Transcript Internet-based Auctions and Markets

Research
Selected Survey of
Sponsored Search Research
at Yahoo! Research
& the 1st & 2nd Workshops on
Sponsored Search Auctions
David Pennock, Yahoo! Research - New York
Contributed slides:
K.Asdemir, H.Bhargava, J.Feng, S.Lahaie, M.Schwarz
Sponsored search auctions
Space next to search results is sold at auction
search “las vegas travel”, Yahoo!
“las vegas travel” auction
Outline
• Yahoo! Research & microeconomics group
• Motivation: Industry facts & figures
• Introduction to sponsored search
– Brief and biased history
– Allocation and pricing: Google vs Yahoo!
– Incentives and equilibrium
• Selected survey of research at Yahoo!
– Mechanism design
• Analytic comparison of mechanisms [Lahaie]
Outline
• Selected survey of research at Yahoo!
– Mechanism Design (cont’d)
• Learning click rates: N-armed bandit formulation
[Pandey & Olsten]
• Simulation I: Static [Feng, Bhargava, Pennock]
• Simulation II: Equilibrium [Lahaie, Pennock]
– Bidding agent design
• Pragmatic robot [Schwarz, Edelman]
Outline
• Brief summaries of the 1st & 2nd Workshops
on Sponsored Search
• Yahoo!/O’Reilly Tech Buzz Game
• Not covered
– Sponsored search: budget optimization, click rate prediction,
content match, engine switching, expressive bidding, intelligent
match, interactivity, inventory prediction, keyword-advertiser
graph clustering/recommendation, long-run effects, pricing,
query classification, & more ...
– General ad systems, algorithmic search, machine learning,
other mechanism design problems
Research
Yahoo! Research
• New, growing, world-class researchers in
search, machine learning, systems, UI, &
microeconomics
• Relatively open, connected to academia, yet
grounded in real problems
• Y!R-NYC in Manhattan: 9 scientists & growing
Sub-concentrations: ML & microeconomics
• Hiring interns & scientists
• Academic outreach, visitors, collaborations
Come visit us!
Auctions: 2000 View
• Yesterday
Going once, …
going twice, ...
• “Today” (~2000)
– eBay: 4 million;
450k new/day
Auctions: 2000 View
• Yesterday
• “Today” (~2000)
Sotheby's (founded 1744)
Ebay (founded 1995)
18.00
16.00
14.00
12.00
10.00
8.00
6.00
4.00
2.00
0.00
Market Capitalization (billions)
Auctions: 2000 View
• Yesterday
• “Today” (~2000)
Auctions: 2006 View
• Yesterday
• Today
– eBay
– Google / Yahoo!
– 200 million/month
– 6 billion/month (US)
Auctions: 2006 View
• Yesterday
• Today
Ebay (founded 1995)
Google (founded 1998)
120.00
100.00
80.00
60.00
40.00
20.00
0.00
Market Capitalization (billions)
Auctions: 2006 View
• Yesterday
• Today
Newsweek June 17, 2002
“The United States of EBAY”
• In 2001: 170 million transactions worth $9.3 billion in
18,000 categories “that together cover virtually the
entire universe of human artifacts—Ferraris,
Plymouths and Yugos; desk, floor, wall and ceiling
lamps; 11 different varieties of pockets watches;
contemporary Barbies, vintage Barbies, and replica
Barbies.”
• “Since everything that transpires on Ebay is
recorded, and most of it is public, the site constitutes
a gold mine of data on American tastes and
preoccupations.”
“The United States of Search”
• 6 billion searches/month
• 50% of web users search every day
• 13% of traffic to commercial sites
• 40% of product searches
• $5 billion 2005 US ad revenue (41% of US
online ads; 2% of all US ads)
• Doubling every year for four years
• Search data: Covers nearly everything that
people think about: intensions, desires,
diversions, interests, buying habits, ...
Research
Introduction to
sponsored search
•
•
•
•
What is it?
Brief and biased history
Allocation and pricing: Google vs Yahoo!
Incentives and equilibrium
Sponsored search auctions
Space next to search results is sold at auction
search “las vegas travel”, Yahoo!
“las vegas travel” auction
Sponsored search auctions
• Search engines auction off space next
to search results, e.g. “digital camera”
• Higher bidders get higher placement
on screen
• Advertisers pay per click: Only pay
when users click through to their site;
don’t pay for uncliked view
(“impression”)
Sponsored search auctions
• Sponsored search auctions are dynamic and
