公开(公告)号：US20140258191A1

公开(公告)日：2014-09-11

申请号：US14286803

申请日：2014-05-23

Applicant: Facebook, Inc.

Inventor： Max Gubin ,  Wayne Kao ,  David Vickrey ,  Alexey Maykov

IPC: G06N99/00 ,  G06F17/30

CPC classification number: G06N99/005 ,  G06F17/30867 ,  G06Q10/06393 ,  G06Q50/01

Abstract: Machine learning models are used for ranking news feed stories presented to users of a social networking system. The social networking system divides its users into different sets, for example, based on demographic characteristics of the users and generates one model for each set of users. The models are periodically retrained. The news feed ranking model may rank news feeds for a user based on information describing other users connected to the user in the social networking system. Information describing other users connected to the user includes interactions of the other users with objects associated with news feed stories. These interactions include commenting on a news feed story, liking a news feed story, or retrieving information, for example, images, videos associated with a news feed story.

Abstract translation: 机器学习模型用于对社交网络系统的用户提供新闻馈送故事。 社交网络系统将用户分为不同的集合，例如基于用户的人口特征，并为每组用户生成一个模型。 这些模型定期进行再培训。 新闻排序模型可以基于描述在社交网络系统中连接到用户的其他用户的信息来为用户排序新闻馈送。 描述连接到用户的其他用户的信息包括其他用户与与新闻馈送故事相关联的对象的交互。 这些互动包括评论新闻Feed故事，喜欢新闻Feed故事或检索信息，例如图像，与新闻Feed故事相关的视频。

公开(公告)号：US10733254B2

公开(公告)日：2020-08-04

申请号：US14964815

申请日：2015-12-10

Applicant: Facebook, Inc.

Inventor： Gregory Matthew Marra ,  David Vickrey ,  Mahmud Sami Tas ,  Yue Zhuo

IPC: G06F16/9538 ,  G06F16/2457 ,  H04L29/08 ,  G06Q50/00 ,  G06F16/9535 ,  G06F16/9536

Abstract: An online system, such as a social networking system, monitors user interactions with news feed stories of the social networking system and divides the user interactions into non-content clicks and content clicks. The non-content clicks indicate a user's interest in news feed stories based on user actions such as comments on, likes, shares, and hides the news feed stories. The content clicks indicate a user's interest in news feed stories based on user actions on different specific portions of multimedia content (e.g., videos) in the news feed stories such as playing, fast forwarding. The social networking system trains a model based on the monitored user interactions with news feed stories and uses the trained model to rank news feed stories for presentation to a user. The ranks of news feed stories for a user are determined based on a likelihood that the user would find the story interesting.

公开(公告)号：US09959503B2

公开(公告)日：2018-05-01

申请号：US14587605

申请日：2014-12-31

Applicant: Facebook, Inc.

Inventor： David Vickrey ,  Jeffrey William Pasternack

IPC: G06F17/00 ,  G06N5/04 ,  H04L12/58 ,  G06F17/27 ,  G06Q50/00 ,  G06Q30/02

CPC classification number: G06N5/04 ,  G06F17/277 ,  G06N99/005 ,  G06Q30/0201 ,  G06Q50/01 ,  H04L51/32

Abstract: A social networking system receives messages from users that include hashtags. The social networking system may use a natural language model to identify terms in the hashtag corresponding to words or phrases of the hashtag. The words or phrases may be used to modify a string of the hashtag. The social networking system may also generate computer models to determine likely membership of a message with various hashtags. Prior to generating the computer models, the social networking system may filter certain hashtags from eligibility for computer modeling, particularly hashtags that are not frequently used or that more typically appear as normal text in a message instead of as a hashtag. The social networking system may also calibrate the computer model outputs by comparing a test message output with outputs of a calibration group that includes positive and negative examples with respect to the computer model output.

公开(公告)号：US20170171139A1

公开(公告)日：2017-06-15

申请号：US14964815

申请日：2015-12-10

Applicant: Facebook, Inc.

