EXPANDING MESSAGE TARGETS

• US 20170032396A1
• Filed: 08/28/2015
• Published: 02/02/2017
• Est. Priority Date: 07/31/2015
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: LinkedIn Corporation (Microsoft Corporation)
• Inventors: Gupta, Rupesh, Vijay, Ravi Kiran Holur, Tseng, Hsiao-Ping, Rosales, Romer E.
• Granted Patent: US 10,643,226 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0203 Market surveys; Market polls
• CPC Class Codes: G06Q 50/01 Social networking
• CPC Class Codes: H04L 51/52 for supporting social networking services
• CPC Class Codes: H04L 67/5651 Reducing the amount or size of exchanged application data

Abstract

This disclosure relates to systems and methods that include configuring a machine learning system to train on a plurality of messages transmitted to target groups of an online social networking service, determining a threshold differential and a weight value using responses to the plurality of messages, and send the input message to the target in response to a differential between the expected number of positive responses and the weight multiplied by the expected number of negative responses being greater than the threshold differential.

Claims

1. A system comprising:

a machine-readable medium having instructions stored thereon, which, when executed by a processor, cause the system to;

configure a machine learning system to train on a plurality of messages transmitted to a portion of members for a target group of an online social networking service, the machine learning system to output an expected number of positive responses and an expected number of negative responses based on an input message being transmitted to a target of the online social networking service;

determine a threshold differential and a weight value using expected responses from the target to minimize a number of messages to send while satisfying one or more constraints, the constraints comprising a maximum number of negative responses and a minimum number of positive responses; and

send the input message to the target in response to a differential between the expected number of positive responses and the weight multiplied by the expected number of negative responses being greater than the threshold differential.

2. The system of claim 1, wherein the operations further comprise receiving a set of target groups, the target group selected from the set of target groups.

3. The system of claim 1, wherein the instructions further cause the system to identify multiple targets from a set of target groups for the input message, and send the input message to each target group in response to the expected number of positive responses and the weight value multiplied by the expected number of negative responses being greater than the threshold differential.

4. The system of claim 1, wherein the target is selected from the group consisting of a language group, a regional group, a cultural group, and a membership group at a network system.

5. The system of claim 1, wherein the positive responses include at least one of a page view, a clicked link, a purchase, a like, and a comment, and the negative responses include at least one of an unsubscribe, a complaint, a dislike, and a spam report.

6. The system of claim 1, wherein the machine learning system is configured to train on responses for a predetermined threshold period of time starting with a first response from a user.

7. The system of claim 1, wherein determining the threshold differential and the weight value uses one of multi-objective optimization and a grid search.

8. A method comprising:

configure a machine learning system to train on a plurality of messages transmitted to a portion of members for a target group of an online social networking service, the machine learning system to output an expected number of positive responses and an expected number of negative responses based on an input message being transmitted to a target of the online social networking service;

determining a threshold differential and a weight value using expected responses from the target to minimize a number of messages to send while satisfying one or more constraints, the constraints comprising a maximum number of negative responses and a minimum number of positive responses; and

send the input message to the target in response to a differential between the expected number of positive responses and the weight multiplied by the expected number of negative responses being greater than the threshold differential.

9. The method of claim 8, further comprising receiving a set of target groups, the target group selected from the set of target groups.

10. The method of claim 8, further comprising identifying multiple targets for the input message, and send the input message to each target in response to the expected number of positive responses and the weight value multiplied by the expected number of negative responses is greater than the threshold differential.

11. The method of claim 8, wherein the input message comprises multiple messages selected from a recent period of time.

12. The method of claim 8, wherein the target is selected from the group consisting of a language group, a regional group, a cultural group, and a membership group at a network system.

13. The method of claim 8, wherein the positive responses include at least one of a page view, a clicked link, a purchase, a like, and a comment, and the negative responses include at least one of an unsubscribe, a complaint, a dislike, and a spam report.

14. The method of claim 8, wherein the machine learning system is configured to train on responses for a predetermined threshold period of time starting with a first response from a user.

15. The method of claim 8, wherein determining the threshold differential and the weight value uses one of multi-objective optimization and a grid search.

16. A machine-readable medium having instructions stored thereon, which, when executed by a processor, cause the processor to perform:

configuring a machine learning system to train on a plurality of messages transmitted to a portion of members for a target group of an online social networking service, the machine learning system to output an expected number of positive responses and an expected number of negative responses based on an input message being transmitted to a target of the online social networking service;

determining a threshold differential and a weight value using expected responses from the target to minimize a number of messages to send while satisfying one or more constraints, the constraints comprising a maximum number of negative responses and a minimum number of positive responses; and

sending the input message to the target in response to a differential between the expected number of positive responses and the weight multiplied by the expected number of negative responses being greater than the threshold differential.

17. The machine-readable medium of claim 16, wherein the operations further comprise receiving a set of target groups, the target group selected from the set of target groups.

18. The machine-readable medium of claim 16, wherein the instructions further cause the processor to perform identifying multiple targets for the input message, and sending the input message to each target in response to the expected number of positive responses and the weight value multiplied by the expected number of negative responses is greater than the threshold differential.

19. The machine-readable medium of claim 16, wherein determining the threshold differential and the weight value uses one of multi-objective optimization and a grid search.

20. The machine-readable medium of claim 16, wherein the machine learning system is configured to train on responses for a predetermined threshold period of time starting with a first response from a user