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公开(公告)号：US10698972B2
公开(公告)日：2020-06-30
申请号：US15707761
申请日：2017-09-18
Applicant: Facebook, Inc.
Inventor： Kai Wang , Komal Kapoor , Bradley Ray Green , Ryan Farina
IPC: G06F16/958 , G06Q50/00 , H04L12/58 , H04L29/06
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.
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2.发明申请公开(公告)号：US20190087747A1
公开(公告)日：2019-03-21
申请号：US15708609
申请日：2017-09-19
Applicant: Facebook, Inc.
Inventor： Komal Kapoor , Apaorn Tanglertsamapan , Ahmed Magdy Hamed Mohamed
Abstract: Systems, methods, and non-transitory computer readable media can obtain a plurality of calls-to-action (CTAs) that can be provided on a page associated with a social networking system. A machine learning model can be trained based on training data including pages and associated CTAs. The plurality of CTAs for a page can be ranked based on the machine learning model. At least one of the ranked CTAs for the page can be provided as a recommended CTA for the page.
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公开(公告)号：US10893070B2
公开(公告)日：2021-01-12
申请号：US16388058
申请日：2019-04-18
Applicant: Facebook, Inc.
Inventor： Haotian Wang , Komal Kapoor , Gaurav Singh Thakur
IPC: G06F7/04 , H04L29/06 , G06N20/20 , H04L29/08 , G06F16/951
Abstract: An online system maintains pages and accesses a graph of nodes representing the pages. Each node is labeled to indicate that a corresponding page is for a real-world entity, an imposter of the real-world entity, or a derived entity complying with or violating a policy. The online system retrieves machine-learning models, each of which is trained based on labels for a set of the nodes and features of corresponding pages. A first model predicts whether a page is for a derived entity based on features of the page. Responsive to predicting the page is not for a derived entity, a second model predicts whether the page is for a real-world entity or an imposter based on features of the page. Responsive to predicting the page is for a derived entity, a third model predicts whether the derived entity complies with or violates the policy based on features of the page.
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公开(公告)号：US20180157663A1
公开(公告)日：2018-06-07
申请号：US15371181
申请日：2016-12-06
Applicant: Facebook, Inc.
Inventor： Komal Kapoor , Aryamman Jain , Bradley Ray Green
CPC classification number: G06F16/24578 , G06F16/334 , G06F16/35 , G06N20/00 , G06Q30/0201 , G06Q50/01
Abstract: Systems, methods, and non-transitory computer-readable media can calculate user similarity scores for a plurality of users on a social networking system with respect to a first user based on user embeddings for the plurality of users and the first user. A set of similar users comprising a plurality of similar users is determined based on the user similarity scores. Page recommendation scores are calculated for a plurality of pages associated with the plurality of similar users based on the user similarity scores. One or more page recommendations are determined for the first user based on the page recommendation scores.
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公开(公告)号：US10733678B2
公开(公告)日：2020-08-04
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
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.
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Patent Agency Ranking
公开(公告)号：US20190087426A1
公开(公告)日：2019-03-21
申请号：US15707113
申请日：2017-09-18
Applicant: Facebook, Inc.
Inventor： Komal Kapoor , Bradley Ray Green , Yunzhi Ye , Yixin Li
Abstract: Systems, methods, and non-transitory computer-readable media can compute a query embedding in a first multi-dimensional space based on a query embedding model. The query embedding is associated with a user query. A plurality of page embeddings are computed in a second multi-dimensional space based on a page embedding model. A query joint embedding and a plurality of page joint embeddings are computed in a third multi-dimensional space based on the query embedding, the plurality of page embeddings, and a joint embedding model. One or more page results are identified for the user query based on the query joint embedding and the plurality of page joint embeddings.
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公开(公告)号：US20180107742A1
公开(公告)日：2018-04-19
申请号：US15297024
申请日：2016-10-18
Applicant: Facebook, Inc.
Inventor： Komal Kapoor , Apaorn Tanglertsampan , Bradley Ray Green , Meiying Li , James Donovan , Hannah Marie Hemmaplardh
CPC classification number: G06F16/9535 , G06F16/24578 , G06F16/248 , G06N20/00 , G06Q50/01
Abstract: Systems, methods, and non-transitory computer-readable media can train a machine learning model to determine predictive search recommendation based on search prediction information. Search prediction information associated with a user is provided to the machine learning model. A predictive search recommendation is presented to the user based on the machine learning model and the search prediction information. A search is performed based on the predictive search recommendation for one or more search results associated with entity pages on a social networking system.
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8.发明申请公开(公告)号：US20180107741A1
公开(公告)日：2018-04-19
申请号：US15297007
申请日：2016-10-18
Applicant: Facebook, Inc.
Inventor： Komal Kapoor , Apaorn Tanglertsampan , Bradley Ray Green , Meiying Li , James Donovan , Hannah Marie Hemmaplardh
IPC: G06F17/30
CPC classification number: G06F16/9535 , G06F21/6218
Abstract: Systems, methods, and non-transitory computer-readable media can present a service directory landing page comprising a plurality of selectable service category options associated with a plurality of pre-defined service categories. A search results page is presented, including one or more search results based on search criteria. Each of the one or more search results is associated with an entity page of a social networking system. The service directory landing page and the search results page are accessible without logging into the social networking system. Each entity page on the social networking system is accessible only when logged into the social networking system.
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公开(公告)号：US20170185685A1
公开(公告)日：2017-06-29
申请号：US14980669
申请日：2015-12-28
Applicant: Facebook, Inc.
Inventor： Jason Brewer , Bradley Ray Green , Jinyi Yao , Komal Kapoor
IPC: G06F17/30
CPC classification number: G06F16/9535 , G06F16/9537
Abstract: Systems, methods, and non-transitory computer-readable media can determine respective geographic locations of a set of users associated with a page that is accessible through a social network. At least one centroid for the page can be generated based at least in part on the respective geographic locations of the set of users. At least one area of influence of the page can be determined based at least in part on the centroid. At least one page recommendation can be presented to one or more users in the set of users based at least in part on the area of influence of the
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公开(公告)号：US20200336509A1
公开(公告)日：2020-10-22
申请号：US16388058
申请日：2019-04-18
Applicant: Facebook, Inc.
Inventor： Haotian Wang , Komal Kapoor , Gaurav Singh Thakur
IPC: H04L29/06 , G06N20/20 , G06F16/951 , H04L29/08
Abstract: An online system maintains pages and accesses a graph of nodes representing the pages. Each node is labeled to indicate that a corresponding page is for a real-world entity, an imposter of the real-world entity, or a derived entity complying with or violating a policy. The online system retrieves machine-learning models, each of which is trained based on labels for a set of the nodes and features of corresponding pages. A first model predicts whether a page is for a derived entity based on features of the page. Responsive to predicting the page is not for a derived entity, a second model predicts whether the page is for a real-world entity or an imposter based on features of the page. Responsive to predicting the page is for a derived entity, a third model predicts whether the derived entity complies with or violates the policy based on features of the page.
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