公开(公告)号：US20190102784A1

公开(公告)日：2019-04-04

申请号：US15722126

申请日：2017-10-02

Applicant: Facebook, Inc.

Inventor： Zheng Chen ,  Robert Oliver Burns Zeldin ,  Shyamsundar Rajaram ,  Hao Song ,  Nimish Rameshbhai Shah

IPC: G06Q30/02

Abstract: A bidding system determines values for impression opportunities on an online system. Values are determined by a set of models. Each model of the set of models is associated with a user response and predicts the likelihood that the associated user response will occur following an impression. The models are ordered based on a predicted chronological ordering of user actions that lead towards a conversion. Each model is weighted based on its relevance to conversion and the accuracy of the model relative to the other models in the set of models. Predictions of the probability of user action generated by each model, as well as the model weights, are used to determine a value for impression opportunities. Data from impression opportunities are then used to further train the models and update the weights assigned to each model for use in determining values for subsequent impression opportunities.

公开(公告)号：US20180114252A1

公开(公告)日：2018-04-26

申请号：US15299330

申请日：2016-10-20

Applicant: Facebook, Inc.

Inventor： Anand Sumatilal Bhalgat ,  Hao Song

IPC: G06Q30/02

CPC classification number: G06Q30/0275 ,  G06Q30/0254

Abstract: An online system selecting content items for presentation to its users accounts for likelihoods of users performing actions associated with content items when selecting content items. The online system maintains models determining likelihoods of users performing various actions. If a content item is associated with an action that infrequently occurs, information for determining the model for the action is limited, so the online system increases a bid amount associated with the content item during a time interval to an amount based on a likelihood of the user performing a more frequently occurring alternative action and an average bid amount for the alternative action from content items previously presented to users. The online system also determines an amount based on the model for the action and the bid amount for during the time interval and stops increasing the bid amount when the rate of change has less than a threshold magnitude.

公开(公告)号：US11222366B2

公开(公告)日：2022-01-11

申请号：US15299330

申请日：2016-10-20

Applicant: Facebook, Inc.

Inventor： Anand Sumatilal Bhalgat ,  Hao Song

IPC: G06Q30/00 ,  G06Q30/02 ,  G05B19/418

Abstract: An online system selecting content items for presentation to its users accounts for likelihoods of users performing actions associated with content items when selecting content items. The online system maintains models determining likelihoods of users performing various actions. If a content item is associated with an action that infrequently occurs, information for determining the model for the action is limited, so the online system increases a bid amount associated with the content item during a time interval to an amount based on a likelihood of the user performing a more frequently occurring alternative action and an average bid amount for the alternative action from content items previously presented to users. The online system also determines an amount based on the model for the action and the bid amount for during the time interval and stops increasing the bid amount when the rate of change has less than a threshold magnitude.

公开(公告)号：US20170352109A1

公开(公告)日：2017-12-07

申请号：US15174865

申请日：2016-06-06

Applicant: Facebook, Inc.

Inventor： Robert Oliver Burns Zeldin ,  Nathan John Davis ,  Anand Sumatilal Bhalgat ,  Harsh Doshi ,  Hao Song

IPC: G06Q50/00 ,  G06Q30/08

CPC classification number: G06Q50/01 ,  G06Q30/08

Abstract: An online system presenting content items to a user generates a model that predicts a latent metric describing user actions that occur at least a reasonable amount of time after presentation of content items. To determine the latent metric, the online system retrieves one or more models predicting likelihoods of the user performing various interactions when presented with the content items and determines weights associated with different retrieved models. Combining the weighted retrieved models generates a model for determining the latent metric. As the retrieved models are based on data accessible to the online system in less than the reasonable amount of time after presenting content items, weighing the retrieved models allows the online system to predict the latent metric describing user actions occurring after content items are presented. When selecting content items for the user, the online system accounts for the latent metric determined by the generated model.

公开(公告)号：US11094021B2

公开(公告)日：2021-08-17

申请号：US15174865

申请日：2016-06-06

Applicant: Facebook, Inc.

Inventor： Robert Oliver Burns Zeldin ,  Nathan John Davis ,  Anand Sumatilal Bhalgat ,  Harsh Doshi ,  Hao Song

IPC: G06Q50/00 ,  G06Q30/08

Abstract: An online system presenting content items to a user generates a model that predicts a latent metric describing user actions that occur at least a reasonable amount of time after presentation of content items. To determine the latent metric, the online system retrieves one or more models predicting likelihoods of the user performing various interactions when presented with the content items and determines weights associated with different retrieved models. Combining the weighted retrieved models generates a model for determining the latent metric. As the retrieved models are based on data accessible to the online system in less than the reasonable amount of time after presenting content items, weighing the retrieved models allows the online system to predict the latent metric describing user actions occurring after content items are presented. When selecting content items for the user, the online system accounts for the latent metric determined by the generated model.

公开(公告)号：US20190026775A1

公开(公告)日：2019-01-24

申请号：US15652412

申请日：2017-07-18

Applicant: Facebook, Inc.

Inventor： Anand Sumatilal Bhalgat ,  Hao Song ,  Ajay Ravindran

IPC: G06Q30/02

Abstract: A content delivery system adjusts bids across publishing channels to account for historical ratios that content was targeted to a content item's audience. A content campaign for users of a content delivery system is devised for two or more sponsored content providers. Targeting criteria for the content item is used to define an audience for the content item, and a sample group from that audience is chosen. The ratio of content impressions among the content providers is identified for the sample group among prior content presentations to these users. For the content item, based on the current ratio of presenting content across a secondary publishing channel and a benchmark publishing channel, a channel control factor is adjusted for the secondary publishing channel based on a numerical comparison of these two ratios. This adjusted channel control factor adjusts the bid price per impression for displaying content by the content campaign.

公开(公告)号：US20180121964A1

公开(公告)日：2018-05-03

申请号：US15340852

申请日：2016-11-01

Applicant: Facebook, Inc.

Inventor： Zhurun Zhang ,  Hao Zhang ,  Junbiao Tang ,  James Theodore Kleban ,  Avi Samuel Gavlovski ,  Hao Song ,  David Benjamin Lue ,  Anand Sumatilal Bhalgat

IPC: G06Q30/02 ,  G06Q50/00 ,  G06N99/00

CPC classification number: G06Q30/0269 ,  G06N20/00 ,  G06Q30/0277 ,  G06Q50/01

Abstract: An online system receives multiple candidate components for including in content items to be presented to online system users. Upon identifying an opportunity to present content to a subject user of the online system, the online system dynamically generates an optimal content item for presentation to the subject user that includes one or more candidate components. Candidate components included in the optimal content item are selected by predicting an affinity of the subject user for each candidate component. The affinity of the subject user for a candidate component may be predicted using a machine-learned model that is trained using historical performance information about content items including the candidate component that were presented to viewing users of the online system having at least a threshold measure of similarity to the subject user. Components of content items used to train the model may be selected using a heuristic (e.g., Thompson sampling).