MACHINE LEARNING MODELS APPLIED TO INTERACTION DATA FOR FACILITATING MODIFICATIONS TO ONLINE ENVIRONMENTS

    公开(公告)号:US20220214957A1

    公开(公告)日:2022-07-07

    申请号:US17703188

    申请日:2022-03-24

    Applicant: Adobe Inc.

    Abstract: In some embodiments, a computing system identifies a current engagement stage of a user with an online platform by applying a stage prediction model based on interaction data associated with the user. The interaction data describe actions performed by the user with respect to the online platform and context data associated with each of the actions. The computing system further identifies one or more critical events for promoting the user to transition from one engagement stage to a higher engagement stage based on the interaction data associated with the user. The computing system can make the identified current engagement stage of the user or the identified critical event to be accessible by the online platform so that user interfaces presented on the online platform can be modified to improve a likelihood of the user to transit from the current stage to a higher engagement stage.

    Machine learning models applied to interaction data for facilitating modifications to online environments

    公开(公告)号:US11775412B2

    公开(公告)日:2023-10-03

    申请号:US17703188

    申请日:2022-03-24

    Applicant: Adobe Inc.

    CPC classification number: G06F11/3438 G06F9/451 G06N20/00

    Abstract: In some embodiments, a computing system identifies a current engagement stage of a user with an online platform by applying a stage prediction model based on interaction data associated with the user. The interaction data describe actions performed by the user with respect to the online platform and context data associated with each of the actions. The computing system further identifies one or more critical events for promoting the user to transition from one engagement stage to a higher engagement stage based on the interaction data associated with the user. The computing system can make the identified current engagement stage of the user or the identified critical event to be accessible by the online platform so that user interfaces presented on the online platform can be modified to improve a likelihood of the user to transit from the current stage to a higher engagement stage.

    User Segment Generation and Summarization

    公开(公告)号:US20210142256A1

    公开(公告)日:2021-05-13

    申请号:US16681056

    申请日:2019-11-12

    Applicant: Adobe Inc.

    Abstract: A user segmentation system is described that is configured to generate use segments and summarize user segments. In one example, the user segmentation system is configured to identify which attributes support a key performance indicator. This is used to generate rules that act as user segments of a user population. Further, the user segmentation system is configured to reduce overlap of user segments through summarization.

    Machine learning models applied to interaction data for facilitating modifications to online environments

    公开(公告)号:US11314616B2

    公开(公告)日:2022-04-26

    申请号:US16775815

    申请日:2020-01-29

    Applicant: Adobe Inc.

    Abstract: In some embodiments, a computing system identifies a current engagement stage of a user with an online platform by applying a stage prediction model based on interaction data associated with the user. The interaction data describe actions performed by the user with respect to the online platform and context data associated with each of the actions. The computing system further identifies one or more critical events for promoting the user to transition from one engagement stage to a higher engagement stage based on the interaction data associated with the user. The computing system can make the identified current engagement stage of the user or the identified critical event to be accessible by the online platform so that user interfaces presented on the online platform can be modified to improve a likelihood of the user to transit from the current stage to a higher engagement stage.

    MACHINE LEARNING MODELS APPLIED TO INTERACTION DATA FOR FACILITATING MODIFICATIONS TO ONLINE ENVIRONMENTS

    公开(公告)号:US20210232478A1

    公开(公告)日:2021-07-29

    申请号:US16775815

    申请日:2020-01-29

    Applicant: Adobe Inc.

    Abstract: In some embodiments, a computing system identifies a current engagement stage of a user with an online platform by applying a stage prediction model based on interaction data associated with the user. The interaction data describe actions performed by the user with respect to the online platform and context data associated with each of the actions. The computing system further identifies one or more critical events for promoting the user to transition from one engagement stage to a higher engagement stage based on the interaction data associated with the user. The computing system can make the identified current engagement stage of the user or the identified critical event to be accessible by the online platform so that user interfaces presented on the online platform can be modified to improve a likelihood of the user to transit from the current stage to a higher engagement stage.

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