METHOD AND DEVICE FOR PUSHING INFORMATION

• US 20200134674A1
• Filed: 12/19/2019
• Published: 04/30/2020
• Est. Priority Date: 06/06/2016
• Status: Active Grant

• Current Assignee: Advanced New Technologies Company Limited (Ant Group Co., Ltd.)
• Original Assignee: Alibaba Group Holding Ltd.
• Inventors: SONG, Nian
• Granted Patent: US 11,074,623 B2
• Time in Patent Office: Days
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0242 Determining effectiveness of advertisements
• CPC Class Codes: G06Q 30/0255 based on user history
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute
• CPC Class Codes: G06Q 50/01 Social networking
• CPC Class Codes: H04L 67/55 Push-based network services
• CPC Class Codes: H04L 67/63 Routing a service request depending on the request content or context

Abstract

The present disclosure discloses a method and device for pushing information to a target user. One example method includes identifying a plurality of users that meet a predetermined condition; selecting a target user from the identified users to be provided with information, where in the target user is selected based on a relationship strength and an influence of the identified users; and transmitting the information to the determined target user over a network., and relates to the field of information technologies.

Claims

1. A computer-implemented method, comprising:

identifying a plurality of users that meet a predetermined condition;

selecting a target user from the identified users to be provided with information, wherein the target user is selected based on a relationship strength and an influence of the identified users; and

transmitting the information to the determined target user over a network.

2. The computer-implemented method of claim 1, further comprising:

obtaining initial relation community labels of the identified users;

determining an updated relation community label of each user from the identified users when the relationship strength for a first user to a second user is greater than a predetermined threshold; and

dividing the identified users into different relation communities based on the relationship strength of the identified users and the updated relation community labels.

3. The computer-implemented method of claim 2, further comprising:

obtaining a relation community label update count of the target user; and

determining whether the relation community label update count of the target user is greater than a predetermined count threshold; and

dividing the identified users into different relation communities based on the relationship strength of the identified users and based on a determination of whether the relation community label update count of the target user is greater than the predetermined count threshold.

4. The computer-implemented method of claim 1, wherein the relationship strength among the users comprises a similarity among attribute information of the multiple users.

5. The computer-implemented method of claim 1, wherein the influence of the identified users comprises a strength with which a first user affects other users.

6. The computer-implemented method of claim 1, wherein identifying the one or more users that meet a predetermined condition further comprises:

scoring attribute data of the identified users using a predetermined score model; and

selecting a subset of the identified users whose scores meet a predetermined score condition.

7. The computer-implemented method of claim 1, further comprising:

determining an influence level for each user in the identified users based on a predetermined influence level table.

8. The computer-implemented method of claim 7, wherein the predetermined influence level table comprises influence threshold intervals that correspond to different influence levels, the different influence levels divided into high, medium, sub-medium, and low.

9. A non-transitory computer-readable storage medium coupled to one or more computers and configured with instructions executable by the one or more computers to:

identifying a plurality of users that meet a predetermined condition;

selecting a target user from the identified users to be provided with information, where in the target user is selected based on a relationship strength and an influence of the identified users; and

transmitting the information to the determined target user over a network.

10. The non-transitory computer-readable storage medium of claim 9, further configured with instructions executable by the one or more computers to:

obtaining initial relation community labels of the identified users;

determining an updated relation community label of each user from the identified users when the relationship strength for a first user to a second user is greater than a predetermined threshold; and

dividing the identified users into different relation communities based on the relationship strength of the identified users and the updated relation community labels.

11. The non-transitory computer-readable storage medium of claim 10, configured with instructions executable by the one or more computers to:

obtaining a relation community label update count of the target user; and

determining whether the relation community label update count of the target user is greater than a predetermined count threshold; and

dividing the identified users into different relation communities based on the relationship strength of the identified users and based on a determination of whether the relation community label update count of the target user is greater than the predetermined count threshold.

12. The non-transitory computer-readable storage medium of claim 9, wherein the relationship strength among the users comprises a similarity among attribute information of the multiple users.

13. The non-transitory computer-readable storage medium of claim 9, wherein the influence of the identified users comprises a strength with which a first user affects other users.

14. The non-transitory computer-readable storage medium of claim 9, wherein identifying the one or more users that meet a predetermined condition includes:

scoring attribute data of the identified users using a predetermined score model; and

selecting a subset of the identified users whose scores meet a predetermined score condition.

15. The non-transitory computer-readable storage medium of claim 9, configured with instructions executable by the one or more computers to:

determining an influence level for each user in the identified users based on a predetermined influence level table.

16. The non-transitory computer-readable storage medium of claim 15, wherein the predetermined influence level table comprises influence threshold intervals that correspond to different influence levels, the different influence levels divided into high, medium, sub-medium, and low.

17. A system, comprising:

one or more computers; and

one or more computer-readable memories coupled to the one or more computers and configured with instructions executable by the one or more computers to;

identifying a plurality of users that meet a predetermined condition;

selecting a target user from the identified users to be provided with information, where in the target user is selected based on a relationship strength and an influence of the identified users; and

transmitting the information to the determined target user over a network.

18. The system of claim 17, further configured with instructions executable by the one or more computers to:

obtaining initial relation community labels of the identified users;

determining an updated relation community label of each user from the identified users when the relationship strength for a first user to a second user is greater than a predetermined threshold; and

dividing the identified users into different relation communities based on the relationship strength of the identified users and the updated relation community labels.

19. The system of claim 18, configured with instructions executable by the one or more computers to:

obtaining a relation community label update count of the target user; and

determining whether the relation community label update count of the target user is greater than a predetermined count threshold; and

dividing the identified users into different relation communities based on the relationship strength of the identified users and based on a determination of whether the relation community label update count of the target user is greater than the predetermined count threshold.

20. The system of claim 17, wherein the relationship strength among the users comprises a similarity among attribute information of the multiple users.