Inventor： Gregory Matthew Marra ,  David Vickrey ,  Mahmud Sami Tas ,  Yue Zhuo

IPC: H04L12/58

Abstract: An online system, such as a social networking system, monitors user interactions with news feed stories of the social networking system and divides the user interactions into non-content clicks and content clicks. The non-content clicks indicate a user's interest in news feed stories based on user actions such as comments on, likes, shares, and hides the news feed stories. The content clicks indicate a user's interest in news feed stories based on user actions on different specific portions of multimedia content (e.g., videos) in the news feed stories such as playing, fast forwarding. The social networking system trains a model based on the monitored user interactions with news feed stories and uses the trained model to rank news feed stories for presentation to a user. The ranks of news feed stories for a user are determined based on a likelihood that the user would find the story interesting.

公开(公告)号：US20160189040A1

公开(公告)日：2016-06-30

申请号：US14587605

申请日：2014-12-31

Applicant: Facebook, Inc.

Inventor： David Vickrey ,  Jeffrey William Pasternack

IPC: G06N5/04 ,  G06N99/00 ,  H04L12/58

CPC classification number: G06N5/04 ,  G06F17/277 ,  G06N99/005 ,  G06Q30/0201 ,  G06Q50/01 ,  H04L51/32

Abstract: A social networking system receives messages from users that include hashtags. The social networking system may use a natural language model to identify terms in the hashtag corresponding to words or phrases of the hashtag. The words or phrases may be used to modify a string of the hashtag. The social networking system may also generate computer models to determine likely membership of a message with various hashtags. Prior to generating the computer models, the social networking system may filter certain hashtags from eligibility for computer modeling, particularly hashtags that are not frequently used or that more typically appear as normal text in a message instead of as a hashtag. The social networking system may also calibrate the computer model outputs by comparing a test message output with outputs of a calibration group that includes positive and negative examples with respect to the computer model output.

Abstract translation: 社交网络系统从包含主题标签的用户接收消息。 社交网络系统可以使用自然语言模型来识别与主题标签的单词或短语相对应的主题标签中的术语。 这些单词或短语可用于修改主题标签的字符串。 社交网络系统还可以生成计算机模型以确定具有各种主题标签的消息的可能成员资格。 在生成计算机模型之前，社交网络系统可以将特定的标签从过滤计算机建模的资格过滤出来，特别是不经常使用的标签，或者通常在消息中更常见地显示为正常文本，而不是主题标签。 社交网络系统还可以通过将测试消息输出与包括关于计算机模型输出的正和负的示例的校准组的输出进行比较来校准计算机模型输出。

公开(公告)号：US20160188607A1

公开(公告)日：2016-06-30

申请号：US15067134

申请日：2016-03-10

Applicant: Facebook, Inc.

Inventor： Dan Yigal Rubinstein ,  David Vickrey ,  Robert William Cathcart ,  Lars Seren Backstrom ,  Romain Jean Thibaux

IPC: G06F17/30

CPC classification number: G06F16/24578 ,  G06F16/9535 ,  G06F17/24 ,  G06Q50/01

Abstract: A social networking system generates a newsfeed for a user to view when accessing the social networking system. Candidate stories associated with users of the social networking system are selected and an expected value score for each candidate story is determined. An expected value score is based on the probability of a user performing various types of interactions with a candidate story and a numerical value for each type of interaction. The numerical value for a type of interaction represents a value to the social networking system of the type of interaction. Based on the expected value scores, the candidate stories are ranked and the ranking used to select candidate stories for the newsfeed.

Abstract translation: 社交网络系统为用户生成访问社交网络系统的新闻源。 选择与社交网络系统的用户相关联的候选故事，并确定每个候选故事的预期值分数。 期望值分数基于用户对候选故事进行各种类型的交互的概率和每种类型的交互的数值。 互动类型的数值代表了交互类型的社交网络系统的价值。 根据预期值得分，候选人的故事排名，排名用于选择新闻源的候选故事。

公开(公告)号：US20160180246A1

公开(公告)日：2016-06-23

申请号：US14577945

申请日：2014-12-19

Applicant: Facebook, Inc.

