Predicting CTR of new ads via click prediction

Abstract

Predicting CTR of ads on the search result page is an urgent topic. The reason for this is that choosing the right advertisement greatly affects revenue of the search engine and advertisers and user's satisfaction. For ads with the large click history it is quite clear how to predict CTR by utilizing statistical data. But for new ads with a poor click history such approach is not robust and reliable. We suggest a model for predicting CTR of such new ads. Contrary to the previous models of predicting CTR of new ads, our model uses events - clicks and skips1 instead of the observed CTR. In addition we have implemented several novel features, that resulted into the increase of the performance of our model. Offline and online experiments on the real search engine system demonstrated that our model outperforms the baseline and the approaches suggested in previous papers.