US 12,093,322 B2
Utilizing a graph neural network to generate visualization and attribute recommendations
Fayokemi Ojo, Baltimore, MD (US); Ryan Rossi, Santa Clara, CA (US); Jane Hoffswell, Seattle, WA (US); Shunan Guo, San Jose, CA (US); Fan Du, Milpitas, CA (US); Sungchul Kim, San Jose, CA (US); Chang Xiao, Sunnyvale, CA (US); and Eunyee Koh, San Jose, CA (US)
Assigned to Adobe Inc., San Jose, CA (US)
Filed by Adobe Inc., San Jose, CA (US)
Filed on Mar. 15, 2022, as Appl. No. 17/654,933.
Prior Publication US 2023/0297625 A1, Sep. 21, 2023
Int. Cl. G06F 16/904 (2019.01); G06N 3/02 (2006.01)
CPC G06F 16/904 (2019.01) [G06N 3/02 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A non-transitory computer readable medium comprising instructions that, when executed by a processing device, cause the processing device to perform operations comprising:
generating, from one or more data sets associated with users, a data attribute graph from data attributes utilized by the users, a visual configuration parameters graph from visual configuration parameters of data visualizations portraying the data attributes, and a combined data attribute visual configuration parameters graph from the data attributes and the visual configuration parameters;
generating, based on the data attribute graph, the visual configuration parameters graph, and the combined data attribute visual configuration parameters graph, a digital graph representation by:
generating visual configuration nodes reflecting the visual configuration parameters of the data visualizations,
generating user nodes corresponding to the users,
generating data attribute nodes corresponding to the data attributes, and
generating edges connecting the visual configuration nodes, the user nodes, and the data attribute nodes within the digital graph representation, wherein the edges reflect historical interactions between the users, the data attributes, and the visual configuration parameters of the data visualizations;
generating, utilizing a graph neural network, user embeddings for the user nodes, data attribute embeddings for the data attribute nodes, and visual configuration embeddings for the visual configuration parameters from the digital graph representation; and
determining a data recommendation comprising:
a target data attribute for a target user utilizing the data attribute embeddings and a target user embedding corresponding to the target user from the user embeddings, or
a target data visualization comprising a set of target visual configuration parameters for the target user utilizing the visual configuration embeddings and the target user embedding corresponding to the target user.