Analysis, Modeling, and Implementation of Publisher-side Ad Request Filtering

Liang Lv, Ke Xu, Haiyang Wang, Meng Shen, Yi Zhao, Minghui Li, Guanhui Geng, Zhichao Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


Online advertising has been a great driving force for the Internet industry. To maintain a steady growth of advertising revenue, advertisement (ad) publishers have made great efforts to increase the impressions as well as the conversion rate. However, we notice that the results of these efforts are not as good as expected. In detail, to show more ads to the consumers, publishers have to waste a significant amount of server resources to process the ad requests that do not result in consumers' clicks. On the other hand, the increasing ads are also impacting the browsing experience of the consumers.In this paper, we explore the opportunity to improve publishers' overall utility by handling a selective number of requests on ad servers. Particularly, we propose a publisher-side proactive ad request filtration solution Win2. Upon receiving an ad request, Win2 estimates the probability that the consumer will click if serving it. The ad request will be served if the clicking probability is above a dynamic threshold. Otherwise, it will be filtered to reduce the publisher's resource cost and improve consumer experience. We implement Win2 in a large-scale ad serving system and the evaluation results confirm its effectiveness.

Original languageEnglish (US)
Title of host publicationINFOCOM 2020 - IEEE Conference on Computer Communications
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages10
ISBN (Electronic)9781728164120
StatePublished - Jul 2020
Event38th IEEE Conference on Computer Communications, INFOCOM 2020 - Toronto, Canada
Duration: Jul 6 2020Jul 9 2020

Publication series

NameProceedings - IEEE INFOCOM
ISSN (Print)0743-166X


Conference38th IEEE Conference on Computer Communications, INFOCOM 2020

Bibliographical note

Publisher Copyright:
© 2020 IEEE.


  • Ad Request Filtering
  • Online Advertising
  • Publisher-side
  • Utility Optimization


Dive into the research topics of 'Analysis, Modeling, and Implementation of Publisher-side Ad Request Filtering'. Together they form a unique fingerprint.

Cite this