Recommendations using session relevance and incremental learning

Patent metadata

Field	Value
Publication number	US-12223554-B2
Application number	US-202016912245-A
Country	US
Kind code	B2
Filing date	Jun 25, 2020
Priority date	Jun 25, 2020
Publication date	Feb 11, 2025
Grant date	Feb 11, 2025

Abstract

In some embodiments, a computer system generates a recommendation for a user of an online service based on user actions that have been performed by the user within a threshold amount of time before the generation of the recommendation. For each user action, the computer system determines an intent classification that identifies an activity of the user and that corresponds to different types of user actions, as well as a preference classification that identifies a target of the activity, and then stores these intent and preference classifications as part of indications of the user actions for use in generating different types of recommendations using different types of recommendation models. Additionally, the computer system may use mini-batches of data from an incoming stream of logged data to train an incremental update to one or more recommendation models.

First claim

What is claimed is:

1. A computer-implemented method performed by a computer system having at least one hardware processor, the computer-implemented method comprising:
receiving a plurality of user actions performed on an online service by a user via a computing device of the user;
generating a plurality of intent classifications corresponding to a plurality of different types of user actions, wherein at least one intent classification corresponds to two or more user actions;
for at least one of the plurality of user actions:
selecting an intent classification from the plurality of intent classifications according to a type of the user action, the selected intent classification identifying an intent of the user in performing the corresponding user action; and
generating, according to the type of the user action, a preference classification identifying a target of the corresponding user action selected from a plurality of different user action targets;

storing the intent classification and the preference classification for each one of the plurality of user actions;
for each of a plurality of different types of user actions, storing a count of a number of occurrences of the corresponding type of user action found in the intent classifications for the plurality of user actions;
generating, at a first point in time, a first recommendation of a first type based on a first recommendation model, the generating of the first recommendation comprising using the stored intent classifications and preference classifications of the plurality of user actions as feature data for the first recommendation model based on the plurality of user actions having been performed within a threshold amount of time before the first point in time;
causing the first recommendation to be displayed on the computing device;
generating, at a second point in time after the first point in time, a second recommendation of a second type different from the first type based on a second recommendation model different from the first recommendation model, the generating of the second recommendation comprising using the stored intent classifications and preference classifications of the plurality of user actions as feature data for the second recommendation model based on the plurality of user actions having been performed within the threshold amount of time before the second point in time; and
causing the second recommendation to be displayed on the computing device.

2. The computer-implemented method of claim 1 , wherein:
the causing the first recommendation to be displayed on the computing device comprises causing the first recommendation to be displayed on a first page of the online service; and
the causing the second recommendation to be displayed on the computing device comprises causing the second recommendation to be displayed on a second page of the online service, the second page being different from the first page.

3. The computer-implemented method of claim 2 , wherein:
the first page comprises a home page of the online service displayed on the computing device of the user in response to the user logging onto the online service; and
the second page is navigated to from the home page via selection by the user of at least one selectable link.

4. The computer-implemented method of claim 1 , wherein:
the first recommendation model is used to generate recommendations of digital content items to be displayed within a personalized feed of the user on a home page; and
the second recommendation model is configured to generate recommendations of other users with whom to form connections or recommendations of online job postings.

5. The computer-implemented method of claim 1 , wherein the plurality of user actions comprise at least one of: posting a comment for a digital content item posted by another user, sending an invitation to connect to another user, following another user, submitting an electronic like of a digital content item posted by another user, sharing a digital content item posted by another user, sending a message to another user, viewing an online job posting via the online service, or applying to an online job posting via the online service.

6. The computer-implemented method of claim 1 , wherein the storing comprises selecting the corresponding intent classification from a plurality of intent classifications based on the corresponding user action, the plurality of intent classifications comprising: building a social network, finding a job, staying informed, or nurturing a relationship.

7. The computer-implemented method of claim 1 , wherein the storing comprises selecting the corresponding preference classification from a plurality of preference classifications based on the corresponding user action, the plurality of preference classifications comprising: another user of the online service, a type of job, a specific company, a type of company, an industry, or a geographic location.

8. A system comprising:
at least one hardware processor; and
a non-transitory machine-readable medium embodying a set of instructions that, when executed by the at least one hardware processor, cause the at least one hardware processor to perform operations, the operations comprising:
receiving a plurality of user actions performed on an online service by a user via a computing device of the user;
generating a plurality of intent classifications corresponding to a plurality of different types of user actions, wherein at least one intent classification corresponds to two or more user actions;
for at least one of the plurality of user actions;
selecting an intent classification from the plurality of intent classifications according to a type of the user action, the selected intent classification identifying an intent of the user in performing the corresponding user action; and
generating, according to the type of the user action, a preference classification identifying a target of the corresponding user action selected from a plurality of different user action targets;

storing the intent classification and the preference classification for each one of the plurality of user actions;
for each of a plurality of different types of user actions, storing a count of a number of occurrences of the corresponding type of user action found in the intent classifications for the plurality of user actions;
generating, at a first point in time, a first recommendation of a first type based on a first recommendation model, the generating of the first recommendation comprising using the stored intent classifications and preference classifications of the plurality of user actions as feature data for the first recommendation model based on the plurality of user actions having been performed within a threshold amount of time before the first point in time;
causing the first recommendation to be displayed on the computing device;
generating, at a second point in time after the first point in time, a second recommendation of a second type different from the first type based on a second recommendation model different from the first recommendation model, the generating of the second recommendation comprising using the stored intent classifications and preference classifications of the plurality of user actions as feature data for the second recommendation model based on the plurality of user actions having been performed within the threshold amount of time before the second point in time; and
causing the second recommendation to be displayed on the computing device.

9. The system of claim 8 , wherein:
the causing the first recommendation to be displayed on the computing device comprises causing the first recommendation to be displayed on a first page of the online service; and

Assignees

• Microsoft Technology Licensing Llc

Inventors

• Ramanath Rohan
• Salomatin Konstantin
• Gee Jeffrey Douglas
• Dalal Onkar Anant
• Polatkan Gungor
• Gerrard Sara Smoot
• Kumar Deepak
• Gupta Rupesh
• Ge Jiaqi
• Weng Lingjie
• Yu Shipeng

Classifications

• G06Q10/40 Primary

  Business processes related to social networking or social networking services · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06F18/214

  Generating training patterns; Bootstrap methods, e.g. bagging or boosting · CPC title

• G06F16/9535

  Search customisation based on user profiles and personalisation · CPC title

• G06F16/958

  Organisation or management of web site content, e.g. publishing, maintaining pages or automatic linking · CPC title