continuous: In principle a new “auction”
clears for each new search query
• Prices can change minute to minute;
React to external effects, cyclical & non-cyc
– “flowers” before Valentines Day
– Fantasy football
– People browse during day, buy in evening
– Vioxx
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Price ($)
Example price volatility: Vioxx
Vioxx
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Date
Sponsored search today
• 2005: ~ $7 billion industry
– 2004: ~ $4B; 2003: ~ $2.5B; 2002: ~ $1B
• $5 billion 2005 US ad revenue (41% of US
online ads; 2% of all US ads)
• Resurgence in web search, web advertising
• Online advertising spending still trailing
consumer movement online
• For many businesses, substitute for eBay
• Like eBay, mini economy of 3rd party
products & services: SEO, SEM
Sponsored Search
A Brief & Biased History
• Idealab  GoTo.com
(no relation to Go.com)
– Crazy (terrible?) idea, meant to combat search spam
– Search engine “destination” that ranks results based on who is
willing to pay the most
– With algorithmic SEs out there, who would use it?
• GoTo 
 Yahoo! Search Marketing
– Team w/ algorithmic SE’s, provide “sponsored results”
– Key: For commercial topics (“LV travel”, “digital camera”)
actively searched for, people don’t mind (like?) it
– Editorial control, “invisible hand” keep results relevant
• Enter Google
– Innovative, nimble, fast, effective
– Licensed Overture patent (one reason for Y!s ~5% stake in G)
Thanks: S. Lahaie
Sponsored Search
A Brief & Biased History
• Overture introduced the first design in
1997: first price, rank by bid
• Google then began running slot
auctions in 2000: second price, rank by
revenue (bid * CTR)
• In 2002, Overture (at this point
acquired by Yahoo!) then switched to
second-price. Still uses rank by bid;
Moving toward rank by revenue
Sponsored Search
A Brief & Biased History
• In the beginning:
– Exact match, rank by bid, pay per click, human
editors
– Mechanism simple, easy to understand, worked,
somewhat ad hoc
• Today & tomorrow:
– “AI” match, rank by expected revenue (Google), pay
per click/impression/conversion, auto editorial,
contextual (AdSense, YPN), local, 2nd price (proxy
bid), 3rd party optimizers, budgeting optimization,
exploration exploitation, fraud, collusion, more
attributes and expressiveness, more automation,
personalization/targeting, better understanding
(economists, computer scientists)
Sponsored Search Research
A Brief & Biased History
•
Weber & Zeng, A model of search intermediaries and paid referrals
•
Bhargava & Feng, Preferential placement in Internet search engines
•
Feng, Bhargava, & Pennock
Implementing sponsored search in web search engines:
Computational evaluation of alternative mechanisms
•
Feng, Optimal allocation mech’s when bidders’ ranking for objects is
common
Asdemir, Internet advertising pricing models
•
•
Asdemir, A theory of bidding in search phrase auctions: Can bidding
wars be collusive?
•
Mehta, Saberi, Vazirani, & Vaziran
AdWords and generalized on-line matching
•
1st & 2nd Workshop on Sponsored Search Auctions at ACM
Electronic Commerce Conference
Allocation and pricing
• Allocation
– Yahoo!: Rank by decreasing bid
– Google: Rank by decreasing bid * E[CTR]
(Rank by decreasing “revenue”)
• Pricing
– Pay “next price”: Min price to keep you in
current position
Research
Yahoo Allocation: Bid Ranking
“las vegas travel” auction
search “las vegas travel”, Yahoo!
pays $2.95
per click
pays $2.94
pays $1.02
... bidder i
pays bidi+1+.01
Research
Google Allocation: $ Ranking
“las vegas travel” auction
x E[CTR] = E[RPS]
x E[CTR] = E[RPS]
x E[CTR] = E[RPS]
x E[CTR] = E[RPS]
x E[CTR] = E[RPS]
Research
Google Allocation: $ Ranking
“las vegas travel” auction
search “las vegas travel”, Google
TripReservations
x
.1
= .301
pays 3.01*.1/.2+.01 = 1.51
per click
Expedia
x
.2
= .588
pays 2.93*.1/.1+.01 = 2.94
LVGravityZone
x
.1
= .293
etc...
x E[CTR] = E[RPS]
x E[CTR] = E[RPS]
pays bidi+1*CTRi+1/CTRi+.01
Aside: Second price auction
(Vickrey auction)
• All buyers submit their bids privately
• buyer with the highest bid wins;
pays the price of the second highest
bid
Only pays $120

