公开(公告)号：US20170186031A1

公开(公告)日：2017-06-29

申请号：US14983432

申请日：2015-12-29

Applicant: Facebook, Inc.

Inventor： Rituraj Kirti ,  Leon R. Cho ,  Yuval Israel Oren ,  Ying Qin

IPC: G06Q30/02

CPC classification number: G06Q30/0246 ,  G06Q30/0244 ,  G06Q30/0277 ,  G06Q50/01

Abstract: An online advertising system evaluates advertising opportunities for online advertising publishers. The online advertising system tracks online users via various tracking methods to receive advertising data and user information for the online users. The online advertising system identifies and segments the online users based on segmenting criteria that are associated with some interest topics (e.g., demographical information). The system calculates projected advertising revenue for each audience segment and generates an inventory optimization dashboard based on the calculated revenue. The inventory optimization dashboard helps the advertising publishers better understand the online advertising traffic and better optimize their advertising inventory. For example, the advertising publishers may advertise to specific audience segments which tend to purchase the advertised products or services.

公开(公告)号：US20180218287A1

公开(公告)日：2018-08-02

申请号：US15421438

申请日：2017-02-01

Applicant: Facebook, Inc.

Inventor： Zhuang Wang ,  Robert Oliver Burns Zeldin ,  Sushma Nagesh Bannur ,  Rami Mahdi ,  Rubinder Singh Sethi ,  Shyamsundar Rajaram ,  Leon R. Cho

IPC: G06N99/00 ,  G06Q30/02 ,  G06N5/04

CPC classification number: G06N20/00 ,  G06N5/04 ,  G06Q30/0201

Abstract: An online system receives content items, for example, from content providers and sends the content items to users. The online system uses machine-learning models for predicting whether a user is likely to interact with a content item. The online system uses stored user interactions to measure the model performance to determine whether the model can be used online. The online system determines a baseline model using stored user interactions. The online system determines whether the machine-learning model performs better than the baseline model or worse for each content provider. The online system determines whether to approve the model for online use based on an aggregate normalized performance metric, for example, a metric representing the fraction of content providers for which the model performs better than the baseline. If the online system determines to reject the model, the online system retrains the model.

公开(公告)号：US20190005575A1

公开(公告)日：2019-01-03

申请号：US15640052

申请日：2017-06-30

Applicant: Facebook, Inc.

Inventor： Robert Oliver Burns Zeldin ,  Chinmay Deepak Karande ,  Shyamsundar Rajaram ,  Leon R. Cho ,  Rami Mahdi ,  Sushma Nagesh Bannur

IPC: G06Q30/08 ,  G06Q30/06

Abstract: An online system calculates bids for content items to display to users based on the value of a product described in the content item and the likelihood of a viewing user purchasing the product. The online system identifies an impression opportunity for an ad request and computes an expected value of the conversion and a likelihood of the conversion. The online system computes a bid amount based on the expected conversion value and the likelihood of the conversion. Bids based on the value of the conversion allow a third party system offering the product to optimize for the value of each conversion instead of the conversion rate.

公开(公告)号：US20160300268A1

公开(公告)日：2016-10-13

申请号：US14681061

申请日：2015-04-07

Applicant: Facebook, Inc.

Inventor： Yi Huang ,  Peng Fan ,  Zhimin Chen ,  Leon R. Cho

IPC: G06Q30/02

CPC classification number: G06Q30/0269 ,  G06Q30/0247

Abstract: An online system receives information describing a target group of online system users from a third party system that includes one or more user properties, which may identify actions to be performed by an online system user for inclusion in the target group. Additionally, information describing the target group includes metadata associated with the user properties identifying access to the user properties by additional third party systems. If an additional third party system requests access to the target group or to the user properties describing the target group, the online system determines whether the additional third party system is authorized to access the target group or the user properties based on the metadata. Further, the online system determines an amount of compensation the third party system is to receive if the additional third party system is authorized to access the target group or the user properties based on the metadata.

Abstract translation: 在线系统从包括一个或多个用户属性的第三方系统接收描述在线系统用户的目标组的信息，其可以标识由在线系统用户执行以包括在目标组中的动作。 另外，描述目标组的信息包括与用户属性相关联的元数据，其通过附加的第三方系统来标识对用户属性的访问。 如果另外的第三方系统请求对目标组或描述目标组的用户属性的访问，则在线系统基于元数据确定附加第三方系统是否被授权访问目标组或用户属性。 此外，如果额外的第三方系统被授权访问目标组或基于元数据的用户属性，则在线系统确定第三方系统要接收的补偿量。

公开(公告)号：US20160232575A1

公开(公告)日：2016-08-11

申请号：US14616543

申请日：2015-02-06

Applicant: Facebook, Inc.

Inventor： Rituraj Kirti ,  Sue Ann Hong ,  Leon R. Cho

IPC: G06Q30/02 ,  G06Q50/00

CPC classification number: G06Q30/0269 ,  G06Q30/0275 ,  G06Q50/01

Abstract: A social networking system receives an advertisement request including multiple sets of targeting criteria. To increase the number of users eligible to be presented with the advertisement request, the social networking system generates a cluster group associated with each set of targeting criteria. A cluster group associated with a set of targeting criteria includes users satisfying the targeting criteria and additional users that do not satisfy the targeting criteria. The social networking system determines an amount of overlap between the cluster groups. If the amount of overlap equals or exceeds a threshold value, the social networking system combines the cluster groups to generate an overall group associated with the advertisement request.

Abstract translation: 社交网络系统接收包括多组目标标准的广告请求。 为了增加有资格获得广告请求的用户数量，社交网络系统生成与每组目标标准相关联的群集组。 与一组定位条件相关联的群集组包括满足定位条件的用户和不满足定位条件的其他用户。 社交网络系统确定集群组之间的重叠量。 如果重叠量等于或超过阈值，则社交网络系统组合群集组以生成与广告请求相关联的整体组。