2,845 research outputs found
On the stability of generalized second price auctions with budgets
The Generalized Second Price (GSP) auction used typically to model sponsored search auctions does not include the notion of budget constraints, which is present in practice. Motivated by this, we introduce the different variants of GSP auctions that take budgets into account in natural ways. We examine their stability by focusing on the existence of Nash equilibria and envy-free assignments. We highlight the differences between these mechanisms and find that only some of them exhibit both notions of stability. This shows the importance of carefully picking the right mechanism to ensure stable outcomes in the presence of budgets.Peer ReviewedPostprint (author's final draft
Multiplicative Bidding in Online Advertising
In this paper, we initiate the study of the multiplicative bidding language
adopted by major Internet search companies. In multiplicative bidding, the
effective bid on a particular search auction is the product of a base bid and
bid adjustments that are dependent on features of the search (for example, the
geographic location of the user, or the platform on which the search is
conducted). We consider the task faced by the advertiser when setting these bid
adjustments, and establish a foundational optimization problem that captures
the core difficulty of bidding under this language. We give matching
algorithmic and approximation hardness results for this problem; these results
are against an information-theoretic bound, and thus have implications on the
power of the multiplicative bidding language itself. Inspired by empirical
studies of search engine price data, we then codify the relevant restrictions
of the problem, and give further algorithmic and hardness results. Our main
technical contribution is an -approximation for the case of
multiplicative prices and monotone values. We also provide empirical
validations of our problem restrictions, and test our algorithms on real data
against natural benchmarks. Our experiments show that they perform favorably
compared with the baseline.Comment: 25 pages; accepted to EC'1
Bid Optimization in Broad-Match Ad auctions
Ad auctions in sponsored search support ``broad match'' that allows an
advertiser to target a large number of queries while bidding only on a limited
number. While giving more expressiveness to advertisers, this feature makes it
challenging to optimize bids to maximize their returns: choosing to bid on a
query as a broad match because it provides high profit results in one bidding
for related queries which may yield low or even negative profits.
We abstract and study the complexity of the {\em bid optimization problem}
which is to determine an advertiser's bids on a subset of keywords (possibly
using broad match) so that her profit is maximized. In the query language model
when the advertiser is allowed to bid on all queries as broad match, we present
an linear programming (LP)-based polynomial-time algorithm that gets the
optimal profit. In the model in which an advertiser can only bid on keywords,
ie., a subset of keywords as an exact or broad match, we show that this problem
is not approximable within any reasonable approximation factor unless P=NP. To
deal with this hardness result, we present a constant-factor approximation when
the optimal profit significantly exceeds the cost. This algorithm is based on
rounding a natural LP formulation of the problem. Finally, we study a budgeted
variant of the problem, and show that in the query language model, one can find
two budget constrained ad campaigns in polynomial time that implement the
optimal bidding strategy. Our results are the first to address bid optimization
under the broad match feature which is common in ad auctions.Comment: World Wide Web Conference (WWW09), 10 pages, 2 figure
Optimizing Your Online-Advertisement Asynchronously
We consider the problem of designing optimal online-ad investment strategies
for a single advertiser, who invests at multiple sponsored search sites
simultaneously, with the objective of maximizing his average revenue subject to
the advertising budget constraint. A greedy online investment scheme is
developed to achieve an average revenue that can be pushed to within
of the optimal, for any , with a tradeoff that the
temporal budget violation is . Different from many existing
algorithms, our scheme allows the advertiser to \emph{asynchronously} update
his investments on each search engine site, hence applies to systems where the
timescales of action update intervals are heterogeneous for different sites. We
also quantify the impact of inaccurate estimation of the system dynamics and
show that the algorithm is robust against imperfect system knowledge
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