$150
$120
$90
$50
Incentive Compatibility
(Truthfulness)
• Telling the truth is optimal in second-price (Vickrey) auction
• Suppose your value for the item is $100;
if you win, your net gain (loss) is $100 - price
• If you bid more than $100:
– you increase your chances of winning at price >$100
– you do not improve your chance of winning for < $100
• If you bid less than $100:
– you reduce your chances of winning at price < $100
– there is no effect on the price you pay if you do win
• Dominant optimal strategy: bid $100
– Key: the price you pay is out of your control
• Vickrey’s Nobel Prize due in large part to this result
Vickrey-Clark-Groves (VCG)
• Generalization of 2nd price auction
• Works for arbitrary number of goods, including
allowing combination bids
• Auction procedure:
– Collect bids
– Allocate goods to maximize total reported value
(goods go to those who claim to value them most)
– Payments: Each bidder pays her externality;
Pays: (sum of everyone else’s value without bidder)
- (sum of everyone else’s value with bidder)
• Incentive compatible (truthful)
Is Google pricing = VCG?
Well, not really …
Put Nobel Prize-winning theories to work.
Google’s unique auction model uses Nobel Prize-winning economic
theory to eliminate the winner’s curse – that feeling that you’ve paid too
much. While the auction model lets advertisers bid on keywords, the
AdWords™ Discounter makes sure that they only pay what they need in
order to stay ahead of their nearest competitor.
https://google.com/adsense/afs.pdf
Yahoo! Confidential
VCG pricing
• (sum of everyone else’s value w/o bidder)
- (sum of everyone else’s value with bidder)
• CTRi = advi * posi (key “separability” assumption)
• pricei = 1/advi*(∑j<ibidj*CTRj + ∑j>ibidj*advj*posj-1
-∑j≠ibidj*CTRj )
= 1/advi*(∑j>ibidj*advj*posj-1 - ∑j>ibidj*CTRj )
• Notes
– For truthful Y! ranking set advi = 1. But Y! ranking
technically not VCG because not efficient allocation.
– Last position may require special handling
Yahoo! Confidential
Next-price equilibrium
• Next-price auction: Not truthful: no dominant strategy
• What are Nash equilibrium strategies? There are many!
• Which Nash equilibrium seems “focal” ?
• Locally envy-free equilibrium [Edelman, Ostrovsky, Schwarz 2005]
Symmetric equilibrium [Varian 2006]
Fixed point where bidders don’t want to move  or 
– Bidders first choose the optimal position for them: position i
– Within range of bids that land them in position i, bidder chooses
point of indifference between staying in current position and
swapping up with bidder in position i-1
• Pure strategy (symmetric) Nash equilibrium
• Intuitive: Squeeze bidder above, but not enough to risk
“punishment” from bidder above
Yahoo! Confidential
Next-price equilibrium
• Recursive solution:
posi-1*advi*bi = (posi-1-posi)*advi*vi+posi*advi+1*bi+1
bi = (posi-1-posi)*advi*vi+posi*advi+1*bi+1
posi-1*advi
• Nomenclature:
Next price = “generalized second price” (GSP)
Yahoo! Confidential
Research
Selected survey of sponsored
search research at Yahoo!
• Analytic comparison of mechanisms [Lahaie]
• Learning click rates: N-armed bandit formulation
[Pandey & Olsten]
• Simulation I: Static [Feng, Bhargava, Pennock]
• Simulation II: Equilibrium [Lahaie, Pennock]
• Pragmatic robot [Schwarz, Edelman]
Source: S. Lahaie
An Analysis of Alternative
Slot Auction Designs for
Sponsored Search
Sebastien Lahaie, Harvard University*
*work partially conducted at Yahoo! Research
ACM Conference on Electronic Commerce,
2006
Source: S. Lahaie
Objective
• Initiate a systematic study of Yahoo! and
Google slot auctions designs.
• Look at both “short-run” incomplete
information case, and “long-run”
complete information case.
Source: S. Lahaie
