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Predicting Winning Price in Real Time Bidding with Censored Data

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

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Predicting Winning Price in Real Time Bidding with Censored Data

  1. 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. 2. Outline ● Introduction of Real-Time Bidding (RTB) ● Introduction of Winning Price ● Modeling Winning Price ● Experiments ● Conclusions
  3. 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. 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. 5. Second Price Auction Source: http://www.science4all.org/le-nguyen-hoang/auction-design/
  6. 6. Outline ● Introduction of Real-Time Bidding (RTB) ● Introduction of Winning Price ● Modeling Winning Price ● Experiments ● Conclusions
  7. 7. Winning Price The highest bidding price from other competitors ● The winning price of purple: 200$ ● The winning price of others: 250$
  8. 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. 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. 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. 11. Outline ● Introduction of Real-Time Bidding (RTB) ● Introduction of Winning Price ● Modeling Winning Price ● Experiments ● Conclusions
  12. 12. Observation ● For losing bids, The bidding price is the lower bound of the winning price. ● It is called right censored
  13. 13. Base Model of the Winning Price
  14. 14. Problem of Linear Regression
  15. 15. Problem of the Censored Regression Model
  16. 16. Mixture Model ● Censored regression model is closer to unobserved data ● Linear regression model is closer to observed data
  17. 17. Challenge of the Mixture Model ● We do not know whether the bid is winning bids or losing bids
  18. 18. 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
  19. 19. Mixture Model ● Learn the linear and censored regression models ● Learn the winning rate ● Combining these models to produce mixture model
  20. 20. Outline ● Introduction of Real-Time Bidding (RTB) ● Introduction of Winning Price ● Modeling Winning Price ● Experiments ● Conclusions
  21. 21. 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
  22. 22. 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
  23. 23. Questions ● (Q1) Different Winning Price Pattern ● (Q2) Censored regression model vs. linear regression model ● (Q3) The Performance of the Mixture model
  24. 24. 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
  25. 25. Inconsistent Pattern of Winning Price (Q1) ● The performance of linear regression based on winning and losing bids are different.
  26. 26. Censored Regression vs. Linear Regression (Q2) βlm is the linear regression βclm is the censored regression The MSE is evaluated on losing bids
  27. 27. 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
  28. 28. 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
  29. 29. Thank You

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