User electronic message system

Patent metadata

Field	Value
Publication number	US-11775601-B2
Application number	US-202217824647-A
Country	US
Kind code	B2
Filing date	May 25, 2022
Priority date	Jul 3, 2019
Publication date	Oct 3, 2023
Grant date	Oct 3, 2023

Abstract

Network site users can be selected to receive a communication based on a network site event, such as incomplete registration. A hybrid user interaction machine learning scheme can select a portion of the selected users based on user interaction estimates and network sampling data. The electronic document sent to the users can have portions that undergo two-pass ranking for ordering of content items to be included in the electronic document, such as an email.

First claim

What is claimed is:

1. A method comprising:
identifying, using one or more processors of a machine, a plurality of users of a network site;
generating, using a machine learning scheme, values of a first interaction type for the plurality of users, the values of the first interaction type indicating a likelihood of user interaction with the network site, the values of the first interaction type grouped into different ranges;
identifying, from the different ranges, one or more top ranges that comprise users that perform an elevated amount of a second interaction type with the network site, the second interaction type being a different user interaction with the network site than the first interaction type;
storing a set of users that have first interaction type values that are in the one or more top ranges; and
transmitting an electronic document to the set of users.

2. The method of claim 1 , wherein identifying the plurality of users comprises:
determining that the plurality of users correspond to a preconfigured network site event.

3. The method of claim 2 , wherein the preconfigured network site event is abandonment of a network site navigation path.

4. The method of claim 3 , wherein the network site navigation path is a network site transaction.

5. The method of claim 1 , wherein the first interaction type is an open electronic document interaction.

6. The method of claim 1 , further comprising:
training the machine learning scheme using training data comprising historical network site document data.

7. The method of claim 6 , wherein the historical network site document data comprises attribute values describing attributes of previous electronic documents of the network site.

8. The method of claim 1 , wherein the machine learning scheme is a decision tree classifier machine learning scheme.

9. The method of claim 1 , wherein the values of the first interaction type are partitioned into ranges using a grouping machine learning scheme.

10. The method of claim 1 , wherein the values of the first interaction type are partitioned into ranges using a set of pre-configured ranges.

11. The method, of claim 1 , further comprising:
generating, using a first ranking machine learning scheme, an unpopulated content scheme ranking data set that ranks a plurality of unpopulated content schemes;
generating, for each of the plurality of users, a plurality of populated content schemes by populating the plurality of unpopulated content schemes with network site content items that matches user profile data of each user;
generating, using a second ranking machine learning scheme, a populated content scheme ranking data set that ranks the plurality of populated content schemes, the second ranking machine learning scheme receiving input data that includes output of the first ranking machine learning scheme that is trained to rank unpopulated content schemes; and
generating, for each of the plurality of users, the electronic document using one or more top ranked populated content schemes.

12. The method of claim 11 , wherein the unpopulated content schemes are network item retrieval schemes that order network site items in a user interface window.

13. The method of claim 11 , wherein the unpopulated content schemes are populated by selecting network items according to each content scheme.

14. A system comprising:
one or more processors of a machine; and
a memory storing instructions that, when executed by the one or more processors, cause the machine to perform operations comprising:
identifying, using one or more processors of a machine, a plurality of users of a network site;
generating, using a machine learning scheme, values of a first interaction type for the plurality of users, the values of the first interaction type indicating a likelihood of user interaction with the network site, the values of the first interaction type grouped into different ranges;
identifying, from the different ranges, one or more top ranges that comprise users that perform an elevated amount of a second interaction type with the network site, the second interaction type being a different user interaction with the network site than the first interaction type;
storing a set of users that have first interaction type values that are in the one or more top ranges; and
transmitting an electronic document to the set of users.

15. The system of claim 14 , wherein identifying the plurality of users comprises:
determining that the plurality of users correspond to a preconfigured network site event.

16. The system of claim 15 , wherein the preconfigured network site event is abandonment of a network site navigation path.

17. The system of claim 16 , wherein the network site navigation path is a network site transaction.

18. The system of claim 14 , wherein the first interaction type is an open electronic document interaction.

19. A machine-readable storage device embodying instructions that, when executed by a machine, cause the machine to perform operations comprising:
identifying, using one or more processors of a machine, a plurality of users of a network site;
generating, using a machine learning scheme, values of a first interaction type for the plurality of users, the values of the first interaction type indicating a likelihood of user interaction with the network site, the values of the first interaction type grouped into different ranges;
identifying, from the different ranges, one or more top ranges that comprise users that perform an elevated amount of a second interaction type with the network site, the second interaction type being a different user interaction with the network site than the first interaction type;
storing a set of users that have first interaction type values that are in the one or more top ranges; and
transmitting an electronic document to the set of users.

20. The machine-readable storage device of claim 19 , wherein identifying the plurality of users comprises: determining that the plurality of users correspond to a preconfigured network site event.

Assignees

• Airbnb Inc

Inventors

• Sun Zhentao
• Wang Dawei
• Odonnell Sean
• Martin Noah
• Sergiyenko Andriy
• Thompson Greg
• Mei Shike

Classifications

• G06F16/9535 Primary

  Search customisation based on user profiles and personalisation · CPC title

• G06F16/9538

  Presentation of query results · CPC title

• G06N20/00

  Machine learning · CPC title

• H04L51/212

  using filtering or selective blocking · CPC title