on this platform. In the first essay, I use a unique data set of
Celtic coins online auctions. These data were obtained from an
anonymous firm and include complete bidding and listing histories.
In contrast, most existing research relies only on the observed
website bids. The complete bidding and listing histories provided by
the data afford additional information that illuminates the insights
into bidder and seller behavior such as bidder valuations and seller
costs.
Using these data from the ancient coins category, I estimate a
structural model that integrates both bidder and seller behavior.
Bidders choose coins and sellers list them to maximize their
respective profits. I then develop a Markov Chain Monte Carlo (MCMC)
estimation approach that enables me, via data augmentation, to infer
unobserved bidder and seller characteristics and to account for
heterogeneity in these characteristics. My findings indicate that:
i) bidder valuations are affected by item characteristics (e.g., the
attributes of the coin), seller (e.g. reputation), and auction
characteristics (e.g., the characteristics of the listing); ii)
bidder costs are affected by bidding behavior, such as the recency
of the last purchase and the number of concurrent auctions; and iii)
seller costs are affected by item characteristics and the number of
concurrent listings from the seller (because acquisition costs
evidence increasing marginal values).
Of special interest, the model enables me to compute fee
elasticities, even though no variation in historical fees exists in
these data. I compute fee elasticities by inferring the role of
seller costs in their historical listing decision and then imputing
how an increase in these costs (which arises from more fees) would
affect the seller's subsequent listing behavior. I find that these
implied commission elasticities exceed per-item fee elasticities
because commissions target high value sellers, and hence, commission
reductions enhance their listing likelihood. By targeting commission
reductions to high value sellers, auction house revenues can be
increased by 3.9%. Computing customer value, I find that attrition
of the largest seller would decrease fees paid to the auction house
by $97. Given that the seller paid $127 in fees, competition
offsets only 24% of the fees paid by the seller. In contrast,
competition largely in the form of other bidders offsets 81% of the
$26 loss from buyer attrition. In both events, the auction house
would overvalue its customers by neglecting the effects of
competition.
A Dynamic Model of Sponsored Search Advertising
Sponsored search advertising is ascendant. Jupiter Research reports
that expenditures rose 28% in 2007 to $8.9B and will continue to
rise at a 26% Compound Annual Growth Rate (CAGR), approaching half
the level of television advertising and making sponsored search
advertising one of the major advertising trends affecting the
marketing landscape. Although empirical studies of sponsored search
advertising are ascending, little research exists that explores how
the interactions of various agents (searchers,
advertisers, and the search engine) in keyword
markets affect searcher and advertiser behavior, welfare and search
engine profits. As in the first essay, sponsored search constitutes
a two-sided network. In this case, bidders (advertisers) and
searchers interact on a common platform, the search engine. The
bidder seeks to maximize profits, and the searcher seeks to maximize
utility.
The structural model I propose serves as a foundation to explore
these outcomes and, to my knowledge, is the first structural model
for keyword search. Not only does the model integrate the behavior
of advertisers and searchers, it also accounts for advertisers
competition in a dynamic setting. Prior theoretical research has
assumed a static orientation to the problem whereas prior empirical
research, although dynamic, has focused solely on estimating the
dynamic sales response to a single firm's keyword advertising
expenditures.
To estimate the proposed model, I have developed a two-step Bayesian
estimator for dynamic games. This approach does not rely on
asymptotics and also facilitates a more flexible model
specification.
I fit this model to a proprietary data set provided by an anonymous
search engine. These data include a complete history of consumer
search behavior from the site's web log files and a complete history
of advertiser bidding behavior across all advertisers. In addition,
the data include search engine information, such as keyword pricing
and website design.
With respect to advertisers, I find evidence of dynamic
bidding behavior. Advertiser valuation for clicks on their sponsored
links averages about $0.27. Given the typical $22 retail price of
the software products advertised on the considered search engine,
this figure implies a conversion rate (sales per click) of about
1.2%, well within common estimates of 1-2% (gamedaily.com). With
respect to consumers, I find that frequent clickers place a
greater emphasis on the position of the sponsored advertising link.
I further find that 10% of consumers perform 90% of the clicks.