Outline
• Incomplete information (one shot game)
•
•
•
•
Incentives
Efficiency
Informational requirements
Revenue
• Complete Information (long-run
equilibrium)
•
•
•
Existence of equilibria
Characterization of equilibria
Efficiency of equilibria (“price of anarchy”)
Source: S. Lahaie
The Model
• slots, bidders
• The type of bidder i consists of
• a value per click of , realization
• a relevance , realization
•
is bidder i’s revenue,
realization
• Ad in slot
So CTRi,k =
is viewed with probability
• Bidder i’s utility function is quasi-linear:
Source: S. Lahaie
The Model (cont’d)
•
is i.i.d on
according to
• is continuous and has full support
• is common knowledge
• Probabilities
are
common knowledge.
• Only bidder i knows realization
• Both seller and bidder i know
other bidders do not
, but
Source: S. Lahaie
Auction Formats
•
•
•
•
•
Rank-by-bid (RBB): bidders are ranked
according to their declared values ( )
Rank-by-revenue (RBR): bidders are
ranked according to their declared
revenues (
)
First-price: a bidder pays his declared
value
Second-price (next-price): For RBB, pays
next highest price. For RBR, pays
All payments are per click
Source: S. Lahaie
Incentives
• First-price: neither RBB nor RBR is
truthful
• Second-price: being truthful is not a
dominant strategy, nor is it an ex post
Nash equilibrium (by example):
1
1
6
4
• Use Holmstrom’s lemma to derive
truthful payment rules for RBB and RBR:
• RBR with truthful payment rule is VCG
Source: S. Lahaie
Efficiency
• Lemma: In a RBB auction with either a
first- or second-price payment rule, the
symmetric Bayes-Nash equilibrium bid is
strictly increasing with value. For RBR it
is strictly increasing with product.
• RBB is not efficient (by example).
0.5
6
1
4
• Proposition: RBR is efficient (proof).
Source: S. Lahaie
First-Price Bidding Equilibria
•
•
is the expected resulting
clickthrough rate, in a symmetric
equilibrium of the RBB auction, to a
bidder with value y and relevance 1.
is defined similarly for bidder with
product y and relevance 1.
• Proposition: Symmetric Bayes-Nash
equilibrium strategies in a first-price RBB
and RBR auction are given by,
respectively:
Source: S. Lahaie
Informational
Requirements
• RBB: bidder need not know his own
relevance, or the distribution over
relevance.
• RBR: must know own relevance and
joint distribution over value and
relevance.
Source: S. Lahaie
Revenue Ranking
• Revenue equivalence principle:
auctions that lead to the same
allocations in equilibrium have the
same expected revenue.
• Neither RBB nor RBR dominates in
terms of revenue, for a fixed number
of agents, slots, and a fixed
.
Source: S. Lahaie
Complete Information
Nash Equilibria
Argument: a bidder always tries to match the nextlowest bid to minimize costs. But it is not an equilibrium
for all to bid 0.
Argument: corollary of characterization lemma.
Source: S. Lahaie
Characterization of
Equilibria
• RBB: same characterization with
replacing
Source: S. Lahaie
Price of Anarchy
Define:
Source: S. Lahaie
Exponential Decay
•
•
•
Typical model of decaying clickthrough
rate:
[Feng et al. ’05] find that their actual
clickthrough data is fit well by such a model
with
In this case
Source: S. Lahaie
•
•
Conclusion
Incomplete information (on-shot game):
•
•
•
•
Neither first- nor second-pricing leads to
truthfulness.
RBR is efficient, RBB is not
RBB has weaker informational requirements
Neither RBB nor RBR is revenue-dominant
Complete information (long-run equilibrium):
•
•
First-price leads to no pure strategy Nash
equilibria, but second-price has many.
Value in equilibrium is constant factor away from
“standard” value.
Source: S. Lahaie
Future Work
• Better characterization of revenue
