標題: Predicting Traffic of Online Advertising in Real-time Bidding Systems from Perspective of Demand-Side Platforms
作者: Lai, Hsu-Chao
Shih, Wen-Yueh
Huang, Jiun-Long
Chen, Yi-Cheng
資訊工程學系
Department of Computer Science
關鍵字: Real Time Bidding;Online Advertisement;Request Traffic;Demand Side Platform
公開日期: 1-Jan-2016
摘要: Online advertising has been all the rage these years. Budget control and traffic prediction turn out to be important issues for the demand-side platforms (DSPs). However, DSPs cannot easily grab the information of audiences and media platforms. Although DSPs might have the information immediately, it is still hard to response the request of advertisements in real-time due to the high volume of features. Therefore, we propose a method predicting traffic of requests from perspective of DSPs. The features we used are simple to be extracted from historical data. The prediction model we chose is regression model with closed-form solution. Both the features and regression model make our prediction adaptive in real-time systems. Our method can detect traffic anomalies and prevent it from overwhelming prediction. Moreover, our method can also keep pace of the trend. Experiment results show that our method's error rate of prediction is about 0.9% in total, and 10% per time unit.
URI: http://hdl.handle.net/11536/146475
期刊: 2016 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA)
起始頁: 3491
結束頁: 3498
Appears in Collections:Conferences Paper