I then conduct several policy simulations to illustrate the effects
of change in search engine policy. First, I find that the
search engine obtains revenue gains of nearly 1.4% by sharing
individual level information with advertisers and enabling them to
vary their bids by consumer segment. This strategy also improves
advertiser profits by 11% and consumer welfare by 2.9%. Second, I
find that a switch from a first to second price auction results in
truth telling (advertiser bids rise to advertiser valuations), which
is consistent with economic theory. However, the second price
auction has little impact on search engine profits. Third, consumer
search tools lead to a platform revenue increase of 3.7% and an
increase of consumer welfare of 5.6%. However, these tools, by
reducing advertising exposure, lower advertiser profits by 4.1%.
Sponsored Search Auctions: Research Opportunities in Marketing
In the final chapter, I systematically review the literature on
keyword search and propose several promising research directions.
The chapter is organized according to each agent in the search
process, i.e., searchers, advertisers and the search engine, and
reviews the key research issues for each. For each group, I outline
the decision process involved in keyword search. For searchers, this
process involves what to search, where to search, which results to
click, and when to exit the search. For advertisers, this process
involves where to bid, which word or words to bid on, how much to
bid, and how searchers and auction mechanisms moderate these
behaviors. The search engine faces choices on mechanism design,
website design, and how much information to share with its
advertisers and searchers. These choices have implications for
customer lifetime value and the nature of competition among
advertisers. Overall, I provide a number of potential areas of
future research that arise from the decision processes of these
various agents.
Foremost among these potential areas of future research are i) the
role of alternative consumer search strategies for information
acquisition and clicking behavior, ii) the effect of advertiser
placement alternatives on long-term profits, and iii) the measure of
customer lifetime value for search engines. Regarding the first
area, a consumer's search strategy (i.e., sequential search and
non-sequential search) affects which sponsored links are more likely
to be clicked. The search pattern of a consumer is likely to be
affected by the nature of the product (experience product vs. search
product), the design of the website, the dynamic orientation of the
consumer (e.g., myopic or forward-looking), and so on. This search
pattern will, in turn, affect advertisers payments, online traffic,
sales, as well as the search engine's revenue. With respect to the
second area, advertisers must ascertain the economic value of
advertising, conditioned on the slot in which it appears, before
making decisions such as which keywords to bid on and how much to
bid. This area of possible research suggests opportunities to
examine how advertising click-through and the number of impressions
differentially affect the value of appearing in a particular
sponsored slot on a webpage, and how this value is moderated by an
appearance in a non-sponsored slot (i.e., a slot in the organic
search results section). With respect to the third area of future
research, customer value is central to the profitability and
long-term growth of a search engine and affects how the firm should
allocate resources for customer acquisition and retention.
Organization
This dissertation is organized as follows. After this brief
introduction, the essay, ``Online Auction Demand,'' serves as a
basis that introduces some concepts of auctions as two-sided
markets. Next, the second essay, ``A Dynamic Model of Sponsored
Search Advertising,'' extends the first essay by considering a
richer context of bidder competition and consumer choice behavior.
Finally, the concluding chapter, which outlines my future research
interests, considers potential extensions that pertain especially to
sponsored search advertising.
Dissertation
Κυροπούλου, Μαρία. "Υπολογιστικά ζητήματα σε στρατηγικά παίγνια και διαδικασίες κοινωνικής επιλογής". Thesis, 2014. http://hdl.handle.net/10889/7774.
Повний текст джерелаIn this dissertation we consider auction markets and examine their properties and how these are affected by the way the participants act. An auction may refer to any mechanism or set of rules governing a resource allocation process. Designing such a mechanism is not an easy task and this is partly due to their vulnerability to strategic manipulation by the participants. Our goal is to examine the theoretical properties of auction mechanisms in order to predict, explain, or even adjust their behavior in practice in terms of some desired features. We focus on sponsored search auctions, which constitute the leading procedure in Internet advertising. We adopt a game-theoretic approach and provide Price of Anarchy bounds in order to measure the efficiency loss due to the strategic behavior of the players. Moreover, we prove revenue guarantees to bound the suboptimality of GSP (generalized second price mechanism) in that respect. Ιn particular, we define variants of the GSP auction mechanism that yield good revenue guarantees. We also consider the problem of designing an optimal auction in the single-item setting. We prove a strong APX-hardness result that applies to the 3-player case. We furthermore give a separation result between the revenue of deterministic and randomized optimal auctions.
