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Traffic Shaping to Optimize Ad Delivery

Deepayan ChakrabartiErik Vee

Traffic Shaping

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Which article summary should

be picked?

Ans: The one with highest expected CTR

Which ad should be displayed?

Ans: The ad that minimizes underdelivery

Article pool

Underdelivery

Advertisers are guaranteed some impressions (say, 1M) over some time (say, 2 months) only to users matching their specs only when they visit certain types of pages only on certain positions on the page

An underdelivering ad is one that is likely to miss its guarantee

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Traffic Shaping

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Which article summary should

be picked?

Ans: The one with highest expected CTR

Which ad should be displayed?

Ans: The ad that minimizes underdelivery

Goal: Combine the two

Traffic Shaping

Goal: Bias the article summary selection to reduce under-delivery but insignificant drop in CTR AND do this in real-time

Outline

Formulation as an optimization problem Real-time solution Empirical results

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Formulation

j:(ads)

ℓ:(user, article, position)“Fully Qualified Impression”

i:(user, article)

k:(user)

ℓj

i

k

Goal: Infer traffic shaping fractions wki

Supply sk

CTR c ki

Traffic

shaping

fractio

n w ki

Demand dj

Ad delivery fraction φℓj

Formulation

Full traffic shaping graph: All forecasted user traffic X

all available articles arriving at the homepage, or directly on article page

Goal: Infer wki But forced to infer φℓj as

wellFull Traffic Shaping Graph

A

B

C

Traffic

shaping

fractio

n w ki

Ad delivery fraction φℓj

CTR c ki

Outline

Formulation as an optimization problem Real-time solution Empirical results

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Formulation Reformulation: {wki, φℓj}→ zℓj

Convex program can be solved optimally

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Formulation

But we have another problem At runtime, we must shape every incoming user

without looking at the entire graph

Solution: Periodically solve the convex problem offline Store a cache derived from this solution Reconstruct the optimal solution for each user at

runtime, using only the cache

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Real-time solution

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Cache these

Reconstruct using these

All constraints can be expressed as constraints on σℓ

Results

Data: Historical traffic logs from April, 2011 25K user nodes

Total supply weight > 50B impressions 100K ads

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Lift in impressionsLi

ft in

impr

essi

ons

deliv

ered

to

unde

rper

form

ing

ads

Fraction of traffic that is not shaped

Nearly threefold improvement via

traffic shaping

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Average CTRA

vera

ge C

TR (a

s pe

rcen

tage

of

max

imum

CTR

)

Fraction of traffic that is not shaped

CTR drop < 10%

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Summary

3x underdelivery reduction with <10% CTR drop 2.6x reduction with 4% CTR drop Runtime application needs only a small cache

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