Ewa Dominowska

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.

First workshop on targeting and ranking for online advertising

Online advertising is a rapidly growing, multi-billion dollar industry. It has become a significant element of the Web browsing experience. Online advertising providers use sophisticated ad targeting and ranking algorithms with the dual aim of maximizing revenue while providing a superior user experience. As a result, advertising optimization is a very complex research problem, since it combines relevance with user interaction models, advertiser valuations, and commercial constraints. Online advertising integrates a number of core research areas: machine learning, data mining, search, auction theory, and user modeling. This workshop is intended to serve as an open forum for discussion of new ideas and current research in the field of online advertising. We expect that the workshop will promote a community of researchers interested in this area and yield future collaboration and exchanges. The research included should address problems faced by advertisers, end-users, advertising platforms, and the market.