Predicting Traffic of Online Advertising in Real-time Bidding Systems from Perspective of Demand-side Platforms

• Main Authors: Lai, Hsu-Chao, 賴旭昭
• Other Authors: Huang, Jiun-Long
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/47200667497798430793

碩士 === 國立交通大學 === 資訊科學與工程研究所 === 105 === 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(DSP). 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 realtime due to the high volume of features. Therefore, we propose a method predicting traffic of requests of advertisements from perspective of DSPs. The features we used are more simple and easy to be extracted from history 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.