公开(公告)号：US10699210B2

公开(公告)日：2020-06-30

申请号：US14671657

申请日：2015-03-27

Applicant: Facebook, Inc.

Inventor： Stuart Michael Bowers ,  Hussein Mohamed Hassan Mehanna ,  Andrey Malevich ,  Sai Nishanth Parepally ,  David Paul Capel ,  Alisson Gusatti Azzolini

IPC: G06N20/00 ,  G06Q30/00 ,  G06Q50/00

Abstract: An online system identifies an additional feature to evaluate for inclusion in a machine learned model. The additional feature is based on characteristics of one or more dimensions of information maintained by the online system. To generate data for evaluating the additional feature, the online system generates various partitions of stored data, where each partition includes characteristics associated with one or more dimensions on which the additional feature is based. Using values of characteristics in a partition, the online system generates values for the additional feature and includes the values of the additional feature in the partition. Values for the additional feature are generated for various partitions based on the values of characteristics in each partition. The online system combines multiple partitions that include values for the additional feature to generate a training set for evaluating a machine learned model including the additional feature.

公开(公告)号：US20180268065A1

公开(公告)日：2018-09-20

申请号：US15463751

申请日：2017-03-20

Applicant: Facebook, Inc.

Inventor： Sai Nishanth Parepally

IPC: G06F17/30 ,  G06N99/00

Abstract: In one embodiment, a method includes generating a reconstructed embedding of a query based on one or more term embeddings associated with the one or more query terms, respectively, on receiving a query with the one or more query terms, formulating an evaluation model based at least on the reconstructed embedding of the query, where the evaluation model calculates a relevance score for posts with respect to the search query based at least on the classifier vectors of the posts, and calculating, for each of the retrieved posts, a relevance score for the post by applying the associated classifier vector to the formulated evaluation model.

公开(公告)号：US10769222B2

公开(公告)日：2020-09-08

申请号：US15463751

申请日：2017-03-20

Applicant: Facebook, Inc.

Inventor： Sai Nishanth Parepally

IPC: G06F16/9535 ,  G06N20/00 ,  G06F16/248 ,  G06F16/2457 ,  G06F16/33

Abstract: In one embodiment, a method includes generating a reconstructed embedding of a query based on one or more term embeddings associated with the one or more query terms, respectively, on receiving a query with the one or more query terms, formulating an evaluation model based at least on the reconstructed embedding of the query, where the evaluation model calculates a relevance score for posts with respect to the search query based at least on the classifier vectors of the posts, and calculating, for each of the retrieved posts, a relevance score for the post by applying the associated classifier vector to the formulated evaluation model.

公开(公告)号：US20200272943A1

公开(公告)日：2020-08-27

申请号：US16869382

申请日：2020-05-07

Applicant: Facebook, Inc.

Inventor： Stuart Michael BOWERS ,  Hussein Mohamed Hassan Mehanna ,  Andrey Malevich ,  Sai Nishanth Parepally ,  David Paul Capel ,  Alisson Gusatti Azzolini

IPC: G06N20/00 ,  G06Q30/00 ,  G06Q50/00

Abstract: An online system identifies an additional feature to evaluate for inclusion in a machine learned model. The additional feature is based on characteristics of one or more dimensions of information maintained by the online system. To generate data for evaluating the additional feature, the online system generates various partitions of stored data, where each partition includes characteristics associated with one or more dimensions on which the additional feature is based. Using values of characteristics in a partition, the online system generates values for the additional feature and includes the values of the additional feature in the partition. Values for the additional feature are generated for various partitions based on the values of characteristics in each partition. The online system combines multiple partitions that include values for the additional feature to generate a training set for evaluating a machine learned model including the additional feature.

公开(公告)号：US20160283863A1

公开(公告)日：2016-09-29

申请号：US14671657

申请日：2015-03-27

Applicant: Facebook, Inc.

Inventor： Stuart Michael Bowers ,  Hussein Mohamed Hassan Mehanna ,  Andrey Malevich ,  Sai Nishanth Parepally ,  David Paul Capel ,  Alisson Gusatti Azzolini

IPC: G06N99/00

CPC classification number: G06N20/00 ,  G06Q30/00 ,  G06Q50/01

Abstract: An online system identifies an additional feature to evaluate for inclusion in a machine learned model. The additional feature is based on characteristics of one or more dimensions of information maintained by the online system. To generate data for evaluating the additional feature, the online system generates various partitions of stored data, where each partition includes characteristics associated with one or more dimensions on which the additional feature is based. Using values of characteristics in a partition, the online system generates values for the additional feature and includes the values of the additional feature in the partition. Values for the additional feature are generated for various partitions based on the values of characteristics in each partition. The online system combines multiple partitions that include values for the additional feature to generate a training set for evaluating a machine learned model including the additional feature.

Abstract translation: 在线系统识别一个附加功能，以评估包含在机器学习模型中。 附加功能基于由在线系统维护的信息的一个或多个维度的特征。 为了生成用于评估附加特征的数据，在线系统生成存储数据的各种分区，其中每个分区包括与附加特征所基于的一个或多个维相关联的特征。 使用分区中的特征值，在线系统生成附加功能的值，并包括分区中附加功能的值。 基于每个分区中的特征值，为各个分区生成附加功能的值。 在线系统组合多个分区，其中包括附加功能的值，以生成用于评估包含附加功能的机器学习模型的训练集。