公开(公告)号：US10380501B2

公开(公告)日：2019-08-13

申请号：US14941495

申请日：2015-11-13

Applicant: Facebook, Inc.

Inventor： Haibin Cheng ,  Xian Xu ,  Yang Pei

IPC: G06F15/18 ,  G06N20/00

Abstract: Lookalike models can select users that are predicted to share characteristics with a specified set of seed users. The processing requirements for lookalike models can be decreased by identifying features that have low impact on model accuracy, and therefore can be excluded from creating models. Also, by identifying preferred seed sources and training parameters, accurate lookalike models can be created with less overhead and in less time. The features and training parameters can be identified by obtaining a sample seed set, extracting seeds with a defined set of features, and using the remaining training seeds to train a model. Performance of this model can be compared to a standard model to see if the model performs well. If so, features excluded from the features used to create the model, a seed source, or training parameters used to create the model can be selected.

公开(公告)号：US20170286997A1

公开(公告)日：2017-10-05

申请号：US15091105

申请日：2016-04-05

Applicant: Facebook, Inc.

Inventor： Sameer Indarapu ,  Pradheep K. Elango ,  Xian Xu

IPC: G06Q30/02 ,  G06N99/00 ,  G06N7/00

CPC classification number: G06Q30/0247 ,  G06N20/00 ,  G06Q30/0277

Abstract: Embodiments are disclosed for predicting target events occurrence for an advertisement campaign. A computing device according to some embodiments assigns a label to an advertisement as unlabeled, in response to a notification that a prerequisite event occurs for the advertisement. The device generates feature vectors based on data that relate to the advertisement. The device further trains a machine learning model using the feature vectors of the unlabeled advertisement based on a first term of an objective function, without waiting for a target event for the advertisement to occur. The first term depends on unlabeled advertisements. The device predicts a probability of a target event occurring for a new advertisement, by feeding data of the new advertisement to the trained machine learning model.

公开(公告)号：US20170140283A1

公开(公告)日：2017-05-18

申请号：US14941495

申请日：2015-11-13

Applicant: Facebook, Inc.

Inventor： Haibin Cheng ,  Xian Xu ,  Yang Pei

IPC: G06N5/04 ,  G06N99/00

CPC classification number: G06N20/00

Abstract: Lookalike models can select users that are predicted to share characteristics with a specified set of seed users. The processing requirements for lookalike models can be decreased by identifying features that have low impact on model accuracy, and therefore can be excluded from creating models. Also, by identifying preferred seed sources and training parameters, accurate lookalike models can be created with less overhead and in less time. The features and training parameters can be identified by obtaining a sample seed set, extracting seeds with a defined set of features, and using the remaining training seeds to train a model. Performance of this model can be compared to a standard model to see if the model performs well. If so, features excluded from the features used to create the model, a seed source, or training parameters used to create the model can be selected.

公开(公告)号：US10592921B2

公开(公告)日：2020-03-17

申请号：US15091105

申请日：2016-04-05

Applicant: Facebook, Inc.

Inventor： Sameer Indarapu ,  Pradheep K. Elango ,  Xian Xu

IPC: G06Q30/00 ,  G06Q30/02 ,  G06N20/00

Abstract: Embodiments are disclosed for predicting target events occurrence for an advertisement campaign. A computing device according to some embodiments assigns a label to an advertisement as unlabeled, in response to a notification that a prerequisite event occurs for the advertisement. The device generates feature vectors based on data that relate to the advertisement. The device further trains a machine learning model using the feature vectors of the unlabeled advertisement based on a first term of an objective function, without waiting for a target event for the advertisement to occur. The first term depends on unlabeled advertisements. The device predicts a probability of a target event occurring for a new advertisement, by feeding data of the new advertisement to the trained machine learning model.