PERSONALIZED HYPERPARAMETER TUNING WITH CONTEXTUAL MULTI-ARM BANDIT AND REINFORCEMENT LEARNING

    公开(公告)号:US20250053853A1

    公开(公告)日:2025-02-13

    申请号:US18232468

    申请日:2023-08-10

    Applicant: ROKU, INC.

    Abstract: Disclosed are system, method and/or computer program product embodiments for improving the performance of a machine learning based algorithm used to provide a user experience to a user via a media device. An embodiment selects a first set of hyperparameter values, implements a first iteration of the algorithm based on the first set of hyperparameter values, utilizes the first iteration of the algorithm to provide a first user experience to the user, determines a response of the user to the first user experience, selects, by a hyperparameter tuning ML model implemented as a contextual multi-arm bandit model or a reinforcement learning model and based on at least the response of the user, a second set of hyperparameter values, implements a second iteration of the algorithm based on the second set of hyperparameter values, and utilizes the second iteration of the algorithm to provide a second user experience to the user.

    CANDIDATE RANKING FOR CONTENT RECOMMENDATION

    公开(公告)号:US20240129565A1

    公开(公告)日:2024-04-18

    申请号:US17965176

    申请日:2022-10-13

    Applicant: ROKU, INC.

    CPC classification number: H04N21/251

    Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for candidate ranking for content recommendation. An embodiment operates by receiving category candidates over a network, wherein each of the category candidates comprises content candidates associated with one or more applications operating on media devices. The embodiment then ranks the category candidates based on a machine model trained using a learning algorithm based on the time series data, and ranks the content candidates in the each of category candidates based on the time series data. The embodiment then causes the ranked category candidates and the ranked content candidates to be outputted for display.

    AUTOMATED TRAILER GENERATION
    4.
    发明公开

    公开(公告)号:US20240196070A1

    公开(公告)日:2024-06-13

    申请号:US18484041

    申请日:2023-10-10

    Applicant: Roku, Inc.

    CPC classification number: H04N21/8549 G06F16/783

    Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product aspects, and/or combinations and sub-combinations thereof, for generating trailers (previews) for multimedia content. An example aspect operates by generating an initial set of candidate points to generate a trailer for a media content; determining conversion data for each of the initial set of candidate points; determining an updated set of candidate points based on the conversion data; determining an estimated mean and upper bound for each of the updated set of candidate points; computing a value for each of the updated set of candidate points; generating a ranked list based on the value computed for each of the updated set of candidate points; and repeating the process until an optimal candidate point is converged upon.

    REDUCING ACTIVE USER BIAS FOR CONTENT RECOMMENDATION MODELS

    公开(公告)号:US20240276041A1

    公开(公告)日:2024-08-15

    申请号:US18107858

    申请日:2023-02-09

    Applicant: ROKU, INC.

    CPC classification number: H04N21/251 G06N20/00 H04N21/25883

    Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for reducing active user or active content category bias in content recommendation systems. An example embodiment operates by modifying a streaming event data set by selecting a voting algorithm. The voting algorithm reduces an impact of highly occurring data points by sampling the streaming event data set to generate a sampled streaming event data set, wherein the highly occurring data points comprise data points generated by the active users or the active content categories. The embodiment further trains, by a machine learning engine and based on the sampled streaming event data set, a machine learning model to generate a reduced bias content recommendation model and generates, based on the reduced bias content recommendation model, content recommendations for subsequent selection and rendering on a media device.

    REINFORCEMENT LEARNING (RL) MODEL FOR OPTIMIZING LONG TERM REVENUE

    公开(公告)号:US20240273575A1

    公开(公告)日:2024-08-15

    申请号:US18108090

    申请日:2023-02-10

    Applicant: ROKU, INC.

