This document discusses demand-side platforms (DSPs) and prediction in DSPs. It explains how DSPs work with supply-side platforms (SSPs) through real-time bidding (RTB) to buy impressions for advertisers. Prediction in DSPs involves estimating click-through rates (CTRs) using models like logistic regression, factorization machines, and follow-the-regularized-leader optimization to determine optimal bid prices. The models are trained on click logs and tested offline before being implemented online through A/B testing.

1. DSP and the prediction
Ahn, Soohan

2. 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

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

4. Why using DSP and SSP?
.
.
.
Advertisers Medias
.
.
.
Effectively!
(Cheaper)
Effectively!
(Wants more
expensive ads.)

5. Why using DSP and SSP?
.
.
.
Advertisers Medias
.
.
.
AdExchange or AdNetwork

6. Why using DSP and SSP?
.
.
.
Advertisers Medias
.
.
.
Too
Complex!!
AdExchange or AdNetwork

7. Why using DSP and SSP?
.
.
.
Advertisers Medias
.
.
.
Make it BlackBox!!

8. Why using DSP and SSP?
DSP -
Demand
Side
Platform
SSP -
Supply
Side
Platform
.
.
.
Advertisers Medias
.
.
.

9. 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.

10. Real Time Bidding(RTB) in the DSP.
IMP!
SSP -
Supply
Side
Platform
Ad Request!

11. Real Time Bidding(RTB) in the DSP.
IMP!
SSP -
Supply
Side
Platform
DSP1
DSP2
DSP3
Bid Request!
(site, app, device, user info..)
DSP4

12. Real Time Bidding(RTB) in the DSP.
IMP!
SSP -
Supply
Side
Platform
DSP1
DSP2
DSP3
Bid Request!
(site, app, device, user info..)
DSP4
Floor Price:
60 (yen)

13. Real Time Bidding(RTB) in the DSP.
DSP1
Advertiser A
100 (yen)
Advertiser A1
Not to buy it.
Advertiser B
Not to buy it.
Advertiser B1
50 (yen)
Bid Request!
(site, app, device, user info..)

14. Real Time Bidding(RTB) in the DSP.
DSP1
Advertiser A
100 (yen)
Advertiser A1
Not to buy it.
Advertiser B
Not to buy it.
Advertiser B1
50 (yen)
Bid Request!
(site, app, device, user info..)

15. Real Time Bidding(RTB) in the DSP.
IMP!
SSP -
Supply
Side
Platform
DSP1
DSP2
DSP3
Bid Response!
Advertiser A : 100 (yen)
Advertiser C : 80 (yen)
Advertiser E: 50 (yen)
DSP4 Not to bid.

16. Real Time Bidding(RTB) in the DSP.
IMP!
SSP -
Supply
Side
Platform
DSP1
DSP2
DSP3
Check for the bidded ADs.
Advertiser A : 100 (yen)
Advertiser C : 80 (yen)
Advertiser E: 50 (yen)

17. Real Time Bidding(RTB) in the DSP.
IMP!
SSP -
Supply
Side
Platform
DSP1
DSP2
DSP3
Check for the price over the floor price.
Advertiser A : 100 (yen)
Advertiser C : 80 (yen)
Advertiser E: 50 (yen)
Floor Price:
60 (yen)

18. Real Time Bidding(RTB) in the DSP.
IMP!
SSP -
Supply
Side
Platform
DSP1
DSP2
Check for the price over the floor price.
Advertiser A : 100 (yen)
Advertiser C : 80 (yen)
Floor Price:
60 (yen)

19. Real Time Bidding(RTB) in the DSP.
IMP!
SSP -
Supply
Side
Platform
DSP1
DSP2
Determine the winner!
Advertiser A : 100 (yen)
Advertiser C : 80 (yen)
Floor Price:
60 (yen)

20. Real Time Bidding(RTB) in the DSP.
IMP!
SSP -
Supply
Side
Platform
DSP1
Charge the price!
Second price auction!
Advertiser A : 100 (yen)
Floor Price:
60 (yen)
Charge price: 81(yen)

21. Real Time Bidding(RTB) in the DSP.
IMP!
SSP -
Supply
Side
Platform
DSP1
DSP2
As the second price was 80(yen),
charge the second price + 1(yen) to the
winner
Advertiser A : 100 (yen)
Advertiser C : 80 (yen)
Floor Price:
60 (yen)

22. Real Time Bidding(RTB) in the DSP.
IMP!
SSP -
Supply
Side
Platform
DSP1
In this case,
Charge the floor price + 1(yen).
Advertiser A : 100 (yen)
Floor Price:
90 (yen)

23. Real Time Bidding(RTB) in the DSP.
IMP!
SSP -
Supply
Side
Platform
DSP1
Charge the price!
Second price auction!
Advertiser A : 100 (yen)
Floor Price:
90 (yen)
Charge price: 91(yen)

24. Prediction for the DSP.
• What do we predict?

25. Real Time Bidding(RTB) in the DSP.
IMP!
SSP -
Supply
Side
Platform
DSP1
DSP2
DSP3
The best price to win the auction!
Advertiser A : 100 (yen)
Advertiser C : 80 (yen)
Advertiser E: 50 (yen)
DSP4 Not to bid.

26. Prediction for the DSP.
• Bid price = Input price * spot score.
• Spot score is usually determined by predicted CTR!
• CTR: Click Through Rate

27. 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

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

29. 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

30. 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.

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

32. FM + FTRL
• Time Complexity
• O(n^2)?
• n: The number of features.
• Usually, quite big number in AdTech.
• Usually, bigger than o(2^24).

33. 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).

34. FM + FTRL
• Model

35. 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.

36. FM + FTRL
• Learning
• Currently, learned and optimized by FTRL.
• Gradient Descent (GD)
https://www.eecs.tufts.edu/~dsculley/papers/ad-click-prediction.pdf

37. 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.

38. References
- Factorization Machines
- http://www.csie.ntu.edu.tw/~b97053/paper/Rendle2010FM.pdf
- アドテク勉強会
• http://www.slideshare.net/shoho/ss-36728773?qid=e69500d6-ae97-
4e49-bf63-8bddd5dddb4b&v=default&b=&from_search=23