Approximating a user location

1. A computer-implemented method for approximating a user location, the method comprising:

accessing, with one or more computing devices, information associated with a user location, the information comprising historical location data and current internet protocol address data;

analyzing, with the one or more computing devices, the historical location data and internet protocol address data to identify a plurality of locations associated with the information;

determining, with the one or more computing devices, a confidence score for each of the plurality of locations, the confidence score expressing a probability of the consensus of the user location with the location associated with the confidence score;

identifying, with the one or more computing devices, two or more locations of the plurality of locations that form a cluster, wherein a cluster comprises two or more locations located within a predefined distance from one another; and

modifying, with the one or more computing devices, the confidence scores for each of the two or more locations that form a cluster by adjusting each confidence score by a weight associated with the cluster.

2. The computer-implemented method of claim 1 , further comprising ranking the plurality of locations based upon the confidence score or modified confidence score, if available, for each of the plurality of locations.

3. The computer-implemented method of claim 1 , wherein the historical location data comprises one or more of historical location data for a device associated with the user, historical internet protocol address data associated with the user, or historical query data associated with the user.

4. The computer-implemented method of claim 1 , wherein the current internet protocol address data is obtained by geolocation of an internet protocol address associated with a request received from a client device associated with the user.

5. The computer-implemented method of claim 1 , wherein the predefined distance is less than or equal to 100 km.

6. The computer-implemented method of claim 1 , wherein the predefined distance is less than or equal to 75 km.

7. The computer-implemented method of claim 1 , wherein the predefined distance is less than or equal to 50 km.

8. The computer-implemented method of claim 1 , wherein the predefined distance is less than or equal to 25 km.

9. The computer-implemented method of claim 1 , wherein each confidence score is based at least in part on an age, prominence, location consistency, or combinations thereof, of the information.

10. A system for approximating a user location, comprising:

one or more computing devices; and

at least one memory storing instructions that, when executed by the one or more computing devices, cause the one or more computing devices to perform operations, the operations comprising:

accessing information associated with a user location, the information comprising historical location data and current internet protocol address data;

analyzing the historical location data and internet protocol address data to identify a plurality of locations associated with the information;

determining a confidence score for each of the plurality of locations, the confidence score expressing a probability of the consensus of the user location with the location associated with the confidence score;

identifying two or more locations of the plurality of locations that form a cluster, wherein a cluster comprises two or more locations located within a predefined distance from one another; and

modifying the confidence scores for each of the two or more locations that form a cluster by adjusting each confidence score by a weight associated with the cluster.

11. The system of claim 10 , wherein the predefined distance is less than or equal to 100 km.

12. The system of claim 10 , wherein the predefined distance is less than or equal to 25 km.

13. The system of claim 10 , wherein each confidence score is based at least in part on an age, prominence, location consistency, or combinations thereof, of the information.

14. A non-transitory computer-readable medium storing instructions that, when executed by one or more computing devices, cause the one or more computing devices to perform operations, the operations comprising:

accessing information associated with a user location, the information comprising historical location data and current internet protocol address data;

analyzing the historical location data and internet protocol address data to identify a plurality of locations associated with the information;

determining a confidence score for each of the plurality of locations, the confidence score expressing a probability of the consensus of the user location with the location associated with the confidence score;

identifying two or more locations of the plurality of locations that form a cluster, wherein a cluster comprises two or more locations located within a predefined distance from one another; and

modifying the confidence scores for each of the two or more locations that form a cluster by adjusting each confidence score by a weight associated with the cluster.

15. The non-transitory computer-readable medium of claim 14 , wherein the operations further comprise ranking the plurality of locations based upon the confidence score or modified confidence score, if available, for each of the plurality of locations.

16. The non-transitory computer-readable medium of claim 14 , wherein the historical location data comprises one or more of historical location data for a device associated with the user, historical internet protocol address data associated with the user, or historical query data associated with the user.

17. The non-transitory computer-readable medium of claim 14 , wherein the predefined distance is less than or equal to 100 km.