Algorithmic Methods for Sponsored Search Advertising

Feldman, Jon; Muthukrishnan, S.

doi:10.1007/978-0-387-79361-0_4

Cited by 26 publications

(19 citation statements)

References 44 publications

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“…According to the ad assignment step (6), if at the beginning of budgeting cycle k, Q i (k) > 1/ , then for this budgeting cycle, the i th row ofM * (t, q, Q(k)) is always a zero vector, i.e., the service provider will not post the ads of client i until Q i (k) falls below 1/ . Since by assumption the number of query arrivals per time slot is upper bounded, for any budgeting cycle k, one can bound the transient length of each overdraft queue as below:…”

Section: An Upper Bound On the Overdraftmentioning

confidence: 99%

“…One is based on auction theory, in which the goal is to design mechanisms in favour of the service provider, and much of the research in this direction considers static bids (e.g. [8]; see [6] for a survey), while dynamic models such the one in [15] are still emerging. The other is from the perspective of online resource allocation without considering the impact of the service provider's mechanisms on the clients' bids, and the main focus of this kind of research is on designing an online algorithm which posts specific ads in response to each search query arriving online, in order to achieve a high competitive ratio with respect to the offline optimal revenue.…”

Section: Introductionmentioning

confidence: 99%

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Online advertisement, optimization and stochastic networks

Tan

Srikant

2011

IEEE Conference on Decision and Control and European Control Conference

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In this paper, we propose a stochastic model to describe how search service providers charge client companies based on users' queries for the keywords related to these companies' ads by using certain advertisement assignment strategies. We formulate an optimization problem to maximize the long-term average revenue for the service provider under each client's long-term average budget constraint, and design an online algorithm which captures the stochastic properties of users' queries and click-through behaviors. We solve the optimization problem by making connections to scheduling problems in wireless networks, queueing theory and stochastic networks. Unlike prior models, we do not assume that the number of query arrivals is known. Due to the stochastic nature of the arrival process considered here, either temporary "free" service, i.e., service above the specified budget (which we call "overdraft") or under-utilization of the budget (which we call "underdraft") is unavoidable. We prove that our online algorithm can achieve a revenue that is within O( ) of the optimal revenue while ensuring that the overdraft or underdraft is O(1/ ), where can be arbitrarily small. With a view towards practice, we also show that one can always operate strictly under the budget.Our algorithm also allows us to quantify the effect of errors in click-through rate estimation on the achieved revenue. We show that we lose at most ∆ 1+∆ fraction of the revenue if ∆ is the relative error in click-through rate estimation.

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Section: An Upper Bound On the Overdraftmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Online advertisement, optimization and stochastic networks

Tan

Srikant

2011

IEEE Conference on Decision and Control and European Control Conference

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“…In general, the motivation for the work that followed is that sponsored search in practice is much more complex than as described by the first models; see [9] for a discussion.…”

Section: Related Workmentioning

confidence: 99%

Sponsored Search Auctions with Markovian Users

Aggarwal

Feldman

Muthukrishnan

et al. 2008

Lecture Notes in Computer Science

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123

146

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Abstract. Sponsored search involves running an auction among advertisers who bid in order to have their ad shown next to search results for specific keywords. The most popular auction for sponsored search is the "Generalized Second Price" (GSP) auction where advertisers are assigned to slots in the decreasing order of their score, which is defined as the product of their bid and click-through rate. One of the main advantages of this simple ranking is that bidding strategy is intuitive: to move up to a more prominent slot on the results page, bid more. This makes it simple for advertisers to strategize. However this ranking only maximizes efficiency under the assumption that the probability of a user clicking on an ad is independent of the other ads shown on the page. We study a Markovian user model that does not make this assumption. Under this model, the most efficient assignment is no longer a simple ranking function as in GSP. We show that the optimal assignment can be found efficiently (even in near-linear time). As a result of the more sophisticated structure of the optimal assignment, bidding dynamics become more complex: indeed it is no longer clear that bidding more moves one higher on the page. Our main technical result is that despite the added complexity of the bidding dynamics, the optimal assignment has the property that ad position is still monotone in bid. Thus even in this richer user model, our mechanism retains the core bidding dynamics of the GSP auction that make it useful for advertisers.

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“…For convenience (with no consequence on the following results) we adopt the normalizing condition P S j¼1 h j ¼ 1, so that we are actually considering the probability of clicking on a specific slot conditioned to the user clicking on a slot (or, alternatively, the user clicks on a slot with probability 1). Though in this paper we implicitly consider the click-through rate being a function of the slot's position only, other authors have considered the more general case of click-through rates being a function of the specific advertiser as well (Feldman andMuthukrishnan 2008, Aggarwal et al 2006). …”

Section: The Click-through Ratementioning

confidence: 99%

The value of location in keyword auctions

Naldi

D'Acquisto²,

Italiano

2010

Electronic Commerce Research and Applications

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a b s t r a c tSponsored links on search engines are an emerging advertising tool, whereby a number of slots are put on sale through keyword auctions. This is also known as contextual advertising. Slot assignment and pricing in keyword auctions are then essential for the search engine's management since provide the main stream of revenues, and are typically accomplished by the Generalized Second Price (GSP) mechanism. In GSP the price of slots is a monotone function of the slot location, being larger for the highest slots. Though a higher location is associated with larger revenues, the lower costs associated with the lowest slots may make them more attractive for the advertiser. The contribution of this research is to show, by analytical and simulation results based on the theory of order statistics, that advertisers may not get the optimal slot they aim at (the slot maximizing their expected profit) and that the GSP mechanism may be unfair to all the winning bidders but the one who submitted the lowest bid.

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Algorithmic Methods for Sponsored Search Advertising

Cited by 26 publications

References 44 publications

Online advertisement, optimization and stochastic networks

Online advertisement, optimization and stochastic networks

Sponsored Search Auctions with Markovian Users

The value of location in keyword auctions

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