Enabling direct interest-aware audience selection

Ariel Fuxman, Anitha Kannan, Zhenhui Li, Panayiotis Tsaparas

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

Abstract

Advertisers typically have a fairly accurate idea of the interests of their target audience. However, today's online advertising systems are unable to leverage this information. The reasons are two-fold. First, there is no agreed upon vocabulary of interests for advertisers and advertising systems to communicate. More importantly, advertising systems lack a mechanism for mapping users to the interest vocabulary. In this paper, we tackle both problems. We present a system for direct interest-aware audience selection. This system takes the query histories of search engine users as input, extracts their interests, and describes them with interpretable labels. The labels are not drawn from a predefined taxonomy, but rather dynamically generated from the query histories, and are thus easy for the advertisers to interpret and use for targeting users. In addition, the system enables seamless addition of interest labels that may be provided by the advertiser.

Original languageEnglish (US)
Title of host publicationCIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management
Pages575-584
Number of pages10
DOIs
StatePublished - 2012
Event21st ACM International Conference on Information and Knowledge Management, CIKM 2012 - Maui, HI, United States
Duration: Oct 29 2012Nov 2 2012

Publication series

NameACM International Conference Proceeding Series

Other

Other21st ACM International Conference on Information and Knowledge Management, CIKM 2012
CountryUnited States
CityMaui, HI
Period10/29/1211/2/12

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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