公开(公告)号：US20250005092A1

公开(公告)日：2025-01-02

申请号：US18883636

申请日：2024-09-12

Applicant: Google LLC

Inventor： Wei HUANG ,  Arne Mauser

IPC: G06F16/9535 ,  G06F16/906 ,  G06N20/00 ,  H04L67/306 ,  H04L67/50 ,  H04L67/55

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting and distributing digital components based on predicted user attributes of users are described. In one aspect, a method includes obtaining data indicating content categories of content of the content pages accessed by the user during the user visits. A determination is made for an aggregate measure of each content category based on a quantity of user visits to content pages of the electronic resource of the publisher that included content classified as belonging to the content category. User attribute prediction data indicating previously predicted user attributes of the user is obtained. User attributes are predicted for the current visit of the user to the electronic resource of the publisher that is further used to select digital components for display with the electronic resource on a client device during the current visit.

公开(公告)号：US12130875B2

公开(公告)日：2024-10-29

申请号：US18008604

申请日：2022-06-02

Applicant: Google LLC

Inventor： Wei Huang ,  Arne Mauser

IPC: G06F16/9535 ,  G06F16/906 ,  G06N20/00 ,  H04L67/306 ,  H04L67/50 ,  H04L67/55

CPC classification number: G06F16/9535 ,  G06F16/906 ,  G06N20/00 ,  H04L67/306 ,  H04L67/535 ,  H04L67/55

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting and distributing digital components based on predicted user attributes of users are described. In one aspect, a method includes obtaining data indicating content categories of content of the content pages accessed by the user during the user visits. A determination is made for an aggregate measure of each content category based on a quantity of user visits to content pages of the electronic resource of the publisher that included content classified as belonging to the content category. User attribute prediction data indicating previously predicted user attributes of the user is obtained. User attributes are predicted for the current visit of the user to the electronic resource of the publisher that is further used to select digital components for display with the electronic resource on a client device during the current visit.

公开(公告)号：US20230274183A1

公开(公告)日：2023-08-31

申请号：US17798152

申请日：2021-04-09

Applicant: Google LLC

Inventor： Arne Mauser ,  Gang Wang

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: A first multi-party computation (MPC) system of an MPC cluster can receive, from an application on a client device, an inference request comprising a first share of a given user profile for a user of the application and a performance threshold. A set of nearest neighbors to the user profile can be identified by performing a secure MPC process using a trained machine learning model in collaboration with one or more second MPC systems. One or more nearest neighbors having a performance measure that satisfies the performance threshold can be selected from the set of nearest neighbors. The first MPC system can transmit data derived from the one or more nearest neighbors to the application.

公开(公告)号：US20230259815A1

公开(公告)日：2023-08-17

申请号：US17996574

申请日：2021-10-28

Applicant: Google LLC

Inventor： Yi Qiao ,  Arne Mauser ,  Chao Wang ,  Yizhong Liang ,  Wei Huang

IPC: G06N20/00

CPC classification number: G06N20/00

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training and using machine learning models. In some aspects, a method includes identifying a first set of data for users of multiple user groups. For each user, a first party user identifier is obtained that identifies the individual user to a first party content provider. A second set of data describing activity of the user with respect to content of the first party content provider is identified. For each user, a contextual analysis of the first set and the second set of data is performed to generate one or more labels indicating user interest. A training dataset is generated based on the first set and the second set of data and a label. The training dataset is then used to train one or more machine learning models to predict user interest.