Predicting traffic of online advertising in real-time bidding systems from perspective of demand-side platforms

Hsu Chao Lai*, Wen-Yueh Shih, Jiun-Long Huang, Yi Cheng Chen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2016 IEEE International Conference on Big Data, Big Data 2016
EditorsRonay Ak, George Karypis, Yinglong Xia, Xiaohua Tony Hu, Philip S. Yu, James Joshi, Lyle Ungar, Ling Liu, Aki-Hiro Sato, Toyotaro Suzumura, Sudarsan Rachuri, Rama Govindaraju, Weijia Xu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3491-3498
Number of pages8
ISBN (Electronic)9781467390040
DOIs
StatePublished - Dec 2016
Event4th IEEE International Conference on Big Data, Big Data 2016 - Washington, United States
Duration: 5 Dec 20168 Dec 2016

Publication series

NameProceedings - 2016 IEEE International Conference on Big Data, Big Data 2016

Conference

Conference4th IEEE International Conference on Big Data, Big Data 2016
CountryUnited States
CityWashington
Period5/12/168/12/16

Keywords

  • Demand Side Platform
  • Online Advertisement
  • Real Time Bidding
  • Request Traffic

Fingerprint Dive into the research topics of 'Predicting traffic of online advertising in real-time bidding systems from perspective of demand-side platforms'. Together they form a unique fingerprint.

Cite this