Inventor： David Vickrey ,  Jeffrey William Pasternack

IPC: G06N99/00 ,  G06N5/04

CPC classification number: G06F16/951 ,  G06N20/00

Abstract: A social networking system receives messages from users that include links to webpages that designate keywords of the webpage. The social networking system identifies webpages linked by users to generate computer models that predict whether a webpage or message should be associated with particular keywords. The social networking system generates computer models that are trained on example webpages and related keywords linked by users in messages. Prior to generating computer models, the social networking system applies one or more filters to exclude webpages and keywords from consideration. The filters may exclude webpages that have low-reliability, are associated with an excessive number of keywords, or keywords that appear on an insufficient number of domains. After training the computer models, messages composed by users may be analyzed and a keyword predicted for the message, which may be suggested to the user to categorize the message.

Abstract translation: 社交网络系统从用户那里接收包含指定网页关键字的网页的链接的消息。 社交网络系统识别用户链接的网页，以生成计算机模型，以预测网页或消息是否应与特定关键字相关联。 社交网络系统生成计算机模型，该模型通过用户在消息中链接的示例网页和相关关键字进行培训。 在生成计算机模型之前，社交网络系统应用一个或多个过滤器来排除网页和关键字的考虑。 过滤器可能会排除具有低可靠性的网页，与过多的关键字或出现在不足域数的关键字相关联。 在训练计算机模型之后，可以对用户组成的消息进行分析，并为消息预测关键字，这可以被建议给用户对消息进行分类。

公开(公告)号：US20150347438A1

公开(公告)日：2015-12-03

申请号：US14586476

申请日：2014-12-30

Applicant: Facebook, Inc.

Inventor： Khalid El Arini ,  David Vickrey

IPC: G06F17/30

CPC classification number: G06F16/9535 ,  G06Q50/01

Abstract: Exemplary methods, apparatuses, and systems determine first and second entities within a social networking system are each associated with a topic. A relationship between the first entity and the second entity is detected. The first entity is determined to be an authority on the topic based upon the detected relationship between the first entity and the second entity. In response to detecting an indication that a user of the social networking system may be interested in the topic, the user is presented with content posted to the social networking system by the first entity based upon determining the first authority is an authority on the topic.

Abstract translation: 确定社交网络系统内的第一和第二实体的示例性方法，装置和系统各自与主题相关联。 检测第一实体和第二实体之间的关系。 基于检测到的第一实体和第二实体之间的关系，第一实体被确定为该主题的权限。 响应于检测到社交网络系统的用户可能对该主题感兴趣的指示，基于确定第一权限是该主题的权限，向用户呈现由第一实体发布到社交网络系统的内容。

公开(公告)号：US10558714B2

公开(公告)日：2020-02-11

申请号：US15393150

申请日：2016-12-28

Applicant: Facebook, Inc.

Inventor： Shengbo Guo ,  Annie Hsin-Wen Liu ,  David Vickrey ,  Khalid Bakry El-Arini

IPC: G06F16/951 ,  G06F16/9535

Abstract: An online system ranks topic-groups for users and presents content items in topic-based feeds. A topic group corresponds to one or more topic(s) and can be used to generate a feed for presenting the content items related to the topic(s). For a particular user, the topic groups are ranked according to the likelihood of the user interacting with content items included in the topic groups. The topic groups are ranked using information of the users and/or users' historical interaction data such as click-based interaction data, post-based interaction data, or engagement-based interaction data. The online system generates and provides a user interface for presenting the topic groups to the client device. Content items that are related to the topic(s) corresponding to the topic group are presented in each topic-based feed such that the user can switch between different topic-based feeds.

公开(公告)号：US10484489B2

公开(公告)日：2019-11-19

申请号：US15394722

申请日：2016-12-29

Applicant: Facebook, Inc.

Inventor： Khalid Bakry El-Arini ,  David Vickrey

IPC: H04L29/08 ,  H04L12/58 ,  G06Q50/00 ,  G06Q30/02

Abstract: An online system generates a feed of content for a user that includes content items provided by, or otherwise related to, other users who are connected to the user via the online system. The online system supplements the feed with additional content items that are not related to users who are connected to the user but are likely to be of interest to the user. The additional content items may be associated with users who are connected to additional users who are connected to the user, content items having received a threshold amount of interacting by other users, content items provided by users who provided other content with which the user interacted, or have other characteristics. The additional content items and content items associated with users connected to the user are included in one or more selection processes that generate the feed for the user.