Presentation of Predicting Winning Price in Real Time Bidding with Censored Data in 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining

1. Predicting Winning Price in Real
Time Bidding with Censored Data
Wush Wu#, Mi-Yen Yeh*, and Ming-Syan Chen#
#: Dept. of Electrical Engineering, National Taiwan University
*:Inst. of Information Science, Academia Sinica

2. Outline
● Introduction of Real-Time Bidding (RTB)
● Introduction of Winning Price
● Modeling Winning Price
● Experiments
● Conclusions

3. Real-Time Bidding
http://www.previewnetworks.com/blog/the-rtb-discussion-for-brands-and-publishers/
Advertisers
Publishers
Demand-Side
Platform (DSP)
Supply-Side
Platform (SSP)
AD Exchange

4. Trading the Impression
● The sellers provide:
– Information of the
publishers
– Identification of the ad
viewer
● The buyers estimate:
– The value of the
impression
Bid Request:
● User Identity
● User IP
● URL
● Ad SlotVisibility
● Ad SlotSize
Advertisers
Publishers
Demand-Side
Platform (DSP)
Supply-Side
Platform (SSP)
Bid Response:
● Bidding Price

5. Second Price Auction
Source: http://www.science4all.org/le-nguyen-hoang/auction-design/

6. Outline
● Introduction of Real-Time Bidding (RTB)
● Introduction of Winning Price
● Modeling Winning Price
● Experiments
● Conclusions

7. Winning Price
The highest bidding price from other competitors
● The winning price of purple: 200$
● The winning price of others: 250$

8. Our Goal: Predicting the Winning Price
● Predicting the winning price of future auctions given the
historical winning/losing bid information the buyer
observed

9. The importance of the Winning Price
● The winning price represents:
– the cost of the impression
– the value of the impression to the competitors
● The winning price helps the bidding strategy
● The winning price improves the estimation of the Click-
Through-Rate(CTR) and the Conversion Rate(CVR)
https://clientmanagementvn.files.wordpress.com/2012/09/competitor-analysis.jpg

10. Challenge of Predicting the Winning Price
● In second price auction, the winning price is unobserved if
the bid is lost.
● No previous work on predicting winning price on buyer
side
– Cui et al. modeled the winning price with the mixture-of-
log-normal distribution on various targeting attributes.

11. Outline
● Introduction of Real-Time Bidding (RTB)
● Introduction of Winning Price
● Modeling Winning Price
● Experiments
● Conclusions

12. Observation
● For losing bids, The bidding price is the lower bound of
the winning price.
● It is called right censored

13. Base Model of the Winning Price

14. Problem of Linear Regression

16. Problem of the Censored
Regression Model

17. Mixture Model
● Censored regression model is closer to
unobserved data
● Linear regression model is closer to
observed data

18. Challenge of the Mixture Model
● We do not know whether the bid is winning
bids or losing bids

19. Winning Rate
● We use the estimated winning rate to classify
whether the bidding will be observed or
censored
– The winning rate is estimated by the
logistic regression

20. Mixture Model
● Learn the linear and censored regression models
● Learn the winning rate
● Combining these models to produce mixture model

21. Outline
● Introduction of Real-Time Bidding (RTB)
● Introduction of Winning Price
● Modeling Winning Price
● Experiments
● Conclusions

22. Datasets
● iPinYou Real-Time Bidding Dataset
– Available at: http://data.computational-advertising.org/
– The codes for related experiments:
https://github.com/wush978/KDD2015wpp
● Bridgewell Inc., the major DSP in Taiwan

23. Preprocessing
● Use real winning bids only
● Set the bidding price to be x% of original bidding price
Original Bidding Price
Simulated Bidding Price
Original Winning Price, not changed
Original Bidding Price
Simulated Bidding Price
Original Winning Price, not changed
Simulated Losing Bids
Simulated Winning Bids

24. Questions
● (Q1) Different Winning Price Pattern
● (Q2) Censored regression model vs. linear regression
model
● (Q3) The Performance of the Mixture model

25. Inconsistent Pattern of Winning
Price (Q1)
● The avg. winning price is different on winning bids and
losing bids
Day Avg. WP on W Avg. WP on L
2013-06-06 52.46772 185.3269
2013-06-07 51.12051 186.9674
2013-06-08 58.48506 189.4200
2013-06-09 58.92701 188.2934

26. Inconsistent Pattern of Winning
Price (Q1)
● The performance of linear regression based on winning and
losing bids are different.

27. Censored Regression vs. Linear
Regression (Q2)
βlm is the linear regression
βclm is the censored regression
The MSE is evaluated on losing bids

28. Performance of the Mixture Model
(Q3)
βlm is the linear regression
βclm is the censored regression
βmix is the mixture model
The MSE is evaluated on losing
bids
- The mixture model usually
outperforms the linear
regression
- The mixture model is more
robust than the censored
regression

29. Conclusion
● We are the first to tackle the winning price prediction
problem from the buyer side
● Prediction performance is improved by taking the censored
information into account

30. Thank You