LEARNING A USER'S ACTIVITY PREFERENCES FROM GPS TRACES AND KNOWN NEARBY VENUES

• US 20090073033A1
• Filed: 09/18/2007
• Published: 03/19/2009
• Est. Priority Date: 09/18/2007
• Status: Active Grant

• Current Assignee: Xerox Corporation (Xerox Holdings Corp.)
• Original Assignee: Palo Alto Research Center, Inc. (Xerox Holdings Corp.)
• Inventors: Price, Robert R.
• Granted Patent: US 7,984,006 B2
• Time in Patent Office: Days
• US Class Current: 342/357.340
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising

Abstract

A method for inferring activities to a user is provided. The system receives at least one location trace and corresponding contextual information. The system then derives a set of venues based on a venue database, wherein a respective hypothetical visit is associated with the contextual information corresponding to the location trace. The system derives a set of activity types associated with a context based on the venues, the corresponding context indicated by the location trace, and a venue-to-activity mapping. In addition, the system receives a user query context and identifies a number of activity types of which the associated contextual information is similar to the user query context. The system further weights a respective identified activity type based on its associated context'"'"'s similarity to the user query context, normalizes weights associated with each identified activity type, and produces an activity-type probability distribution, thereby facilitating inferring activities associated with the user.

Claims

1. A method for inferring activities associated with a user, the method comprising:

receiving at least one location trace and corresponding contextual information;

deriving a set of venues based on the location trace based on a venue database, wherein a respective hypothetical visit is associated with the contextual information corresponding to the location trace;

deriving a set of activity types associated with a context based on the venues, the corresponding context indicated by the location trace, and a venue-to-activity mapping;

receiving a user query context;

identifying a number of activity types of which the associated contextual information is similar to the user query context;

weighting a respective identified activity type based on its associated context'"'"'s similarity to the user query context;

normalizing weights associated with each identified activity type; and

producing an activity-type probability distribution, thereby facilitating inferring activities associated with the user.

2. The method of claim 1,

wherein the context indicated by the location trace includes one or more of:

a location;

an indication of a time of a day or a time range;

an indication of a day of a week or a range of days of a week; and

an indication of a weather condition.

3. The method of claim 1,

wherein the query context includes one or more of:

a location;

a time;

a day of a week; and

a weather condition.

4. The method of claim 1,

wherein the location trace includes a set of coordinates with corresponding time stamps.

5. The method of claim 1,

wherein deriving the set of venues comprises:

identifying a location based on one or more locations indicated by the location trace within a time period; and

identifying a number of venues in the vicinity of the identified location based on the venue database.

6. The method of claim 1,

wherein the venue database indicates a number of venues and their corresponding coordinates.

7. The method of claim 1,

further comprising pooling activity-type-to-context mappings associated with multiple, similar users by explicitly clustering similar users or by using latent-variable methods to implicitly cluster similar users.

8. A computer readable storage medium storing instructions which when executed by a computer cause the computer to perform a method for inferring activities associated with a user, the method comprising:

receiving at least one location trace and corresponding contextual information;

deriving a set of venues based on the location trace based on a venue database, wherein a respective hypothetical visit is associated with the contextual information corresponding to the location trace;

deriving a set of activity type associated with a context based on the venues, the corresponding context indicated by the location trace, and a venue-to-activity mapping;

receiving a user query context;

identifying a number of activity types of which the associated contextual information is similar to the user query context;

weighting a respective identified activity type based on its associated context'"'"'s similarity to the user query context;

normalizing weights associated with each identified activity type; and

producing an activity-type probability distribution, thereby facilitating inferring activities associated with the user.

9. The computer readable storage medium of claim 8,

wherein the context indicated by the location trace includes one or more of:

a location;

an indication of a time of a day or a time range;

an indication of a day of a week or a range of days of a week; and

an indication of a weather condition.

10. The computer readable storage medium of claim 8,

wherein the query context includes one or more of:

a location;

a time;

a day of a week; and

a weather condition.

11. The computer readable storage medium of claim 8,

wherein the location trace includes a set of coordinates with corresponding time stamps.

12. The computer readable storage medium of claim 8,

wherein deriving the set of venues comprises:

identifying a location based on one or more locations indicated by the location trace within a time period; and

identifying a number of venues in the vicinity of the identified location based on the venue database.

13. The computer readable storage medium of claim 8,

wherein the venue database indicates a number of venues and their corresponding coordinates.

14. The computer readable storage medium of claim 8,

wherein the method further comprises pooling activity-type-to-context mappings associated with multiple, similar users by explicitly clustering similar users or by using latent-variable methods to implicitly cluster similar users.

16. The computer system of claim 14,

wherein the context indicated by the location trace includes one or more of:

a location;

an indication of a time of a day or a time range;

an indication of a day of a week or a range of days of a week; and

an indication of a weather condition.

17. The computer system of claim 14,

wherein the query context includes one or more of:

a location;

a time;

a day of a week; and

a weather condition.

18. The computer system of claim 14,

wherein the location trace includes a set of coordinates with corresponding time stamps.

19. The computer system of claim 14,

wherein while deriving the set of venues, the location trace preprocessing module is further configured to:

identify a location based on one or more locations indicated by the location trace within a time period; and

identify a number of venues in the vicinity of the identified location based on the venue database.

20. The computer system of claim 14,

wherein the venue database indicates a number of venues and their corresponding coordinates.

21. The computer system of claim 14,

further comprising a pooling mechanism configured to pool activity-type-to-context mappings associated with multiple, similar users by explicitly clustering similar users or by using latent-variable methods to implicitly cluster similar users.

15. A computer system for recommending leisure activities to a user, the computer system comprising:

a location trace receiving mechanism configured to receive at least one location trace and corresponding contextual information;

a location trace preprocessing module coupled to the location trace receiving mechanism and configured to;

derive a set of venues based on a venue database, wherein a respective hypothetical visit is associated with the contextual information corresponding to the location trace; and

derive a set of activity type associated with a context based on the venues, the corresponding context indicated by the location trace, and a venue-to-activity mapping;

a query receiving mechanism configured to receive a user query context; and

an inferring mechanism configured to;

identify a number of activity types of which the associated contextual information is similar to the user query context;

weight a respective identified activity type based on its associated context'"'"'s similarity to the user query context;

normalize weights associated with each identified activity type; and

produce an activity-type probability distribution, thereby facilitating inferring activities associated with the user.