Mobile device-related measures of affinity

• US 9,654,591 B2
• Filed: 10/01/2012
• Issued: 05/16/2017
• Est. Priority Date: 10/01/2012
• Status: Active Grant

• Current Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Original Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Inventors: Matus, Jonathan Arie
• Primary Examiner(s): Serrao, Ranodhi
• Assistant Examiner(s): FIORILLO, JAMES N
• Application Number:
  US13/632,869
• Publication Number:
  US 20140095606A1
• Time in Patent Office: 1,688 Days
• Field of Search: None
• CPC Class Codes: G06F 21/00 Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06Q 10/10 Office automation; Time management
• CPC Class Codes: G06Q 50/01 Social networking
• CPC Class Codes: H04L 65/403 Arrangements for multi-party communication, e.g. for conferences data switching systems for conference H04L12/18; arrangements for connecting several subscribers to a common circuit, i.e. affording conference facilities H04M3/56; television conferencing systems H04N7/15
• CPC Class Codes: H04L 67/306 User profiles
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34
• CPC Class Codes: H04W 4/50 Service provisioning or reconfiguring

Abstract

In one embodiment, a method includes receiving a request for a measure of affinity for a particular action associated with a user of the social-networking system; and determining results for each predictor function based at least in part upon a number of actions previously preformed by the user with respect to the mobile-computing device. Each predictor function calculating a likelihood the user performs one or more actions; The method also includes computing a measure of affinity associated with the user based on the results for the predictor functions; and providing the computed measure of affinity.

Claims

1. A method comprising:

receiving, at a social-networking system, data about a plurality of actions previously performed by a user of the social-networking system with respect to a mobile-computing device associated with the user, wherein each of the previously performed actions comprise data exchanged between the mobile-computing device and a third-party system regarding an interaction between the user and another person or a concept;

receiving, at the social-networking system, a request from a third-party process for a measure of affinity that the user has for a person or a concept;

determining, by one or more processors of one or more computer servers associated with the social-networking system, results for one or more predictor functions based at least in part upon the received data, and upon one or more properties associated with the plurality of actions previously performed by the user,

wherein each predictor function calculates likelihood that the user will perform one or more actions in relation to the person or the concept, and

wherein the one or more properties associated with the plurality of actions comprises a duration of time associated with an action of the plurality of actions, a frequency with which an action of the plurality of actions is taken, or statistics associated with an action of the plurality of actions;

specifying one or more weights to be attributed to the one or more predictor functions;

computing, by the one or more processors, the measure of affinity based on the results for one or more of the predictor functions and the specified weights; and

providing, to the third party process, the computed measure of affinity, the computed measure of affinity being based at least in part on a level of interest with respect to the user.

2. The method of claim 1, further comprising creating a node in a social graph for a non-member user who is not associated with the social network in response to the computed measure of affinity being related to the non-member user.

3. The method of claim 2, further comprising adding additional information associated with a particular action to the node created for the non-member user.

4. The method of claim 3, further comprising:

determining content to provide to the user based on the computed measure of affinity, the additional information, or any combination thereof; and

providing the content or a portion thereof to the user.

5. The method of claim 4, wherein determining the content to provide to the user comprises:

providing the computed measure of affinity, the additional information, or any combination thereof to a third party; and

receiving the content to provide to the user of the social-networking system from the third party.

6. The method of claim 2, further comprising creating an edge in the social graph between a node for the user of the social networking system and the node for the non-member user.

7. The method of claim 6, further comprising adding additional information associated with a particular action to the edge created between the node created for the non-member user and the node for the user of the social-networking system.

8. The method of claim 1, wherein one or more of the plurality of actions previously performed by the user with respect to the mobile-computing device comprises actions using a telephone feature of the mobile-computing device, accessing content through a network connection, actions using contact information stored on the mobile-computing device, or actions affecting one or more applications on the mobile-computing device, or any combination thereof.

9. The method of claim 8, wherein one or more of the applications provides interaction with a user of another mobile-computing device.

10. The method of claim 8, wherein one or more of the applications is a game, a networking application, a database of contact information, a web browser, or any combination thereof.

11. The method of claim 1, wherein one or more of the predictor functions is determined as a function of statistics related to a history of the plurality of actions previously performed by the user on the mobile-computing device.

12. The method of claim 11, wherein the function of statistics on the history of the plurality of actions previously performed by the user implements a time decay by assigning a higher relative importance to recent actions.

13. The method of claim 11, wherein the function of statistics is determined through one or more machine-learning algorithms.

14. The method of claim 1, wherein each predictor function calculates a likelihood for to one or more actions to be performed with respect to the third-party process.

15. One or more computer-readable non-transitory storage media embodying software that is operable when executed to:

receive, at a social-networking system, data about a plurality of actions previously performed by a user of the social-networking system with respect to a mobile-computing device associated with the user, wherein each of the previously performed actions comprise data exchanged between the mobile-computing device and a third-party system regarding an interaction between the user and another person or a concept;

receive, at the social-networking system, a request from a third-party process for a measure of affinity that the user has for a person or a concept;

determine, by one or more processors of one or more computer servers associated with the social-networking system, results for one or more predictor functions based at least in part upon the received data, and upon one or more properties associated with the plurality of actions previously performed by the user,

wherein each predictor function calculates a likelihood that the user will perform one or more actions in relation to the person or the concept, and

wherein the one or more properties associated with the plurality of actions comprises a duration of time associated with an action of the plurality of actions, a frequency with which an action of the plurality of actions is taken, or statistics associated with an action of the plurality of actions;

specify one or more weights to be attributed to the one or more predictor functions;

compute, by the one or more processors, the measure of affinity based on the results for one or more of the predictor functions and the specified weights; and

provide, to the third-party process, the computed measure of affinity, the computed measure of affinity being based at least in part on a level of interest with respect to the user.

