公开(公告)号：US11968165B1

公开(公告)日：2024-04-23

申请号：US18086138

申请日：2022-12-21

Applicant: Microsoft Technology Licensing, LLC

Inventor： Ivan Lopez Moreno ,  Xuexin Ren ,  Ying Han ,  Shaunak Chatterjee ,  Ajith Muralidharan

IPC: H04L12/58 ,  G06N20/00 ,  G06Q50/00 ,  H04L51/10 ,  H04L51/52

CPC classification number: H04L51/52 ,  G06N20/00 ,  G06Q50/01 ,  H04L51/10

Abstract: Methods, systems, and computer programs are presented for selecting notifications based on an affinity score between a content generator and a viewer of the content. One method includes capturing interactions of content generators with notifications, received by the content generators, associated with viewer responses to creator-generated content items. The method further includes training a machine-learning model based on the interactions, and detecting a first set of notifications, for a first content generator, associated with interactions of a set of viewers to first-content generator content. The ML model calculates an affinity score between the first content generator and each viewer, and the set of first notifications are ranked based on the affinity scores of the first content generator and the viewer associated with each notification. A set of second notifications is selected based on the ranked first notifications; and generating notifications are generated, for the first content generator, for the selected set of second notifications.

公开(公告)号：US20230196070A1

公开(公告)日：2023-06-22

申请号：US17556218

申请日：2021-12-20

Applicant: Microsoft Technology Licensing, LLC

Inventor： Yuan Sun ,  Ye Tu ,  Ying Han ,  Chun Lo ,  Shaunak Chatterjee ,  Vrishti Gulati

IPC: G06N3/04 ,  G06N20/20

CPC classification number: G06N3/0454 ,  G06N20/20 ,  G06N3/0472

Abstract: In an example embodiment, a separate mimicry machine-learned model is trained for each of a plurality of different item types. Each of these models is trained to estimate an effect of mimicry for a user (i.e., a user whose user profile or other information is passed to the corresponding mimicry machine-learned model at prediction-time). The output of these models may be either used on its own to perform various actions, such as modifying a location of a user interface element of a user interface, or may be passed as input to an interaction machine-learned model that is trained to determine a likelihood of a user (i.e., a user whose user profile or other information is passed to the interaction machine-learned model at prediction-time) interacting with a particular item, such as a potential feed item.