There has been a lot of recent research in the use of unlabeled data for applications where labeled data is expensive or difficult to collect. This paper presents a problem that involves detecting invalid clicks on online paid search listings that is difficult to solve using traditional supervised learning algorithms. The process of generating labeled data by manually marking clicks as valid or invalid is very time consuming and difficult, which results in a data set that is mostly unlabeled. Yet having a good way of identifying invalid clicks is essential to Overture's business, so making better use of the available unlabeled data is a problem of great interest.
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