Dsp and the prediction

DSP and the prediction

Ahn, Soohan

Introduction

• Ahn, Soohan• 2015.8 ~ : Software Engineer at FreakOut Inc.• 2014.10 ~ 2015.07 : Software Engineer at DRECOM• 2012.03 ~ 2014.02 : Hanyang University, M.S in Computer Science Engineering

• Bioinformatics, String Matching.• 2005.03 ~ 2012.02 : Hanyang University, M.S in Computer Science Engineering

Introduction

• RTB(Real Time Bidding) in the DSP.• Words in the RTB.• Prediction in the DSP.

Why using DSP and SSP?

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Advertisers Medias

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Effectively! (Cheaper)

Effectively! (Wants more expensive ads.)


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Advertisers Medias

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AdExchange or AdNetwork


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Advertisers Medias

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Too Complex!!

AdExchange or AdNetwork


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Advertisers Medias

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.Make it BlackBox!!


DSP -Demand

SidePlatform

SSP -SupplySide

Platform

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Advertisers Medias

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Real Time Bidding(RTB) in the DSP.

• RTB? => Real Time Bidding.• One of the way of exchanging to serve the AD.• Real Time bidding for every impression.

• Usually, SSP decide the rule of RTB.• Second Price auction.• First Price sealed-bid auctions• Open ascending bid auctions.


IMP!SSP -SupplySide

Platform Ad Request!


IMP!SSP -SupplySide

Platform

DSP1

DSP2

DSP3

Bid Request!(site, app, device, user info..)

DSP4


IMP!SSP -SupplySide

Platform

DSP1

DSP2

DSP3


DSP4

Floor Price: 60 (yen)


DSP1

Advertiser A100 (yen)

Advertiser A1Not to buy it.

Advertiser BNot to buy it.

Advertiser B150 (yen)



DSP1

Advertiser A100 (yen)

Advertiser A1Not to buy it.

Advertiser BNot to buy it.

Advertiser B150 (yen)



IMP!SSP -SupplySide

Platform

DSP1

DSP2

DSP3

Bid Response!

Advertiser A : 100 (yen)

Advertiser C : 80 (yen)

Advertiser E: 50 (yen)

DSP4 Not to bid.


IMP!SSP -SupplySide

Platform

DSP1

DSP2

DSP3

Check for the bidded ADs.





IMP!SSP -SupplySide

Platform

DSP1

DSP2

DSP3

Check for the price over the floor price.






IMP!SSP -SupplySide

Platform

DSP1

DSP2

Check for the price over the floor price.





IMP!SSP -SupplySide

Platform

DSP1

DSP2

Determine the winner!





IMP!SSP -SupplySide

Platform

DSP1

Charge the price!Second price auction!



Charge price: 81(yen)


IMP!SSP -SupplySide

Platform

DSP1

DSP2

As the second price was 80(yen), charge the second price + 1(yen) to the winner





IMP!SSP -SupplySide

Platform

DSP1

In this case,Charge the floor price + 1(yen).




IMP!SSP -SupplySide

Platform

DSP1

Charge the price!Second price auction!



Charge price: 91(yen)

Prediction for the DSP.

• What do we predict?


IMP!SSP -SupplySide

Platform

DSP1

DSP2

DSP3

The best price to win the auction!




DSP4 Not to bid.

Prediction for the DSP.

• Bid price = Input price * spot score.• Spot score is usually determined by predicted CTR!

• CTR: Click Through Rate

Words in the DSP.

• Imp: impression.• Click• Conversion

• CPM: Cost Per Mille• CPC: Cost Per Click = Cost / Click• CPA: Cost per Action = Cost / Conversion

• CTR: Click Through Rate = Click / Impression• CVR: Conversion Through Rate = Conversion / Click

Logistic Regression

• A method for classifying data into discrete outcomes.• x: Sparse feature vector (from click logs.)• y: -1: not-clicked, 1: clicked

Logistic Regression

• The advantage of the logistic regression• Could parallelized easily to handle large scale problems.• The sparse nature of data.

• Almost all of the data (usually in x) are 0.

• May be implemented by the Hivemall.• http://www.slideshare.net/myui/hivemall-hadoop-summit-2014-san-jose?related=1• https://github.com/myui/hivemall

https://github.com/myui/hivemall

FM + FTRL

• Recently, Factorization Machine + Follow The Regularized Leader.• FM: Model, FTRL: Optimizer• FM: A new model class that combines the advantages of Support Vector Machines

(SVM) with factorization models.• Model all interactions between variables(features).

• Interactions may give better precision.

FM + FTRL

• Model• https://www.ismll.uni-hildesheim.de/pub/pdfs/Rendle2010FM.pdf

FM + FTRL

• Time Complexity• O(n^2)?• n: The number of features.

• Usually, quite big number in AdTech.• Usually, bigger than o(2^24).

FM + FTRL

• Time Complexity• O(kn)• k: The dimensionality of the factorization

• Ususally, 10 < k < 100.• n: The number of features.

• Usually, quite big number in AdTech.• Usually, bigger than o(2^24).

FM + FTRL

• Model

FM + FTRL

• Recently, Factorization Machine + Follow The Rebularized Leader.• FM: Model, FTRL: Optimizer• FTRL could reduce the dimension without losing the precision.

• https://www.eecs.tufts.edu/~dsculley/papers/ad-click-prediction.pdf• Prediction is still based on the Logistic Regression.

FM + FTRL

• Learning• Currently, learned and optimized by FTRL.

• Gradient Descent (GD)

https://www.eecs.tufts.edu/~dsculley/papers/ad-click-prediction.pdf

Applying the model to the real system.

• Offline test• Test the model on the local(non-real) system.

• Online test• A/B test.

• Apply it!• With increasing the ratio of the A/B test.

References

- Factorization Machines- http://www.csie.ntu.edu.tw/~b97053/paper/Rendle2010FM.pdf

- アトテク勉強会• http://www.slideshare.net/shoho/ss-36728773?qid=e69500d6-ae97-4e4

9-bf63-8bddd5dddb4b&v=default&b=&from_search=23

http://www.csie.ntu.edu.tw/~b97053/paper/Rendle2010FM.pdf

http://www.slideshare.net/shoho/ss-36728773?qid=e69500d6-ae97-4e49-bf63-8bddd5dddb4b&v=default&b=&from_search=23

http://www.slideshare.net/shoho/ss-36728773?qid=e69500d6-ae97-4e49-bf63-8bddd5dddb4b&v=default&b=&from_search=23