document: A search-based method for forecasting ad impression in contextual advertising Contextual advertising ( also called content match ) refers to the placement of small textual ads within the content of a generic web page . It has become a significant source of revenue for publishers ranging from individual bloggers to major newspapers . At the same time it is an important way for advertisers to reach their intended audience . This reach depends on the total number of exposures of the ad ( impressions ) and its click-through-rate ( CTR ) that can be viewed as the probability of an end-user clicking on the ad when shown . These two orthogonal , critical factors are both difficult to estimate and even individually can still be very informative and useful in planning and budgeting advertising campaigns . In this paper , we address the problem of forecasting the number of impressions for new or changed ads in the system . Producing such forecasts , even within large margins of error , is quite challenging : 1 ) ad selection in contextual advertising is a complicated process based on tens or even hundreds of page and ad features ; 2 ) the publishers ' content and traffic vary over time ; and 3 ) the scale of the problem is daunting : over a course of a week it involves billions of impressions , hundreds of millions of distinct pages , hundreds of millions of ads , and varying bids of other competing advertisers . We tackle these complexities by simulating the presence of a given ad with its associated bid over weeks of historical data . We obtain an impression estimate by counting how many times the ad would have been displayed if it were in the system over that period of time . We estimate this count by an efficient two-level search algorithm over the distinct pages in the data set . Experimental results show that our approach can accurately forecast the expected number of impressions of contextual ads in real time . We also show how this method can be used in tools for bid selection and ad evaluation .
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