- 专利标题: Machine-learning based multi-step engagement strategy modification
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申请号: US17355907申请日: 2021-06-23
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公开(公告)号: US11816696B2公开(公告)日: 2023-11-14
- 发明人: Pankhri Singhai , Sundeep Parsa , Piyush Gupta , Nupur Kumari , Nikaash Puri , Mayank Singh , Eshita Shah , Balaji Krishnamurthy , Akash Rupela
- 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: FIG. 1 Patents
- 主分类号: G06Q30/00
- IPC分类号: G06Q30/00 ; G06Q30/0242 ; G06Q30/0251 ; G06N20/00 ; G06N5/00 ; G05B19/418
摘要:
Machine-learning based multi-step engagement strategy modification is described. Rather than rely heavily on human involvement to manage content delivery over the course of a campaign, the described learning-based engagement system modifies a multi-step engagement strategy, originally created by an engagement-system user, by leveraging machine-learning models. In particular, these leveraged machine-learning models are trained using data describing user interactions with delivered content as those interactions occur over the course of the campaign. Initially, the learning-based engagement system obtains a multi-step engagement strategy created by an engagement-system user. As the multi-step engagement strategy is deployed, the learning-based engagement system randomly adjusts aspects of the sequence of deliveries for some users. Based on data describing the interactions of recipients with deliveries served according to both the user-created and random multi-step engagement strategies, the machine-learning models generate a modified multi-step engagement strategy.
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