公开(公告)号：US10127573B2

公开(公告)日：2018-11-13

申请号：US14940794

申请日：2015-11-13

Applicant: Facebook, Inc

Inventor： Xingyao Ye ,  David Townsend Martin ,  Michael Phillip Salem ,  Yi Wu ,  Jie Xiong ,  Mark Teng Xia

IPC: G06Q30/00 ,  G06Q30/02

Abstract: An online system predicts, using a first targeting model, a first group of users as candidates to be in a targeting cluster, and predicts, using a second targeting model, a second group of users as candidates to be in the targeting cluster. The online system determines a first set of users that are not part of the first group of users, and a second set of users that are not part of the second group of users, and provides surveys to the first and second set of users. The online system determines a first subgroup of the first group of users and a second subgroup of the second group of users, and provides an ad preferences tool to the first subgroup and the second subgroup. The online system scores the first and second targeting models based in part on responses to the surveys and/or the ad preferences tools.

公开(公告)号：US20170140416A1

公开(公告)日：2017-05-18

申请号：US14940794

申请日：2015-11-13

Applicant: Facebook, Inc

Inventor： Xingyao Ye ,  David Townsend Martin ,  Michael Phillip Salem ,  Yi Wu ,  Jie Xiong ,  Mark Teng Xia

IPC: G06Q30/02

CPC classification number: G06Q30/0243 ,  G06Q30/0245 ,  G06Q30/0255 ,  G06Q30/0277

Abstract: An online system predicts, using a first targeting model, a first group of users as candidates to be in a targeting cluster, and predicts, using a second targeting model, a second group of users as candidates to be in the targeting cluster. The online system determines a first set of users that are not part of the first group of users, and a second set of users that are not part of the second group of users, and provides surveys to the first and second set of users. The online system determines a first subgroup of the first group of users and a second subgroup of the second group of users, and provides an ad preferences tool to the first subgroup and the second subgroup. The online system scores the first and second targeting models based in part on responses to the surveys and/or the ad preferences tools.

公开(公告)号：US10937053B1

公开(公告)日：2021-03-02

申请号：US16158309

申请日：2018-10-12

Applicant: Facebook, Inc.

Inventor： Xingyao Ye ,  David Townsend Martin ,  Michael Phillip Salem ,  Yi Wu ,  Jie Xiong ,  Mark Teng Xia

IPC: G06Q30/00 ,  G06Q30/02

Abstract: An online system predicts, using a first targeting model, a first group of users as candidates to be in a targeting cluster, and predicts, using a second targeting model, a second group of users as candidates to be in the targeting cluster. The online system determines a first set of users that are not part of the first group of users, and a second set of users that are not part of the second group of users, and provides surveys to the first and second set of users. The online system determines a first subgroup of the first group of users and a second subgroup of the second group of users, and provides an ad preferences tool to the first subgroup and the second subgroup. The online system scores the first and second targeting models based in part on responses to the surveys and/or the ad preferences tools.

公开(公告)号：US20180336589A1

公开(公告)日：2018-11-22

申请号：US15599192

申请日：2017-05-18

Applicant: Facebook, Inc.

Inventor： Xingyao Ye ,  Okhee Kim ,  Mark Teng Xia ,  Duy Ha

IPC: G06Q30/02 ,  H04L29/08

Abstract: An online system suggests targeting criteria to advertisers creating new ads in the online system by generating a seed group of targeting criteria. The seed targeting criteria include targeting criteria already selected (if any), targeting criteria previously used, and targeting criteria extracted from the ad being created (e.g., from ad components) or a page being promoted by the ad. The seed targeting criteria are expanded via collaborative filtering on advertisers, collaborative filtering on targeted users, and determination of relationships within topic hierarchies. The online system selects a subset of the expanded targeting criteria by applying a machine learning model to each targeting criterion to determine a probability of the advertiser selecting the targeting criterion if it were suggested. The targeting criteria are ranked based on the determined probabilities and selected based on the ranking. The suggested targeting criteria may also be ordered in the user interface based on the ranking.