610 research outputs found
Generalized Second Price Auction with Probabilistic Broad Match
Generalized Second Price (GSP) auctions are widely used by search engines
today to sell their ad slots. Most search engines have supported broad match
between queries and bid keywords when executing GSP auctions, however, it has
been revealed that GSP auction with the standard broad-match mechanism they are
currently using (denoted as SBM-GSP) has several theoretical drawbacks (e.g.,
its theoretical properties are known only for the single-slot case and
full-information setting, and even in this simple setting, the corresponding
worst-case social welfare can be rather bad). To address this issue, we propose
a novel broad-match mechanism, which we call the Probabilistic Broad-Match
(PBM) mechanism. Different from SBM that puts together the ads bidding on all
the keywords matched to a given query for the GSP auction, the GSP with PBM
(denoted as PBM-GSP) randomly samples a keyword according to a predefined
probability distribution and only runs the GSP auction for the ads bidding on
this sampled keyword. We perform a comprehensive study on the theoretical
properties of the PBM-GSP. Specifically, we study its social welfare in the
worst equilibrium, in both full-information and Bayesian settings. The results
show that PBM-GSP can generate larger welfare than SBM-GSP under mild
conditions. Furthermore, we also study the revenue guarantee for PBM-GSP in
Bayesian setting. To the best of our knowledge, this is the first work on
broad-match mechanisms for GSP that goes beyond the single-slot case and the
full-information setting
Optimising Trade-offs Among Stakeholders in Ad Auctions
We examine trade-offs among stakeholders in ad auctions. Our metrics are the
revenue for the utility of the auctioneer, the number of clicks for the utility
of the users and the welfare for the utility of the advertisers. We show how to
optimize linear combinations of the stakeholder utilities, showing that these
can be tackled through a GSP auction with a per-click reserve price. We then
examine constrained optimization of stakeholder utilities.
We use simulations and analysis of real-world sponsored search auction data
to demonstrate the feasible trade-offs, examining the effect of changing the
allowed number of ads on the utilities of the stakeholders. We investigate both
short term effects, when the players do not have the time to modify their
behavior, and long term equilibrium conditions.
Finally, we examine a combinatorially richer constrained optimization
problem, where there are several possible allowed configurations (templates) of
ad formats. This model captures richer ad formats, which allow using the
available screen real estate in various ways. We show that two natural
generalizations of the GSP auction rules to this domain are poorly behaved,
resulting in not having a symmetric Nash equilibrium or having one with poor
welfare. We also provide positive results for restricted cases.Comment: 18 pages, 10 figures, ACM Conference on Economics and Computation
201
Born to trade: a genetically evolved keyword bidder for sponsored search
In sponsored search auctions, advertisers choose a set of keywords based on products they wish to market. They bid for advertising slots that will be displayed on the search results page when a user submits a query containing the keywords that the advertiser selected. Deciding how much to bid is a real challenge: if the bid is too low with respect to the bids of other advertisers, the ad might not get displayed in a favorable position; a bid that is too high on the other hand might not be profitable either, since the attracted number of conversions might not be enough to compensate for the high cost per click.
In this paper we propose a genetically evolved keyword bidding strategy that decides how much to bid for each query based on historical data such as the position obtained on the previous day. In light of the fact that our approach does not implement any particular expert knowledge on keyword auctions, it did remarkably well in the Trading Agent Competition at IJCAI2009
Internet Advertising and the Generalized Second Price Auction: Selling Billions of Dollars Worth of Keywords
We investigate the "generalized second price" auction (GSP), a new mechanism which is used by search engines to sell online advertising that most Internet users encounter daily. GSP is tailored to its unique environment, and neither the mechanism nor the environment have previously been studied in the mechanism design literature. Although GSP looks similar to the Vickrey-Clarke-Groves (VCG) mechanism, its properties are very different. In particular, unlike the VCG mechanism, GSP generally does not have an equilibrium in dominant strategies, and truth-telling is not an equilibrium of GSP. To analyze the properties of GSP in a dynamic environment, we describe the generalized English auction that corresponds to the GSP and show that it has a unique equilibrium. This is an ex post equilibrium that results in the same payoffs to all players as the dominant strategy equilibrium of VCG.
Multi-keyword multi-click advertisement option contracts for sponsored search
In sponsored search, advertisement (abbreviated ad) slots are usually sold by
a search engine to an advertiser through an auction mechanism in which
advertisers bid on keywords. In theory, auction mechanisms have many desirable
economic properties. However, keyword auctions have a number of limitations
including: the uncertainty in payment prices for advertisers; the volatility in
the search engine's revenue; and the weak loyalty between advertiser and search
engine. In this paper we propose a special ad option that alleviates these
problems. In our proposal, an advertiser can purchase an option from a search
engine in advance by paying an upfront fee, known as the option price. He then
has the right, but no obligation, to purchase among the pre-specified set of
keywords at the fixed cost-per-clicks (CPCs) for a specified number of clicks
in a specified period of time. The proposed option is closely related to a
special exotic option in finance that contains multiple underlying assets
(multi-keyword) and is also multi-exercisable (multi-click). This novel
structure has many benefits: advertisers can have reduced uncertainty in
advertising; the search engine can improve the advertisers' loyalty as well as
obtain a stable and increased expected revenue over time. Since the proposed ad
option can be implemented in conjunction with the existing keyword auctions,
the option price and corresponding fixed CPCs must be set such that there is no
arbitrage between the two markets. Option pricing methods are discussed and our
experimental results validate the development. Compared to keyword auctions, a
search engine can have an increased expected revenue by selling an ad option.Comment: Chen, Bowei and Wang, Jun and Cox, Ingemar J. and Kankanhalli, Mohan
S. (2015) Multi-keyword multi-click advertisement option contracts for
sponsored search. ACM Transactions on Intelligent Systems and Technology, 7
(1). pp. 1-29. ISSN: 2157-690
Statistical Arbitrage Mining for Display Advertising
We study and formulate arbitrage in display advertising. Real-Time Bidding
(RTB) mimics stock spot exchanges and utilises computers to algorithmically buy
display ads per impression via a real-time auction. Despite the new automation,
the ad markets are still informationally inefficient due to the heavily
fragmented marketplaces. Two display impressions with similar or identical
effectiveness (e.g., measured by conversion or click-through rates for a
targeted audience) may sell for quite different prices at different market
segments or pricing schemes. In this paper, we propose a novel data mining
paradigm called Statistical Arbitrage Mining (SAM) focusing on mining and
exploiting price discrepancies between two pricing schemes. In essence, our
SAMer is a meta-bidder that hedges advertisers' risk between CPA (cost per
action)-based campaigns and CPM (cost per mille impressions)-based ad
inventories; it statistically assesses the potential profit and cost for an
incoming CPM bid request against a portfolio of CPA campaigns based on the
estimated conversion rate, bid landscape and other statistics learned from
historical data. In SAM, (i) functional optimisation is utilised to seek for
optimal bidding to maximise the expected arbitrage net profit, and (ii) a
portfolio-based risk management solution is leveraged to reallocate bid volume
and budget across the set of campaigns to make a risk and return trade-off. We
propose to jointly optimise both components in an EM fashion with high
efficiency to help the meta-bidder successfully catch the transient statistical
arbitrage opportunities in RTB. Both the offline experiments on a real-world
large-scale dataset and online A/B tests on a commercial platform demonstrate
the effectiveness of our proposed solution in exploiting arbitrage in various
model settings and market environments.Comment: In the proceedings of the 21st ACM SIGKDD international conference on
Knowledge discovery and data mining (KDD 2015
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