Implicit geolocation of social networking users
In one embodiment, one or more computing systems receive a request for a location prediction for a user from a service. The computing systems access one or more real-time location signals and one or more aggregated location signals. The aggregated location signals may comprise one or more previous location signals. The computing systems may then generate one or more location predictions from the one or more real-time location signals and the one or more aggregated location signals, and calculate a single location prediction for the user from the one or more location predictions. The computing systems may then send, in response to the request, the single location prediction for the user to the requesting service.
1. A method comprising, by one or more computing systems:
receiving, from a requesting service, a request for a location prediction for a user;
generating a current location prediction from a plurality of previous location signals, wherein each previous location signal comprises an indication of the user's location at a respective time prior to a predetermined time span, and wherein each previous location signal is weighted based on (1) a type of the previous location signal and (2) a difference between a current time and the respective time for the previous location signal; and
sending, in response to the request, the current location prediction for the user to the requesting service.
2. The method of claim 1 , wherein the plurality of previous location signals comprise the user's IP address.
3. The method of claim 1 , wherein the plurality of previous location signals comprise a self-declared user location.
4. The method of claim 1 , wherein the plurality of previous location signals comprise real-time location signals received before the predetermined time span.
5. The method of claim 1 , wherein generating the current location prediction further comprises weighting the plurality of previous location signals based on a relative reliability of each previous location signal.
6. The method of claim 1 , further comprising storing, when each previous location signal is received, each previous location signal in a histogram, wherein each previous location signal is weighted based on predetermined time decay for the histogram.
7. The method of claim 1 , further comprising storing, when each previous location signal is received, each previous location signal in one of a fast decay histogram or a slow decay histogram, wherein the fast-decay histogram comprises previous location signals of a first type, and the slow-decay histogram comprises previous location signals of a second type.
8. The method of claim 1 , further comprising:
generating a plurality of location predictions; and
filtering the plurality of location predictions based on a set of rules to generate the current location prediction, wherein the set of rules comprises filtering out location predictions that exceed a predetermined geographic radius from a location associated with the user's IP address.
9. The method of claim 1 , wherein generating the current location prediction comprises calculating a weighted average for a plurality of location predictions, wherein a weight of each prediction is based at least in part on a source of signals from which the prediction was generated.
10. The method of claim 1 , wherein generating the current location prediction comprises weighting each previous location signal, and wherein the weight applied to each previous location signals is determined via a machine learning algorithm.
11. One or more computer-readable non-transitory storage media embodying software that is operable when executed to:
receive, from a requesting service, a request for a location prediction for a user;
generate a current location prediction from a plurality of previous location signals, wherein each previous location signal comprises an indication of the user's location at a respective time prior to a predetermined time span, and wherein each previous location signal is weighted based on (1) a type of the previous location signal, and (2) a difference between a current time and the respective time for the previous location signal; and
send, in response to the request, the current location prediction for the user to the requesting service.
12. The media of claim 11 , wherein the plurality of previous location signals comprise the user's IP address.
13. The media of claim 11 , wherein the plurality of previous location signals comprise a self-declared user location.
14. The media of claim 11 , wherein the plurality of previous location signals comprise real-time location signals received before the predetermined time span.
15. The media of claim 11 , wherein generating the current location prediction further comprises weighting the plurality of previous location signals based on a relative reliability of each previous location signal.
16. The media of claim 11 , wherein the software is further operable to store, when each previous location signal is received, each previous location signal in a histogram, wherein each previous location signal is weighted based on predetermined time decay for the histogram.
17. The media of claim 11 , wherein the software is further operable to store, when each previous location signal is received, each previous location signal in one of a fast decay histogram or a slow decay histogram, wherein the fast-decay histogram comprises previous location signals of a first type, and the slow-decay histogram comprises previous location signals of a second type.
18. The media of claim 11 , wherein the software is further operable to:
generate a plurality of location predictions; and
filter the plurality of location predictions based on a set of rules to generate the current location prediction, wherein the set of rules comprises filtering out location predictions that exceed a predetermined geographic radius from a location associated with the user's IP address.
19. The media of claim 11 , wherein generating the current location prediction comprises calculating a weighted average for a plurality of location predictions, wherein a weight of each prediction is based at least in part on a source of signals from which the prediction was generated.
20. A system comprising:
one or more processors; and
a memory coupled to the processors comprising instructions executable by the processors, the processors being operable when executing the instructions to:
receive, from a requesting service, a request for a location prediction for a user;
generate a current location prediction from a plurality of previous location signals, wherein each previous location signal comprises an indication of the user's location at a respective time prior to a predetermined time span, and wherein each previous location signal is weighted based on (1) a type of the previous location signal, and (2) a difference between a current time and the respective time for the previous location signal; and
send, in response to the request, the current location prediction for the user to the requesting service.