公开(公告)号：US20200311487A1

公开(公告)日：2020-10-01

申请号：US16371460

申请日：2019-04-01

Applicant: Adobe Inc.

Inventor： Jun He ,  Zhenyu Yan ,  Yi-Hong Kuo ,  Wuyang Dai ,  Shiyuan Gu ,  Abhishek Pani

IPC: G06K9/62 ,  G06F17/27 ,  G06N7/00

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for determining send times to provide electronic communications based on predicted response rates by utilizing a Bayesian approach and multi-armed bandit algorithms. For example, the disclosed systems can generate predicted response rates by training and utilizing one or more response rate prediction models to generate a weighted combination of user-specific response information and population-specific response information. The disclosed systems can further utilize a Bayes upper-confidence-bound send time model to determine send times that are more likely to elicit user responses based on the predicted response rates and further based on exploration and exploitation considerations. In addition, the disclosed systems can update the response rate prediction models and/or the Bayes upper-confidence-bound send time model based on providing additional electronic communications and receiving additional responses to modify model weights.

公开(公告)号：US11710065B2

公开(公告)日：2023-07-25

申请号：US16371460

申请日：2019-04-01

Applicant: Adobe Inc.

Inventor： Jun He ,  Shiyuan Gu ,  Zhenyu Yan ,  Wuyang Dai ,  Yi-Hong Kuo ,  Abhishek Pani

IPC: G06F40/279 ,  G06N20/00 ,  G06F18/2415 ,  G06F18/214 ,  G06N7/01

CPC classification number: G06N20/00 ,  G06F18/214 ,  G06F18/24155 ,  G06F40/279 ,  G06N7/01

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for determining send times to provide electronic communications based on predicted response rates by utilizing a Bayesian approach and multi-armed bandit algorithms. For example, the disclosed systems can generate predicted response rates by training and utilizing one or more response rate prediction models to generate a weighted combination of user-specific response information and population-specific response information. The disclosed systems can further utilize a Bayes upper-confidence-bound send time model to determine send times that are more likely to elicit user responses based on the predicted response rates and further based on exploration and exploitation considerations. In addition, the disclosed systems can update the response rate prediction models and/or the Bayes upper-confidence-bound send time model based on providing additional electronic communications and receiving additional responses to modify model weights.