Top Friend Prediction for Users in a Social Networking System

• US 20120271722A1
• Filed: 04/25/2011
• Published: 10/25/2012
• Est. Priority Date: 04/25/2011
• Status: Abandoned Application

• Current Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Original Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Inventors: Juan, Yun-Fang, Hua, Ming
• Application Number:
  US13/093,744
• Publication Number:
  US 20120271722A1
• Time in Patent Office: Days
• US Class Current: 705/14.72
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202

Abstract

A social networking system predicts a user'"'"'s top friends among the user'"'"'s connections in a social networking system. A top friend prediction model receives static data and statistics related to the historical interactions of the connection and the user as input singles. The model may be trained using a training set of data associated with the connections of users, where users have explicitly indicated that other users are or are not their top (or “best” or “closest”) friends. The trained model outputs a score for each of a particular user'"'"'s connections, and the score is used to predict whether the connection is a top friend of that user. Whether a user'"'"'s connection is one of that user'"'"'s top friends thus indicates a closeness of that relationship in the real world, which may differ from how likely the users are to interact with each other within the social networking system.

Claims

1. A method for predicting a set of top friends for a user in a social networking system, the method comprising:

training a prediction model by;

identifying one or more users of a social networking system who have explicitly indicated a level of closeness of a relationship between the user and another user of the social networking system with whom the user has established a connection,

obtaining a training set of data that comprises information about the identified users and the corresponding relationships, and

training the prediction model using the obtained training set of data, wherein the prediction model computes a score indicative a level of closeness to a user of another user of the social networking system with whom that user has established a connection;

receiving a request from a process running on a computer system, the request for a set of top friends for a subject user of the social networking system;

predicting a set of top friends for the subject user by;

obtaining information about the subject user and one or more other users with whom the subject user has established a connection the social networking system,

providing the obtained information to the trained prediction model,

computing a score using the prediction model for each of a plurality of connections of the subject user, and

determining a set of top friends based on at least in part the computed scores; and

providing the determined set of top friends to the requesting process.

2. The method of claim 1, wherein for the identifying step, a user has explicitly indicated a level of closeness of a relationship with another user by adding the other user to a friends list associated with a predetermined keyword.

3. The method of claim 1, wherein for the identifying step, a user has explicitly indicated a level of closeness of a relationship with another user by excluding the other user from a friends list associated with a predetermined keyword.

4. The method of claim 1, wherein for the identifying step, a user has explicitly indicated a level of closeness of a relationship with another user by responding to a suggestion to interact with the other user.

5. The method of claim 1, wherein the trained prediction model is configured to receive a plurality of input signals based on historical information about interactions between the identified users and their connections and static information about profiles of the identified users and their connections.

6. The method of claim 1, wherein the obtained information comprises a plurality of input signals based on historical information about interactions between the subject user and the one or more other users.

7. The method of claim 6, wherein one or more of the interactions are selected from a group consisting of:

one user observing information about another user;

one user communicating with another user;

two users coincidentally associated with a content item; and

a farmed response of one user to a suggestion to interact with another user.

8. The method of claim 1, wherein the obtained information comprises a plurality of input signals based on static information about profiles of the subject user and the one or more other users.

9. The method of claim 1, wherein one or more elements of the obtained information are weighted by recency.

10. The method of claim 1, wherein one or more elements of the obtained information are normalized based on a frequency of a user'"'"'s interactions in the social networking system.

11. The method of claim 1, wherein determining the set of top friends comprises identifying a connection as a top friend if the connection'"'"'s computed score is above a threshold.

12. The method of claim 1, wherein determining the set of top friends comprises selecting a number of connections having the highest computed scores.

13. The method of claim 1, wherein the process comprises an advertisement creation process that is creating an advertisement for the subject user.

