公开(公告)号：US11341516B2

公开(公告)日：2022-05-24

申请号：US16877385

申请日：2020-05-18

Applicant: Adobe Inc.

Inventor： Xinyue Liu ,  Suofei Wu ,  Chang Liu ,  Jun He ,  Zhenyu Yan ,  Wuyang Dai ,  Shengyun Peng

IPC: G06Q30/02 ,  H04L51/56 ,  G06Q10/10

Abstract: Introduced here are approaches for identifying the optimal send time for messages by accounting for hidden confounders, such as the content of those messages, delivery channel, etc. These approaches use a causal inference framework to discover and then remove the impact of hidden confounders. These approaches may be employed by a marketing and analytics platform (or simply “marketing platform”) that may be used to design, implement, or review digital marketing campaigns. The marketing platform can consider the send time as a treatment and then employ machine learning (ML) models that consider the send time, features of the recipient, and hidden confounders to produce a ranked series of send times with the effect of the hidden confounders marginalized. Approaches to performing offline evaluations that mimic A/B tests using data related to existing field experiments are also introduced here.

公开(公告)号：US20210075875A1

公开(公告)日：2021-03-11

申请号：US16564768

申请日：2019-09-09

Applicant: Adobe Inc.

Inventor： Xinyue Liu ,  Jun He ,  Zhenyu Yan ,  Wuyang Dai ,  Abhishek Pani

IPC: H04L29/08 ,  G06F16/2457 ,  G06N3/04 ,  G06N3/08

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for determining send times for distributing digital content to client devices utilizing a recommendation system approach. For example, the disclosed systems can utilize a recommendation system model such as a matrix factorization model, a factorization machine model, and/or a neural network to implement collaborative filtering to generate predicted response rates for particular candidate send times. Based on the predicted response rates indicating likelihoods of receiving responses for particular send times, the disclosed system can generate a distribution schedule to provide electronic communications at one or more of the send times.

公开(公告)号：US20220309523A1

公开(公告)日：2022-09-29

申请号：US17664601

申请日：2022-05-23

Applicant: Adobe Inc.

Inventor： Xinyue Liu ,  Suofei Wu ,  Chang Liu ,  Jun He ,  Zhenyu Yan ,  Wuyang Dai ,  Shengyun Peng

IPC: G06Q30/02 ,  G06Q10/10 ,  H04L51/56

Abstract: Introduced here are approaches for identifying the optimal send time for messages by accounting for hidden confounders, such as the content of those messages, delivery channel, etc. These approaches use a causal inference framework to discover and then remove the impact of hidden confounders. These approaches may be employed by a marketing and analytics platform (or simply “marketing platform”) that may be used to design, implement, or review digital marketing campaigns. The marketing platform can consider the send time as a treatment and then employ machine learning (ML) models that consider the send time, features of the recipient, and hidden confounders to produce a ranked series of send times with the effect of the hidden confounders marginalized. Approaches to performing offline evaluations that mimic A/B tests using data related to existing field experiments are also introduced here.

公开(公告)号：US20210357952A1

公开(公告)日：2021-11-18

申请号：US16877385

申请日：2020-05-18

Applicant: Adobe Inc.

Inventor： Xinyue Liu ,  Suofei Wu ,  Chang Liu ,  Jun He ,  Zhenyu Yan ,  Wuyang Dai ,  Shengyun Peng

IPC: G06Q30/02 ,  G06Q10/10 ,  H04L12/58

Abstract: Introduced here are approaches for identifying the optimal send time for messages by accounting for hidden confounders, such as the content of those messages, delivery channel, etc. These approaches use a causal inference framework to discover and then remove the impact of hidden confounders. These approaches may be employed by a marketing and analytics platform (or simply “marketing platform”) that may be used to design, implement, or review digital marketing campaigns. The marketing platform can consider the send time as a treatment and then employ machine learning (ML) models that consider the send time, features of the recipient, and hidden confounders to produce a ranked series of send times with the effect of the hidden confounders marginalized. Approaches to performing offline evaluations that mimic A/B tests using data related to existing field experiments are also introduced here.

公开(公告)号：US11038976B2

公开(公告)日：2021-06-15

申请号：US16564768

申请日：2019-09-09

Applicant: Adobe Inc.

Inventor： Xinyue Liu ,  Jun He ,  Zhenyu Yan ,  Wuyang Dai ,  Abhishek Pani

IPC: H04L29/08 ,  G06N3/08 ,  G06N3/04 ,  G06F16/2457

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for determining send times for distributing digital content to client devices utilizing a recommendation system approach. For example, the disclosed systems can utilize a recommendation system model such as a matrix factorization model, a factorization machine model, and/or a neural network to implement collaborative filtering to generate predicted response rates for particular candidate send times. Based on the predicted response rates indicating likelihoods of receiving responses for particular send times, the disclosed system can generate a distribution schedule to provide electronic communications at one or more of the send times.