公开(公告)号：US10147041B2

公开(公告)日：2018-12-04

申请号：US14799517

申请日：2015-07-14

Applicant: Facebook, Inc.

Inventor： Tianshi Gao ,  Shyamsundar Rajaram ,  Stuart Michael Bowers ,  Mircea Grecu

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

Abstract: Some embodiments include a method of generating a compatibility score for a grouping of objects based on correlations between attributes of the objects. An example grouping is a pair of user and ad. The method may be implemented using a multi-threaded pipeline architecture that utilizes a learning model to compute the compatibility score. The learning model determines correlations between a first object's attributes (e.g., user's liked pages, user demographics, user's apps installed, pixels visited, etc.) and a second object's attributes (e.g., expressed or implied). Example expressed attributes can be targeting keywords; example implied attributes can be object IDs associated with the ad.

公开(公告)号：US20160275554A1

公开(公告)日：2016-09-22

申请号：US14662222

申请日：2015-03-18

Applicant: Facebook, Inc.

Inventor： Feng Yan ,  Shyamsundar Rajaram ,  Hao Zhang ,  Lu Zheng ,  Tianshi Gao ,  David Michael Viner

IPC: G06Q30/02

CPC classification number: G06Q30/0255 ,  G06Q30/0277

Abstract: An online system tracks stores information identifying content provided by third party systems and accessed by online system users as well as interactions with advertisements performed by online system users. When the online system identifies an opportunity to present an advertisement to a viewing user, the online system identifies content from third party systems accessed by the viewing user and content from third party systems accessed by additional online system users who interacted with advertisements. A score is computed for various advertisements based at least in part on correlations between content from third party systems accessed by the viewing user and content from third party systems accessed by additional online system users who interacted with advertisements. The online system selects candidate advertisements to evaluate for presentation to the viewing user based on the scores.

Abstract translation: 在线系统跟踪识别由第三方系统提供并由在线系统用户访问的内容以及与在线系统用户执行的广告的交互的商店信息。 当在线系统识别向观看用户呈现广告的机会时，在线系统识别由观看用户访问的第三方系统的内容和来自与广告交互的其他在线系统用户访问的第三方系统的内容。 至少部分地基于由观看用户访问的来自第三方系统的内容与来自与由广告进行了互动的其他在线系统用户访问的第三方系统的内容的内容之间的相关性来计算各种广告的得分。 在线系统基于分数来选择候选广告来评估呈现给观看者。

公开(公告)号：US20190108557A1

公开(公告)日：2019-04-11

申请号：US15727410

申请日：2017-10-06

Applicant: Facebook, Inc.

Inventor： Dinkar Jain ,  Ethan Batraski ,  Nikita Igorevych Lytkin ,  Rachel Antoinette Hamilton ,  Shyamsundar Rajaram

IPC: G06Q30/02

Abstract: An online system selects items for display in content provided to users by considering the value of each item to third-party content providers as well as user's interests. The online system receives a catalog including items that are each associated with weights from a third-party content provider for inclusion in sponsored content to be presented to users of an online system. The weights have values indicating measures of importance of the items to the third-party content provider on a per-item basis. The online system identifies a request for sponsored content, and selects one or more items from the catalog for inclusion in a dynamic sponsored content item. The online system calculates a weighted user preference score using a weight associated with an item and affinity information describing the user's affinity for the item.

公开(公告)号：US20180336620A1

公开(公告)日：2018-11-22

申请号：US15600645

申请日：2017-05-19

Applicant: Facebook, Inc.

Inventor： Pradheep K. Elango ,  Shyamsundar Rajaram ,  Apurva Jadhav ,  Yanxi Pan ,  Shike Mei ,  Aashish Pant ,  Amit Madaan ,  Shashikant Khandelwal

IPC: G06Q30/06 ,  G06Q30/02 ,  G06F17/30

Abstract: An online system generates a content item for a user based on products likely to be of interest to the user. The online system receives information about products associated one or more third party systems accessed by users of the online system. When the online system identifies an opportunity to present to a user, the online system identifies candidate products for inclusion in the content item based on products previously accessed by the users. For example, the online system identifies candidate products based on products accessed by the user and by one or more other users. Based on likelihoods of the user accessing content items including different candidate products, the online system selects a candidate product and includes the content item having information about the selected candidate product in one or more selection processes that select content for presentation to the user.

公开(公告)号：US20180218399A1

公开(公告)日：2018-08-02

申请号：US15422187

申请日：2017-02-01

Applicant: Facebook, Inc.

Inventor： Shyamsundar Rajaram ,  Pradheep K. Elango ,  Yanxi Pan ,  Apurva Jadhav

IPC: G06Q30/02

CPC classification number: G06Q30/0255

Abstract: An online system generates a content item for a user based on products likely to be of interest to the user. The online system receives information about content provided by one or more third party systems the user accessed and determines products associated with accessed content. When the online system identifies an opportunity to present to a user, the online system retrieves products maintained by the online system and identifies candidate products for inclusion in the content item based on relevance of the products to the user. The online system determines probabilities of the user accessing the content item including different candidate products and removes combinations of the content item and candidate products having less than a threshold probability of user interaction. The online system includes one or more combinations of the content item and candidate products in one or more selection processes selecting content for presentation to the user.

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：US20170017886A1

公开(公告)日：2017-01-19

申请号：US14799517

申请日：2015-07-14

Applicant: Facebook, Inc.

Inventor： Tianshi Gao ,  Shyamsundar Rajaram ,  Stuart Michael Bowers

IPC: G06N5/04 ,  G06F17/30 ,  G06N99/00

CPC classification number: G06N5/04 ,  G06N99/005 ,  G06Q30/02 ,  G06Q30/0241 ,  G06Q50/01

Abstract: Some embodiments include a method of generating a compatibility score for a grouping of objects based on correlations between attributes of the objects. An example grouping is a pair of user and ad. The method may be implemented using a multi-threaded pipeline architecture that utilizes a learning model to compute the compatibility score. The learning model determines correlations between a first object's attributes (e.g., user's liked pages, user demographics, user's apps installed, pixels visited, etc.) and a second object's attributes (e.g., expressed or implied). Example expressed attributes can be targeting keywords; example implied attributes can be object IDs associated with the ad.

Abstract translation: 一些实施例包括基于对象的属性之间的相关性来生成对象分组的兼容性分数的方法。 示例分组是一对用户和广告。 该方法可以使用利用学习模型来计算兼容性分数的多线程流水线架构来实现。 学习模型确定第一对象的属性（例如，用户喜爱的页面，用户人口统计，安装的用户的应用，被访问的像素等）与第二对象的属性（例如，表示或暗示的）之间的相关性。 示例表示的属性可以是定位关键字; 示例隐含的属性可以是与广告相关联的对象ID。