公开(公告)号：US11886964B2

公开(公告)日：2024-01-30

申请号：US17322108

申请日：2021-05-17

Applicant: Adobe Inc.

Inventor： Atanu R. Sinha ,  Xiang Chen ,  Sungchul Kim ,  Omar Rahman ,  Jean Bernard Hishamunda ,  Goutham Srivatsav Arra ,  Shiv Kumar Saini

IPC: G06N20/00 ,  G06F3/0484 ,  H04L67/50

CPC classification number: G06N20/00 ,  G06F3/0484 ,  H04L67/535

Abstract: Methods and systems disclosed herein relate generally to systems and methods for using a machine-learning model to predict user-engagement levels of users in response to presentation of future interactive content. A content provider system accesses a machine-learning model, which was trained using a training dataset including previous user-device actions performed by a plurality of users in response to previous interactive content. The content provider system receives user-activity data of a particular user and applies the machine-learning model to the user-activity data, in which the user-activity data includes user-device actions performed by the particular user in response to interactive content. The machine-learning model generates an output including a categorical value that represents a predicted user-engagement level of the particular user in response to a presentation of the future interactive content.

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：US11550859B2

公开(公告)日：2023-01-10

申请号：US16569484

申请日：2019-09-12

Applicant: Adobe Inc.

Inventor： Ryan A. Rossi ,  Sungchul Kim ,  Eunyee Koh ,  Anup Bandigadi Rao ,  Russell R. Stringham

IPC: G06F16/9535 ,  G06F16/955

Abstract: Techniques and systems are described for analytics system entity resolution. Typed higher-order node combinations are determined within a dataset, and an amount of similarity between two arbitrary nodes within the dataset is determined based on the typed higher-order node combinations. The amount of similarity enables the digital analytics to accurately perform source resolution of portions of the dataset to a respective source, and may be utilized to control output of digital content to a client device.

公开(公告)号：US11507878B2

公开(公告)日：2022-11-22

申请号：US16380566

申请日：2019-04-10

Applicant: Adobe Inc.

Inventor： Xiaowei Jia ,  Sheng Li ,  Handong Zhao ,  Sungchul Kim

IPC: G06N20/00

Abstract: Techniques are disclosed for the generation of adversarial training data through sequence perturbation, for a deep learning network to perform event sequence analysis. A methodology implementing the techniques according to an embodiment includes applying a long short-term memory attention model to an input data sequence to generate discriminative sequence periods and attention weights associated with the discriminative sequence periods. The attention weights are generated to indicate the relative importance of data in those discriminative sequence periods. The method further includes generating perturbed data sequences based on the discriminative sequence periods and the attention weights. The generation of the perturbed data sequences employs selective filtering or conservative adversarial training, to preserve perceptual similarity between the input data sequence and the perturbed data sequences. The input data sequence may be created by vectorizing a temporal input data stream comprising words, symbols, and the like, into a multidimensional vectorized numerical data sequence format.

公开(公告)号：US20220300836A1

公开(公告)日：2022-09-22

申请号：US17207959

申请日：2021-03-22

Applicant: Adobe Inc.

Inventor： Ryan Rossi ,  Xin Qian ,  Tak Yeon Lee ,  Sungchul Kim ,  Sana Lee ,  Fan Du ,  Eunyee Koh

IPC: G06N5/04 ,  G06N20/00

Abstract: A visualization recommendation system generates recommendation scores for multiple visualizations that combine data attributes of a dataset with visualization configurations. The visualization recommendation system maps meta-features of the dataset to a meta-feature space and configuration attributes of the visualization configurations to a configuration space. The visualization recommendation system generates meta-feature vectors that describe the mapped meta-features, and generates configuration attribute sets that describe the attributes of the visualization configurations. The visualization recommendation system applies multiple scoring models to the meta-feature vectors and configuration attribute sets, including a wide scoring model and a deep scoring model. In some cases, the visualization recommendation system trains the multiple scoring models using the meta-feature vectors and configuration attribute sets.

公开(公告)号：US20220147540A1

公开(公告)日：2022-05-12

申请号：US17091941

申请日：2020-11-06

Applicant: ADOBE INC.

