A note on search based forecasting of ad volume in contextual advertising

  • Authors:
  • Xuerui Wang;Andrei Broder;Marcus Fontoura;Vanja Josifovski

  • Affiliations:
  • University of Massachusetts, Amherst, MA, USA;Yahoo! Research, Santa Clara, CA, USA;PUC-Rio, Rio de Janeiro, Brazil;Yahoo! Research, Santa Clara, CA, USA

  • Venue:
  • Proceedings of the 17th ACM conference on Information and knowledge management
  • Year:
  • 2008

Quantified Score

Hi-index 0.00

Visualization

Abstract

In contextual advertising, estimating the number of impressions of an ad is critical in planning and budgeting advertising campaigns. However, producing this forecast, even within large margins of error, is quite challenging. We attack this problem by simulating the presence of a given ad with its associated bid over historical data, involving billions of impressions. This apparently enormous computational task is reduced to a search task involving only the set of distinct pages in the data. Furthermore the search is made more efficient using a two-level search process. Experimental results show that our approach can accurately forecast the expected number of impressions of contextual ads in real time.