Transcript ppt

Traffic Shaping to Optimize Ad
Delivery
Deepayan Chakrabarti
Erik Vee
1
Traffic Shaping
Which article
summary should
be picked?
Which ad should
be displayed?
Ans: The one
with highest
expected CTR
Ans: The ad that
minimizes
underdelivery
Article pool
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Underdelivery

Advertisers are guaranteed some impressions
(say, 1M) over some time (say, 2 months)


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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
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
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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
ℓ
i
Supply sk
j
Demand dj
k
k:(user)
j:(ads)
i:(user, article)
ℓ:(user, article, position)
“Fully Qualified Impression”
Goal: Infer traffic shaping fractions wki
Formulation

Full traffic shaping graph:

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All forecasted user traffic X
all available articles
arriving at the homepage,
or directly on article page
A
B
Goal: Infer wki

But forced to infer φℓj as
well
C
Full Traffic Shaping Graph
Outline
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
Formulation as an optimization problem
Real-time solution
Empirical results
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Formulation

Reformulation: {wki, φℓj}→ zℓj
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Convex program  can be solved optimally
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Formulation

But we have another problem

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At runtime, we must shape every incoming user
without looking at the entire graph
Solution:
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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
Cache
these
Reconstruct
using these
All constraints can be expressed as
constraints on σℓ
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Results
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Data:
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Historical traffic logs from April, 2011
25K user nodes


Total supply weight > 50B impressions
100K ads
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Lift in impressions delivered to
underperforming ads
Lift in impressions
Nearly threefold
improvement via
traffic shaping
Fraction of traffic that is not shaped
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Average CTR (as percentage
of maximum CTR)
Average CTR
CTR drop
< 10%
Fraction of traffic that is not shaped
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Summary
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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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