Online learning from click data for sponsored search

Abstract

Sponsored search is one of the enabling technologies for today's Web search engines. It corresponds to matching and showing ads related to the user query on the search engine results page. Users are likely to click on topically related ads and the advertisers pay only when a user clicks on their ad. Hence, it is important to be able to predict if an ad is likely to be clicked, and maximize the number of clicks. We investigate the sponsored search problem from a machine learning perspective with respect to three main sub-problems: how to use click data for training and evaluation, which learning framework is more suitable for the task, and which features are useful for existing models. We perform a large scale evaluation based on data from a commercial Web search engine. Results show that it is possible to learn and evaluate directly and exclusively on click data encoding pairwise preferences following simple and conservative assumptions. Furthermore, we find that online multilayer perceptron learning, based on a small set of features representing content similarity of different kinds, significantly outperforms an information retrieval baseline and other learning models, providing a suitable framework for the sponsored search task.