METHOD AND SYSTEM FOR DETECTING FRAUDULENT USER-CONTENT PROVIDER PAIRS
The present teaching generally relates to identifying fraudulent content provider-user device pairs. In one embodiment, an initial user risk value and an initial content provider risk value may be determined. A first functional representation of a user risk value may be generated based on the initial user risk value and relational data. A second functional representation of a content provider risk value may be generated based on the initial content provider risk value and the relational data. A converged user risk value and a converged content provider risk value associated with the first and second representations converging may be determined. A pair risk value may be determined based on the converged user risk value and the converged content provider risk value. A fraudulent label may then be applied to interaction events detected by the content provider from the user in response to the risk pair value satisfying a condition.
1 . A method for identifying fraudulent content provider-user device pairs, the method comprising:
determining a click-through-rate for a user device and a click-through-rate for a content provider;
generating a first representation of a user risk value and a second representation of a content provider risk value, wherein the first representation at an iteration is dependent on the second representation at a previous iteration and the click-through-rate for the user device and a weighted transition matrix, while the second representation at an iteration is dependent on the first representation at a previous iteration and the click-through-rate for the content provider and the weighted transition matrix;
performing iterations for the first and second representations until the first and second representations converge to determine a converged user risk value and a converged content provider risk value;
determining a pair risk value dependent on the converged user risk value and the converged content provider risk value; and
applying a fraudulent label to the content provider and the user device in response to the pair risk value satisfying a condition.
2 . The method of claim 1 , further comprising:
detecting, via the fraudulent labels, subsequent interactions involving the content provider and the user device; and
determining the subsequent interactions as fraudulent.
3 . The method of claim 1 , wherein the weighted transition matrix is generated by weighing each value in a transition matrix using a corresponding transition probability.
4 . The method of claim 3 , wherein the transition matrix records a number of clicks detected by the content provider from the user device.
5 . The method of claim 1 , further comprising:
editing a network identifier of the content provider and a network identifier of the user device to create the fraudulence labels therefor.
6 . The method of claim 5 , wherein the editing is in response to the pair risk value being greater than or equal to a threshold risk value.
7 . The method of claim 5 , wherein the edited network identifiers are stored in a database.
8 . A non-transitory, computer-readable medium having information recorded thereon for identifying fraudulent content provider-user device pairs, wherein the information, when read by at least one processor, effectuates operations comprising:
determining a click-through-rate for a user device and a click-through-rate for a content provider;
generating a first representation of a user risk value and a second representation of a content provider risk value, wherein the first representation at an iteration is dependent on the second representation at a previous iteration and the click-through-rate for the user device and a weighted transition matrix, while the second representation at an iteration is dependent on the first representation at a previous iteration and the click-through-rate for the content provider and the weighted transition matrix;
performing iterations for the first and second representations until the first and second representations converge to determine a converged user risk value and a converged content provider risk value;
determining a pair risk value dependent on the converged user risk value and the converged content provider risk value; and
applying a fraudulent label to the content provider and the user device in response to the pair risk value satisfying a condition.
9 . The medium of claim 8 , wherein the operations further comprise:
detecting, via the fraudulent labels, subsequent interactions involving the content provider and the user device; and
determining the subsequent interactions as fraudulent.
10 . The medium of claim 8 , wherein the weighted transition matrix is generated by weighing each value in a transition matrix using a corresponding transition probability.
11 . The medium of claim 10 , wherein the transition matrix records a number of clicks detected by the content provider from the user device.
12 . The medium of claim 8 , wherein the operations further comprise:
editing a network identifier of the content provider and a network identifier of the user device to create the fraudulence labels therefor.
13 . The medium of claim 12 , wherein the editing is in response to the pair risk value being greater than or equal to a threshold risk value.
14 . The medium of claim 12 , wherein the edited network identifiers are stored in a database.
15 . A system for identifying fraudulent content provider-user device pairs, the system comprising:
memory storing computer program instructions; and
one or more processors that, in response to executing the computer program instructions, effectuate operations comprising:
determining a click-through-rate for a user device and a click-through-rate for a content provider;
generating a first representation of a user risk value and a second representation of a content provider risk value, wherein the first representation at an iteration is dependent on the second representation at a previous iteration and the click-through-rate for the user device and a weighted transition matrix, while the second representation at an iteration is dependent on the first representation at a previous iteration and the click-through-rate for the content provider and the weighted transition matrix;
performing iterations for the first and second representations until the first and second representations converge to determine a converged user risk value and a converged content provider risk value;
determining a pair risk value dependent on the converged user risk value and the converged content provider risk value; and
applying a fraudulent label to the content provider and the user device in response to the pair risk value satisfying a condition.
16 . The system of claim 15 , wherein the operations further comprise:
detecting, via the fraudulent labels, subsequent interactions involving the content provider and the user device; and
determining the subsequent interactions as fraudulent.
17 . The system of claim 15 , wherein the weighted transition matrix is generated by weighing each value in a transition matrix using a corresponding transition probability.
18 . The system of claim 17 , wherein the transition matrix records a number of clicks detected by the content provider from the user device.
19 . The system of claim 15 , wherein the operations further comprise:
editing a network identifier of the content provider and a network identifier of the user device to create the fraudulence labels therefor.
20 . The system of claim 19 , wherein the editing is in response to the pair risk value being greater than or equal to a threshold risk value.