Paper Info
Title
A Gamma-Based Regression For Winning Price Estimation In Real-Time Bidding Advertising
Abstract
In Real-Time Bidding (RTB) advertising, estimating the winning price is an important task in evaluating the bid cost of bid requests in Demand-Side Platforms (DSPs). The prior works utilize censored linear regression for winning price estimation by considering both winning and losing bid records. In the traditional regression models, the winning price of each bid request is based on Gaussian distribution. However, the property of Gaussian distribution is not suitable for the winning price of each bid request, and it is hard to link the physical meaning of Gaussian distribution and the winning price. Therefore, in this paper, based on our observation and analysis, the winning price of each bid request is modeled by a unique gamma distribution with respect to its features. Then we propose a gamma-based censored linear regression with regularization for winning price estimation. To derive the parameters of our proposed complicated model based on bid records, our approach is to divide this hard problem into two sub-problems, which are easier to solve. In practice, we also provide four heuristic initial parameter settings that are able to greatly reduce the computation cost when deriving the parameters. The experimental results demonstrate that our approach is highly effective for estimating the winning price compared with the state-of-the-art approaches in three real datasets.
Year
DOI
Venue
2017
10.1109/BigData.2017.8258095
2017 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA)
DocType
ISSN
Citations 
Conference
2639-1589
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Wen-Yuan Zhu1895.57
Wen-Yueh Shih211.72
Ying-Hsuan Lee300.34
Wen-Chih Peng41645106.49
Jiun-Long Huang500.68