Predicting Winning Price in Real Time Bidding with Censored Data

In the aspect of a Demand-Side Platform (DSP), which is the agent of advertisers, we study how to predict the winning price such that the DSP can win the bid by placing a proper bidding value in the real-time bidding (RTB) auction. We propose to leverage the machine learning and statistical methods to train the winning price model from the bidding history. A major challenge is that a DSP usually suffers from the censoring of the winning price, especially for those lost bids in the past. To solve it, we utilize the censored regression model, which is widely used in the survival analysis and econometrics, to fit the censored bidding data. Note, however, the assumption of censored regression does not hold on the real RTB data. As a result, we further propose a mixture model, which combines linear regression on bids with observable winning prices and censored regression on bids with the censored winning prices, weighted by the winning rate of the DSP. Experiment results show that the proposed mixture model in general prominently outperforms linear regression in terms of the prediction accuracy.