公开(公告)号：US20220004918A1

公开(公告)日：2022-01-06

申请号：US16946779

申请日：2020-07-06

Applicant: Google LLC

Inventor： Spurthi Amba Hombaiah ,  Vladimir Ofitserov ,  Mike Bendersky ,  Marc Alexander Najork

IPC: G06N20/00 ,  G06N5/04 ,  G06F16/9038

Abstract: Implementations relate to training a model that can be used to process values for defined features, where the values are specific to a user account, to generate a predicted user measure that reflects both popularity and quality of the user account. The model is trained based on losses that are each generated as a function of both a corresponding generated popularity measure and a corresponding generated quality measure of a corresponding training instance. Accordingly, the model can be trained to generate, based on values for a given user account, a single measure that reflects both quality and popularity of the given user account. Implementations are additionally or alternatively directed to utilizing such predicted user measures to restrict provisioning of content items that are from user accounts having respective predicted user measures that fail to satisfy a threshold.

公开(公告)号：US10055467B1

公开(公告)日：2018-08-21

申请号：US15608083

申请日：2017-05-30

Applicant: Google LLC

Inventor： Navneet Panda ,  Vladimir Ofitserov ,  Kaihua Zhu

IPC: G06F17/30 ,  G06F7/00

CPC classification number: G06F16/248 ,  G06F16/951

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for receiving a search query from a client device; receiving search result data identifying a first plurality of search result resources and respective initial scores for each of the first plurality of search result resources; identifying a respective group of resources to which each of the search result resources belongs; determining a respective group-based modification factor for each group of resources; and adjusting the initial score for each of the search result resources based at least in part on the group-specific modification factor for the group of resources to which the search result resource belongs to generate a respective second score for each of the search result resources.

公开(公告)号：US20230094198A1

公开(公告)日：2023-03-30

申请号：US18074774

申请日：2022-12-05

Applicant: GOOGLE LLC

Inventor： Spurthi Amba Hombaiah ,  Vladimir Ofitserov ,  Mike Bendersky ,  Marc Alexander Najork

IPC: G06N20/00 ,  G06F16/9038 ,  G06N5/04

Abstract: Implementations relate to training a model that can be used to process values for defined features, where the values are specific to a user account, to generate a predicted user measure that reflects both popularity and quality of the user account. The model is trained based on losses that are each generated as a function of both a corresponding generated popularity measure and a corresponding generated quality measure of a corresponding training instance. Accordingly, the model can be trained to generate, based on values for a given user account, a single measure that reflects both quality and popularity of the given user account. Implementations are additionally or alternatively directed to utilizing such predicted user measures to restrict provisioning of content items that are from user accounts having respective predicted user measures that fail to satisfy a threshold.

公开(公告)号：US11551150B2

公开(公告)日：2023-01-10

申请号：US16946779

申请日：2020-07-06

Applicant: Google LLC

Inventor： Spurthi Amba Hombaiah ,  Vladimir Ofitserov ,  Mike Bendersky ,  Marc Alexander Najork

IPC: G06N20/00 ,  G06F16/9038 ,  G06N5/04

Abstract: Implementations relate to training a model that can be used to process values for defined features, where the values are specific to a user account, to generate a predicted user measure that reflects both popularity and quality of the user account. The model is trained based on losses that are each generated as a function of both a corresponding generated popularity measure and a corresponding generated quality measure of a corresponding training instance. Accordingly, the model can be trained to generate, based on values for a given user account, a single measure that reflects both quality and popularity of the given user account. Implementations are additionally or alternatively directed to utilizing such predicted user measures to restrict provisioning of content items that are from user accounts having respective predicted user measures that fail to satisfy a threshold.

公开(公告)号：US12236322B2

公开(公告)日：2025-02-25

申请号：US18074774

申请日：2022-12-05

Applicant: GOOGLE LLC

Inventor： Spurthi Amba Hombaiah ,  Vladimir Ofitserov ,  Mike Bendersky ,  Marc Alexander Najork

IPC: G06N20/00 ,  G06F16/9038 ,  G06N5/04

Abstract: Implementations relate to training a model that can be used to process values for defined features, where the values are specific to a user account, to generate a predicted user measure that reflects both popularity and quality of the user account. The model is trained based on losses that are each generated as a function of both a corresponding generated popularity measure and a corresponding generated quality measure of a corresponding training instance. Accordingly, the model can be trained to generate, based on values for a given user account, a single measure that reflects both quality and popularity of the given user account. Implementations are additionally or alternatively directed to utilizing such predicted user measures to restrict provisioning of content items that are from user accounts having respective predicted user measures that fail to satisfy a threshold.