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1.发明申请公开(公告)号：US20190073592A1
公开(公告)日：2019-03-07
申请号：US15694321
申请日：2017-09-01
Applicant: Facebook, Inc.
Inventor： Enming Luo , Yang Mu , Emanuel Alexandre Strauss , Taiyuan Zhang , Daniel Olmedilla de la Calle
Abstract: A content review system for an online system automatically determines if received content items to be displayed to users violate any policies of the online system. The content review system generates a semantic vector representing the semantic features of a content item, for example, using a neural network. By comparing the semantic vector for the content item with semantic vectors of content items previously determined to violate one or more policies, the content review system determines whether the content item also violates one or more policies. The content review system may also maintain templates corresponding to portions of semantic vectors shared by multiple content items. An analysis of historical content items that conform to the template is performed to determine a probability that received content items that conform to the template violate a policy.
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Patent Agency Ranking
公开(公告)号：US10936952B2
公开(公告)日：2021-03-02
申请号：US15694339
申请日：2017-09-01
Applicant: Facebook, Inc.
Inventor： Enming Luo , Yang Mu , Emanuel Alexandre Strauss , Taiyuan Zhang , Daniel Olmedilla de la Calle
Abstract: A content review system for an online system automatically determines if received content items to be displayed to users violate any policies of the online system. The content review system generates a semantic vector representing the semantic features of a content item, for example, using a neural network. By comparing the semantic vector for the content item with semantic vectors of content items previously determined to violate one or more policies, the content review system determines whether the content item also violates one or more policies. The content review system may also maintain templates corresponding to portions of semantic vectors shared by multiple content items. An analysis of historical content items that conform to the template is performed to determine a probability that received content items that conform to the template violate a policy.
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公开(公告)号：US20190073593A1
公开(公告)日：2019-03-07
申请号：US15694339
申请日：2017-09-01
Applicant: Facebook, Inc.
Inventor： Enming Luo , Yang Mu , Emanuel Alexandre Strauss , Taiyuan Zhang , Daniel Olmedilla de la Calle
Abstract: A content review system for an online system automatically determines if received content items to be displayed to users violate any policies of the online system. The content review system generates a semantic vector representing the semantic features of a content item, for example, using a neural network. By comparing the semantic vector for the content item with semantic vectors of content items previously determined to violate one or more policies, the content review system determines whether the content item also violates one or more policies. The content review system may also maintain templates corresponding to portions of semantic vectors shared by multiple content items. An analysis of historical content items that conform to the template is performed to determine a probability that received content items that conform to the template violate a policy.
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公开(公告)号：US10853838B2
公开(公告)日：2020-12-01
申请号：US15608803
申请日：2017-05-30
Applicant: Facebook, Inc.
Inventor： Yang Mu , Emanuel Alexandre Strauss , Daniel Olmedilla de la Calle
Abstract: For various content campaigns (or content), an online system predicts a likelihood score of context violations (e.g., account term violations) of a content campaign. The online system derives a plurality of feature vectors of the content campaign. The online system predicts a likelihood score of context violation of the content campaign using a memorization model based on the plurality of feature vectors. The memorization model comprises a plurality of categories and a plurality of items of each category. Each of the plurality of categories has a category weight, and each of the plurality of items of each category has an item weight. The predicted likelihood score is based on a combination of a plurality of category weights and a plurality of item weights associated with the plurality of feature vectors. The online system performs an action affecting the content campaign based in part on the predicted likelihood score.
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公开(公告)号：US20180349942A1
公开(公告)日：2018-12-06
申请号：US15608803
申请日：2017-05-30
Applicant: Facebook, Inc.
Inventor： Yang Mu , Emanuel Alexandre Strauss , Daniel Olmedilla de la Calle
Abstract: For various content campaigns (or content), an online system predicts a likelihood score of context violations (e.g., account term violations) of a content campaign. The online system derives a plurality of feature vectors of the content campaign. The online system predicts a likelihood score of context violation of the content campaign using a memorization model based on the plurality of feature vectors. The memorization model comprises a plurality of categories and a plurality of items of each category. Each of the plurality of categories has a category weight, and each of the plurality of items of each category has an item weight. The predicted likelihood score is based on a combination of a plurality of category weights and a plurality of item weights associated with the plurality of feature vectors. The online system performs an action affecting the content campaign based in part on the predicted likelihood score.
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公开(公告)号：US11195099B2
公开(公告)日：2021-12-07
申请号：US15694321
申请日：2017-09-01
Applicant: Facebook, Inc.
Inventor： Enming Luo , Yang Mu , Emanuel Alexandre Strauss , Taiyuan Zhang , Daniel Olmedilla de la Calle
Abstract: A content review system for an online system automatically determines if received content items to be displayed to users violate any policies of the online system. The content review system generates a semantic vector representing the semantic features of a content item, for example, using a neural network. By comparing the semantic vector for the content item with semantic vectors of content items previously determined to violate one or more policies, the content review system determines whether the content item also violates one or more policies. The content review system may also maintain templates corresponding to portions of semantic vectors shared by multiple content items. An analysis of historical content items that conform to the template is performed to determine a probability that received content items that conform to the template violate a policy.
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