Abstract
The demand-side platform (DSP) is a technological ingredient that fits into the larger real-time-bidding (RTB) ecosystem. DSPs enable advertisers to purchase ad impressions from a wide range of ad slots, generally via a second-price auction mechanism. In this aspect, predicting the auction winning price notably enhances the decision for placing the right bid value to win the auction and helps with the advertiser’s campaign planning and traffic reallocation between campaigns. This is a difficult task because the observed winning price distribution is biased due to censorship; the DSP only observes the win price in case of winning the auction. For losing bids, the win price remains censored. Erstwhile, there has been little work that utilizes censored information in the learning process. In this article, we generalize the winning price model to incorporate a gradient boosting framework adapted to learn from both observed and censored data. Experiments show that our approach yields the hypothesized boost in predictive performance in comparison to classic linear censored regression.
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Paliwal, P., Renov, O. (2019). Gradient Boosting Censored Regression for Winning Price Prediction in Real-Time Bidding. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11448. Springer, Cham. https://doi.org/10.1007/978-3-030-18590-9_43
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DOI: https://doi.org/10.1007/978-3-030-18590-9_43
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