Click fraud resistant learning of click through rate

摘要

Click-based algorithms are leveraged to provide protection against fraudulent user clicks of online advertisements. This enables mitigation of short term losses due to the fraudulent clicks and also mitigates long term advantages caused by the fraud. The techniques employed utilize “expected click wait” instead of CTR to determine the likelihood that a future click will occur. An expected click wait is based on the number of events that occur before a certain number of clicks are obtained. The events can also include advertisement impressions and/or sale and the like. This flexibility allows for fraud detection of other systems by transforming the other systems to clock-tick fraud based systems. Averages, including weighted averages, can also be utilized with the systems and methods herein to facilitate in providing a fraud resistant estimate of the CTR.