Inventor： RYAN Rossi ,  Vasanthi Holtcamp ,  Tak Yeon Lee ,  Sungchul Kim ,  Sana Lee ,  Nathan Ross ,  John Anderson ,  Fan Du ,  Eunyee Koh ,  Xin Qian

IPC: G06F16/26 ,  G06F11/30 ,  G06F11/34 ,  G06F3/0482

Abstract: Systems and methods for personalized visualization recommendation are described. Embodiments of the described systems and methods are configured to identify a first matrix representing user interactions with a plurality of data attributes corresponding to a plurality of datasets, a second matrix representing user interactions with a plurality of visualizations, and a third matrix representing a plurality of meta-features for each of the data attributes; compute low-dimensional embeddings representing user characteristics, the data attributes, visualization configurations, and the meta-features using joint factorization of the first matrix, the second matrix and the third matrix; generate a model for predicting visualization preference weights based on the low-dimensional embeddings; predict the visualization preference weights for a user corresponding to a plurality of candidate visualizations of dataset using the model; and generate a personalized visualization of the dataset for the user based on the predicted visualization preference weights.

公开(公告)号：US10963527B2

公开(公告)日：2021-03-30

申请号：US16254125

申请日：2019-01-22

Applicant: ADOBE INC.

Inventor： Sungchul Kim ,  Nedim Lipka ,  Eunyee Koh

IPC: G06F16/29 ,  G06F16/9535 ,  G06F16/335

Abstract: A method for clustering geolocations using geo-point density includes receiving a user log of geolocation data extracted from user interactions with at least one electronic device. A density is determined relative to other geo-points for each geo-point in a set of geo-points extracted from the user log. Lower density geo-points in the set are merged into higher density geo-points in the set to result in a merged set of geo-points, and clusters of geo-points are identified from the merged set. Merging the geo-points tends to preserve frequently occurring geo-points while reducing those that constitute noise, which improves the reliability of identifying the clusters. Core geo-points of the user log are selected from the clusters. The core geo-points of the user log can be compared to core geo-points of other use logs to identify associations between the user logs.

公开(公告)号：US20210081473A1

公开(公告)日：2021-03-18

申请号：US16569484

申请日：2019-09-12

Applicant: Adobe Inc.

Inventor： Ryan A. Rossi ,  Sungchul Kim ,  Eunyee Koh ,  Anup Bandigadi Rao ,  Russell R. Stringham

IPC: G06F16/9535 ,  G06F16/955

Abstract: Techniques and systems are described for analytics system entity resolution. Typed higher-order node combinations are determined within a dataset, and an amount of similarity between two arbitrary nodes within the dataset is determined based on the typed higher-order node combinations. The amount of similarity enables the digital analytics to accurately perform source resolution of portions of the dataset to a respective source, and may be utilized to control output of digital content to a client device.

公开(公告)号：US10728105B2

公开(公告)日：2020-07-28

申请号：US16204616

申请日：2018-11-29

Applicant: Adobe Inc.

Inventor： Ryan A. Rossi ,  Eunyee Koh ,  Sungchul Kim ,  Anup Bandigadi Rao

IPC: H04L12/24 ,  G06F16/901 ,  G06N20/00 ,  H04L29/06

Abstract: In implementations of higher-order network embedding, a computing device maintains interconnected data in the form of a graph that represents a network, the graph including nodes that each represent entities in the network and node associations that each represent edges between the nodes in the graph. The computing device includes a network embedding module that is implemented to determine a frequency of k-vertex motifs for each of the edges in the graph, and derive motif-based matrices from the frequency of each of the k-vertex motifs in the graph. The network embedding module is also implemented to determine a higher-order network embedding for each of the nodes in the graph from each of the motif-based matrices. The network embedding module can then concatenate the higher-order network embeddings into a matrix representation.

公开(公告)号：US20200162340A1

公开(公告)日：2020-05-21

申请号：US16192313

申请日：2018-11-15

Applicant: Adobe Inc.

Inventor： Ryan A. Rossi ,  Sungchul Kim ,  Eunyee Koh

IPC: H04L12/24 ,  H04L12/26

Abstract: In implementations of time-dependent network embedding, a computing device maintains time-dependent interconnected data in the form of a time-based graph that includes nodes and node associations that each represent an edge between two of the nodes in the time-based graph based at least in part on a temporal value that indicates when the two nodes were associated. The computing device includes a network embedding module that is implemented to traverse one or more of the nodes in the time-based graph along the node associations, where the traversal is performed with respect to the temporal value of each of the edges that associate the nodes. The network embedding module is also implemented to determine a time-dependent embedding for each of the nodes traversed in the time-based graph, the time-dependent embedding for each of the respective nodes being representative of feature values that describe the respective node.