properties: under what conditions on
does either RBB or RBR dominate?
• Revenue results for complete
information case (relation to Edelman
et al.’s “locally envy-free equilibria”).
Source: S. Lahaie
Research Problem: Online
Estimation of Clickrates
•
•
•
•
Make virtually no assumptions on clickrates.
Each different ranking yields (1) information on
clickrates and (2) revenue.
Tension between optimizing current revenue
based on current information, and gaining more
info on clickrates to optimize future revenue
(multi-armed bandit problem...)
Twist: chosen policy determines rankings,
which will affect agent’s equilibrium behavior.
Research
Handling Advertisements of
Unknown Quality in Search
Advertising
Sandeep Pandey, Carnegie Mellon University
Christopher Olston, Yahoo! Research and CMU
Neural Information Processing Systems, 2006
Research
CTR estimation
• Explore/exploit tradeoff
• Exploit: Use current CTR est’s to rank
• Explore: Try new or low rank advertisers
in higher positions to improve CTR est’s
QuickTime™ and a
TIFF (LZW) decompressor
are needed to see this picture.
Research
Analytic results
• Unbudgeted: Cast as independent
multi-armed bandits, propose “MIX”
policy
• Budgeted: New budgeted multi-armed
multi-bandit formulation (BMMP)
bpol(N) >= opt(N)/2 + O(ln N)
Research
Experiments: Real Y! data
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TIFF (LZW) decompressor
are needed to see this picture.
Research
Extensions
• Using prior information
e.g. algorithmic relevance of listing
• Allowing ads to come and go at any
time
• Additional performance bounds
Research
Implementing sponsored
search in web search engines:
Computational evaluation of
alternative mechanisms
Jane Feng, University of Florida
Hemant Bhargava, University of California Davis
David Pennock, Yahoo! Research
Informs Journal on Computing, forthcoming
Research
Simulation model
Source: J. Feng
Research
Simulation model
•  = relevance  click rate (CTR)
• v = advertiser value
• (,v) = bivariate normal
• Revenue:
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Research
Allocation Rules Tested
•
•
•
•
Bid ranking
Revenue ranking
Relevance ranking
Posted price
Research
Simulation Results
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TIFF (LZW) decompressor
are needed to see this picture.
Research
Simulation Results
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TIFF (LZW) decompressor
are needed to see this picture.
Number of paid slots
Research
Simulation Results
Effect of
editorial
control
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TIFF (LZW) decompressor
are needed to see this picture.
Research
Simulation Results
Effect of
naive
learning
of 
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Research
QuickTime™ and a
TIFF (LZW) decompressor
are needed to see this picture.
Research
Equilibrium revenue
simulations of hybrid
sponsored search mechanisms
Sebastien Lahaie, Harvard University*
*work conducted at Yahoo! Research
David Pennock, Yahoo! Research
Source: S. Lahaie
Monte-Carlo simulations
• 10 bidders, 10 positions
• Value and relevance are i.i.d. and have
lognormal marginals with mean and
variance (1,0.2) and (1,0.5) resp.
• Spearman correlation between value and
relevance is varied between -1 and 1.
• Standard errors are within 2% of plotted
estimates.
Yahoo! Confidential
Revenue effects
Y! today
Highest bid wins
Google/Panama
Highest bid*CTR wins
Hybrid
Highest bid*(CTR)s wins
s=0
s=1
s=1/2 ?
s=3/4 ?
• What gives most revenue?
– Key: If rules change, advertiser bids will change
– Use Edelman et al. envy-free equilibrium solution
Yahoo! Confidential
Source: S. Lahaie
Yahoo! Confidential
Source: S. Lahaie
Yahoo! Confidential
