公开(公告)号：US20230297625A1

公开(公告)日：2023-09-21

申请号：US17654933

申请日：2022-03-15

Applicant: Adobe Inc.

Inventor： Fayokemi Ojo ,  Ryan Rossi ,  Jane Hoffswell ,  Shunan Guo ,  Fan Du ,  Sungchul Kim ,  Chang Xiao ,  Eunyee Koh

IPC: G06F16/904 ,  G06N3/02

CPC classification number: G06F16/904 ,  G06N3/02

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize a graph neural network to generate data recommendations. The disclosed systems generate a digital graph representation comprising user nodes corresponding to users, data attribute nodes corresponding to data attributes, and edges reflecting historical interactions between the users and the data attributes; Moreover, the disclosed systems generate, utilizing a graph neural network, user embeddings for the user nodes and data attribute embeddings for the data attribute nodes from the digital graph representation. In addition, the disclosed systems generate, utilizing a graph neural network, user embeddings for the user nodes and data attribute embeddings for the data attribute nodes from the digital graph representation. Furthermore, the disclosed systems determine a data recommendation for a target user utilizing the data attribute embeddings and a target user embedding corresponding to the target user from the user embeddings.

公开(公告)号：US20250036858A1

公开(公告)日：2025-01-30

申请号：US18225906

申请日：2023-07-25

Applicant: Adobe Inc.

Inventor： Ryan Rossi ,  Ryan Aponte ,  Shunan Guo ,  Nedim Lipka ,  Jane Hoffswell ,  Chang Xiao ,  Eunyee Koh ,  Yeuk-yin Chan

IPC: G06F40/154 ,  G06F40/117 ,  G06F40/143

Abstract: Techniques discussed herein generally relate to applying machine-learning techniques to design documents to determine relationships among the different style elements within the document. In one example, hypergraph model is trained on a corpus of hypertext markup language (HTML) documents. The trained model is utilized to identifying one or more candidate style elements for a candidate fragment and/or a candidate fragment. Each of the candidates are scored, and at least a portion of the scored candidates are presented as design options for generating a new document.

公开(公告)号：US20240311623A1

公开(公告)日：2024-09-19

申请号：US18183387

申请日：2023-03-14

Applicant: Adobe Inc.

Inventor： Ryan Rossi ,  Eunyee Koh ,  Jane Hoffswell ,  Nedim Lipka ,  Shunan Guo ,  Sudhanshu Chanpuriya ,  Sungchul Kim ,  Tong Yu

IPC: G06N3/049

CPC classification number: G06N3/049

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods for building time-decayed line graphs from temporal graph networks for efficiently and accurately generating time-aware recommendations. For example, the time-decayed line graph system creates a line graph of the temporal graph network by deriving interaction nodes from temporal edges (e.g., timed interactions) and connecting interactions that share an endpoint node. Then, the time-decayed line graph system determines the edge weights in the line graph based on differences in time between interactions, with interactions that occur closer together in time being connected with higher weights. Notably, by using this method, the derived time-decayed line graph directly represents topological proximity and temporal proximity. Upon generating the time-decayed line graphs, the system performs downstream predictive modeling such as predicted edge classifications and/or temporal link predictions.

公开(公告)号：US12093322B2

公开(公告)日：2024-09-17

申请号：US17654933

申请日：2022-03-15

Applicant: Adobe Inc.

Inventor： Fayokemi Ojo ,  Ryan Rossi ,  Jane Hoffswell ,  Shunan Guo ,  Fan Du ,  Sungchul Kim ,  Chang Xiao ,  Eunyee Koh

IPC: G06F16/904 ,  G06N3/02

CPC classification number: G06F16/904 ,  G06N3/02

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize a graph neural network to generate data recommendations. The disclosed systems generate a digital graph representation comprising user nodes corresponding to users, data attribute nodes corresponding to data attributes, and edges reflecting historical interactions between the users and the data attributes; Moreover, the disclosed systems generate, utilizing a graph neural network, user embeddings for the user nodes and data attribute embeddings for the data attribute nodes from the digital graph representation. In addition, the disclosed systems generate, utilizing a graph neural network, user embeddings for the user nodes and data attribute embeddings for the data attribute nodes from the digital graph representation. Furthermore, the disclosed systems determine a data recommendation for a target user utilizing the data attribute embeddings and a target user embedding corresponding to the target user from the user embeddings.