公开(公告)号：US11521221B2

公开(公告)日：2022-12-06

申请号：US15909723

申请日：2018-03-01

Applicant: Adobe Inc.

Inventor： Shiv Kumar Saini ,  Vishwa Vinay ,  Vaibhav Nagar ,  Aishwarya Mittal

IPC: G06Q30/02 ,  G06N3/04 ,  G06N7/00 ,  G06F16/9535

Abstract: This disclosure involves predictive modeling with entity representations computed from neural network models simultaneously trained on multiple tasks. For example, a method includes a processing device performing operations including accessing input data for an entity and transforming the input data into a dense vector entity representation representing the entity. Transforming the input data includes applying, to the input data, a neural network including simultaneously trained propensity models. Each propensity model predicts a different task based on the input data. Transforming the input data also includes extracting the dense vector entity representation from a common layer of the neural network to which the propensity models are connected. The operations performed by the processing device include computing a predicted behavior by applying a predictive model to the dense vector entity representation and transmitting the predicted behavior to a computing device that customizes a presentation of electronic content at a remote user device.

公开(公告)号：US20190272553A1

公开(公告)日：2019-09-05

申请号：US15909723

申请日：2018-03-01

Applicant: Adobe Inc.

Inventor： Shiv Kumar Saini ,  Vishwa Vinay ,  Vaibhav Nagar ,  Aishwarya Mittal

IPC: G06Q30/02 ,  G06N7/00 ,  G06N3/04 ,  G06F17/30

Abstract: This disclosure involves predictive modeling with entity representations computed from neural network models simultaneously trained on multiple tasks. For example, a method includes a processing device performing operations including accessing input data for an entity and transforming the input data into a dense vector entity representation representing the entity. Transforming the input data includes applying, to the input data, a neural network including simultaneously trained propensity models. Each propensity model predicts a different task based on the input data. Transforming the input data also includes extracting the dense vector entity representation from a common layer of the neural network to which the propensity models are connected. The operations performed by the processing device include computing a predicted behavior by applying a predictive model to the dense vector entity representation and transmitting the predicted behavior to a computing device that customizes a presentation of electronic content at a remote user device.