16. The media of claim 15, wherein the software is further operable when executed to create a node in a social graph for a non-member user who is not associated with the social network in response to the computed measure of affinity being related to the non-member user.

17. The media of claim 16, wherein the software is further operable when executed to add additional information associated with a particular action to the node created for the non-member user.

18. The media of claim 17, wherein the software is further operable when executed to:

determine content to provide to the user based on the computed measure of affinity, the additional information, or any combination thereof; and

provide the content or a portion thereof to the user.

19. The media of claim 18, wherein the software is further operable when executed to:

provide the computed measure of affinity, the additional information, or any combination thereof to a third party; and

receive the content to provide to the user of the social-networking system from the third party.

20. The media of claim 16, wherein the software is further operable when executed to create an edge in the social graph between a node for the user of the social networking system and the node for the non-member user.

21. The media of claim 20, wherein the software is further operable when executed to add additional information associated with a particular action to the edge created between the node created for the non-member user and the node for the user of the social-networking system.

22. The media of claim 15, wherein one or more of the plurality of actions previously performed by the user with respect to the mobile-computing device comprises actions using a telephone feature of the mobile-computing device, accessing content through a network connection, actions using contact information stored on the mobile-computing device, or actions affecting one or more applications on the mobile-computing device, or any combination thereof.

23. The media of claim 22, wherein one or more of the applications provides interaction with a user of another mobile-computing device.

24. The media of claim 22, wherein one or more of the applications is a game, a networking application, a database of contact information, a web browser, or any combination thereof.

25. The media of claim 15, wherein one or more of the predictor functions is determined as a function of statistics related to a history of the plurality of actions previously performed by the user on the mobile-computing device.

26. The media of claim 25, wherein the function of statistics on the history of the plurality of actions previously performed by the user implements a time decay by assigning a higher relative importance to recent actions.

27. The media of claim 25, wherein the function of statistics is determined through one or more machine-learning algorithms.

28. The media of claim 15, wherein each predictor function calculates a likelihood for one or more actions to be performed with respect to the third-party process.

30. The system of claim 16, wherein the processors are further operable when executing the instructions to create a node in a social graph for a non-member user who is not associated with the social network in response to the computed measure of affinity being related to the non-member user.

31. The system of claim 30, wherein the processors are further operable when executing the instructions to add additional information associated with a particular action to the node created for the non-member user.

32. The system of claim 31, wherein the processors are further operable when executing the instructions to:

determine content to provide to the user based on the computed measure of affinity, the additional information, or any combination thereof; and

provide the content or a portion thereof to the user.

33. The system of claim 32, wherein the processors are further operable when executing the instructions to:

provide the computed measure of affinity, the additional information, or any combination thereof to a third party; and

receive the content to provide to the user of the social-networking system from the third party.

34. The system of claim 30, wherein the processors are further operable when executing the instructions to create an edge in the social graph between a node for the user of the social networking system and the node for the non-member user.

35. The system of claim 34, wherein the processors are further operable when executing the instructions to add additional information associated with a particular action to the edge created between the node created for the non-member user and the node for the user of the social-networking system.

36. The system of claim 16, wherein one or more of the plurality of actions previously performed by the user with respect to the mobile-computing device comprises actions using a telephone feature of the mobile-computing device, accessing content through a network connection, actions using contact information stored on the mobile-computing device, or actions affecting one or more applications on the mobile-computing device, or any combination thereof.

37. The system of claim 36, wherein one or more of the applications provides interaction with a user of another mobile-computing device.

38. The system of claim 36, wherein one or more of the applications is a game, a networking application, a database of contact information, a web browser, or any combination thereof.

39. The system of claim 16, wherein one or more of the predictor functions is determined as a function of statistics related to a history of the plurality of actions previously performed by the user on the mobile-computing device.

40. The system of claim 39, wherein the function of statistics on the history of the plurality of actions previously performed by the user implements a time decay by assigning a higher relative importance to recent actions.

41. The system of claim 39, wherein the function of statistics is determined through one or more machine-learning algorithms.

42. The system of claim 16, wherein each predictor function calculates a likelihood for one or more actions to be performed with respect to the third-party process.

29. A system comprising:

one or more processors associated with one or more computer servers of a social-networking system; and

a memory coupled to the processors comprising instructions executable by the processors, the processors operable when executing the instructions to;

receive data about a plurality of actions previously performed by a user of the social-networking system with respect to a mobile-computing device associated with the user, wherein the previously performed actions comprise data exchanged between the mobile-computing device and a third-party system regarding an interaction between the user and another person or a concept;

receive a request from a third-party process for a measure of affinity that the user has for a person or a concept;

determine results for one or more predictor functions based at least in part upon the received data, and upon one or more properties associated with the plurality of actions previously performed by the user,

wherein each predictor function calculates a likelihood that the user will perform one or more actions in relation to the person or the concept, and

wherein the one or more properties associated with the plurality of actions comprises a duration of time associated with an action of the plurality of actions, a frequency with which an action of the plurality of actions is taken, or statistics associated with an action of the plurality of actions;

specify one or more weights to be attributed to the one or more predictor functions;

compute the measure of affinity based on the results for one or more of the predictor functions and the specified weights; and

provide, to the third party process, the computed measure of affinity, the computed measure of affinity being based at least in part on a level of interest with respect to the user.