SYSTEMS AND METHODS FOR EVALUATING MODELS THAT GENERATE RECOMMENDATIONS

    公开(公告)号:US20220014822A1

    公开(公告)日:2022-01-13

    申请号:US17305612

    申请日:2021-07-12

    摘要: A device may receive content data, a first model, and a second model. The first model may be trained on different types of metadata than the second model. The content data may include a first identifier of a first content item and a first set of metadata associated with the first content item. The device may process the first set of metadata to generate first recommendations from the first model and second recommendations from the second model. The device may provide the first identifier and a combination of the first recommendations and the second recommendations to client devices. The device may receive, from the client devices, user-generated target recommendations based on the combination. The device may process the user-generated target recommendations, the first recommendations, and the second recommendations, to provide feedback to update the first model and the second model.

    SYSTEM AND METHOD FOR UN-BIASING USER PERSONALIZATIONS AND RECOMMENDATIONS

    公开(公告)号:US20230057423A1

    公开(公告)日:2023-02-23

    申请号:US17406199

    申请日:2021-08-19

    摘要: Disclosed are systems and methods for art electronic framework that enables un-biasing of personalizations for users. The disclosed framework provides controls that can enable users to selectively escape from previously conceived notions of a user's preferred tastes and/or interests. Upon a user requesting content, the disclosed framework can analyze the type of request as well as the modeled behavior and preferences of the user, and automatically un-bias or depersonalize content for the user, thereby availing the user to a broader range of content from a larger pool of content then previously made available to the user.

    SYSTEMS AND METHODS FOR DISCOVERY AND GENERALIZED EXPERIMENTATION WITH DIFFERENT TYPES OF SOFTWARE COMPONENTS

    公开(公告)号:US20240329939A1

    公开(公告)日:2024-10-03

    申请号:US18192005

    申请日:2023-03-29

    IPC分类号: G06F8/10 G06F11/36

    CPC分类号: G06F8/10 G06F11/3608

    摘要: A device may store, in a data structure, a plurality of feature variants associated with a schema of software and signatures generated based on the schema, and may provide a user interface that requests experiment information. The device may receive, via the user interface, the experiment information, and may identify, in the data structure, a set of feature variants, from the plurality of feature variants, based on the experiment information. The device may identify a set of corresponding signatures for the set of feature variants and may compare signatures of the set of corresponding signatures to identify compliant signatures of the set of corresponding signatures. The device may generate compliant feature variants based on the compliant signatures and may define segments and metrics. The device may generate the software experiment based on the compliant feature variants, the segments, and the metrics, and may execute the software experiment to generate results.

    SYSTEMS AND METHODS FOR EVALUATING MODELS THAT GENERATE RECOMMENDATIONS

    公开(公告)号:US20220303626A1

    公开(公告)日:2022-09-22

    申请号:US17805889

    申请日:2022-06-08

    摘要: A device may receive content data, a first model, and a second model. The first model may be trained on different types of metadata than the second model. The content data may include a first identifier of a first content item and a first set of metadata associated with the first content item. The device may process the first set of metadata to generate first recommendations from the first model and second recommendations from the second model. The device may provide the first identifier and a combination of the first recommendations and the second recommendations to client devices. The device may receive, from the client devices, user-generated target recommendations based on the combination. The device may process the user-generated target recommendations, the first recommendations, and the second recommendations, to provide feedback to update the first model and the second model.