公开(公告)号：US20240103893A1

公开(公告)日：2024-03-28

申请号：US18530995

申请日：2023-12-06

Applicant: GOOGLE LLC

Inventor： Deepak Ramachandran ,  Sarvjeet Singh ,  Tania Bedrax-Weiss

IPC: G06F9/451 ,  H04L51/02

CPC classification number: G06F9/453 ,  H04L51/02

Abstract: Techniques are disclosed that enable the generation of candidate endorsements for recommended items of content using an ensemble of nominators. Various implementations include each nominator in the ensemble providing a candidate endorsement for each recommended item of content. Additionally or alternatively, an endorsement is selected to present to the user based on a score determined for each candidate endorsement.

公开(公告)号：US11842206B2

公开(公告)日：2023-12-12

申请号：US17608700

申请日：2019-05-31

Applicant: GOOGLE LLC

Inventor： Deepak Ramachandran ,  Sarvjeet Singh ,  Tania Bedrax-Weiss

IPC: G06F9/451 ,  H04L51/02

CPC classification number: G06F9/453 ,  H04L51/02

Abstract: Techniques are disclosed that enable the generation of candidate endorsements for recommended items of content using an ensemble of nominators. Various implementations include each nominator in the ensemble providing a candidate endorsement for each recommended item of content. Additionally or alternatively, an endorsement is selected to present to the user based on a score determined for each candidate endorsement.

公开(公告)号：US20190311287A1

公开(公告)日：2019-10-10

申请号：US16322433

申请日：2017-01-24

Applicant: Google LLC

Inventor： Sue Yi Chew ,  Deepak Ramamurthi Sivaramapuram Chandrasekaran ,  Bo Fu ,  Prachi Gupta ,  Kunal Jain ,  Thomas Price ,  Sarvjeet Singh ,  Jierui Xie

IPC: G06N20/00 ,  G06F11/34 ,  G06N7/00

Abstract: Balancing content distribution between a machine learning model and a statistical model provides a baseline assurance in combination with the benefits of a well-trained machine learning model for content selection. In some implementations, a server receiving requests for a content item assigns a first proportion of the received requests to a first group and assigns remaining requests to a second group. The server uses a machine learning model to select variations of the requested content item for responding to requests assigned to the first group and uses a statistical model to select content variations for requests assigned to the second group. The server obtains performance information, e.g., acceptance rates for the different variations, and compares performance of the different models used for content selection. Audience share assigned to the machine learning model is increased when it outperforms the statistical model and decreased when it underperforms the statistical model.