Matching anonymized user identifiers across differently anonymized data sets

1. A method of generating user profiles, the method comprising:

obtaining a plurality of location data sets from different providers of user geolocation history, each location data set including a plurality of user-activity records, each user-activity records being associated with a user identifier and including geolocations of the corresponding user and times that the corresponding user was at the geolocations, the different providers having different user identifiers for a given corresponding user, the different user identifiers each uniquely identifying the given corresponding user within a corresponding one of the plurality of location data sets but being otherwise uncorrelated with an identity of the given corresponding user;

generating user-location records from the user-activity records by extracting geolocations and normalizing the extracted geolocations and the times that the corresponding user was at the geolocations;

matching, by one or more processors, the user identifiers between the location data sets based on the normalized geolocations of the corresponding user and the normalized times that the corresponding user was at the geolocations;

storing the matched user identifiers in association with one another in corresponding user profiles;

selecting the user location records of one or more of the corresponding user profiles;

generating profile attribute records for one or more given corresponding user based on information in the selected user location records; and

delivering an advertisement based on the profile attribute records.

2. The method of claim 1 , comprising:

calculating a confidence value for a respective match of the user identifiers based on a sample size of the user-activity records upon which the respective match is based and an amount of similarity between the geolocations of the corresponding user in the different location data sets subject to the respective match, an amount of similarity between the times that the user was at the geolocations in the different data sets subject to the respective match, or both.

3. The method of claim 1 , wherein normalizing the extracted geolocations comprises:

translating the extracted geolocations into corresponding tiles of a geographic information system.

4. The method of claim 1 , wherein normalizing the extracted geolocations comprises:

translating the extracted geolocations and times that the corresponding user was at the geolocations into time-tiles associated with tile-time records of a geographic information system.

5. The method of claim 1 , wherein matching the user identifiers between the location data sets comprises:

converting the geolocations of the corresponding user and times that the corresponding user was at the geolocations into time-location vectors;

clustering the time-location vectors; and

determining that the user identifiers across the location data sets match based on a measure of similarity of clusters from the locations data sets.

6. The method of claim 5 , wherein the measure of similarity includes a distance in vector space between a measure of central tendency of two of the clusters.

7. The method of claim 6 , wherein the measure of central tendency is a centroid vector of the corresponding cluster.

8. The method of claim 1 , wherein matching the user identifiers between the location data sets comprises:

vectorizing the geolocations of the corresponding user and times that the corresponding user was at the geolocations to produce a measure of central tendency of two of the clusters;

clustering the time-location vectors within each location data set to produce clusters for each location data set; and

matching a cluster selected from one location data set among the plurality of location data sets with another cluster from another location data set among the plurality of location data sets.

9. The method of claim 8 , wherein matching the cluster selected from one location data set with the other cluster from the other location data set comprises:

calculating a similarity score for the cluster and the other cluster; and determining that the similarity score satisfies a threshold.

10. The method of claim 8 , wherein matching the cluster selected from one location data set with the other cluster from the other location data set comprises:

calculating a Euclidean distance between centroids of the cluster selected from one location data set with the other cluster from the other location data set.

11. The method of claim 8 , wherein matching the cluster selected from one location data set with the other cluster from the other location data set comprises:

calculating a volume of overlap of convex hulls of the cluster selected from one location data set with the other cluster from the other location data set.

12. A method of matching an identifier of a user's new computing device to an identifier of an older computing device of the same user, the method comprising:

obtaining a data set including a plurality of records, each record being associated with a user identifier and including geolocations of the corresponding user and times that the corresponding user was at the geolocations, each user identifier uniquely identifying the corresponding user within a corresponding one of the plurality of records but being otherwise uncorrelated with an identity of the corresponding user;

generating user-location records from the plurality of records by extracting geolocations and normalizing the extracted geolocations and the times that the corresponding user was at the geolocations;

determining that two user identifiers refer to the same user by matching, with one or more processors, the user identifiers within the data set based on the normalized geolocations of the corresponding user and the normalized times that the corresponding user was at the geolocations;

storing the matched user identifiers in a user profile of the corresponding user;

selecting the user location records of one or more corresponding user profile;

generating profile attribute records for one or more given corresponding user based on information in the selected user location records; and

delivering an advertisement based on the profile attribute records.

13. A system for generating user profiles, the system comprising:

one or more processors; and

memory storing instructions that when executed by the one or more processors effectuates operations comprising:

obtaining a plurality of location data sets from different providers of user geolocation history, each location data set including a plurality of user-activity records, each user-activity records being associated with a user identifier and including geolocations of the corresponding user and times that the corresponding user was at the geolocations, the different providers having different user identifiers for a given corresponding user, the different user identifiers each uniquely identifying the given corresponding user within a corresponding one of the plurality of location data sets but being otherwise uncorrelated with an identity of the given corresponding user;

generating user-location records from the user-activity records by extracting geolocations and normalizing the extracted geolocations and the times that the corresponding user was at the geolocations;

matching, by one or more processors, the user identifiers between the location data sets based on the normalized geolocations of the corresponding user and the normalized times that the corresponding user was at the geolocations;

storing the matched user identifiers in association with one another in corresponding user profiles;

selecting the user location records of one or more of the corresponding user profiles;

generating profile attribute records for one or more given corresponding user based on information in the selected user location records; and delivering an advertisement based on the profile attribute records.

14. The system of claim 13 , comprising:

calculating a confidence value for a respective match of the user identifiers based on a sample size of the user-activity records upon which the respective match is based and an amount of similarity between the geolocations of the corresponding user in the different location data sets subject to the respective match, an amount of similarity between the times that the user was at the geolocations in the different data sets subject to the respective match, or both.

15. The system of claim 13 , wherein normalizing the extracted geolocations comprises:

translating the extracted geolocations into corresponding tiles of a geographic information system.