公开(公告)号：US20230089961A1

公开(公告)日：2023-03-23

申请号：US18071308

申请日：2022-11-29

Applicant: Google LLC

Inventor： Scott Tadashi Davies ,  Kai Chen ,  Michael Jee-Kai Wang ,  Wei Jiang ,  Maryam Tavafi ,  Peter Zaimis Tipton

IPC: G06N20/00 ,  G06F16/22 ,  G06F16/435 ,  H04L67/01 ,  H04L67/06

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for optimizing content presentation. In one aspect, a system includes a training database that stores training data including attribute information about users and corresponding proxy metrics quantifying behavior by the users following content presentation; a content database; a model generator that accesses the training data and trains a model for content distribution; and a content distribution server that receives a content request, uses the model to select content, transmits data identifying the selected content, wherein the model: obtains a set of attributes for a user associated with the request, receives information about a given content, predicts a proxy metric based on the set of attributes and the information about the content, the predicted proxy metric providing information about subject retention or awareness; and identifies the given content for distribution if the predicted proxy metrics meet a threshold.

公开(公告)号：US11531925B2

公开(公告)日：2022-12-20

申请号：US15183335

申请日：2016-06-15

Applicant: Google LLC

Inventor： Scott Tadashi Davies ,  Kai Chen ,  Michael Jee-Kai Wang ,  Wei Jiang ,  Maryam Tavafi ,  Peter Zaimis Tipton

IPC: G06N20/00 ,  G06F16/22 ,  G06F16/435 ,  H04L67/01 ,  H04L67/06 ,  G06Q30/02

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for optimizing content presentation. In one aspect, a system includes a training database that stores training data including attribute information about users and corresponding proxy metrics quantifying behavior by the users following content presentation; a content database; a model generator that accesses the training data and trains a model for content distribution; and a content distribution server that receives a content request, uses the model to select content, transmits data identifying the selected content, wherein the model: obtains a set of attributes for a user associated with the request, receives information about a given content, predicts a proxy metric based on the set of attributes and the information about the content, the predicted proxy metric providing information about subject retention or awareness; and identifies the given content for distribution if the predicted proxy metrics meet a threshold.