公开(公告)号：US20160179968A1

公开(公告)日：2016-06-23

申请号：US14579710

申请日：2014-12-22

Applicant: Facebook, Inc.

Inventor： Holly Marie Ormseth ,  Elad Gerson ,  Guy Dassa ,  Khalid Bakry El-Arini ,  Gaurav Shankar ,  Yuanxuan Wang ,  Varun Kacholia ,  Prasoon Mishra ,  David Vickrey ,  Sanjeet Uday Hajarnis ,  Sahil P. Thaker

IPC: G06F17/30

CPC classification number: G06F17/30867

Abstract: Systems, methods, and non-transitory computer readable media configured to detect access by a user to an original content item relating to a story. At least one of a comments based technique, a token based technique, and a tag based technique is performed on content items. Constraints are applied to identify at least one follow up content item from the content items relating to a development of the story.

Abstract translation: 被配置为检测用户对与故事相关的原始内容项的访问的系统，方法和非暂时计算机可读介质。 对内容项目执行基于注释的技术，基于令牌的技术和基于标签的技术中的至少一个。 应用限制来从与故事发展相关的内容项目中识别至少一个后续内容项目。

公开(公告)号：US20180276561A1

公开(公告)日：2018-09-27

申请号：US15469399

申请日：2017-03-24

Applicant: Facebook, Inc.

Inventor： Jeffrey William Pasternack ,  David Vickrey ,  Justin MacLean Coughlin ,  Prasoon Mishra ,  Austen Norment McDonald ,  Max Christian Eulenstein ,  Jianfu Chen ,  Kritarth Anand ,  Polina Kuznetsova

IPC: G06N99/00 ,  G06N7/00 ,  G06F17/30

CPC classification number: G06N20/00 ,  G06F16/353 ,  G06N5/041 ,  G06N20/20

Abstract: An online system predicts topics for content items. The online system provides one or more topic labels for a user to apply concurrently while a user is composing a post, in response to requests periodically received from the user's device. A request includes information such as content composed by the user and contextual information. The online system employs machine learning techniques to analyze content composed by a user and contextual information thereby to predict topic labels. Different machine learning models for classifying individual topic labels, identifying relevant topic labels, and/or detecting changes in existing topic predictions are developed. Some machine learning models predict topics for full content and some predict topics for partial content. The online system trains the machine learning models to ensure accurate topic predictions are provided timely. The online system employs various machine learning model training methods such as active training and gradient training.

公开(公告)号：US10740690B2

公开(公告)日：2020-08-11

申请号：US15469399

申请日：2017-03-24

Applicant: Facebook, Inc.

Inventor： Jeffrey William Pasternack ,  David Vickrey ,  Justin MacLean Coughlin ,  Prasoon Mishra ,  Austen Norment McDonald ,  Max Christian Eulenstein ,  Jianfu Chen ,  Kritarth Anand ,  Polina Kuznetsova

IPC: G06N20/00 ,  G06F16/35 ,  G06N5/04 ,  G06N20/20

Abstract: An online system predicts topics for content items. The online system provides one or more topic labels for a user to apply concurrently while a user is composing a post, in response to requests periodically received from the user's device. A request includes information such as content composed by the user and contextual information. The online system employs machine learning techniques to analyze content composed by a user and contextual information thereby to predict topic labels. Different machine learning models for classifying individual topic labels, identifying relevant topic labels, and/or detecting changes in existing topic predictions are developed. Some machine learning models predict topics for full content and some predict topics for partial content. The online system trains the machine learning models to ensure accurate topic predictions are provided timely. The online system employs various machine learning model training methods such as active training and gradient training.

公开(公告)号：US10318597B2

公开(公告)日：2019-06-11

申请号：US14579710

申请日：2014-12-22

Applicant: Facebook, Inc.

Inventor： Holly Marie Ormseth ,  Elad Gerson ,  Guy Dassa ,  Khalid Bakry El-Arini ,  Gaurav Shankar ,  Yuanxuan Wang ,  Varun Kacholia ,  Prasoon Mishra ,  David Vickrey ,  Sanjeet Uday Hajarnis ,  Sahil P. Thaker

IPC: G06F17/30 ,  G06Q30/02 ,  G06F3/0482 ,  G06F16/9535 ,  G06Q50/00

Abstract: Systems, methods, and non-transitory computer readable media configured to detect access by a user to an original content item relating to a story. At least one of a comments based technique, a token based technique, and a tag based technique is performed on content items. Constraints are applied to identify at least one follow up content item from the content items relating to a development of the story.

公开(公告)号：US20190109871A1

公开(公告)日：2019-04-11

申请号：US15729528

申请日：2017-10-10

Applicant: Facebook, Inc.

Inventor： David Vickrey ,  Alexander Calan Leavitt ,  Anavil Tripathi ,  Harsh Mayur Selani ,  Prasoon Mishra ,  Sara Lee Su ,  Grace Louise Jackson

IPC: H04L29/06 ,  G06F7/02

Abstract: The present disclosure relates to techniques for determining trustworthiness of a domain among users. The determination may be based upon trust scores provided by the users for the domain. When all users have specified a trust score for the domain, an overall trust score may be computed based upon the specified trust scores. When some users have not specified a trust score for the domain, trust scores may be computed for the users based upon the specified trust scores, and an overall trust score may be computed based upon the specified trust scores and the computed trust scores. Based on the overall trust score, a social networking system may send content to users of the social networking system.