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Ad Exchanges:
Research Issues
S. Muthukrishnan
Google Inc.
Presented by Tova Wiener, CS286r
11/16/2009
Framework
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Advertising on the Internet involves three parties:
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Users
Publishers
Advertisers
An Ad Exchange brings sellers (publishers) and buyers
(advertisers) together to a single marketplace
Generally, contracts are sold in advance; however,
publishers who have ads that are not sold can send their
slots to the exchange to sell.
Websites customize ads based on their knowledge about
the current user.
Ad exchanges ease the burden on website publishers by
accepting bids from many networks and selecting the
revenue maximizing bid for the publisher.
Sequence of Events
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User u visits webpage w of publisher P(w)
Publisher P(w) contacts the exchange E with
(w,P(u),p)
The exchange E contacts the ad networks
with (E(w),E(u),p)
Website
Exchange
w
Networks
(E(w), E(u), p)
(w, P(u), p)
E
a1
ai
am
Advertisers
Sequence of Events
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Each ad network returns (bi,di) on behalf of its
advertisers.
Exchange E determines a winner i* for the slot
and a price ci*.
P(w) serves the webpage w with the ad to the
user u.
Website
Exchange
w
Networks
(E(w), E(u), p)
(w, P(u), p)
ai
E
(i*,ci*)
a1
(bi,di)
am
Advertisers
Observations on the Model
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The units traded are impressions
 Display ads focus on marketing, not actions.
 Difficult to collect accurate click-through data.
Who determines what the advertisers know about the website?
How is “fit” between advertisers and websites measured?
Computation must happen in microseconds.
Can each website publisher only be involved with a single
exchange?
 The publisher must accept the price returned by the Exchange,
and trust the Exchange to do content monitoring
The advertisers must trust the networks to bid to maximize their
individual values.
How does this differ from the financial auction model?
 Heterogeneity and perishable goods
Basic Auction at the Exchange
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Assume there is a single slot being auctioned, and
each ad network submits a bid for its advertisers,
and that the Exchange uses a second price auction.
Define book value to be the second largest value of
all bidders
Problem 1: Assuming p is exogenous and
assuming the advertisers reveal their bids
truthfully to the networks, is there a possibly
truthful auction at the exchange that will extract
a large fraction of the book value?
Basic Auction at the Exchange
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Why would a network ever reveal their second
highest bidder?
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More advertising spots on each webpage
Auctions run in the “long-run” where the highest bidder
has budget constraints
Other possible incentives?
Can we reasonably bound the expected difference
between the second highest bidder of one network
and the highest bidder of another network?
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If there are enough networks, and each is big enough, will
demand really be that widely distributed?
Auction and Bidding by Ad Networks
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Problem 2 Assuming p is exogenous, and the
exchange runs a second price auction with
reserve price r > p, ie., E(p) = r, and advertisers
are captive, that is, remain with their choice of
ad network throughout, what is a revenue
optimal mechanism for an ad network?
This refers to the mechanism that the network will
use to extract bids from the advertisers.
Why is this auction different than standard auctions
in which we can use VCG?
Auction and Bidding by Ad Networks
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Why is this auction non-standard?
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The networks may not know the distribution of bids
beforehand.
They are selling a contingent good: there is some
probability ®(b), when the network submits some bid b
that they will win, based on the bids of other networks.
How will strategies change as networks learn
about the types of advertisers in other networks?
How should a network bid within the exchange to
maximize its own revenue? This is the of the
Ghosh et al. paper.
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How should networks charge their bidders?
What would be optimal if they know that the Exchange is
running GSP or VCG?
Auctioning with Heterogeneous Valuations
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The optimal revenue for an auction is R* = maxi vi, where
vi, is bidder i’s valuation for the impression. How close
can we get to generating revenue R*?
If values are truly heterogeneous, then VCG and GSP (ie,
looking at second prices) will not work.
The problem has been solved for the case where a prior
distribution over the valuations is known.
No truthful mechanism exists with (roughly) expected
revenue £(R*/log R*), but a randomized algorithm has
been shown with revenue close to that.