Chen, Shu-han, and 陳姝含. "A Study on Auction Strategy of Keyword Search for Sponsors." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/56835705820188457837.
Повний текст джерела南華大學
資訊管理學系碩士班
99
Online advertising has been one of the few advertising platforms that have shown revenue growth fast over the past years. Especially, Search engines including Yahoo and Google utilize the Keyword Auction for ranking the advertisements displayed around the search results in the web page. In the early years of Keyword Auctions, the Generalized First Price (GFP) auction was used. Since all winners pay their bid in the first price, they have incentive to under-bid to reduce their payment. As a result, biding prices oscillate, and the outcome becomes quite unstable. To overcome the problem, the Generalized Second Price (GSP) auction was applied in the current search engine. This paper studied the auction strategy of Keyword Search for sponsors in order to find the relationship between the revenue of search engine and the speed of auction.
Garg, Dinesh. "Design Of Innovative Mechanisms For Contemporary Game Theoretic Problems In Electronic Commerce." Thesis, 2006. https://etd.iisc.ac.in/handle/2005/360.
Повний текст джерелаКниги з теми "Sponsored Search Auctions"
1970-, Schwarz Michael, and Harvard Business School, eds. Optimal auction design and equlibrium selection in sponsored search auctions. [Boston]: Harvard Business School, 2010.
Знайти повний текст джерелаMela, Carl F., and Song Yao. Sponsored Search Auctions: Research Opportunities in Marketing. Now Publishers, 2009.
Знайти повний текст джерелаBudget Constraints and Optimization in Sponsored Search Auctions. Elsevier, 2014. http://dx.doi.org/10.1016/c2012-0-13544-4.
Повний текст джерелаBudget Constraints and Optimization in Sponsored Search Auctions. Elsevier Science & Technology Books, 2013.
Знайти повний текст джерелаYang, Yanwu, and Feiyue Wang. Budget Constraints and Optimization in Sponsored Search Auctions. Elsevier Science & Technology Books, 2013.
Знайти повний текст джерелаYang, Yanwu, and Dajun Zeng. Budget Constraints and Optimization in Sponsored Search Auctions. Elsevier Science & Technology Books, 2013.
Знайти повний текст джерелаЧастини книг з теми "Sponsored Search Auctions"
Goel, Sharad, Sébastien Lahaie, and Sergei Vassilvitskii. "Contract Auctions for Sponsored Search." In Lecture Notes in Computer Science, 196–207. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10841-9_19.
Повний текст джерелаCaragiannis, Ioannis, Christos Kaklamanis, Panagiotis Kanellopoulos, and Maria Kyropoulou. "Revenue Guarantees in Sponsored Search Auctions." In Algorithms – ESA 2012, 253–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33090-2_23.
Повний текст джерелаNarahari, Y., Ramasuri Narayanam, Dinesh Garg, and Hastagiri Prakash. "Mechanism Design for Sponsored Search Auctions." In Game Theoretic Problems in Network Economics and Mechanism Design Solutions, 1–51. London: Springer London, 2009. http://dx.doi.org/10.1007/978-1-84800-938-7_3.
Повний текст джерелаAggarwal, Gagan, Jon Feldman, S. Muthukrishnan, and Martin Pál. "Sponsored Search Auctions with Markovian Users." In Lecture Notes in Computer Science, 621–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-92185-1_68.
Повний текст джерелаFeuerstein, Esteban, Pablo Ariel Heiber, Matías Lopez-Rosenfeld, and Marcelo Mydlarz. "Optimal Auctions Capturing Constraints in Sponsored Search." In Algorithmic Aspects in Information and Management, 188–201. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02158-9_17.