    CPC classification number: G06Q30/0269 G06Q30/0261

    Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for optimizing user experience/engagement and revenue. An example embodiment operates by a computer-implemented method for providing one or more advertisements to a media device. The method includes receiving, by at least one computer processor, a user state associated with a user of the media device, where the user state corresponds to a time step. The method further includes receiving a revenue value associated with the user of the media device, where the revenue value corresponds to the time step. The method also include determining an action associated with the user based on the user state and the revenue value. The action includes one or more parameters associated with the one or more advertisements. The method further includes providing the action to the user.

    DEMOGRAPHIC PREDICTIONS FOR CONTENT ITEMS
    7.
    发明公开

    公开(公告)号:US20240205479A1

    公开(公告)日:2024-06-20

    申请号:US18076955

    申请日:2022-12-07

    Applicant: ROKU, INC.

    CPC classification number: H04N21/251 H04N21/25883

    Abstract: Disclosed herein are system, apparatus, article of manufacture, method, and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for demographic predictions for content items. An example embodiment operates by assigning weights representing demographics to a first plurality of nodes of a predictive model and assigning predictive values representing predicted demographics to a second plurality of nodes of the model. Pairwise distances between the predictive values for the nodes of the second plurality of nodes and the weighted values of the first plurality of nodes may be calculated and the shortest calculated pairwise distances may be used to assign demographics for content items corresponding to nodes of the first plurality of nodes to content items corresponding nodes of the second plurality of nodes. When content is requested, a content item for which the same demographic has been assigned may be recommended to the requestor.

    RECOMMENDATION SYSTEM FORWARD SIMULATOR
    8.
    发明公开

    公开(公告)号:US20240155195A1

    公开(公告)日:2024-05-09

    申请号:US17983138

    申请日:2022-11-08

    Applicant: ROKU, INC.

    CPC classification number: H04N21/4668 H04N21/25883 H04N21/4826

    Abstract: Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for utilizing a content acquisition recommendation system to generating a set of candidate content assets, generate embeddings and popularity score estimates for the set of candidate content assets, aggregate the set of candidate content assets with a set of existing content assets to generate a simulation set of content assets, determine a target set of users for the simulation set of content assets, generate, for at least a portion of the target set of users and based on a trained machine learning model, a result set of recommended content assets, determining an impact of the candidate content assets located in the result set of recommended content assets and generate a proposal for an acquisition of candidate content assets.

    CONTENT DISPLAY AND CLUSTERING SYSTEM
    9.
    发明公开

    公开(公告)号:US20240086466A1

    公开(公告)日:2024-03-14

    申请号:US17943526

    申请日:2022-09-13

    Applicant: ROKU, INC.

    CPC classification number: G06F16/906

    Abstract: Disclosed herein are various embodiments, for a content display and clustering system. An example embodiment operates by receiving a request to display the plurality of content items. At each of multiple levels different pairs of content items are identified and a similarity score is computed for each pair. A subset of pairs for which their similarity score exceeds a similarity threshold for the respective level are identified and clustered. This process is repeated for one or more iterations at the same level, and then the process is repeated for each of the multiple levels. A final clustered subset is identified, and output for display, responsive to the request to display the plurality of content items.

    RECOMMENDATION SYSTEM WITH REDUCED BIAS BASED ON A VIEW HISTORY

    公开(公告)号:US20240064354A1

    公开(公告)日:2024-02-22

    申请号:US17890491

    申请日:2022-08-18

    Applicant: ROKU, INC.

    CPC classification number: H04N21/252 H04N21/25891

    Abstract: Disclosed are mechanisms for selecting a recommended item for a current item being viewed by a user account based on a view history of the user account with reduced bias. For a current item being viewed by the user account represented by a current node of a co-watch graph, embodiments can select a recommended item represented by an associated node in the co-watch graph likely being viewed by the user account, and determine a probability of the recommended item likely being viewed. The co-watch graph can be generated based on a view history of the user account. An edge between a first node and a second node of the co-watch graph can have a weight representing a number of co-occurrence times when the first item represented by the first node and the second item represented by the second node are viewed in sequence within a predetermined time interval.

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