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    Dynamic correlated topic model
    1.
    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

    申请人： Spotify AB

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

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

    CPC分类号： G06F40/44 ,  G06F16/355 ,  G06F17/15 ,  G06F40/20 ,  G06F40/295 ,  G06F40/30

    摘要： 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.

    System and method for generating models representing users of a media providing service
    3.
    发明授权
    System and method for generating models representing users of a media providing service 有权

    公开(公告)号：US11540017B1

    公开(公告)日：2022-12-27

    申请号：US17325049

    申请日：2021-05-19

    申请人： Spotify AB

    发明人： Dmitrii Moor ,  Rishabh Mehrotra ,  Mounia Lalmas-Roelleke

    IPC分类号： H04N21/466

    摘要： A method of recommending media items to a user is provided. The method includes receiving historical data for a user of a media providing service. The historical data indicates past interactions of the user with media items. The method includes generating a model of the user. The model includes a first set of parameters, each of the first set of parameters quantifying a predicted latent preference of the user for a respective media item provided by the media providing service. The method includes evaluating the predicted latent preferences of the user for the respective media items against the historical data indicating the past interactions of the user with the media items provided by the media providing service. The method includes selecting a recommender system from a plurality of recommender systems using the model of the user, including the first set of parameters. The method includes providing a media item to a second user using the selected recommender system.

    Dynamic word correlated topic machine learning model
    4.
    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

    申请人： Spotify AB

    发明人： Federico Tomasi ,  Zhenwen Dai ,  Mounia Lalmas-Roelleke

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

    CPC分类号： G06F40/295 ,  G06F16/355 ,  G06F40/40

    摘要： 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
    6.
    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

    申请人： Spotify AB

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

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

    摘要： 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.

    Systems and methods for selecting content using a multiple objective, multi-arm bandit model
    9.
    发明授权
    Systems and methods for selecting content using a multiple objective, multi-arm bandit model 有权

    公开(公告)号：US11556828B2

    公开(公告)日：2023-01-17

    申请号：US17170543

    申请日：2021-02-08

    申请人： Spotify AB

    发明人： Rishabh Mehrotra ,  Niannan Xue ,  Mounia Lalmas-Roelleke

    IPC分类号： G06F3/048 ,  G06N7/00 ,  G06F9/451 ,  G06F3/0482

    摘要： An electronic device for a first session of a user, for each of a plurality of lists of media content items, determines a respective value for each objective of a first set of objectives and a second set of objectives by accessing contextual data for the first session of the user. The first set of objectives corresponds to the user and the second set of objectives corresponds to a second party distinct from the user. The electronic device, using a multi-arm bandit model, identifies a first list of media content items, from the plurality of lists of media content items, to present to the user, including: calculating a score for each list in the plurality of lists of media items; and probabilistically selecting the first list of media content items according to the respective scores corresponding to the respective lists in the plurality of lists of media items.