公开(公告)号：US20190155929A1

公开(公告)日：2019-05-23

申请号：US15817123

申请日：2017-11-17

Applicant: Facebook, Inc.

Inventor： Komal Kapoor ,  Bradley Ray Green ,  Yunzhi Ye ,  Yixin Li

IPC: G06F17/30

Abstract: Systems, methods, and non-transitory computer-readable media can receive a user query comprising one or more search terms. One or more synonyms are identified for the user query based on a dynamic thesaurus generated using automated synonym extraction. An expanded query is generated based on the user query and the one or more synonyms. One or more search results are identified based on the expanded query.

公开(公告)号：US20190095841A1

公开(公告)日：2019-03-28

申请号：US15717907

申请日：2017-09-27

Applicant: Facebook, Inc.

Inventor： Yunzhi Ye ,  Komal Kapoor ,  Bradley Ray Green ,  Yixin Li

IPC: G06Q10/06 ,  G06Q50/00 ,  G06F7/02 ,  G06N99/00 ,  G06T11/20

Abstract: Systems, methods, and non-transitory computer readable media can obtain a plurality of page engagement graphs, each of the plurality of page engagement graphs associated with a page engagement type of a plurality of page engagement types. Respective weights associated with the plurality of page engagement types can be determined. An aggregated page engagement graph can be generated based on the plurality of page engagement graphs and the respective weights. Pages in the aggregated page engagement graph can be ranked.

公开(公告)号：US20190087430A1

公开(公告)日：2019-03-21

申请号：US15707761

申请日：2017-09-18

Applicant: Facebook, Inc.

Inventor： Kai Wang ,  Komal Kapoor ,  Bradley Ray Green ,  Ryan Farina

IPC: G06F17/30 ,  G06Q50/00 ,  H04L12/58

Abstract: Systems, methods, and non-transitory computer-readable media can determine a change made to a page that is accessible through a social networking system. A page update story that describes the change can be generated. The page update story to be published through the social networking system.

公开(公告)号：US20180103005A1

公开(公告)日：2018-04-12

申请号：US15289729

申请日：2016-10-10

Applicant: Facebook, Inc.

Inventor： Ashish Kumar Yadav ,  Komal Kapoor ,  Daniel Dinu ,  Bradley Ray Green ,  Naman Jain

IPC: H04L12/58

CPC classification number: H04L51/32 ,  H04L51/24

Abstract: Systems, methods, and non-transitory computer readable media are configured to receive values associated with features corresponding to an instance involving a page of a social networking system and an administrator of the page. The values associated with the features are applied to a machine learning model. A probability that the administrator of the page will take action on the page in response to receipt of an electronic notification provided to the administrator is determined based on the machine learning model.

公开(公告)号：US20180060973A1

公开(公告)日：2018-03-01

申请号：US15254906

申请日：2016-09-01

Applicant: Facebook, Inc.

Inventor： Komal Kapoor ,  Jonathan Daniel Sorg ,  Bradley Ray Green ,  Jason Eric Brewer

IPC: G06Q50/00 ,  G06Q30/02 ,  G06F17/30

CPC classification number: G06Q50/01 ,  G06F16/24578 ,  G06F16/9535 ,  G06Q30/0243 ,  G06Q30/0255 ,  H04L67/02 ,  H04L67/20 ,  H04L67/22 ,  H04L67/26 ,  H04L67/306

Abstract: Systems, methods, and non-transitory computer-readable media can determine a plurality of candidate entities for recommendation to a user of a social networking system based on candidate criteria. A recommendation pace rating is determined for each of the plurality of candidate entities based on historical recommendation data. A first entity of the plurality of candidate entities is selected for recommendation to the user based on the recommendation pace ratings.

公开(公告)号：US20170186101A1

公开(公告)日：2017-06-29

申请号：US14981029

申请日：2015-12-28

Applicant: Facebook, Inc.

Inventor： Komal Kapoor ,  Jonathan Daniel Sorg ,  Bradley Ray Green ,  Jason Brewer ,  David Tomotsu Sasaki

IPC: G06Q50/00

Abstract: Systems, methods, and non-transitory computer-readable media can determine a plurality of candidate entities for recommendation to a user of a social networking system. A predicted activity objective value model configured to calculate activity stores for candidate entities is established. The activity score is indicative of the probability of future activity on the social networking system by a candidate entity. A first activity score is determined for each of the plurality of candidate entities based on the predicted activity object value model and a first set of feature values. A second activity score is determined for each of the plurality of candidate entities based on the predicted activity object value model and a second set of feature values that is different from the first set of feature values. A first entity is selected of the plurality of candidate entities based on the first and second activity scores.