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

Outline

● Introduction of Real-Time Bidding (RTB)● Introduction of Winning Price

● Modeling Winning Price

● Experiments

● Conclusions

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

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

Second Price Auction

Source: http://www.science4all.org/le-nguyen-hoang/auction-design/

Outline

● Introduction of Real-Time Bidding (RTB)

● Introduction of Winning Price● Modeling Winning Price

● Experiments

● Conclusions

Winning Price

The highest bidding price from other competitors

● The winning price of purple: 200$

● The winning price of others: 250$

Our Goal: Predicting the Winning Price

● Predicting the winning price of future auctions given the historical winning/losing bid information the buyer observed

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

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.

Outline

● Introduction of Real-Time Bidding (RTB)

● Introduction of Winning Price

● Modeling Winning Price● Experiments

● Conclusions

Observation

● For losing bids, The bidding price is the lower bound of the winning price.

● It is called right censored

Base Model of the Winning Price

Problem of Linear Regression

Problem of the Censored Regression Model

Mixture Model ● Censored regression model is closer to

unobserved data● Linear regression model is closer to

observed data

Challenge of the Mixture Model● We do not know whether the bid is winning

bids or losing bids

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

Mixture Model

● Learn the linear and censored regression models

● Learn the winning rate

● Combining these models to produce mixture model

Outline

● Introduction of Real-Time Bidding (RTB)

● Introduction of Winning Price

● Modeling Winning Price

● Experiments● Conclusions

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

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

Questions

● (Q1) Different Winning Price Pattern

● (Q2) Censored regression model vs. linear regression model

● (Q3) The Performance of the Mixture model

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

Inconsistent Pattern of Winning Price (Q1)

● The performance of linear regression based on winning and losing bids are different.

Censored Regression vs. Linear Regression (Q2)

βlm is the linear regressionβclm is the censored regressionThe MSE is evaluated on losing bids

Performance of the Mixture Model (Q3)

βlm is the linear regressionβclm is the censored regressionβmix is the mixture modelThe MSE is evaluated on losing bids- The mixture model usually outperforms the linear regression- The mixture model is more robust than the censored regression

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

Thank You