Contextually Relevant Affinity Prediction in a Social Networking System

• US 20120166532A1
• Filed: 12/23/2010
• Published: 06/28/2012
• Est. Priority Date: 12/23/2010
• 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:
  US12/978,265
• Publication Number:
  US 20120166532A1
• Time in Patent Office: Days
• US Class Current: 709/204
• CPC Class Codes: G06Q 30/0224 based on user history
• CPC Class Codes: G06Q 50/01 Social networking

Abstract

A tunable affinity function serves one or more processes running in a social networking environment, where each process may request a measure of affinity for a particular user. A module that implements the affinity function computes the requested measure of affinity by combining (e.g., adding) a weighted set of predictor functions, where each predictor function predicts whether the user will perform a different action. The weights are provided by the process that requests the measure of affinity, which allows the requesting process to weight the predictor functions differently and thus tune the affinity function for its own purpose.

Claims

1. A method for providing a measure of affinity for a user of a social networking system, the method comprising:

receiving a request for a measure of affinity for a user of a social networking system from a process, the request specifying one or more weights related to a plurality of actions in the social networking system;

weighting each of a plurality of predictor functions using the received weights, wherein each predictor function predicts whether the user will perform an action associated with the weight applied thereto;

computing a measure of affinity for the user by combining the weighted plurality of predictor functions; and

providing the computed measure of affinity to the process.

2. The method of claim 1, wherein computing the measure of affinity for the user involves linearly combining the weighted set of predictor functions.

3. The method of claim 1, wherein the weights specified in the request are binary.

4. The method of claim 1, wherein the action is representative of a user'"'"'s communication in the social networking system.

5. The method of claim 1, wherein the action is representative of a user'"'"'s observation of content in the social networking system.

6. The method of claim 1, wherein the action is representative of two or more users'"'"' coincidence in the social networking system.

7. The method of claim 1, wherein the action is selected from a group consisting of:

commenting on a content item, using a third party application, inviting a connection to perform an activity, playing a game, and expressing an affinity for an object in the social networking system.

8. The method of claim 1, wherein the predictor function predicts whether the user will perform an action associated with the weight based on the user'"'"'s interest in the action.

9. The method of claim 1, wherein the predictor function predicts whether the user will perform an action associated with the weight based on the user'"'"'s historical activity.

10. The method of claim 1, wherein the predictor functions are determined as functions of statistics on user'"'"'s historical activity.

11. The method of claim 10, wherein the functions of statistics on user'"'"'s historical activity implement time decay by assigning recent activity higher relative importance.

12. The method of claim 10, wherein the functions of statistics in the predictor functions are determined through machine learning algorithms.

13. The method of claim 1, wherein each of the plurality of predictor functions are determined as a ratio of a numerator affine function of statistics on user'"'"'s historical activity and a denominator affine function of statistics on user'"'"'s historical activity.

14. The method of claim 13, wherein the denominator affine function of statistics on user'"'"'s historical activity represents a normalization of the numerator affine function of statistics on user'"'"'s historical activity.

15. A method for obtaining a prediction of an action of a user of a social networking system, the method comprising:

sending a request for a measure of affinity for a user of a social networking system from a process, the request specifying one or more weights related to a first plurality of actions in the social networking system;

receiving the measure of affinity computed by combining a plurality of predictor functions corresponding to the first plurality of actions; and

predicting a second plurality of actions the user will perform in the process according to the received measure of affinity.

16. The method of claim 15, wherein the process is executed within the social networking system.

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

18. The method of claim 15, wherein the process is performed by a platform application provided by third party and executed within the social networking system.

19. The method of claim 15, wherein the action comprises a user'"'"'s communication to another user in the social networking system.

20. The method of claim 15, wherein the action comprises a user'"'"'s observation of content in the social networking system.

21. The method of claim 15, wherein the action is selected from a group consisting of:

commenting on a content item, using a third party application, inviting connections to perform an action, playing a game, and expressing an affinity for an object in the social networking system.