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

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.