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Predicting Winning Price in Real Time Bidding with Censored Data
2015
Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '15
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
doi:10.1145/2783258.2783276
dblp:conf/kdd/WuYC15
fatcat:dix7w3an6nbcbfuvuxxp3aqnbe