2017 IEEE International Conference on Big Data (Big Data) 2017
DOI: 10.1109/bigdata.2017.8258095
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A gamma-based regression for winning price estimation in real-time bidding advertising

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Cited by 22 publications
(17 citation statements)
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“…Wu et al [29] proposed another deep learning framework, which models the winning price as Gumbel distribution, to solve the censor problem. Zhu et al [30] proposed a gamma-based censored linear regression and a two-step optimization method to learn the model. Wang et al [31] proposed a novel decision tree-based method and utilized the non-parametric survival models to predict winning price without making any assumption for winning price distribution.…”
Section: B Winning Price Predictionmentioning
confidence: 99%
“…Wu et al [29] proposed another deep learning framework, which models the winning price as Gumbel distribution, to solve the censor problem. Zhu et al [30] proposed a gamma-based censored linear regression and a two-step optimization method to learn the model. Wang et al [31] proposed a novel decision tree-based method and utilized the non-parametric survival models to predict winning price without making any assumption for winning price distribution.…”
Section: B Winning Price Predictionmentioning
confidence: 99%
“…Wu et al [32] proposed a regression model based on Gaussian distribution to fit the market price. Recently, Gamma distribution for market price modeling has also been studied in the work [43]. The main drawback of these distributional methods is that these restricted empirical preassumptions may lose the effectiveness of handling various dynamic data and they even ignore the sophisticated real data divergence as we show in Figure 2.…”
Section: Related Workmentioning
confidence: 99%
“…Wu et al [31,32] proposed a censored regression model using the lost auction data to alleviate the data bias problem. Nevertheless, the Gaussian distribution or other distributional assumptions [43] turn out to be too restricted while lacking of flexibility for modeling sophisticated yet practical distributions. Another problem is that these regression models [31,32,43] can only provide a point estimation, i.e., the expectation of the market price without standard deviation, which fails to provide winning probability estimation given an arbitrary bid price to support the subsequent bidding decision [26].…”
Section: Related Workmentioning
confidence: 99%
“…[1], [2], [3], [4], [5]). Some works such as [6], [7], [8] try to estimate or predict the winning bid (possibly in the presence of censoring) by using a historical dataset containing the top two bids and additional features relating to the context of the user visiting a website. These papers use different methods in order to predict the winning bid.…”
Section: Related Literaturementioning
confidence: 99%
“…These papers use different methods in order to predict the winning bid. In [6] a deep neural network is used, in [7] a regression model with a gamma distribution is used to deal with censoring, and in [8] a mixture model is used. These studies however do not focus on setting optimal reserve prices.…”
Section: Related Literaturementioning
confidence: 99%