- 专利标题: Identifying touchpoint contribution utilizing a touchpoint attribution attention neural network
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申请号: US17656782申请日: 2022-03-28
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公开(公告)号: US11816272B2公开(公告)日: 2023-11-14
- 发明人: Zhenyu Yan , Fnu Arava Venkata Kesava Sai Kumar , Chen Dong , Abhishek Pani , Ning Li
- 申请人: Adobe Inc.
- 申请人地址: US CA San Jose
- 专利权人: Adobe Inc.
- 当前专利权人: Adobe Inc.
- 当前专利权人地址: US CA San Jose
- 代理机构: Keller Preece PLLC
- 主分类号: G06Q30/00
- IPC分类号: G06Q30/00 ; G06F3/01 ; G06N3/08 ; G06F3/0484 ; G06F3/0481 ; H04L67/50
摘要:
The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating and utilizing a touchpoint attribution attention neural network to identify and measure performance of touchpoints in digital content campaigns. For example, a deep learning attribution system trains a touchpoint attribution attention neural network using touchpoint sequences, which include user interactions with content via one or more digital media channels. In one or more embodiments, the deep learning attribution system utilizes the trained touchpoint attribution attention neural network to determine touchpoint attributions of touchpoints in a target touchpoint sequence. In addition, the deep learning attribution system can utilize the trained touchpoint attribution attention neural network to generate conversion predictions for target touchpoint sequences and to provide targeted digital content over specific digital media channels to client devices of individual users.
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