Learning the click-through rate for rare/new ads from similar ads

Abstract

Ads on the search engine (SE) are generally ranked based on their Click-through rates (CTR). Hence, accurately predicting the CTR of an ad is of paramount importance for maximizing the SE's revenue. We present a model that inherits the click information of rare/new ads from other semantically related ads. The semantic features are derived from the query ad click-through graphs and advertisers account information. We show that the model learned using these features give a very good prediction for the CTR values.