FEATURE GENERATION FOR ONLINE/OFFLINE MACHINE LEARNING

    公开(公告)号:US20210168184A1

    公开(公告)日:2021-06-03

    申请号:US17174317

    申请日:2021-02-11

    Applicant: Netflix, Inc.

    Abstract: A system for utilizing models derived from offline historical data in online applications is provided. The system includes a processor and a memory storing machine-readable instructions for determining a set of contexts of the usage data, and for each of the contexts within the set of contexts, collecting service data from services supporting the media service and storing that service data in a database. The system performing an offline testing process by fetching service data for a defined context from the database, generating a first set of feature vectors based on the fetched service data, and providing the first set to a machine-learning module. The system performs an online testing process by fetching active service data from the services supporting the media streaming service, generating a second set of feature vectors based on the fetched active service data, and providing the second set to the machine-learning module.

    Page simulation system
    2.
    发明授权

    公开(公告)号:US11409637B2

    公开(公告)日:2022-08-09

    申请号:US16746795

    申请日:2020-01-17

    Applicant: Netflix, Inc.

    Abstract: The disclosed computer-implemented method may include accessing updated data structures that are to be included in a user interface functionality test, where the updated data structures contribute to a user interface. The method may also include accessing live or snapshotted data captured from services running in a production environment, initiating generation of a first user interface instance using the updated data structures and using the accessed live or snapshotted data, and initiating generation of a second user interface instance using a different version of the data structures and using the same accessed live or snapshotted data. The method further includes comparing the first user interface instance to the second user interface instance to identify differences and then determine which outcome-defining effects the updated data structures had on the user interface based on the identified differences between the user interfaces. Various other methods, systems, and computer-readable media are also disclosed.

    Feature generation for online/offline machine learning

    公开(公告)号:US10958704B2

    公开(公告)日:2021-03-23

    申请号:US16557558

    申请日:2019-08-30

    Applicant: Netflix, Inc.

    Abstract: A system for utilizing models derived from offline historical data in online applications is provided. The system includes a processor and a memory storing machine-readable instructions for determining a set of contexts of the usage data, and for each of the contexts within the set of contexts, collecting service data from services supporting the media service and storing that service data in a database. The system performing an offline testing process by fetching service data for a defined context from the database, generating a first set of feature vectors based on the fetched service data, and providing the first set to a machine-learning module. The system performs an online testing process by fetching active service data from the services supporting the media streaming service, generating a second set of feature vectors based on the fetched active service data, and providing the second set to the machine-learning module.

    Feature generation for online/offline machine learning

    公开(公告)号:US10432689B2

    公开(公告)日:2019-10-01

    申请号:US15331106

    申请日:2016-10-21

    Applicant: NETFLIX, Inc

    Abstract: A system for utilizing models derived from offline historical data in online applications is provided. The system includes a processor and a memory storing machine-readable instructions for determining a set of contexts of the usage data, and for each of the contexts within the set of contexts, collecting service data from services supporting the media service and storing that service data in a database. The system performing an offline testing process by fetching service data for a defined context from the database, generating a first set of feature vectors based on the fetched service data, and providing the first set to a machine-learning module. The system performs an online testing process by fetching active service data from the services supporting the media streaming service, generating a second set of feature vectors based on the fetched active service data, and providing the second set to the machine-learning module.

    Page simulation system
    5.
    发明授权

    公开(公告)号:US11782821B2

    公开(公告)日:2023-10-10

    申请号:US17853648

    申请日:2022-06-29

    Applicant: Netflix, Inc.

    CPC classification number: G06F11/3664 G06F11/3608 G06F16/22

    Abstract: The disclosed computer-implemented method may include accessing updated data structures that are to be included in a user interface functionality test, where the updated data structures contribute to a user interface. The method may also include accessing live or snapshotted data captured from services running in a production environment, initiating generation of a first user interface instance using the updated data structures and using the accessed live or snapshotted data, and initiating generation of a second user interface instance using a different version of the data structures and using the same accessed live or snapshotted data. The method further includes comparing the first user interface instance to the second user interface instance to identify differences and then determine which outcome-defining effects the updated data structures had on the user interface based on the identified differences between the user interfaces. Various other methods, systems, and computer-readable media are also disclosed.

    PAGE SIMULATION SYSTEM
    6.
    发明申请

    公开(公告)号:US20210141712A1

    公开(公告)日:2021-05-13

    申请号:US16746795

    申请日:2020-01-17

    Applicant: Netflix, Inc.

    Abstract: The disclosed computer-implemented method may include accessing updated data structures that are to be included in a user interface functionality test, where the updated data structures contribute to a user interface. The method may also include accessing live or snapshotted data captured from services running in a production environment, initiating generation of a first user interface instance using the updated data structures and using the accessed live or snapshotted data, and initiating generation of a second user interface instance using a different version of the data structures and using the same accessed live or snapshotted data. The method further includes comparing the first user interface instance to the second user interface instance to identify differences and then determine which outcome-defining effects the updated data structures had on the user interface based on the identified differences between the user interfaces. Various other methods, systems, and computer-readable media are also disclosed.

    FEATURE GENERATION FOR ONLINE/OFFLINE MACHINE LEARNING

    公开(公告)号:US20190394252A1

    公开(公告)日:2019-12-26

    申请号:US16557558

    申请日:2019-08-30

    Applicant: Netflix, Inc.

    Abstract: A system for utilizing models derived from offline historical data in online applications is provided. The system includes a processor and a memory storing machine-readable instructions for determining a set of contexts of the usage data, and for each of the contexts within the set of contexts, collecting service data from services supporting the media service and storing that service data in a database. The system performing an offline testing process by fetching service data for a defined context from the database, generating a first set of feature vectors based on the fetched service data, and providing the first set to a machine-learning module. The system performs an online testing process by fetching active service data from the services supporting the media streaming service, generating a second set of feature vectors based on the fetched active service data, and providing the second set to the machine-learning module.

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