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    Dynamic word correlated topic machine learning model
    3.
    发明授权
    Dynamic word correlated topic machine learning model 有权

    公开(公告)号：US12050872B2

    公开(公告)日：2024-07-30

    申请号：US17526845

    申请日：2021-11-15

    Applicant: Spotify AB

    Inventor： Federico Tomasi ,  Zhenwen Dai ,  Mounia Lalmas-Roelleke

    IPC: G06F40/279 ,  G06F16/35 ,  G06F40/284 ,  G06F40/295 ,  G06F40/35 ,  G06F40/40

    CPC classification number: G06F40/295 ,  G06F16/355 ,  G06F40/40

    Abstract: A system implements a dynamic word correlated topic model (DWCTM) to model an evolution of topic popularity, word embedding, and topic correlation within a set of documents, or other dataset, that spans a period of time. For example, the DWCTM receives the set of documents and a quantity of topics for modeling. The DWCTM processes the set computing, for each topic, various distributions to capture a popularity, word embedding, and correlation with other topics across the period of time. In other examples, a dataset of user listening sessions comprised of media content items for modeling by the DWCTM. Media content metadata (e.g., artist or genre) of the media content items, similar to words of a document, can be modeled by the DWCTM.

    DYNAMIC CORRELATED TOPIC MODEL
    4.
    发明申请
    DYNAMIC CORRELATED TOPIC MODEL 有权

    公开(公告)号：US20220019750A1

    公开(公告)日：2022-01-20

    申请号：US16932323

    申请日：2020-07-17

    Applicant: Spotify AB

    Inventor： Praveen Chandar Ravichandran ,  Mounia Lalmas-Roelleke ,  Federico Tomasi ,  Zhenwen Dai ,  Gal Levy-Fix

    IPC: G06F40/44 ,  G06F16/35 ,  G06F40/295 ,  G06F17/15

    Abstract: A system implements a dynamic correlated topic model (DCTM) to model an evolution of topic popularity, topic representation, and topic correlation within a set of documents, or other dataset, that spans a period of time. For example, the DCTM receives the set of documents and a quantity of topics for modeling. The DCTM processes the set by analyzing words of the documents, identifying word clusters representing the topics, and computing, for each topic, various distributions using continuous processes to capture a popularity, representation, and correlation with other topics across the period of time. In other examples, the dataset are user listening sessions comprised of media content items. Media content metadata (e.g., artist or genre) of the media content items, similar to words of a document, can be analyzed and clustered to represent topics for modeling by the DCTM.

    REINFORCEMENT LEARNING FOR DIVERSE CONTENT GENERATION
    5.
    发明公开
    REINFORCEMENT LEARNING FOR DIVERSE CONTENT GENERATION 审中-公开

    公开(公告)号：US20230419187A1

    公开(公告)日：2023-12-28

    申请号：US17851426

    申请日：2022-06-28

    Applicant: Spotify AB

    Inventor： Zhenwen Dai ,  Joseph Cauteruccio ,  Federico Tomasi ,  Mehdi Ben Ayed

    IPC: G06N20/10

    CPC classification number: G06N20/10

    Abstract: Methods, systems and computer program products are provided for content generation. A distribution of policies is defined based on an action space. Distribution parameters are received from a reinforcement learning (RL) algorithm. In turn, a policy is randomly sampled from the distribution of policies. A candidate content item is generated using the sampled policy. A quality of the candidate content item is measured based on a predefined quality criteria and a parameter model is adjusted as specified by the reinforcement learning algorithm to obtain a plurality of updated distribution parameters. Environment settings are passed to a trained parameter model to obtain a plurality of policy distribution parameters. A predetermined number of policies from the distribution of policies are then sampled and the plurality of environment settings are passed to the predetermined number of sampled policies to obtain at least one content item.

    Dynamic correlated topic model
    6.
    发明授权
    Dynamic correlated topic model 有权

    公开(公告)号：US11727221B2

    公开(公告)日：2023-08-15

    申请号：US16932323

    申请日：2020-07-17

    Applicant: Spotify AB

    Inventor： Praveen Chandar Ravichandran ,  Mounia Lalmas-Roelleke ,  Federico Tomasi ,  Zhenwen Dai ,  Gal Levy-Fix

    IPC: G06F40/44 ,  G06F40/295 ,  G06F16/35 ,  G06F40/20 ,  G06F40/30 ,  G06F17/15

    CPC classification number: G06F40/44 ,  G06F16/355 ,  G06F17/15 ,  G06F40/20 ,  G06F40/295 ,  G06F40/30

    Abstract: A system implements a dynamic correlated topic model (DCTM) to model an evolution of topic popularity, topic representation, and topic correlation within a set of documents, or other dataset, that spans a period of time. For example, the DCTM receives the set of documents and a quantity of topics for modeling. The DCTM processes the set by analyzing words of the documents, identifying word clusters representing the topics, and computing, for each topic, various distributions using continuous processes to capture a popularity, representation, and correlation with other topics across the period of time. In other examples, the dataset are user listening sessions comprised of media content items. Media content metadata (e.g., artist or genre) of the media content items, similar to words of a document, can be analyzed and clustered to represent topics for modeling by the DCTM.

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