Source: S. Lahaie
Yahoo! Confidential
Source: S. Lahaie
Preliminary Conclusions
• With perfectly negative correlation
(-1), revenue, efficiency, and relevance
exhibits threshold behavior
• Squashing up to this threshold can
improve revenue without too much
sacrifice in efficiency or relevance
• Squashing can significantly improve
revenue with positive correlation
Yahoo! Confidential
Source: M. Schwarz
Pragmatic Robots and
Equilibrium Bidding in GSP
Auctions
Michael Schwarz, Yahoo! Research
Ben Edelman, Harvard University
Thanks: M. Schwarz
Testing game theory
• Empirical game theory
– Analytic solutions intractable in all but simplest settings
– Laboratory experiments cumbersome, costly
– Agent-based simulation: easy, cheap, allow massive
exploration; Key: modeling realistic strategies
• Ideal for agent-based simulation: when real economic
decisions are already delegated to software
“If pay-per-click marketing is so strategic, how can it be
automated? That’s why we developed Rules-Based Bidding.
Rules-Based Bidding allows you to apply the kind of rules you
would use if you were managing your bids manually.” Atlas
http://www.atlasonepoint.com/products/bidmanager/rulesbased
Yahoo! Confidential
Source: M. Schwarz
Bidders’ actual strategies
Yahoo! Confidential
Source: M. Schwarz
Models of GSP
1.
Static game of complete information
2.
Generalized English Auction (simple dynamic model)
More realistic model
•
Each period one random bidder can change his bid
•
Before the move a bidder observes all standing bids
Yahoo! Confidential
Source: M. Schwarz
Pragmatic Robot (PR)
• Find current optimal position i
Implies range of possible bids: Static best
response (BR set)
• Choose envy-free point inside BR set:
Bid up to point of indifference between
position i and position i-1
• If start in equilibrium PRs stay in
equilibrium
Yahoo! Confidential
Convergence of PR
Simulation
Source: M. Schwarz
1.6
Total Surplus
Search Engine Revenue
Advertiser Surplus
Computed Equilibrium
1.4
1.2
1
0.8
0.6
0.4
0.2
0
Yahoo! Confidential
100
200
300
400
500
simulation rounds - convergence to 0.000001 after 329 iterations
600
700
800
Source: M. Schwarz
Convergence of PR
Yahoo! Confidential
Source: M. Schwarz
Convergence of PR
• The fact that PR converges supports
the assertion that the equilibrium of a
simple model informs us about the
outcome of intractable dynamic game
that inspired it
?
Complex game that we
can not solve
Yahoo! Confidential
Simple model inspired by
a complex game
Source: M. Schwarz
Playing with Ideal Subjects
Largest Gap (commercially available strategy)
Moves your keyword listing to the largest bid
gap within a specified set of positions
Regime One: 15 robots all play Largest Gap
Regime Two: one robot becomes pragmatic
By becoming Pragmatic pay off is up 16%
Other assumptions: values are log normal, mean valuation 1, std
dev 0.7 of the underlying normal, bidders move sequentially in
random order
Yahoo! Confidential
Source: M. Schwarz
ROI
• Setting ROI target is a popular strategy
• For any ROI goal the advertiser who
switches to pragmatic gets higher
payoff
Yahoo! Confidential
Source: M. Schwarz
If others play ROI targeter
• Bidders 1,...,K-1 bid according to the ROI
targeting strategy
• What is K’s
best response?
bidder payoffs if bidder K
plays
ROI
bidder targeting
1
…
K-1
K
Yahoo! Confidential
0.0387
PR
0.0457
Reinforcement Learner
vs Pragmatic Robot
• Pragmatic learner outperforms
reinforcement learner (that we tried)
• Remark: reinforcement learning does
not converge in a problem with big BR
set
Yahoo! Confidential
Source: M. Schwarz
Thanks: M. Schwarz
Conclusion
• A strategy inspired by theory seems useful in
practice: PR beats commercially available
