Click fraud resistant learning of click through rate

• US 20070073579A1
• Filed: 09/23/2005
• Published: 03/29/2007
• Est. Priority Date: 09/23/2005
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Mahdian, Mohammad, Jain, Kamal, Talwar, Kunal, Immorlica, Nicole
• Application Number:
  US11/234,476
• Publication Number:
  US 20070073579A1
• Time in Patent Office: Days
• US Class Current: 705/14.470
• CPC Class Codes: G06Q 30/0248 Avoiding fraud
• CPC Class Codes: G06Q 30/0267 Wireless devices

Abstract

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.

Claims

1. A system that facilitates online advertisement data predictions, comprising:

a receiving component that receives at least one event data set relating to an online advertisement; and

a probability component that determines an occurrence value of a first event type from the obtained event data based on an occurrence value of a second event type from the obtained event data that is a conversion of the first event type and learns an expected event wait based on the first and second event type occurrence values.

2. The system of claim 1, the event type comprising a non-clicked advertisement impression, a clicked advertisement impression, and/or an acquisition relating to an advertisement.

3. The system of claim 1, the first event type comprising a non-clicked advertisement impression, the second event type comprising a clicked advertisement impression, and the expected event wait comprising an expected click wait.

4. The system of claim 3, the probability component employs click-based processes to facilitate in learning the expected click wait.

5. The system of claim 1, the probability component employs an averaging process to facilitate in learning the expected event wait.

6. The system of claim 5, the averaging process comprising a weighted averaging process over a last fixed number of converted event type occurrences.

7. An advertisement auction system that employs the system of claim 1 to facilitate in determining advertising parameters.

8. An advertisement auction system that employs the system of claim 1 to mitigate the effects of click fraud, impression fraud, and/or acquisition fraud.

9. A method for facilitating online advertisement data predictions, comprising:

receiving at least one event data set relating to an online advertisement; and

learning an expected wait of an event from the event data set via integration of the learning over a past fixed number of events.

10. The method of claim 9, the expected event wait comprising an expected click wait.

11. The method of claim 10 further comprising:

utilizing the expected click wait instead of a click through rate to facilitate in establishing a predicted click probability of an online advertisement.

12. The method of claim 9 further comprising:

employing the expected event wait in an advertisement auction to facilitate in determining advertising parameters.

13. The method of claim 9 further comprising:

employing the expected event wait to facilitate in mitigating effects of click fraud, impression fraud, and/or acquisition fraud.

14. The method of claim 9 further comprising:

determining an occurrence value of a first event type from the obtained event data based on an occurrence value of a second event type from the obtained event data that is a conversion of the first event type; and

learning the expected event wait based on the first and second event type occurrence values.

15. The method of claim 9 further comprising:

learning the expected event wait utilizing an averaging process over a last fixed number of an event occurrence.

16. The method of claim 15, the averaging process comprising a weighted averaging process.

17. A pay-per-acquisition advertisement auction method that employs the method of claim 9.

20. A device employing the method of claim 9 comprising at least one selected from the group consisting of a computer, a server, and a handheld electronic device.

18. A method of auctioning online advertisements, comprising:

employing an expected click wait to facilitate in determining a likelihood of a future click on an advertisement impression by a user; and

utilizing the likelihood to facilitate in determining a pricing structure to charge an advertiser for each future click of the advertisement impression.

19. The method of claim 18 further comprising:

employing the expected click wait to facilitate in learning an expected acquisition rate associated with an advertisement impression; and

utilizing the expected acquisition rate to facilitate in determining a pay-per-acquisition pricing structure for an advertiser.