To swing or not to swing: learning when (not) to advertise

• Authors: Andrei Broder;Massimiliano Ciaramita;Marcus Fontoura;Evgeniy Gabrilovich;Vanja Josifovski;Donald Metzler;Vanessa Murdock;Vassilis Plachouras
• Venue: Proceedings of the 17th ACM conference on Information and knowledge management
• Year: 2008

Web textual advertising can be interpreted as a search problem over the corpus of ads available for display in a particular context. In contrast to conventional information retrieval systems, which always return results if the corpus contains any documents lexically related to the query, in Web advertising it is acceptable, and occasionally even desirable, not to show any results. When no ads are relevant to the user's interests, then showing irrelevant ads should be avoided since they annoy the user and produce no economic benefit. In this paper we pose a decision problem "whether to swing", that is, whether or not to show any of the ads for the incoming request. We propose two methods for addressing this problem, a simple thresholding approach and a machine learning approach, which collectively analyzes the set of candidate ads augmented with external knowledge. Our experimental evaluation, based on over 28,000 editorial judgments, shows that we are able to predict, with high accuracy, when to "swing" for both content match and sponsored search advertising.