14. The method of claim 1, wherein the process is executed within the social networking system.

15. The method of claim 1, wherein the process is executed by a server external to the social networking system.

16. A method for predicting a set of top friends for a user in a social networking system, the method comprising:

receiving a request from a process running on a computer system, the request for a set of top friends for a subject user of the social networking system;

obtaining information about the subject user and one or more other users with whom the subject user has established a connection the social networking system;

providing the obtained information to a prediction model, wherein the prediction model has been trained by;

identifying one or more users of a social networking system who have explicitly indicated a level of closeness of a relationship between the user and another user of the social networking system with whom the user has established a connection,

obtaining a training set of data that comprises information about the identified users and the corresponding relationships, and

training the prediction model using the obtained training set of data, wherein the prediction model computes a score indicative a level of closeness to a user of another user of the social networking system with whom that user has established a connection;

computing a score using the prediction model for each of a plurality of connections of the subject user;

determining a set of top friends based on at least in part the computed scores; and

providing the determined set of top friends to the requesting process.

17. The method of claim 16, wherein the obtained information comprises a plurality of input signals based on:

historical information about interactions between the subject user and the one or more other users, wherein one or more of the interactions are selected from a group consisting of;

one user observing information about another user;

one user communicating with another user;

two users coincidentally associated with a content item; and

a farmed response of one user to a suggestion to interact with another user; and

static information about profiles of the subject user and the one or more other users.

18. The method of claim 16, wherein one or more elements of the obtained information are weighted by recency.

19. The method of claim 16, wherein one or more elements of the obtained information are normalized based on a frequency of a user'"'"'s interactions in the social networking system.

20. The method of claim 16, wherein determining the set of top friends comprises identifying a connection as a top friend if the connection'"'"'s computed score is above a threshold.

21. The method of claim 16, wherein determining the set of top friends comprises selecting a number of connections having the highest computed scores.

22. The method of claim 16, wherein the process comprises an advertisement creation process that is creating an advertisement for the subject user.

23. The method of claim 16, wherein the process is executed within the social networking system.

24. The method of claim 16, wherein the process is executed by a server external to the social networking system.

25. A method for training a prediction model for predicting a set of top friends for a user in a social networking system, the method comprising:

identifying one or more users of a social networking system who have explicitly indicated a level of closeness of a relationship between the user and another user of the social networking system with whom the user has established a connection;

obtaining a training set of data that comprises information about the identified users and the corresponding relationships; and

training the prediction model using the obtained training set of data, wherein the prediction model computes a score indicative a level of closeness to a user of another user of the social networking system with whom that user has established a connection.

26. The method of claim 25 wherein for the identifying step, a user has explicitly indicated a level of closeness of a relationship with another user by adding the other user to a friends list associated with a predetermined keyword.

27. The method of claim 25, wherein for the identifying step, a user has explicitly indicated a level of closeness of a relationship with another user by excluding the other user from a friends list associated with a predetermined keyword.

28. The method of claim 25, wherein for the identifying step, a user has explicitly indicated a level of closeness of a relationship with another user by responding to a suggestion to interact with the other user.

29. The method of claim 25, wherein the trained prediction model is configured to receive a plurality of input signals based on historical information about interactions between the identified users and their connections and static information about profiles of the identified users and their connections.

30. A method for predicting a plurality of top objects for a user from among objects in the social networking system, the method comprising:

receiving a request for a plurality of top objects for a user of a social networking system;

receiving statistics of the historical interactions of the user with a plurality of objects on the social networking system;

receiving static data for the user and the objects on the social networking system;

computing a score for each object through a scoring function based on the received statistics and static data, wherein the statistics are weighted by recency ;

determining a plurality of top objects based on at least in part the previously computed scoring functions; and

outputting the plurality of top objects.

31. The method of claim 30, wherein the objects comprise pages residing on the social networking system.

32. The method of claim 30, wherein the objects comprise search results requested by the user of the social networking system.

33. The method of claim 30, wherein the objects comprise applications on the social networking system.

34. The method of claim 30, wherein the objects are associated with locations for which users of the social networking system can check in.

35. The method of claim 30, wherein the objects comprise groups that users of the social networking system can join.