strategies and other reasonable baselines
• Since PR converges and performs well, the
equilibrium concept is sound in spite the fact that
some theoretical assumptions are violated and
there are plenty of players who are “irrational”
• When bidding agents are used for real economic
decisions (e.g., search engine optimization), we
have an ideal playground for empirical game theory
simulations
Yahoo! Confidential
Research
First Workshop on
Sponsored Search Auctions
at ACM Electronic Commerce, 2005
Organizers:
Kursad Asdemir, University of Alberta
Hemant Bharghava, University of California Davis
Jane Feng, University of Florida
Gary Flake, Microsoft
David Pennock, Yahoo! Research
Research
Papers
• Mechanism Design
• Pay-Per-Percentage of Impressions: An Advertising
Method that is Highly Robust to Fraud, J.Goodman
•
• Stochastic and Contingent-Payment Auctions,
C.Meek,D.M.Chickering, D.B.Wilson
• Optimize-and-Dispatch Architecture for Expressive
Ad Auctions, D.Parkes, T.Sandholm
•
• Sponsored Search Auction Design via Machine
Learning, M.-F. Balcan, A.Blum, J.D.Hartline, Y.Mansour
• Knapsack Auctions, G.Aggarwal, J.D. Hartline
• Designing Share Structure in Auctions of Divisible
Goods, J.Chen, D.Liu, A.B.Whinston
Research
Papers
• Bidding Strategies
• Strategic Bidder Behavior in Sponsored Search
Auctions, Benjamin Edelman, Michael Ostrovsky
• A Formal Analysis of Search Auctions Including
Predictions on Click Fraud and Bidding Tactics,
B.Kitts, P.Laxminarayan, B.LeBlanc, R.Meech
• User experience
• Examining Searcher Perceptions of and Interactions
with Sponsored Results, B.J.Jansen, M. Resnick
• Online Advertisers' Bidding Strategies for Search,
Experience, and Credence Goods: An Empirical
Investigation, A.Animesh, V. Ramachandran,
• S.Vaswanathan
•
Research
Stochastic Auctions
C.Meek,D.M.Chickering, D.B.Wilson
• Ad ranking allocation rule is stochastic
• Why?
• Reduces incentive for “bid jamming”
• Naturally incorporates explore/exploit mix
• Incentive for low value bidders to join/stay?
• Derive truthful pricing rule
• Investigate contingent-payment auctions:
Pay per click, pay per action, etc.
• Investigate bid jamming, exploration
strategies
Research
Expressive Ad Auctions
D.Parkes, T.Sandholm
• Propose expressive bidding semantics for
ad auctions (examples next)
• Good: Incr. economic efficiency, incr. revenue
• Bad: Requires combinatorial optimization;
Ads need to be displayed within milliseconds
• To address computational complexity,
propose “optimize and dispatch”
architecture: Offline scheduler “tunes” an
online (real-time) dispatcher
Research
Expressive bidding I
• Multi-attribute bidding
Advertiser
1
Advertiser
2
Advertiser
1
Advertiser
2
Male users
(50%)
$1
$2
Pre-qualified
(50%)
$2
$2
Female users
(50%)
$2
$1
Other (50%)
$1
$1
Undifferentiated
$1.50
$1.50
Undifferentiated
$1.50
$1.50
Research
Expressive bidding II
• Competition constraints
b xCTR = RPS
3 x .05 = .15
1 x .05 = .05
Research
Expressive bidding II
• Competition constraints
monopoly bid
b xCTR = RPS
4 x .07 = .28
Research
Expressive bidding III
•
•
•
•
•
•
•
•
•
Guaranteed future delivery
Decreasing/increasing marginal value
All or nothing bids
Pay per: impression, click, action, ...
Type/id of distribution site (content match)
Complex search query properties
Algo results properties (“piggyback bid”)
Ad infinitum
Keys: What advertisers want; what
advertisers value differently; controlling
cognitive burden; computational complexity
Source: K. Asdemir
Second Workshop on
Sponsored Search Auctions
Organizing Committee
Kursad Asdemir, University of Alberta
Jason Hartline, Microsoft Research
Brendan Kitts, Microsoft
Chris Meek, Microsoft Research
Objectives

