公开(公告)号：US11269870B2

公开(公告)日：2022-03-08

申请号：US16149418

申请日：2018-10-02

Applicant: Adobe Inc.

Inventor： Vidit Bhatia ,  Vijeth Lomada ,  Haichun Chen

IPC: G06F16/00 ,  G06F16/242 ,  G06N3/08 ,  G06N7/00 ,  G06F16/26 ,  G06F16/248 ,  G06F16/901

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for expanding user segments automatically utilizing user embedding representations generated by a trained neural network. For example, a user embeddings system expands a segment of users by identifying holistically similar users from uniform user embeddings that encode behavior and/or realized traits of the users. Further, the user embeddings system facilitates the expansion of user segments in a particular direction and focus to improve the accuracy of user segments.

公开(公告)号：US20220156257A1

公开(公告)日：2022-05-19

申请号：US17587372

申请日：2022-01-28

Applicant: Adobe Inc.

Inventor： Vidit Bhatia ,  Vijeth Lomada ,  Haichun Chen

IPC: G06F16/242 ,  G06N3/08 ,  G06N7/00 ,  G06F16/26 ,  G06F16/248 ,  G06F16/901

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for expanding user segments automatically utilizing user embedding representations generated by a trained neural network. For example, a user embeddings system expands a segment of users by identifying holistically similar users from uniform user embeddings that encode behavior and/or realized traits of the users. Further, the user embeddings system facilitates the expansion of user segments in a particular direction and focus to improve the accuracy of user segments.

公开(公告)号：US20200226489A1

公开(公告)日：2020-07-16

申请号：US16247297

申请日：2019-01-14

Applicant: Adobe Inc.

Inventor： Yancheng Li ,  Moumita Sinha ,  Haichun Chen

IPC: G06N20/00 ,  G06N5/02

Abstract: In some embodiments, a computing system generates de-biased training data for fairness-aware predictive models to facilitate online resource access. The computing system extracts latent features from training data of a first machine learning model for predicting an access flag for a user indicating the ability of the user to access an online environment. Based on the latent features, the computing system trains a second machine learning model to generate de-biased training data by applying a loss function that includes loss terms associated with an individual bias and a group bias of the training data. The de-biased training data are utilized to train the first machine learning model and to update the access flag for the user by applying the first machine learning model to attributes of the user. A user device associated with the user can be provided with access to the online environment according to the updated access flag.

公开(公告)号：US11461634B2

公开(公告)日：2022-10-04

申请号：US16149347

申请日：2018-10-02

Applicant: Adobe Inc.

Inventor： Vidit Bhatia ,  Vijeth Lomada ,  Haichun Chen

IPC: G06N3/08 ,  G06N5/02 ,  G06F16/28

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating user embeddings utilizing an interaction-to-vector neural network. For example, a user embeddings system transforms unorganized data of user interactions with content items into structured user interaction data. Further, the user embeddings system can utilize the structured user interaction data to train a neural network in a semi-supervised manner and generate uniform vectorized user embeddings for each of the users.