In the Mood to Click? Towards Inferring Receptiveness to Search Advertising

Abstract—We present a method for modeling, and automatically inferring, the current interest of a user in search advertising. Our task is complementary to that of predicting ad relevance or commercial intent of a query in the aggregate, since the user intent may vary significantly for the same query. To achieve this goal, we develop a fine-grained user interaction model for inferring searcher receptiveness to advertising. We show that modeling the search context and behavior can significantly improve the accuracy of ad clickthrough prediction for the current user, compared to the existing state-of-the-art classification methods that do not model this additional sessionlevel contextual and interaction information. In particular, our experiments over thousands of search sessions from hundreds of real users demonstrate that our model is more effective at predicting ad clickthrough within the same search session. Our work has other potential applications, such as improving search int...

• Type: Conference
• Year: 2009
• Where: WEBI
• Authors: Qi Guo, Eugene Agichtein, Charles L. A. Clarke, Azin Ashkan
• Keywords: Ad Clickthrough, Internet Technology, User, User Interaction Model, WEBI 2009