Google’s AdWords Market: How Theory Influenced Practice · nMaximize Google’s revenue...

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Google’s AdWords Market: How Theory Influenced Practice Vijay V. Vazirani University of California, Irvine

Transcript of Google’s AdWords Market: How Theory Influenced Practice · nMaximize Google’s revenue...

Page 1: Google’s AdWords Market: How Theory Influenced Practice · nMaximize Google’s revenue –efficient solution! nGREEDY:Display the ad of highest bidder ¨Assume: only 1 ad is shown

Google’s AdWords Market:How Theory Influenced Practice

Vijay V. Vazirani

University of California, Irvine

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Two major achievements of Google

1). Search: That is robust to spam

2). Advertising: Business model

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Revolution in advertising

n Matching merchants with customers¨ How can a merchant pitch ads in a targeted way

to customers who are interested in his goods.

n Difficulty: How to find out needs/desires of customers in a super-efficient manner?

n Insight: A user’s search queries reveals to Google a succinct window into her mind/needs

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n Solution: Auction off queried keywords to advertisers!

n This converts a giant undefined matching problem into a gigantic auction!

n Google is world’s biggest auction house: billions daily!

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How to allocate keywords to advertisers?

n Advertisers bid for specific keywordsn Maximize Google’s revenue – efficient solution!

n GREEDY: Display the ad of highest bidder ¨ Assume: only 1 ad is shown

n Charge bid, or second highest bid (Vickery auction)

This soln. was being used!

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Problem …

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Example:

$1 $0.99

$1 $0

Book

CD

Bidder1 Bidder 2

B1 = B2 = $100

Queries: 100 Books then 100 CDs

Bidder 1 Bidder 2

Algorithm Greedy

LOSTRevenue$100

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Example:

$1 $0.99

$1 $0

Book

CD

Bidder1 Bidder 2

B1 = B2 = $100

Queries: 100 Books then 100 CDs

Bidder 1 Bidder 2

Optimal Allocation

Revenue$199

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Must incorporate budgets in solution

n Large fraction of advertisers are budget-constrained

n Budget distribution is heavy-tailed

Must handle small budget advertisers adequately

n Solution must be real time and particularly simple!

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Adwords Problem

Mehta, Saberi, Vazirani & V, 2005:

Simple framework that captures essential features.

Will add bells and whistles later!

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The Adwords Problem

N advertisers. ¨ Daily Budgets B1, B2, …, BN = $1¨ Each advertiser provides bids for keywords he is interested in.

Search EngineSelect one Ad

Advertiser pays his bid

queries (online)

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Online competitive analysis

n Compare revenue of online algorithm with that of best offline allocation.

n E.g., GREEDY has competitive ratio of ½.

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Algorithm

n Will use agents’ ¨Bid ¨Fraction of budget spent

n “Correct” tradeoff between them given by a special function f

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Algorithm

Award next query to bidder with max bid (fraction of budget spent)×"

" # = 1 − '((*(+)

1 − 1/' competitive ratio, assuming bid << budget Optimal! Simple, minimalistic solution!

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Impact

n Provided a general framework for thinking about budget constrained auctions for many Ad products

n Bid scaling used widely in industry for ad auctions and for display ads:¨ Very fast: one operation per bid¨ Low on memory: one number per bid¨ No extra communication

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New algorithmic work

n Numerous models: ¨Queries arrive in random order¨Queries are picked from a distribution¨Submodular constraints on allocations¨Use historical data about query arrival¨ . . .

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Solution has roots in “pure” theory,in particular, Matching Theory!

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Online bipartite matching

!

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Online bipartite matching

! "

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Online bipartite matching

! "

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Online bipartite matching

! "

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Online bipartite matching

! "

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Online bipartite matching

! "

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n Any deterministic algorithm has competitive ratio of 1/2.

n Karp, Vazirani & V., 1990:1 − 1/$ factor randomized algorithm

Optimal!

n Same as simple case of Adwords problem:All budgets $1, all bids $0/1.

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Online !-matching

n Kalyanasundaram & Pruhs, 1996:1 − 1/% factor algorithm for b-matching:Daily budgets $!, bids $0/1, b>>1

BALANCE: Assign query to interested bidder who has spent least so far.

Optimal!

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Example: Balance

$1 $1

$1 $0

Book

CD

Bidder1 Bidder 2

B1 = B2 = $100

Queries: 100 Books then 100 CDs

Bidder 1 Bidder 2

Balance Algorithm

Revenue$1.50

¾-competitive

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Where did this tradeoff function come from?

New Proof forBALANCE {0,1}

Factor Revealing LP

Modify LP for arbitrary bids [0,1]

Use dual to get Tradeoff function

Tradeoff Revealing LP

1-1/e

1-1/e

(Jain, Mahdian, Markakis, Saberi & V., ’03)

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New proof of BALANCE

n N bidders, $1 each. Each bid is $!.

n OPT: optimal offline, ALG: BALANCE

n OPT = N

n Will show: ALG ≥ $ 1 − '(

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n Idea: Upper bound no. of bidders who spent less!

n Assume k large

n !": Bidders who spent #"$%& , ("&

n Let |!"| = *". Will constrain *%, *% + *,, …

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!"!#

ALG

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n Partition revenue of ALG

n $" ∈ $%&'( ) if bidder had *+,-. , 0+.when this money was spent.

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!"!#

ALGLayer 2Layer 1

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n Partition revenue of ALG

n $" ∈ $%&'( ) if bidder had *+,-. , 0+.when this money was spent.

n BALANCE assigns next query to interested bidder who has spent least so far.

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The picture can't be displayed.

•First Constraint: !" = $" ≤ &'()* 1 ≤ ,-

Layer 1 ≤ ,-

!"

Layer1

OPT

q

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Second Constraint: !" ∪ !$ = &" + &$ ≤ () +

(+,-)

Layer 2 ≤ (+,-)

Layer 2+

Layer 1!"!$

OPT

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Lower bound on ALG

LP(N): min ∑" "# $"s.t. constraints on $%, $' , … $#

ALG ≥ * 1 − 1 − %##

≥ * 1 − %-

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Factor revealing LP

n Jain, Mahdian, Markakis, Saberi & V., ’03

n Family of LPs: LP(N) encodes problem of finding lower bound for instance of size !.

n Infimum of optimal solutions gives approximation factor

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Where did this tradeoff function come from?

New Proof forBALANCE {0,1}

Factor Revealing LP

Modify LP for arbitrary bids [0,1]

Use dual to get Tradeoff function

Tradeoff Revealing LP

1-1/e

1-1/e

(Jain, Mahdian, Markakis, Saberi & V., ’03)

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Larger Theme

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AdWords, Amazon, eBay, Yahoo!, Alibaba, Uber,Apple (iTunes), Airbnb,

Cloud Computing …

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Markets on the Internet

n Numerous new algorithmic and game-theoretic issues raised.

n Much scope for creative work that can have a huge impact!