Methods and systems for optimizing messages to users of a social network

• US 9,882,863 B2
• Filed: 02/27/2017
• Issued: 01/30/2018
• Est. Priority Date: 05/31/2012
• Status: Active Grant

• Current Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Original Assignee: Meta Platforms, Inc. (f/k/a Facebook, Inc.)
• Inventors: Arquette, Lex, Chen, David Y., Grewal, Emily B., Moreno, Denise, Ratiu, Florin, Shi, Yanxin, Sidhu, Kiranjit Singh, Weng, Ching-Chih, Yang, Huan
• Primary Examiner(s): Vincent, David
• Application Number:
  US15/442,964
• Publication Number:
  US 20170171142A1
• Time in Patent Office: 337 Days
• Field of Search: 706 12, 706 20, 706 45, 706 62
• CPC Class Codes: G06N 5/048 Fuzzy inferencing
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: H04L 51/52 for supporting social networking services
• CPC Class Codes: H04L 67/10 in which an application is distributed across nodes in the network software deployment G06F8/60; multiprogramming arrangements G06F9/46
• 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

Abstract

Techniques to optimize messages sent to a user of a social networking system. In one embodiment, information about the user may be collected by the social networking system. The information may be applied to train a model for determining likelihood of a desired action by the user in response to candidate messages that may be provided for the user. The social networking system may provide to the user a message from the candidate messages with a selected likelihood of causing the desired action.

Claims

1. A method comprising:

determining a type of user of an online system, the type identifying a subset of characteristics of users of the online system;

logging one or more user activities associated with users of the online system associated with the type, the user activities including one or more selected from a group consisting of;

responses by a user associated with the type to one or more messages sent to the user associated with the type, one or more times the user associated with the type is active on the social networking system, one or more activities performed by the user associated with the type, and any combination thereof;

identifying a message response prediction model for the type of user, the message response prediction using one or more selected from a group consisting of;

the logged one or more user activities associated with users associated with the type, one or more attributes of messages sent to users associated with the type, times when the messages were sent by the social networking system to users associated with the type, and any combination thereof;

identifying one or more candidate messages, each candidate message including a link associated with a desired activity;

determining a likelihood of the user associated with the type performing the desired activity included in each candidate message by applying the message response prediction model to each candidate message;

selecting a candidate message based at least in part on the determined likelihoods; and

sending the selected candidate message from the social networking system to the user associated with the type at a specified time based at least in part on the determined likelihood.

2. The method of claim 1, wherein user activities associated with users of the online system associated with the type further include activities performed on the online system by other users connected to the user associated with the type.

3. The method of claim 1, wherein characteristics of users of the online system include at least one selected from a group consisting of:

user demographics, behavior of the users, behavior of other users connected to at least one user via the online system, and any combination thereof.

4. The method of claim 3, wherein the behavior of the users includes at least one selected from a group consisting of:

date and time of activities of one or more users, types of activities of one or more users, extent of activities of one or more users, responses of one or more users to previous messages provided by the online system, and any combination thereof.

5. The method of claim 3, wherein the behavior of users connected to at least one user via the online system at least one selected from a group consisting of:

date and time of activities of the users connected to the at least one user via the online system, types of activities of the users connected to the at least one user via the online system, extent of activities of the users connected to the at least one user via the online system, and any combination thereof.

6. The method of claim 1, wherein the characteristics of the users are based on user activities that occurred during a selected period of time.

7. The method of claim 1, wherein the message response prediction model for the type of user further uses at least one selected from a group consisting of:

a day of the week a candidate message is to be sent, a time the candidate message is to be sent by the online system to one or more user associated with the type, and any combination thereof.

8. The method of claim 1, further comprising updating the message response prediction model for the type of user with changed information about one or more users associated with the type.

9. The method of claim 8, wherein the updating is performed periodically during a selected interval.

10. The method of claim 8, wherein the updating is performed continuously during a selected interval.

11. The method of claim 1, further comprising updating the message response prediction model for the type of user with an action taken by the user associated with the type in response to the selected candidate message.

12. The method of claim 1, further comprising training the message response prediction model using information from user profiles of users associated with the type and responses by users associated with the type to one or more messages previously sent to the users associated with the type.

13. A computer program product comprising a non-transitory computer storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to:

determine a type of user of an online system, the type identifying a subset of characteristics of users of the online system;

log one or more user activities associated with users of the online system associated with the type, the user activities including one or more selected from a group consisting of;

responses by a user associated with the type to one or more messages sent to the user associated with the type, one or more times the user associated with the type is active on the social networking system, one or more activities performed by the user associated with the type, and any combination thereof;

identify a message response prediction model for the type of user, the message response prediction using one or more selected from a group consisting of;

the logged one or more user activities associated with users associated with the type, one or more attributes of messages sent to users associated with the type, times when the messages were sent by the social networking system to users associated with the type, and any combination thereof;

identify one or more candidate messages, each candidate message including a link associated with a desired activity;

determine a likelihood of the user associated with the type performing the desired activity included in each candidate message by applying the message response prediction model to each candidate message;

select a candidate message based at least in part on the determined likelihoods; and

send the selected candidate message from the social networking system to the user associated with the type at a specified time based at least in part on the determined likelihood.

14. The computer program product of claim 13, wherein user activities associated with users of the online system associated with the type further include activities performed on the online system by other users connected to the user associated with the type.

15. The computer program product of claim 13, wherein characteristics of users of the online system include at least one selected from a group consisting of:

user demographics, behavior of the users, behavior of other users connected to at least one user via the online system, and any combination thereof.

16. The computer program product of claim 15, wherein the behavior of the users includes at least one selected from a group consisting of:

date and time of activities of one or more users, types of activities of one or more users, extent of activities of one or more users, responses of one or more users to previous messages provided by the online system, and any combination thereof.

17. The computer program product of claim 15, wherein the behavior of users connected to at least one user via the online system at least one selected from a group consisting of:

date and time of activities of the users connected to the at least one user via the online system, types of activities of the users connected to the at least one user via the online system, extent of activities of the users connected to the at least one user via the online system, and any combination thereof.

18. The computer program product of claim 13, wherein the characteristics of the users are based on user activities that occurred during a selected period of time.

19. The computer program product of claim 13, wherein the non-transitory computer storage medium having instructions encoded thereon that, when executed by a processor, cause the processor to:

update the message response prediction model for the type of user with an action taken by the user associated with the type in response to the selected candidate message.

20. A system comprising:

at least one processor; and

a memory storing instructions configured to instruct the at least one processor to;

determine a type of user of an online system, the type identifying a subset of characteristics of users of the online system;

log one or more user activities associated with users of the online system associated with the type, the user activities including one or more selected from a group consisting of;

responses by a user associated with the type to one or more messages sent to the user associated with the type, one or more times the user associated with the type is active on the social networking system, one or more activities performed by the user associated with the type, and any combination thereof;

identify a message response prediction model for the type of user, the message response prediction using one or more selected from a group consisting of;

the logged one or more user activities associated with users associated with the type, one or more attributes of messages sent to users associated with the type, times when the messages were sent by the social networking system to users associated with the type, and any combination thereof;

identify one or more candidate messages, each candidate message including a link associated with a desired activity;

determine a likelihood of the user associated with the type performing the desired activity included in each candidate message by applying the message response prediction model to each candidate message;

select a candidate message based at least in part on the determined likelihoods; and

send the selected candidate message from the social networking system to the user associated with the type at a specified time based at least in part on the determined likelihood.