Повний текст джерелаLi, Sai-Ming, Mohammad Mahdian, and R. Preston McAfee. "Value of Learning in Sponsored Search Auctions." In Lecture Notes in Computer Science, 294–305. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17572-5_24.
Повний текст джерелаChe, Yeon-Koo, Syngjoo Choi, and Jinwoo Kim. "An Experimental Study of Sponsored-Search Auctions." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30913-7_7.
Повний текст джерелаColini-Baldeschi, Riccardo, Monika Henzinger, Stefano Leonardi, and Martin Starnberger. "On Multiple Keyword Sponsored Search Auctions with Budgets." In Automata, Languages, and Programming, 1–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31585-5_1.
Повний текст джерелаGonen, Rica, and Sergei Vassilvitskii. "Sponsored Search Auctions with Reserve Prices: Going Beyond Separability." In Lecture Notes in Computer Science, 597–608. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-92185-1_66.
Повний текст джерелаXu, Wenlin, Kun Yue, Jin Li, Liang Duan, Suiye Liu, and Weiyi Liu. "An Approach for Sponsored Search Auctions Based on the Coalitional Game Theory." In Lecture Notes in Computer Science, 458–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41154-0_34.
Повний текст джерелаТези доповідей конференцій з теми "Sponsored Search Auctions"
Colini-Baldeschi, Riccardo. "Sponsored search auctions." In the sixth ACM international conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2433396.2433488.
Повний текст джерелаAggarwal, Gagan, and S. Muthukrishnan. "Theory of Sponsored Search Auctions." In 2008 IEEE 49th Annual IEEE Symposium on Foundations of Computer Science (FOCS). IEEE, 2008. http://dx.doi.org/10.1109/focs.2008.88.
Повний текст джерелаMahdian, Mohammad, and Mukund Sundararajan. "Robust Mechanisms for Sponsored Search." In The Third Conference on Auctions, Market Mechanisms and Their Applications. ACM, 2015. http://dx.doi.org/10.4108/eai.8-8-2015.2260361.
Повний текст джерелаPapadimitriou, Panagiotis, and Hector Garcia-Molina. "Sponsored search auctions with conflict constraints." In the fifth ACM international conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2124295.2124332.
Повний текст джерелаPin, Furcy, and Peter Key. "Stochastic variability in sponsored search auctions." In the 12th ACM conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/1993574.1993586.
Повний текст джерелаCavallo, Ruggiero, Prabhakar Krishnamurthy, Maxim Sviridenko, and Christopher A. Wilkens. "Sponsored Search Auctions with Rich Ads." In WWW '17: 26th International World Wide Web Conference. Republic and Canton of Geneva, Switzerland: International World Wide Web Conferences Steering Committee, 2017. http://dx.doi.org/10.1145/3038912.3052703.
Повний текст джерелаMartin, David J., and Joseph Y. Halpern. "Shared Winner Determination in Sponsored Search Auctions." In 2009 IEEE 25th International Conference on Data Engineering (ICDE). IEEE, 2009. http://dx.doi.org/10.1109/icde.2009.88.
Повний текст джерелаMartin, David J., Johannes Gehrke, and Joseph Y. Halpern. "Toward Expressive and Scalable Sponsored Search Auctions." In 2008 IEEE 24th International Conference on Data Engineering (ICDE 2008). IEEE, 2008. http://dx.doi.org/10.1109/icde.2008.4497432.
Повний текст джерелаRoberts, Bem, Dinan Gunawardena, Ian A. Kash, and Peter Key. "Ranking and tradeoffs in sponsored search auctions." In EC '13: ACM Conference on Electronic Commerce. New York, NY, USA: ACM, 2013. http://dx.doi.org/10.1145/2482540.2482568.
Повний текст джерелаRoberts, Bem, Dinan Gunawardena, Ian A. Kash, and Peter Key. "Ranking and tradeoffs in sponsored search auctions." In the fourteenth ACM conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2492002.2482568.
Повний текст джерелаЗвіти організацій з теми "Sponsored Search Auctions"
Nonparametric estimation of sponsored search auctions and impacts of ad quality on search revenue. Cemmap, March 2023. http://dx.doi.org/10.47004/wp.cem.2023.0523.
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