document: Predicting clicks : estimating the click-through rate for new ads Search engine advertising has become a significant element of the Web browsing experience . Choosing the right ads for the query and the order in which they are displayed greatly affects the probability that a user will see and click on each ad . This ranking has a strong impact on the revenue the search engine receives from the ads . Further , showing the user an ad that they prefer to click on improves user satisfaction . For these reasons , it is important to be able to accurately estimate the click-through rate of ads in the system . For ads that have been displayed repeatedly , this is empirically measurable , but for new ads , other means must be used . We show that we can use features of ads , terms , and advertisers to learn a model that accurately predicts the click-though rate for new ads . We also show that using our model improves the convergence and performance of an advertising system . As a result , our model increases both revenue and user satisfaction .
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