Problem 3 Design a non-truthful mechanism for priorfree auction of a single slot with near-optimal
revenue, but with good equilibrium properties.
Auctioning with Heterogeneous Valuations
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By good equilibrium properties, they mean a
Complete Information Nash Equilibrium.
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The ad networks have priors about the other networks
bidding behavior, but the situation is prior free because the
Exchange does not have these priors.
They leave the idea of truthfulness in order to
increase revenue: what are the properties of this
tradeoff.
Quasi-proportional Allocations: the ith bidder is
selected with probability f(bi)/i f(bi). If f(x) = x this is
simply the proportional allocation rule.
They hypothesize that quasi-proportional allocations
are the right thing to do in this case. Why?
Auctioning with Heterogeneous Valuations
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What if we relax the assumption that the networks
have priors about the bidding practices of other
networks?
Problem 4 Design (even non-truthful)
mechanisms for prior-free bidding of ad
networks in AdX, with good equilibrium
properties and (near-)optimal revenue.
How does being an incomplete information setting
change things?
Again, they need to relax truthfulness to allow for
prior-free bidding.
What is the revenue of the ad networks in the first
place? Do they charge a constant reserve price, or
take a percentage or what is paid to the exchange?
AdX integrity
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The networks and advertisers must trust AdX to participate
Problem 8 Design a cryptographically sound real-time
auction protocol so that any participating party in AdX can
verify that (a) all communication, accounting and
computations were performed correctly, and (b) auction was
closed envelope, that is, no bidder sees others’ bids prior to
the auction. This has to work for repeated auction of
impressions in AdX where some information is revealed
between impressions.
While such cryptographic protocols have been explored, they
need to be fast.
Also, must be expressed through a clear model so that networks
can prove the strong cryptographic properties of various
protocols.
Callout Optimization
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How should the Exchange get bids from the ad
networks without consuming too much bandwidth?
Consider the approach where E makes http calls to
the networks servers and waits for the networks to
reply with bids.
Does the exchange need to communicate with each
network for every ad?
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Networks can inform exchanges about the general types of
impressions that their customers would be interested in,
and then the exchange can only query certain networks.
This implies that the networks cover different parts of the
advertising market. How much competition can we expect
on each sector, as opposed to how much cooperation
between networks to cover the whole market space?
Callout Optimization
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Problem 5 Each ad network i has bandwidth budget Bi. Say E
has bandwidth budget of B. Design an online algorithm for E
that for each incoming call (wj, uj, ½j), chooses a subset Sj µ
S(E(wj ),E(uj ),½j) of networks to call such that no ad network i gets
more than Bi calls per second, E make fewer than B calls per
second, and optimizes the expected:
 number of bids, ie, number of nonempty (bi(j), di(j))’s received
at E, or
 efficiency j maxi bi(j), or
 sales revenue j maxi|bi(j)  maxi bi(j) bi(j), or
 profit for E.
Moreover, we need to learn the probability that each network will
bid a non-empty, or relevant, value.
What would the optimization problems look like for each of these
cases?
Conclusions
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The research topics presented here are important
for the future growth of the market
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Game Theory of Advertisers: Advertisers may go to multiple
networks, or choose networks strategically. How does this
affect exchange dynamics?
Ad Quality: We require a quality metric to price incentives
endogenously. A proposal is to generate a suitable Markov
model for users that will capture even the long term impact
of ad impressions. (That seems like a pretty hard problem,
how would we categorize users?)
On a higher level, how can we think about this
“multi-level” auction set-up? Are there other
instances where this kind of contingency would be a
factor? Could one imagine three or four level
auctions?
Publisher Optimization and Strategies
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Now we deal with how the website publisher should
choose to interact with AdX.
Why would Google be trying to solve this problem?
Problem 6 Given models for impressions
inventory (w,u), models for bids (bi*,di*) from E,
models of ad sales and prices through other
channels, design an algorithm that on each
impression (a) decides whether to go to AdX, (b)
chooses disclosed or undisclosed inventory at
AdX, and (c) selects min price p, in order to
optimize the expected overall (long term)
revenue.