Diversity

Participants



Industry: Search engines and search engine marketers
Academia: Engineering, business, economics schools
Approaches




Source: K. Asdemir
Mechanism Design
Empirical
Data mining / machine learning
New Ideas
History & Overview

First Workshop on S.S.A.





Vancouver, BC 2005
~25 participants
10 papers + Open discussion
4 papers from Microsoft Research
Second Workshop on S.S.A.



~40-50 participants
10 papers + Panel
3 papers from Yahoo! Research
Source: K. Asdemir
Participants

Industry



Source: K. Asdemir
Yahoo!, Microsoft, Google
Iprospect (Isobar), Efficient Frontier, HP Labs, Bell
Labs, CommerceNet
Academia

Several schools
Papers

Mechanism design





Bidding behavior





Edelman, Ostrovsky, and Schwarz
Iyengar and Kumar
Liu, Chen, and Whinston
Borgs et al.
Zhou and Lukose
Szymanski and Lee
Asdemir
Borgs et al.
Data mining


Regelson and Fain
Sebastian, Bartz, and Murthy
Source: K. Asdemir
Source: K. Asdemir
Panel: Models of Sponsored Search:
What are the Right Questions?

Proposed by


Lance Fortnow and Rakesh Vohra
Panel members




Kamal Jain, Microsoft Research
Rakesh Vohra, Northwestern University
Michael Schwarz, Yahoo! Inc
David Pennock, Yahoo! Inc
Panel Discussions

Mechanisms





Hard or a soft constraint
Flighting (How to spend the budget over time?)
Pay-per-what? CPM, CPC, CPS



Competition between mechanisms
Ambiguity vs Transparency: “Pricing” versus “auctions”
Involving searchers
Budget


Source: K. Asdemir
Risk sharing
Fraud resistance
Transcript available!
Research
Web resources
• 1st Workshop website & papers:
http://research.yahoo.com/workshops/ssa2005/
• 1st Workshop notes (by Rohit Khare):
http://wiki.commerce.net/wiki/RK_SSA_WS_Notes
• 2nd Workshop website & papers:
http://www.bus.ualberta.ca/kasdemir/ssa2/
• 2nd Workshop panel transcript:
(thanks Hartline & friends!)
http://research.microsoft.com/~hartline/papers/
panel-SSA-06.pdf
Research
http://buzz.research.yahoo.com
•
•
•
Yahoo!,O’Reilly launched Buzz Game 3/05 @ETech
Research testbed for investigating prediction markets
Buy “stock” in hundreds of technologies
•
Earn dividends based on actual search “buzz”
•
•
•
API interface
Exchange mechanism is Yahoo! invention (dynamic parimutuel)
Cross btw stock market and horse race betting
Research
Technology forecasts
• iPod phone
• What’s next?
Google Calendar?
price
QuickTime™ and a
TIFF (LZW) decompressor
are needed to see this picture.
search
buzz
QuickTime™ and a
TIFF (LZW) decompressor
are needed to see this picture.
8/28: buzz gamers
begin bidding
up iPod phone
8/29: Apple
invites press
to “secret”
unveiling
9/7: Apple
announces
Rokr
9/8-9/18: searches
for iPod phone soar;
early buyers profit
• Another Apple unveiling
10/12; iPod Video?
QuickTime™ and a
TIFF (LZW) decompressor
are needed to see this picture.
9am 10/5
Research
Forecast
accuracy
Early lessons
learned
• Average forecast error
across 352 stocks
• Market closing deadline
focuses traders
• Dividend levels matter
• Intelligent strategies work
forecast error
rapidly declines
as traders zero in
on correct
predictions
end of phase 1
contest period
• Randomized bots lost
money to real traders
• Contest winner followed
optimal buzz trading
strategy (prices  buzz);
Went from 4th to 1st place in
final days
• Forecast error does
decrease over time
Research
Forecast accuracy
• Stocks categorized by
Day 0 implied buzz /
actual buzz
• Graph shows movement
of actual buzz for each
category
Research
Tech Buzz Game
Research
Pari-mutuel market
Basic idea
1
1
Research
Dynamic pari-mutuel market
Basic idea
Research
How are prices set?
• A price function pi(n) gives the
instantaneous price of an infinitesimal
additional share beyond the nth
• Cost of buying n shares: 0 pi(n) dn
n
• Different reasonable assumptions lead
to different price functions
Research
Share-ratio price function
• One can view DPM as a market maker
• Shares pay equal portion of total $$:
C(Qfinal)/qo >= $1
• Ratio of shares qi/qj = ratio of prices pi/pj
• Cost Function:
C (Q ) 
n
 qi
2
i 1
• Price Function:
pi (Q ) 
qi
n
qj
j 1
2
More Challenges
• Predicting click through rates
• Detecting click spam
• Pay per “action” / conversion
• Number of ad slots
• Improved targeting / expressiveness
• Content match